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Background: Since the molecular mechanisms of cervical cancer (CC) have not been completely discovered, it is of great significance to identify the hub genes and pathways of this disease to reveal the molecular mechanisms of cervical cancer., Objective: The study aimed to identify the biological functions and prognostic value of hub genes in cervical cancer., Methods: The gene expression data of CC patients were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The core genes were screened out by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). R software, the STRING online tool and Cytoscape software were used to screen out the hub genes. The GEPIA public database was used to further verify the expression levels of the hub genes in normal tissues and tumour tissues and determine the disease-free survival (DFS) rates of the hub genes. The protein expression of the survival-related hub genes was identified with the Human Protein Atlas (HPA) database., Results: A total of 64 core genes were screened, and 10 genes, including RFC5, POLE3, RAD51, RMI1, PALB2, HDAC1, MCM4, ESR1, FOS and E2F1, were identified as hub genes. Compared with that in normal tissues, RFC5, POLE3, RAD51,RMI1, PALB2, MCM4 and E2F1 were all significantly upregulated in cervical cancer, ESR1 was significantly downregulated in cervical cancer, and RFC5 expression in CC patients was significantly related to OS. In the DFS analysis, no significant difference was observed in the expression level of RFC5 in cervical cancer patients. Finally, RFC5 protein levels verified by the HPA database were consistently upregulated with mRNA levels in CC samples., Conclusions: RFC5 may play important roles in the occurrence and prognosis of CC. It could be further explored and validated as a potential predictor and therapeutic target for CC.

The fruit fly Drosophila melanogaster can be used as a model organism for studying various problems in biomedicine and pest management. A large number of fruit fly transcriptomes have been profiled in various cell types, tissues, development stages, toxicological exposures, and other conditions by microarray. Until now, there are still no database developed for exploring those precious data. Microarray data for 4,367 samples from National Center for Biotechnology Information Gene Expression Omnibus was collected, and analyzed by weighted gene coexpression network analysis algorithm. Fifty one gene coexpression modules that are related to cell types, tissues, development stages, and other experimental conditions were identified. The high dimensional gene expression was reduced to tens of modules that were associated with experiments/traits, representing signatures for phenotypes. Six modules were enriched with genomic regions of clustered genes. Hub genes could also be screened by intramodule connectivity. By analyzing higher order module networks, we found that cell signaling modules are more connected than other modules. Module-based gene function identification may help to discover novel gene function. An easy-to-use database was developed, which provides a new source for gene function study in the fruit fly (http://bioinformatics.fafu.edu.cn/fly/)., (© 2020 Wiley Periodicals LLC.)

Background: To understand the gene expression networks controlling flower color formation in alfalfa, flowers anthocyanins were identified using two materials with contrasting flower colors, namely Defu and Zhongtian No. 3, and transcriptome analyses of PacBio full-length sequencing combined with RNA sequencing were performed, across four flower developmental stages., Results: Malvidin and petunidin glycoside derivatives were the major anthocyanins in the flowers of Defu, which were lacking in the flowers of Zhongtian No. 3. The two transcriptomic datasets provided a comprehensive and systems-level view on the dynamic gene expression networks underpinning alfalfa flower color formation. By weighted gene coexpression network analyses, we identified candidate genes and hub genes from the modules closely related to floral developmental stages. PAL, 4CL, CHS, CHR, F3'H, DFR, and UFGT were enriched in the important modules. Additionally, PAL6, PAL9, 4CL18, CHS2, 4 and 8 were identified as hub genes. Thus, a hypothesis explaining the lack of purple color in the flower of Zhongtian No. 3 was proposed., Conclusions: These analyses identified a large number of potential key regulators controlling flower color pigmentation, thereby providing new insights into the molecular networks underlying alfalfa flower development.

This research endeavors to investigate the functions of N6-methyladenosine (m6A) regulatory genes and key genes linked to m6A modifications within the context of breast cancer (BC). The objective is to identify a promising predictive biomarker related to m6A modifications and validate its significance in BC through experimental methodologies. Utilizing data from The Cancer Genome Atlas (TCGA) database, a model for predicting prognosis was developed. Key genes connected to m6A modifications were discerned using weighted gene co-expression network analysis (WGCNA) coupled with LASSO and Cox regression analyses, which were then utilized to construct a predictive model. The influence of ZNF260 within BC was probed experimentally. The predictive model formulated using m6A regulatory genes and key m6A-associated genes demonstrated the capability to categorize BC patients into distinct risk groups effectively (all P < 0.001). Clinical sample analyses revealed notably elevated expression levels of ZNF260 in hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR + /HER2−) BC tissues compared to adjacent non-tumor tissues (all P < 0.001). Reduction in ZNF260 expression was shown to inhibit the proliferation, clonogenicity, migration, and invasiveness of MCF-7 cells while concomitantly enhancing apoptosis (all P < 0.001).This investigation uniquely uncovered ZNF260 as a novel key gene, suggesting its potential utility as a predictive biomarker associated with m6A modifications specifically in HR + /HER2− BC. [ABSTRACT FROM AUTHOR]

Background: Oral squamous cell carcinoma (OSCC) is a prevalent and aggressive oral cancer with a poor prognosis. Its polygenic risk is likely influenced by complex transcriptional disorders involving networks of co-expressed and functionally related genes, though such investigations are limited in OSCC. Methods: We analyzed the GSE37991 dataset, comprising 40 OSCC and 40 normal oral tissue samples from the Gene Expression Omnibus. Tumor-specific modules were identified using weighted correlation network analysis (WGCNA), leading to the selection of hub mRNAs and lncRNAs. These lncRNAs were used to construct lncRNA–mRNA and competing endogenous RNA networks. We further examined the expression profiles and survival data of these genes from the Cancer Genome Atlas. Prognostic markers were identified and validated through 5-year survival analysis and Cox proportional hazards modeling. RT-qPCR was used to validate the expression levels in clinical OSCC tissues. Results: We identified 1847 differentially expressed genes in OSCC tissues. WGCNA revealed four OSCC-specific modules, screening 120 hub mRNAs and five hub lncRNAs. Two prognostic markers (AQP5, IL-26) from hub mRNAs and three (FRMD5, INHBB, GUCY1A3) from the lncRNA–mRNA network were associated with survival. Validation showed lower expression of AQP5 and GUCY1A3, and higher expression of FRMD5 and INHBB in OSCC compared to normal tissues. Conclusion: This study enhances our understanding of transcriptional dysregulation in OSCC and may highlights AQP5, IL-26, FRMD5, INHBB, and GUCY1A3 as promising prognostic biomarkers. [ABSTRACT FROM AUTHOR]

Heart failure (HF) is a terminal condition of multiple cardiovascular disorders. Cancer is a deadly disease worldwide. The relationship between HF and cancer remains poorly understood. The Gene Expression Omnibus database was used to download the RNA sequencing data of 356 patients with hypertrophic cardiomyopathy-induced HF and non-HF. A co-expression network was established through the weighted correlation network analysis (WGCNA) to identify hub genes of HF and cancer. Cox risk analysis was performed to predict the prognostic risks of HF hub genes in pan-cancer. HF was linked to immune response pathway by the analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A positive correlation was observed between the expression levels of 4 hub genes and the infiltration of CD8+T-cells in pan-cancer. 4 hub genes were identified as beneficial prognostic factors in several cancers. Western blotting and real-time polymerase chain reaction validated the high expression of GZMM, NKG7, and ZAP70 in both mice and patients with HF compared to control groups. Our study highlights the shared immune pathogenesis of HF and cancer and provides valuable insights for developing novel therapeutic strategies, offering new opportunities for improving the management and treatment outcomes of both HF and cancer. [ABSTRACT FROM AUTHOR]

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Hypopharyngeal cancer is a disease that is associated with EGFR‐mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases. [ABSTRACT FROM AUTHOR]

In this study, we aimed to identify the hub genes responsible for increased vascular endothelial cell permeability. Methods: We applied the weighted Gene Expression Omnibus (GEO) database to mine dataset GSE178331 and obtained the most relevant high-throughput sequenced genes for an increased permeability of vascular endothelial cells due to inflammation. We constructed two weighted gene co-expression network analysis (WGCNA) networks, and the differential expression of high-throughput sequenced genes related to endothelial cell permeability were screened from the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differential genes. Their degree values were obtained from the topological properties of protein-protein interaction (PPI) networks of differential genes, and the hub genes associated with an increased endothelial cell permeability were analyzed. Reverse transcription-polymerase chain reaction (RT-PCR) and western blotting techniques were used to detect the presence of these hub genes in TNF-α induced mRNA and the protein expression in endothelial cells. Results: In total, 1,475 differential genes were mainly enriched in the cell adhesion and TNF-α signaling pathway. With TNF-α inducing an increase in the endothelial cell permeability and significantly increasing mRNA and protein expression levels, we identified three hub genes, namely PTGS2, ICAM1, and SNAI1. There was a significant difference in the high-dose TNF-α group and in the low-dose TNF-α group compared to the control group, in the endothelial cell permeability experiment (p = 0.008 vs. p = 0.02). Measurement of mRNA and protein levels of PTGS2, ICAM1, and SNAI1 by western blotting analysis showed that there was a significant impact on TNF-α and that there was a significant dose-dependent relationship (p < 0.05 vs. p < 0.01). Conclusions: The three hub genes identified through bioinformatics analyses in the present study may serve as biomarkers of increased vascular endothelial cell permeability. The findings offer valuable insights into the progress and mechanism of vascular endothelial cell permeability. [ABSTRACT FROM AUTHOR]

Simple Summary: The reproduction of goats is a highly complex and dynamic process of life regulation, which is synergistically regulated by various aspects such as central nervous system regulation, reproductive system development, oocyte maturation, and fertilized egg development. In goats, there is still an urgent need to explore marker genes related to reproductive performance. This study identified the RPL4 gene as a key marker gene related to reproductive capacity in goat oocytes through various bioinformatics analysis methods including WGCNA, PPI network analysis, and differential gene expression analysis. The results reveal that the RPL4 gene in oocytes has the potential to serve as a biological marker for determining the reproductive rate of goats, deepening our understanding of the regulatory mechanisms of goat reproductive performance. Reproduction in goats is a highly complex and dynamic process of life regulation, involving coordinated regulation from various aspects such as central nervous system regulation, reproductive system development, oocyte maturation, and fertilized egg development. In recent years, researchers have identified numerous genes associated with goat reproductive performance through high-throughput sequencing, single-cell sequencing, gene knockout, and other techniques. However, there is still an urgent need to explore marker genes related to goat reproductive performance. In this study, a single-cell RNA sequencing dataset of oocytes (GSE136005) was obtained from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to identify modules highly correlated with goat litter size. Through gene function enrichment analysis, it was found that genes within the modules were mainly enriched in adhesive junctions, cell cycle, and other signaling pathways. Additionally, the top 30 hub genes with the highest connectivity in WGCNA were identified. Subsequently, using Protein–Protein Interaction (PPI) network analysis, the top 30 genes with the highest connectivity within the modules were identified. The intersection of hub genes, key genes in the PPI network, and differentially expressed genes (DEGs) led to the identification of the RPL4 gene as a key marker gene associated with reproductive capacity in goat oocytes. Overall, our study reveals that the RPL4 gene in oocytes holds promise as a biological marker for assessing goat litter size, deepening our understanding of the regulatory mechanisms underlying goat reproductive performance. [ABSTRACT FROM AUTHOR]

Objective: We aimed to screen novel gene signatures for ovarian cancer (OC) and explore the role of biomarkers in OC via regulating pyroptosis using bioinformatics analysis. Methods: Differentially expressed genes (DEGs) of OC were screened from GSE12470 and GSE16709 datasets. Hub genes were determined from protein–protein interaction networks after bioinformatics analysis. The role of Centromeric protein M (CENPM) in OC was assessed by subcutaneous tumor experiment using hematoxylin–eosin and immunohistochemical staining. Tumor metastasis was evaluated by detecting epithelial-mesenchymal transition-related proteins. The proliferation, migration, and invasion were determined using cell counting kit and transwell assay. Enzyme-linked immunosorbent assay was applied to measure inflammatory factors. The mRNA and protein expression were detected using real-time quantitative PCR and western blot. Results: We determined 9 hub genes (KIFC1, PCLAF, CDCA5, KNTC1, MCM3, OIP5, CENPM, KIF15, and ASF1B) with high prediction value for OC. In SKOV3 and A2780 cells, the expression levels of hub genes were significantly up-regulated, compared with normal ovarian cells. CENPM was selected as a key gene. Knockdown of CENPM suppressed proliferation, migration, and invasion of OC cells. Subcutaneous tumor experiment revealed that CENPM knockdown significantly suppressed tumor growth and metastasis. Additionally, pyroptosis was promoted in OC cells and xenograft tumors after CENPM knockdown. Furthermore, CENPM knockdown activated cGAS-STING pathway and the pathway inhibitor reversed the inhibitory effect of CENPM knockdown on viability, migration, and invasion of OC cells. Conclusion: CENPM was a novel biomarker of OC, and knockdown of CENPM inhibited OC progression by promoting pyroptosis and activating cGAS-STING pathway. [ABSTRACT FROM AUTHOR]

Abdominal subcutaneous fat deposition (ASFD) is not only related to meat quality in the pig industry but also to human health in medicine. It is of great value to elucidate the potential molecular mechanisms of ASFD. The present study aims to identify obese-specific biomarkers and key pathways correlated with ASFD in pigs. The ASF-related mRNA expression dataset GSE136754 was retrieved from the Gene Expression Omnibus (GEO) database and systematically analyzed using a comprehensive bioinformatics method. A total of 565 differentially expressed genes (DEGs) were identified between three obese and three lean pigs, and these DEGs were mainly involved in the p53 signaling pathway, MAPK signaling pathway and fatty acid metabolism. A protein-protein interaction (PPI) network, consisting of 540 nodes and 1,065 edges, was constructed, and the top ten genes with the highest degree scores—ABL1, HDAC1, CDC42, HDAC2, MRPS5, MRPS10, MDM2, JUP, RPL7L1 and UQCRFS1—were identified as hub genes in the whole PPI network. Especially HDAC1, MDM2, MRPS10 and RPL7L1 were identified as potential robust obese-specific biomarkers due to their significant differences in single gene expression levels and high ROC area; this was further verified by quantitative real-time PCR (qRT-PCR) on abdominal subcutaneous fat samples from obese-type (Saba) and lean-type (Large White) pigs. Additionally, a mRNA-miRNA-lncRNA ceRNA network consisting of four potential biomarkers, 15 miRNAs and 51 lncRNAs was established, and two targeted lncRNAs with more connections, XIST and NEAT1, were identified as potentially important regulatory factors. The findings of this study may provide novel insights into the molecular mechanism involved in ASFD. [ABSTRACT FROM AUTHOR]

Esophageal adenocarcinoma (EAC) occurs following a series of histological changes through epithelial-mesenchymal transition (EMT). A variable expression of normal and aberrant genes in the tissue can contribute to the development of EAC through the activation or inhibition of critical molecular signaling pathways. Gene expression is regulated by various regulatory factors, including transcription factors and microRNAs (miRs). The exact profile of miRs associated with the pathogenesis of EAC is largely unknown, though some candidate miRNAs have been reported in the literature. To identify the unique miR profile associated with EAC, we compared normal esophageal tissue to EAC tissue using bulk RNA sequencing. RNA sequence data was verified using qPCR of 18 selected genes. Fourteen were confirmed as being upregulated, which include CDH11, PCOLCE, SULF1, GJA4, LUM, CDH6, GNA12, F2RL2, CTSZ, TYROBP, and KDELR3 as well as the downregulation of UGT1A1. We then conducted Ingenuity Pathway Analysis (IPA) to analyze for novel miR-gene relationships through Causal Network Analysis and Upstream Regulator Analysis. We identified 46 miRs that were aberrantly expressed in EAC compared to control tissues. In EAC tissues, seven miRs were associated with activated networks, while 39 miRs were associated with inhibited networks. The miR-gene relationships identified provide novel insights into potentially oncogenic molecular pathways and genes associated with carcinogenesis in esophageal tissue. Our results revealed a distinct miR profile associated with dysregulated genes. The miRs and genes identified in this study may be used in the future as biomarkers and serve as potential therapeutic targets in EAC. [ABSTRACT FROM AUTHOR]

AIM: Using bioinformatics analysis methods to identify the hub genes involved in myocardial isch-emia-reperfusion injury (MIRI). METHODS: Firstly, the rat MIRI related dataset GSE122020, E-MEXP-2098, and E-GE0D-4105 were downloaded from the database. Secondly, differentially expressed genes (DEGs) were screened from each dataset using the linear models for microarray data (limma) package, and robust DEGs were filtered using the robust rank aggregation (RRA) method. In addition, the surrogate variable analysis (SVA) package was used to merge all datas-ets into one, and merged DEGs were screened using the limma package. The common DEGs were obtained by taking the intersection of the two channels of DEGs. Next, the protein-protein interaction (PPI) network of common DEGs was constructed, and the hub genes were identified using the density-maximizing neighborhood component (DMNC) algorithm. The receiver operating characteristic curve (ROC) was plotted to evaluate the diagnostic performance of the hub gene. Then, the mRNA and protein expression levels of hub genes were detected in the rat MIRI model, and the literature review analysis was carried out on the involvement of hub genes in MIRI. Finally, the gene set enrichment analysis (GSEA) was performed on hub gene to further reveal the possible mechanism in mediating MIRI. RESULTS: A total of 143 robust DEGs and 48 merged DEGs were identified. After taking the intersection of the two, 48 common DEGs were obtained. In the PPI network of common DEGs, 5 hub genes were screened out, namely MYC proto-oncogene bHLH transcription factor (MYC), prostaglandin-endoperoxide synthase 2 (PTGS2), heme oxygenase 1 (HMOX1), caspase-3 (CASP3), and plasminogen activator urokinase receptor (PLAUR). The ROC results showed that the area under the curve values for all hub genes were greater than 0. 8. MYC, PTGS2, CASP3, and PLAUR showed high mRNA and protein expression in rat MIRI, while there was no difference in mRNA and protein expression for HMOX1. The literature review revealed that among the 5 hub genes, only PLAUR has not been reported to be involved in MIRI. The GSEA results for PLAUR indicated that its functional enrichment mainly focused on pathways such as NOD-like receptor signaling pathway, P53 signaling pathway, Toll-like receptor signaling pathway, apoptosis, and fatty acid metabolism. CONCLUSION: MYC, PTGS2, CASP3, HMOX1, and PLAUR are involved in the pathological process of MIRI. PLAUR is a potential hub gene that can mediate MIRI by regulating pathways such as NOD like receptor signaling, P53 signaling, Toll like receptor signaling, cell apoptosis, and fatty acid metabolism. The results can provide reference for further investigation into the molecular mechanisms and therapeutic targets of MIRI. [ABSTRACT FROM AUTHOR]

Background: Intramuscular fat (IMF) is an important factor in meat quality, and triglyceride (TG) and Phospholipids (PLIP), as the main components of IMF, are of great significance to the improvement of meat quality. Results: In this study, we used 30 RNA sequences generated from the transcriptome of chicken breast muscle tissues at different developmental stages to construct a gene expression matrix to map RNA sequence reads to the chicken genome and identify the transcript of origin. We used weighted gene co-expression network analysis (WGCNA) and identified 27 co-expression modules, 10 of which were related to TG and PLIP. We identified 150 highly-connected hub genes related to TG and PLIP, respectively, which were found to be mainly enriched in the adipocytokine signaling pathway, MAPK signaling pathway, mTOR signaling pathway, FoxO signaling pathway, and TGF-beta signaling pathway. Additionally, using the BioMart database, we identified 134 and 145 candidate genes related to fat development in the TG-related module and PLIP-related module, respectively. Among them, RPS6KB1, BRCA1, CDK1, RPS3, PPARGC1A, ACSL1, NDUFAB1, NDUFA9, ATP5B and PRKAG2 were identified as candidate genes related to fat development and highly-connected hub genes in the module, suggesting that these ten genes may be important candidate genes affecting IMF deposition. Conclusions: RPS6KB1, BRCA1, CDK1, RPS3, PPARGC1A, ACSL1, NDUFAB1, NDUFA9, ATP5B and PRKAG2 may be important candidate genes affecting IMF deposition. The purpose of this study was to identify the co-expressed gene modules related to chicken IMF deposition using WGCNA and determine key genes related to IMF deposition, so as to lay a foundation for further research on the molecular regulation mechanism underlying chicken fat deposition. [ABSTRACT FROM AUTHOR]

Melanoma is a malignant skin tumor. This study aimed to explore and assess the effect of novel biomarkers on the progression of melanoma. Differently expressed genes (DEGs) were screened from GSE3189 and GSE46517 datasets of Gene Expression Omnibus database using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted based on the identified DEGs. Hub genes were identified and assessed using protein–protein interaction networks, principal component analysis, and receiver operating characteristic curves. Quantitative real-time polymerase chain reaction was employed to measure the mRNA expression levels. TIMER revealed the association between aldehyde dehydrogenase 2 (ALDH2) and tumor immune microenvironment. The viability, proliferation, migration, and invasion were detected by cell counting kit-8, 5-ethynyl-2′-deoxyuridine, wound healing, and transwell assays. Total 241 common DEGs were screened out from GSE3189 and GSE46517 datasets. We determined 6 hub genes with high prediction values for melanoma, which could distinguish tumor samples from normal samples. ALDH2, ADH1B, ALDH3A2, DPT, EPHX2, and GATM were down-regulated in A375 and SK-MEL-2 cells, compared with the human normal melanin cell line (PIG1 cells). ALDH2 was selected as the candidate gene in this research, presenting a high diagnostic and predictive value for melanoma. ALDH2 had a positive correlation with the infiltrating levels of immune cells in melanoma microenvironment. Overexpression of ALDH2 inhibited cell viability, proliferation, migration, and invasion of A375/SK-MEL-2 cells. ALDH2 is a new gene biomarker of melanoma, which exerts an inhibitory effect on melanoma. [ABSTRACT FROM AUTHOR]

Objective: The study aimed to screen key genes in early diabetic kidney disease (DKD) and predict their biological functions and signaling pathways using bioinformatics analysis of gene chips interrelated to early DKD in the Gene Expression Omnibus database. Methods: Gene chip data for early DKD was obtained from the Gene Expression Omnibus expression profile database. We analyzed differentially expressed genes (DEGs) between patients with early DKD and healthy controls using the R language. For the screened DEGs, we predicted the biological functions and relevant signaling pathways by enrichment analysis of Gene Ontology (GO) biological functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways. Using the STRING database and Cytoscape software, we constructed a protein interaction network to screen hub pathogenic genes. Finally, we performed immunohistochemistry on kidney specimens from the Beijing Hospital to verify the above findings. Results: A total of 267 differential genes were obtained using GSE142025, namely, 176 upregulated and 91 downregulated genes. GO functional annotation enrichment analysis indicated that the DEGsweremainly involved in immune inflammatory response and cytokine effects. KEGG pathway analysis indicated that C-C receptor interactions and the IL-17 signaling pathway are essential for early DKD. We identified FOS, EGR1, ATF3, and JUN as hub sites of protein interactions using a protein-protein interaction network and module analysis. We performed immunohistochemistry (IHC) on five samples of early DKD and three normal samples from the Beijing Hospital to label the proteins. This demonstrated that FOS, EGR1, ATF3, and JUN in the early DKD group were significantly downregulated. Conclusion: The four hub genes FOS, EGR1, ATF3, and JUN were strongly associated with the infiltration of monocytes, M2 macrophages, and T regulatory cells in early DKD samples. We revealed that the expression of immune response or inflammatory genes was suppressed in early DKD. Meanwhile, the FOS group of low-expression genes showed that the activated biological functions included mRNA methylation, insulin receptor binding, and protein kinase A binding. These genes and pathways may serve as potential targets for treating early DKD. [ABSTRACT FROM AUTHOR]

Background: The pathological damage mechanism of type 2 diabetes (T2D) and macroangiopathy is extremely complex, and T2D and arteriosclerosis obliterans have different biological behaviors and clinical features. To explore the mechanism of lower extremity arteriosclerosis occlusion (LEAOD) in T2D patients, we utilized RNA-seq to identify unique gene expression signatures of T2D and LEAOD through transcriptomic analysis. Methods: We obtained blood samples and performed RNA sequencing from four patients with T2D, five of whom had LEAOD. Another six age- and gender-matched blood samples from healthy volunteers were used for control. By exploring the general and specific differential expression analysis after transcriptome sequencing, specific gene expression patterns of T2D and LEAOD were verified. Results: Transcriptome analysis found differentially expressed genes in T2D, and T2D + LEAOD (vs normal) separately, of which 35/486 (T2D/T2D + LEAOD) were up-regulated and 1290/2970 (T2D/T2D + LEAOD) were down-regulated. A strong overlap of 571 genes across T2D, LEAOD, and coexisting conditions was mainly involved in extracellular exosomes and the transcription process. By exploring the sex difference gene expression features between T2D, T2D + LEAOD, and healthy controls, we noticed that sex chromosome-associated genes do not participate in the sexual dimorphism gene expression profiles of T2D and LEAOD. Protein–Protein Interaction Network analysis and drug target prediction provided the drug candidates to treat T2D and LEAOD. Conclusion: This study provides some evidence at the transcript level to uncover the association of T2D with LEAOD. The screened hub genes and predicted target drugs may be therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: Chronic myeloid leukemia (CML) is a kind of malignant blood tumor, which is prone to drug resistance and relapse. This study aimed to identify novel diagnostic and therapeutic targets for CML. Methods: Differentially expressed genes (DEGs) were obtained by differential analysis of the CML cohort in the GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify CML-related co-expressed genes. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen hub genes and construct a risk score model based on hub genes. Consensus clustering algorithm was used for the identification of molecular subtypes. Clinical samples and in vitro experiments were used to verify the expression and biological function of hub genes. Results: A total of 378 DEGs were identified by differential analysis. 369 CMLrelated genes were identified by WGCNA analysis, which were mainly enriched in metabolism-related signaling pathways. In addition, CML-related genes are mainly involved in immune regulation and anti-tumor immunity, suggesting that CML has some immunodeficiency. Immune infiltration analysis confirmed the reduced infiltration of immune killer cells such as CD8+ T cells in CML samples. 6 hub genes (LINC01268, NME8, DMXL2, CXXC5, SCD and FBN1) were identified by LASSO regression analysis. The receiver operating characteristic (ROC) curve confirmed the high diagnostic value of the hub genes in the analysis and validation cohorts, and the risk score model further improved the diagnostic accuracy. hub genes were also associated with cell proliferation, cycle, and metabolic pathway activity. Two molecular subtypes, Cluster A and Cluster B, were identified based on hub gene expression. Cluster B has a lower risk score, higher levels of CD8+ T cell and activated dendritic cell infiltration, and immune checkpoint expression, and is more sensitive to commonly used tyrosine kinase inhibitors. Finally, our clinical samples validated the expression and diagnostic efficacy of hub genes, and the knockdown of LINC01268 inhibited the proliferation of CML cells, and promoted apoptosis. Conclusion: Through WGCNA analysis and LASSO regression analysis, our study provides a new target for CML diagnosis and treatment, and provides a basis for further CML research. [ABSTRACT FROM AUTHOR]

Sepsis ranks among the most common health problems worldwide, characterized by organ dysfunction resulting from infection. Excessive inflammatory responses, cytokine storms, and immune-induced microthrombosis are pivotal factors influencing the progression of sepsis. Our objective was to identify novel immune-related hub genes for sepsis through bioinformatic analysis, subsequently validating their specificity and potential as diagnostic and prognostic biomarkers in an animal experiment involving a sepsis mice model. Gene expression profiles of healthy controls and patients with sepsis were obtained from the Gene Expression Omnibus (GEO) and analysis of differentially expressed genes (DEGs) was conducted. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to analyze genes within crucial modules. The functional annotated DEGs which related to the immune signal pathways were used for constructing protein–protein interaction (PPI) analysis. Following this, two hub genes, FERMT3 and CD3G, were identified through correlation analyses associated with sequential organ failure assessment (SOFA) scores. These two hub genes were associated with cell adhesion, migration, thrombosis, and T-cell activation. Furthermore, immune infiltration analysis was conducted to investigate the inflammation microenvironment influenced by the hub genes. The efficacy and specificity of the two hub genes were validated through a mice sepsis model study. Concurrently, we observed a significant negative correlation between the expression of CD3G and IL-1β and GRO/KC. These findings suggest that these two genes probably play important roles in the pathogenesis and progression of sepsis, presenting the potential to serve as more stable biomarkers for sepsis diagnosis and prognosis, deserving further study. [ABSTRACT FROM AUTHOR]

Introduction: Climate change is likely to lead to not only increased global temperatures but also a more variable climate where unseasonal periods of heat stress are more prevalent. This has been evidenced by the observation of spring-time temperatures approaching 40°C in some of the main springwheat producing countries, such as the USA, in recent years. With an optimum growth temperature of around 20°C, wheat is particularly prone to damage by heat stress. A warming climate with increasingly common fluctuations in temperature therefore threatens wheat crops and subsequently the lives and livelihoods of billions of people who depend on the crop for food. To futureproof wheat against a variable climate, a better understanding of the response to early heat stress is required. Methods: Here, we utilised DESeq2 to identify 7,827 genes which were differentially expressed in wheat landraces after early heat stress exposure. Candidate hub genes, which may regulate the transcriptional response to early heat stress, were identified via weighted gene co-expression network analysis (WGCNA), and validated by qRT-PCR. Results: Two of the most promising candidate hub genes (TraesCS3B02G409300 and TraesCS1B02G384900) may downregulate the expression of genes involved in the drought, salinity, and cold responses—genes which are unlikely to be required under heat stress—as well as photosynthesis genes and stress hormone signalling repressors, respectively. We also suggest a role for a poorly characterised sHSP hub gene (TraesCS4D02G212300), as an activator of the heat stress response, potentially inducing the expression of a vast suite of heat shock proteins and transcription factors known to play key roles in the heat stress response. Discussion: The present work represents an exploratory examination of the heatinduced transcriptional change in wheat landrace seedlings and identifies several candidate hub genes which may act as regulators of this response and, thus, may be targets for breeders in the production of thermotolerant wheat varieties. [ABSTRACT FROM AUTHOR]

Objective: To use bioinformatics technology to analyse differentially expressed genes in chronic rejection after renal transplantation, we can screen out potential pathogenic targets associated with the development of this disease, providing a theoretical basis for finding new therapeutic targets. Methods: Gene microarray data were downloaded from the Gene Expression Profiling Integrated Database (GEO) and cross-calculated to identify differentially expressed genes (DEGs). Analysis of differentially expressed genes (DEGs) with gene ontology (GO) is a method used to study the differences in gene expression under different conditions as well as their functions and interrelationships, while Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis is a tool used to explore the functions and pathways of genes in specific biological processes. By calculating the distribution of immune cell infiltration, the result of immune infiltration in the rejection group can be analysed as a trait in Weighted Gene Co-Expression Network Analysis (WGCNA) for genes associated with rejection. Then, protein-protein interaction networks (PPI) were constructed using the STRING database and Cytoscape software to identify hub gene markers. Results: A total of 60 integrated DEGs were obtained from 3 datasets (GSE7392, GSE181757, GSE222889). By GO and KEGG analysis, the GEDs were mainly concentrated in the regulation of immune response, defence response, regulation of immune system processes, and stimulation response. The pathways were mainly enriched in antigen processing and presentation, EBV infection, graft-versus-host, allograft rejection, and natural killer cell-mediated cytotoxicity. After further screening using WGCNA and PPI networks, HLA-A, HLA-B, HLA-F, and TYROBP were identified as hub genes (Hub genes). The data GSE21374 with clinical information was selected to construct the diagnostic efficacy and risk prediction model plots of the four hub genes, and the results concluded that all four Hub genes had good diagnostic value (area under the curve in the range of 0.794-0.819). From the inference, it can be concluded that the four genes, HLA-A, HLA-B, HLA-F and TYROBP, may have an important role in the development and progression of chronic rejection after renal transplantation. Conclusion: DEGs play an important role in the study of the pathogenesis of chronic rejection after renal transplantation, and can provide theoretical support for further research on the pathogenesis of chronic rejection after renal transplantation and the discovery of new therapeutic targets through enrichment analysis and pivotal gene screening, as well as inferential analyses of related diagnostic efficacy and disease risk prediction. [ABSTRACT FROM AUTHOR]

The incidence of acute myocardial infarction (AMI) in hemodialysis (HD) patients is high and the prognosis is extremely poor. However, the potential connection between HD and AMI, and its regulatory mechanisms remain unclear. In this study, the gene expression profiles of HD (GSE15072) and AMI (GSE66360) were downloaded from the Gene Expression Omnibus database, common differentially expressed genes (DEGs) were obtained using the limma R package, the biological functions were analyzed according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, machine learning was conducted to identify hub genes. Receiver operating characteristic curves and gene set enrichment analyses were used to explore the characters and biological function of hub genes, networks were used for candidate identification of transcription factor (TF), microRNA (miRNA), and drug. After a total of 255 common DEGs were selected, GO and KEGG analyses indicated that neutrophil extracellular trap (NET) may be a potential connection between HD and AMI, LILRB2, S100A12, CYBB, ITGAM, and PPIF were finally identified as hub genes. The area under curve of LILRB2, S100A12, and PPIF was higher than 0.8 in both datasets. Networks show the relationship between hub genes, TF, and miRNA, also the relationship between potential drugs and protein. In conclusion, NETs may be the potential connection between AMI and HD. The potential hub gene, signaling pathways, and drugs provided by this study may contribute to future AMI prevention and intervention in HD patients. [ABSTRACT FROM AUTHOR]

Although the prognosis of multiple myeloma (MM) has remarkably improved with the emerge of novel agents, it remains incurable and relapses inevitably. The molecular mechanisms of MM have not been well-studied. Herein, this study aimed to identify key genes in MM. The GSE39754 dataset was used to screen differentially expressed genes (DEGs) and construct a co-expression network. Hub nodes were identified in the protein and protein interaction (PPI) network. Datasets GSE13591 and GSE2658 were used to validate hub genes. Moreover, function and gene set enrichment analyses were performed to elucidate the molecular pathogenesis of MM. In this study, 11 genes were found to be hub genes in the co-expression network, among which four genes (CD68, FCER1G, PLAUR and LCP2) were also identified as hub nodes. In the test dataset GSE13591, CD68 and FCER1G were significantly downregulated in MM. Besides, the areas under the curve (AUCs) of CD68 and FCER1G were greater than 0.8 in both the training dataset and the test dataset. Our results also confirmed that FCER1G highly expressed patients had remarkably longer survival times in MM. Function and pathway enrichment analyses suggested that hub genes were associated with epithelial mesenchymal transition, TNF-α signaling via NF-κB and inflammatory response. GSEA in our study indicated that FCER1G participated in NK cell mediated cytotoxicity and the NOD-like receptor signaling pathway. Our study identified FCER1G as a key gene in MM, providing a novel biomarker and potential molecular mechanisms of MM for further studies. [ABSTRACT FROM AUTHOR]

Introduction: Many clinical studies reported the coexistence of Alzheimer's disease (AD) and multiple sclerosis (MS), but the common molecular signature between AD and MS remains elusive. The purpose of our study was to explore the genetic linkage between AD and MS through bioinformatic analysis, providing new insights into the shared signatures and possible pathogenesis of two diseases. Methods: The common differentially expressed genes (DEGs) were determined between AD and MS from datasets obtained from Gene Expression Omnibus (GEO) database. Further, functional and pathway enrichment analysis, protein-protein interaction network construction, and identification of hub genes were carried out. The expression level of hub genes was validated in two other external AD and MS datasets. Transcription factor (TF)-gene interactions and gene-miRNA interactions were performed in NetworkAnalyst. Finally, receiver operating characteristic (ROC) curve analysis was applied to evaluate the predictive value of hub genes. Results: A total of 75 common DEGs were identified between AD and MS. Functional and pathway enrichment analysis emphasized the importance of exocytosis and synaptic vesicle cycle, respectively. Six significant hub genes, including CCL2, CD44, GFAP, NEFM, STXBP1, and TCEAL6, were identified and verified as common hub genes shared by AD and MS. FOXC1 and hsa-mir-16-5p are the most common TF and miRNA in regulating hub genes, respectively. In the ROC curve analysis, all hub genes showed good efficiency in helping distinguish patients from controls. Conclusion: Our study first identified a common genetic signature between AD and MS, paving the road for investigating shared mechanism of AD and MS. [ABSTRACT FROM AUTHOR]

Introduction: Sepsis is a life-threatening disease that damages multiple organs and induced by the host's dysregulated response to infection with high morbidity and mortality. Heart remains one of the most vulnerable targets of sepsis-induced organ damage, and sepsis-induced cardiomyopathy (SIC) is an important factor that exacerbates the death of patients. However, the underlying genetic mechanism of SIC disease needs further research. Methods: The transcriptomic dataset, GSE171564, was downloaded from NCBI for further analysis. Gene expression matrices for the sample group were obtained by quartile standardization and log2 logarithm conversion prior to analysis. The time series, protein-protein interaction (PPI) network, and functional enrichment analysis via Gene Ontology and KEGG Pathway Databases were used to identify key gene clusters and their potential interactions. Predicted miRNA-mRNA relationships from multiple databases facilitated the construction of a TF-miRNA-mRNA regulatory network. In vivo experiments, along with qPCR and western blot assays, provided experimental validation. Results: The transcriptome data analysis between SIC and healthy samples revealed 221 down-regulated, and 342 up-regulated expressed genes across two distinct clusters. Among these, Tpt1, Mmp9 and Fth1 were of particular significance. Functional analysis revealed their role in several biological processes and pathways, subsequently, in vivo experiments confirmed their overexpression in SIC samples. Notably, we found TPT1 play a pivotal role in the progression of SIC, and silencing TPT1 showed a protective effect against LPS-induced SIC. Conclusion: In our study, we demonstrated that Tpt1, Mmp9 and Fth1 have great potential to be biomarker of SIC. These findings will facilitated to understand the occurrence and development mechanism of SIC. [ABSTRACT FROM AUTHOR]

To explore the potential network markers and related signaling pathways of human B cells infected by COVID-19, we performed standardized integration and analysis of single-cell sequencing data to construct conditional cell-specific networks (CCSN) for each cell. Then the peripheral blood cells were clustered and annotated based on the conditional network degree matrix (CNDM) and gene expression matrix (GEM), respectively, and B cells were selected for further analysis. Besides, based on the CNDM of B cells, the hub genes and 'dark' genes (a gene has a significant difference between case and control samples not in a gene expression level but in a conditional network degree level) closely related to COVID-19 were revealed. Interestingly, some of the 'dark' genes and differential degree genes (DDGs) encoded key proteins in the JAK-STAT pathway, which had antiviral effects. The protein p21 encoded by the 'dark' gene CDKN1A was a key regulator for the COVID-19 infection-related signaling pathway. Elevated levels of proteins encoded by some DDGs were directly related to disease severity of patients with COVID-19. In short, the proteins encoded by 'dark' genes complement some missing links in COVID-19 and these signaling pathways played an important role in the growth and activation of B cells. [ABSTRACT FROM AUTHOR]

BACKGROUND: Osteoarthritis is inseparable from synovitis, so it has important clinical significance to explore osteoarthritis pathogenesis based on synovial tissue. OBJECTIVE: To explore the diagnosis and therapeutic targets of osteoarthritis from the perspective of synovium through analyzing the transcriptome data of synovial tissues between patients with osteoarthritis and healthy controls based on bioinformatics methods, as well as to provide follow-up research ideas for osteoarthritis. METHODS: Datasets containing normal and osteoarthritis synovial tissues were screened from Gene Expression Omnibus (GEO) database. GSE55457 and GSE55235 were selected, both of which contained 10 synovial tissue samples of osteoarthritis and 10 synovial tissue samples of healthy controls. GEO2R was utilized to perform differential gene expression analysis of GSE55457 and GSE55235 datasets, and genes with adjusted P values (adj.P) < 0.05 were collected. The online tool Xiantao Academy was used to obtained common up-regulated and down-regulated differentially expressed genes in the two datasets. GO functional annotation and KEGG pathway enrichment analysis for differentially expressed genes were performed. The STRING database was used to perform protein-protein interaction analysis. The results were visualized in Cytoscape software through CytoHubba App by 7 algorithms (BottleNeck, Clossness, Degree, DNNC, EPC, NNC and MCC). The top 10 genes with the highest scores were picked out for each algorithm, and the intersected genes of 7 algorithms were selected as the hub genes of osteoarthritis. RESULTS AND CONCLUSION: 200 common up-regulated differential genes and 124 common down-regulated differential genes were gained from the above two datasets. The results of GO analysis for the 324 differential genes showed that these genes were mainly associated with the DNA-binding transcription factor binding, RNA polymerase II specific DNA-binding transcription factor binding, poly(A) binding, platelets-derived growth factor receptor binding, glucocorticoid receptor binding, etc. KEGG analysis showed that the differential genes were mainly enriched in MAPK signaling pathway, insulin signaling pathway, osteoclast differentiation as well as parathyroid hormone synthesis, secretion and action pathways. Protein-protein interaction network analysis screened two hub genes of osteoarthritis, namely heat shock protein 90α class A member 1 (HSP90AA1) and suppressors of cytokine signaling 3 (SOCS3). These findings confirm that HSP90AA1 and SOCS3 are differentially expressed in synovial tissue of patients with osteoarthritis and healthy subjects, and may serve as surveillance markers and therapeutic target, which provide sound ideas for further study of molecular mechanisms of osteoarthritis. [ABSTRACT FROM AUTHOR]

BACKGROUND: Osteoarthritis is a degenerative disease characterized by joint pain, stiffness and swelling. At present, the pathogenesis of osteoarthritis is not clear. OBJECTIVE: To screen out Hub genes in osteoarthritis-related data sets based on bioinformatics and then identify them using cell experiments to screen the key biomarkers of osteoarthritis. METHODS: Osteoarthritis-related data sets were searched from GEO database and differentially expressed genes were identified by GEO2R analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed for differentially expressed genes. Meanwhile, a gene set enrichment analysis was performed for all genes in the data set. Protein-protein network was constructed by inputting differentially expressed genes on String website. The functional modules of the protein-protein network were analyzed by MCODE plug-in in Cytoscape software and the top 10 Hub genes were identified by CytoHubba plug-in. The knee meniscus cells of Sprague-Dawley rats were extracted. In the experimental group, interleukin-1β (10 ng/mL) was used to intervene the cells for 24 hours. Cells with no intervention were used as controls. The expression of Hub genes was detected by RT-qPCR. RESULTS AND CONCLUSION: A total of 147 differentially expressed genes were up-regulated and 212 down-regulated. Gene ontology, Kyoto Encyclopedia of Genes and Genomes enrichment and gene set enrichment analyses showed that the enriched pathways and biological processes mainly involved collagen fiber organization, extracellular matrix interaction, Th17 cell differentiation and interleukin-17. CytoHubba, a plug-in in Cytoscape software, was used to identify the top 10 Hub genes in MCC algorithm, including COL1A1, COL3A1, COL5A1, COL5A2, COL6A3, LOX, LOXL1, LOXL2, POSTN, and PLOD1. RT-qPCR results showed that compared with the control group, the mRNA expression of COL1A1, COL3A1, COL5A1, COL5A2, COL6A3, LOXL1, and LOXL2 decreased (P < 0.000 1), while the mRNA expression of LOX and POSTN increased (P < 0.000 1). However, there was no significant difference in the PLOD1 expression between the two groups (P > 0.05). To conclude, differentially expressed Hub genes in osteoarthritis may provide new insights into the development of osteoarthritis in the future. [ABSTRACT FROM AUTHOR]

This study was designed to identify the differentially expressed miRNAs (DEMs) and genes (DEGs) in metastatic cervical cancer using bioinformatic tools. In this study, fifty-seven DEMs (48 downregulated and 9 upregulated) were identified, among which miR-4459 and miR-3195 expression was negatively associated with overall survival of cervical cancer patients. Then, 476 target DEGs were determined, and protein–protein interaction (PPI) network was constructed. Seventeen hub genes (LONRF2, CCNE2, AURKA, SYT1, NEGR1, PPP1R12B, GABRP, RAD51, CDK1, FBLN5, PRKG1, CDC6, CACNA1C, MEOX2, ANLN, MYLK, and EDNRB) were finally selected to construct the miRNA-hub gene network. Overall, our study discovered the key miRNAs and mRNAs related to lymph node metastasis (LNM) in cervical cancer, which helps discover candidate therapeutic targets for cervical cancer. [ABSTRACT FROM AUTHOR]

Background: Dendritic cells (DCs) are most potent antigen-processing cells and play key roles in host defense against Mycobacterium tuberculosis (MTB) infection. In this study, hub genes in DCs during MTB infection were first investigated using bioinformatics approaches and further validated in Monocyte-derived DCs. Methods: Microarray datasets were obtained from Gene Expression Omnibus (GEO) database. Principal component analysis (PCA) and immune infiltration analysis were performed to select suitable samples for further analysis. Differential analysis and functional enrichment analysis were conducted on DC samples, comparing live MTB-infected and non-infected (NI) groups. The CytoHubba plugin in Cytoscape was used to identify hub genes from the differentially expressed genes (DEGs). The expression of the hub genes was validated using two datasets and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in human monocyte-derived DCs. Enzyme-linked immunosorbent assay (ELISA) was used to validate interferon (IFN) secretion. Transcription factors (TFs) and microRNAs (miRNAs) that interact with the hub genes were predicted using prediction databases. The diagnostic value of the hub genes was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values. Results: A total of 1835 common DEGs among three comparison groups (18 h, 48 h, 72 h after MTB infection) were identified. Six DEGs (IFIT1, IFIT2, IFIT3, ISG15, MX1, and RSAD2) were determined as hub genes. Functions enrichment analysis revealed that all hub genes all related to IFN response. RT-qPCR showed that the expression levels of six hub genes were significantly increased after DC stimulated by live MTB. According to the results of ELISA, the secretion of IFN-γ, but not IFN-α/β, was upregulated in MTB-stimulated DCs. AUC values of six hub genes ranged from 84 to 94% and AUC values of 5 joint indicators of two hub genes were higher than the two hub genes alone. Conclusion: The study identified 6 hub genes associated with IFN response pathway. These genes may serve as potential diagnostic biomarkers in tuberculosis (TB). The findings provide insights into the molecular mechanisms involved in the host immune response to MTB infection and highlight the diagnostic potential of these hub genes in TB. [ABSTRACT FROM AUTHOR]

Fat deposition is an economically important trait in pigs. Ningxiang pig, one of the four famous indigenous breeds in China, is characterized by high fat content. The underlying gene expression pattern in different developmental periods of backfat tissue remains unclear, and the purpose of this investigation is to explore the potential molecular regulators of backfat tissue development in Ningxiang pigs. Backfat tissue (three samples for each stage) was initially collected from different developmental stages (60, 120, 180, 240, 300, and 360 days after birth), and histological analysis and RNA sequencing (RNA-seq) were then conducted. Fragments per kilobase of transcript per million (FPKM) method was used to qualify gene expressions, and differentially expressed genes (DEGs) were identified. Furthermore, strongly coexpressed genes in modules, which were named by color, were clustered by Weighted gene co-expression network analysis (WGCNA) based on dynamic tree cutting algorithm. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment were subsequently implemented, and hub genes were described in each module. Finally, QPCR analysis was employed to validate RNA-seq data. The results showed that adipocyte area increased and adipocyte number decreased with development of backfat tissue. A total of 1,024 DEGs were identified in five comparison groups (120 days vs. 60 days, 180 days vs. 120 days, 240 days vs. 180 days, 300 days vs. 240 days, and 360 days vs. 300 days). The turquoise, red, pink, paleturquoise, darkorange, and darkgreen module had the highest correlation coefficient with 60, 120, 180, 240, 300, and 360 days developmental stage, while the tan, black and turquoise module had strong relationship with backfat thickness, adipocyte area, and adipocyte number, respectively. Thirteen hub genes (ACSL1, ACOX1, FN1, DCN, CHST13, COL1A1, COL1A2, COL6A3, COL5A1, COL14A1, OAZ3, DNM1, and SELP) were recognized. ACSL1 and ACOX1 might perform function in the early developmental stage of backfat tissue (60 days), and FN1, DCN, COL1A1, COL1A2, COL5A1, COL6A3, and COL14A1 have unignorable position in backfat tissue around 120 days developmental stage. Besides, hub genes SELP and DNM1 in modules significantly associated with backfat thickness and adipocyte area might be involved in the process of backfat tissue development. These findings contribute to understand the integrated mechanism underlying backfat tissue development and promote the progress of genetic improvement in Ningxiang pigs. [ABSTRACT FROM AUTHOR]

Background: Hypertrophic cardiomyopathy (HCM), an autosomal dominant genetic disease, is the main cause of sudden death in adolescents and athletes globally. Hypoxia and immune factors have been revealed to be related to the pathology of HCM. There is growing evidence of a role for hypoxia and inflammation as triggers and enhancers in the pathology in HCM. However, the role of hypoxia- and immune-related genes in HCM have not been reported. Methods: Firstly, we obtained four HCM-related datasets from the Gene Expression Omnibus (GEO) database for differential expression analysis. Immune cells significantly expressed in normal samples and HCM were then screened by a microenvironmental cell population counter (MCP-counter) algorithm. Next, hypoxia- and immune-related genes were screened by the LASSO + support vector machine recursive feature elimination (SVM-RFE) and weighted gene co-expression network analysis (WGCNA). Single-gene enrichment analysis and expression validation of key genes were then performed. Finally, we constructed a competing endogenous RNA (ceRNA) network of key genes. Results: In this study, 35 differentially expressed hypoxia genes were found. By using LASSO + SVM-RFE analysis, 10 more targets with differentially expressed hypoxia genes were identified. The MCP-count algorithm yielded five differentially expressed immune cells, and after assessing them for WGCNA characteristics, 612 immune genes were discovered. When hypoxia and immune genes were combined for cross-tabulation analysis, three hypoxia- and immune-related genes (ATP2A2, DDAH1, and OMA1) were identified. Conclusion: Based on hypoxia characteristic genes, three key genes were identified. These were also significantly related to immune activation, which proves a theoretical basis and reference value for studying the relationship between HCM and hypoxia and immunity. [ABSTRACT FROM AUTHOR]

SUMMARY: Triticum aestivum L. (bread wheat) is a crop relied upon by billions of people around the world, as a major source of both income and calories. Rising global temperatures, however, pose a genuine threat to the livelihood of these people, as wheat growth and yields are extremely vulnerable to damage by heat stress. Here we present the YoGI wheat landrace panel, comprising 342 accessions that show remarkable phenotypic and genetic diversity thanks to their adaptation to different climates. We quantified the abundance of 110 790 transcripts from the panel and used these data to conduct weighted co‐expression network analysis and to identify hub genes in modules associated with abiotic stress tolerance. We found that the expression of three hub genes, all heat‐shock proteins (HSPs), were significantly correlated with early thermotolerance in a validation panel of landraces. These hub genes belong to the same module, with one (TraesCS4D01G207500.1) being a candidate master‐regulator potentially controlling the expression of the other two hub genes, as well as a suite of other HSPs and heat‐stress transcription factors (HSFs). In this work, therefore, we identify three validated hub genes, the expression of which can serve as markers of thermotolerance during early development, and suggest that TraesCS4D01G207500.1 is a potential master regulator of HSP and HSF expression – presenting the YoGI landrace panel as an invaluable tool for breeders wishing to determine and introduce novel alleles into modern varieties, for the production of climate‐resilient crops. Significance Statement: Global wheat yields and crop growth are vulnerable to damage by heat stress, a threat that is likely to become more common globally in the coming years through climate change. Conducting weighted co‐expression network analysis on 337 diverse landrace accessions, we identified and validated TraesCS4D01G207500.1 as a hub gene that can be used as a marker of early thermotolerance, seemingly via the regulation of the expression of a suite of HSP and HSF genes. [ABSTRACT FROM AUTHOR]

Objective. Bladder cancer is a clinical and social conundrum due to its high incidence and recurrence rate. It is urgent to find new targets for the diagnosis and treatment of bladder cancer and improve the prognosis and survival rate of bladder cancer patients. We sought a prognosis-related gene, built related models of evaluated bladder cancer and identified the function of the hub gene in bladder cancer. Methods. We downloaded the data of bladder cancer patients from the TCGA database, and used differentially expressed genes (DEGs), copy number variation (CNV) and survival analysis to scan the hub genes associated with prognosis in bladder cancer. Then, multi-factor cox regression was used to obtain the bladder cancer prognosis correlation model. Then, we analyzed the relationship between the expression of hub gene and immune microenvironment of bladder cancer. The relationship between the expression of hub gene and prognosis in bladder cancer patients was verified by immunohistochemistry. Cell proliferation assay and drug sensitivity test in vivo were used to verify the inhibition of bladder cancer by targeted inhibitors. Results. In bladder cancer, we screened seven hub genes (ACLY, CNP, NKIRAS2, P3H4, PDIA6, VPS25 and XPO1) associated with survival. Moreover, the multifactor regression model constructed with hub gene can well distinguish the prognosis of bladder cancer. Hub gene is mostly associated with immune microenvironment. Immunohistochemical results basically confirmed the importance of XPO1 in bladder cancer. Selinexor (an inhibitor of XPO1) could effectively inhibit the proliferation of bladder cancer in the cell proliferation experiments by CCK-8 assays and it could suppress the growth of bladder cancer in mouse bladder cancer model. Conclusions. In this study, a prognostic model with seven hub genes has provided great help for the prognosis prediction of bladder cancer patients. And XPO1 is an important target affecting the prognosis of bladder cancer, and inhibition of XPO1 can effectively inhibit bladder cancer proliferation and growth. [ABSTRACT FROM AUTHOR]

Background: Patients with anaphylaxis are at risk for ST-segment elevation myocardial infarction (STEMI). However, the pathological links between anaphylaxis and STEMI remain unclear. Here, we aimed to explore shared biological processes, immune effector cells, and hub genes of anaphylaxis and STEMI. Methods: Gene expression data for anaphylactic (GSE69063) and STEMI (GSE60993) patients with corresponding healthy controls were pooled from the Gene Expression Omnibus database. Differential expression analysis, enrichment analysis, and CIBERSORT were used to reveal transcriptomic signatures and immune infiltration profiles of anaphylaxis and STEMI, respectively. Based on common differentially expressed genes (DEGs), Gene Ontology analysis, cytoHubba algorithms, and correlation analyses were performed to identify biological processes, hub genes, and hub gene-related immune cells shared by anaphylaxis and STEMI. The robustness of hub genes was assessed in external anaphylactic (GSE47655) and STEMI (GSE61144) datasets. Furthermore, a murine model of anaphylaxis complicated STEMI was established to verify hub gene expressions. The logistic regression analysis was used to evaluate the diagnostic efficiency of hub genes. Results: 265 anaphylaxis-related DEGs were identified, which were associated with immune-inflammatory responses. 237 STEMI-related DEGs were screened, which were involved in innate immune response and myeloid leukocyte activation. M0 macrophages and dendritic cells were markedly higher in both anaphylactic and STEMI samples compared with healthy controls, while CD4+ naïve T cells and CD8+ T cells were significantly lower. Enrichment analysis of 33 common DEGs illustrated shared biological processes of anaphylaxis and STEMI, including cytokine-mediated signaling pathway, response to reactive oxygen species, and positive regulation of defense response. Six hub genes were identified, and their expression levels were positively correlated with M0 macrophage abundance and negatively correlated with CD4+ naïve T cell abundance. In external anaphylactic and STEMI samples, five hub genes (IL1R2, FOS, MMP9, DUSP1, CLEC4D) were confirmed to be markedly upregulated. Moreover, experimentally induced anaphylactic mice developed impaired heart function featuring STEMI and significantly increased expression of the five hub genes. DUSP1 and CLEC4D were screened as blood diagnostic biomarkers of anaphylaxis and STEMI based on the logistic regression analysis. Conclusion: Anaphylaxis and STEMI share the biological processes of inflammation and defense responses. Macrophages, dendritic cells, CD8+ T cells, and CD4+ naïve T cells constitute an immune cell population that acts in both anaphylaxis and STEMI. Hub genes (DUSP1 and CLEC4D) identified here provide candidate genes for diagnosis, prognosis, and therapeutic targeting of STEMI in anaphylactic patients. [ABSTRACT FROM AUTHOR]

Background: Odontogenic keratocyst (OKC) is a relatively common odontogenic lesion characterized by local invasion in the maxillary and mandibular bones. In the pathological tissue slices of OKC, immune cell infiltrations are frequently observed. However, the immune cell profile and the molecular mechanism for immune cell infiltration of OKC are still unclear. We aimed to explore the immune cell profile of OKC and to explore the potential pathogenesis for immune cell infiltration in OKC. Methods: The microarray dataset GSE38494 including OKC and oral mucosa (OM) samples were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in OKC were analyzed by R software. The hub genes of OKC were performed by protein-protein interaction (PPI) network. The differential immune cell infiltration and the potential relationship between immune cell infiltration and the hub genes were performed by single-sample gene set enrichment analysis (ssGSEA). The expression of COL1A1 and COL1A3 were confirmed by immunofluorescence and immunohistochemistry in 17 OKC and 8 OM samples. Results: We detected a total of 402 differentially expressed genes (DEGs), of which 247 were upregulated and 155 were downregulated. DEGs were mainly involved in collagen-containing extracellular matrix pathways, external encapsulating structure organization, and extracellular structure organization. We identified ten hub genes, namely FN1, COL1A1, COL3A1, COL1A2, BGN, POSTN, SPARC, FBN1, COL5A1, and COL5A2. A significant difference was observed in the abundances of eight types of infiltrating immune cells between the OM and OKC groups. Both COL1A1 and COL3A1 exhibited a significant positive correlation with natural killer T cells and memory B cells. Simultaneously, they demonstrated a significant negative correlation with CD56dim natural killer cells, neutrophils, immature dendritic cells, and activated dendritic cells. Immunohistochemistry analysis showed that COL1A1 (P = 0.0131) and COL1A3 (P < 0.001) were significantly elevated in OKC compared with OM. Conclusions: Our findings provide insights into the pathogenesis of OKC and illuminate the immune microenvironment within these lesions. The key genes, including COL1A1 and COL1A3, may significantly impact the biological processes associated with OKC. [ABSTRACT FROM AUTHOR]

Background: Aortic dissection (AD) is a life-threatening cardiovascular disease. Pathophysiologically, it has been shown that aortic wall inflammation promotes the occurrence and development of aortic dissection. Thus, the aim of the current research was to determine the inflammation-related biomarkers in AD. Methods: In this study, we conducted differentially expressed genes (DEGs) analysis using the GSE153434 dataset containing 10 type A aortic dissection (TAAD) and 10 normal samples downloaded from the Gene Expression Omnibus (GEO) database. The intersection of DEGs and inflammation-related genes was identified as differential expressed inflammation-related genes (DEIRGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for DEIRGs. We then constructed the protein–protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and identified hub genes using the Cytoscape plugin MCODE. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was used to construct a diagnostic model. Results: A total of 1728 DEGs were identified between the TAAD and normal samples. Thereafter, 61 DEIRGs are obtained by taking the intersection of DEGs and inflammation-related genes. The GO indicated that DEIRGs were mainly enriched in response to lipopolysaccharide, in response to molecules of bacterial origin, secretory granule membrane, external side of plasma, receptor ligand activity, and signaling receptor activator activity. KEGG analysis indicated that DEIRGs were mainly enriched in cytokine–cytokine receptor interaction, TNF signaling pathway, and proteoglycans in cancer. We identified MYC, SELL, HIF1A, EDN1, SERPINE1, CCL20, IL1R1, NOD2, TLR2, CD69, PLAUR, MMP14, and HBEGF as hub genes using the MCODE plug-in. The ROC indicated these genes had a good diagnostic performance for TAAD. Conclusion: In conclusion, our study identified 13 hub genes in the TAAD. This study will be of significance for the future development of a preventive therapy of TAAD. [ABSTRACT FROM AUTHOR]

Background: Massive cerebral infarction (MCI) causes severe neurological deficits, coma and can even result in death. Here, we identified hub genes and pathways after MCI by analyzing microarray data from a murine model of ischemic stroke and identified potential therapeutic agents for the treatment of MCI. Methods: Microarray expression profiling was performed using the GSE28731 and GSE32529 datasets from the Gene Expression Omnibus (GEO) database. Data from a sham group (n = 6 mice) and a middle cerebral artery occlusion (MCAO) group (n = 7 mice) were extracted to identify common dierentially expressed genes (DEGs). After identifying gene interactions, we generated a protein-protein interaction (PPI) network with Cytoscape software. Then, the MCODE plug-in in Cytoscape was used to determine key sub-modules according to MCODE scores. Enrichment analyses were then conducted on DEGs in the key sub-modules to evaluate their biological functions. Furthermore, hub genes were identified by generating the intersections of several algorithms in the cytohubba plug-in; these genes were then verified in other datasets. Finally, we used Connectivity MAP (CMap) to identify potential agents for MCI therapy. Results: A total of 215 common DEGs were identified and a PPI network was generated with 154 nodes and 947 edges. The most significant key sub-module had 24 nodes and 221 edges. Gene ontology (GO) analysis showed that the DEGs in this sub-module showed enrichment in inflammatory response, extracellular space and cytokine activity in terms of biological process, cellular component and molecular function, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that TNF signaling was the most enriched pathway. Myd88 and Ccl3 were identified as hub genes and TWS-119 was identified as the most potential therapeutic agent by CMap. Conclusions: Bioinformatic analysis identified two hub genes (Myd88 and Ccl3) for ischemic injury. Further analysis identified TWS-119 as the best potential candidate for MCI therapy and that this target may be associated with TLR/MyD88 signaling. [ABSTRACT FROM AUTHOR]

Introduction: The polypeptide molecule epidermal growth factor (EGF) is a multifactorial cytokine acting in cell growth, proliferation, and wound healing. Parkinson's disease, a number of cancers and inflammatory disorders have all been reported as correlated with serum EGF levels. Therefore, this study aims at investigating the association between pancreatic cancer and periodontitis via EGF and angiogenesis pathways. Materials and methods: Differentially expressed genes (DEGs) of periodontitis and pancreatic cancer were identified from the datasets retrieved from the Gene Expression Omnibus database. GeneMANIA was used to visualize the molecular interaction networks. By ranking the scores of each node of molecular interaction, important nodes of protein interactions were obtained. The hub genes were ranked and those with a degree = 20 were selected. A network of genes and their co-expression genes were analyzed. Gene ontology of the corresponding genes was also assessed. Results and discussion: There is a significant correlation between periodontitis and pancreatic cancer. The most significant biological process for upregulated genes involves angiogenesis, EGF receptor signaling pathway, cytokine mediated signaling pathway and chemokine signaling pathway. Molecular function enrichment assessment for most significant upregulated genes included chemokine receptor binding, catalytic activity, molecular function regulator, molecular transducer activity and transcription regulator activity. The top five ranked hub genes involved in the interactome are SLF2, PECAM1, LIFR, ESYT2 and GSDMC. Conclusions: A positive gene to gene interaction interactome between periodontal disease and pancreatic cancer was observed. [ABSTRACT FROM AUTHOR]

Objective: Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) are collectively known as Lewy body dementia (LBD). Considering the heterogeneous nature of LBD and the different constellations of symptoms with which patients can present, the exact molecular mechanism underlying the differences between these two isoforms is still unknown. Therefore, this study aimed to explore the biomarkers and potential mechanisms that distinguish between PDD and DLB. Methods: The mRNA expression profile dataset of GSE150696 was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between 12 DLB and 12 PDD were identified from Brodmann area 9 of human postmortembrains using GEO2R. A series of bioinformaticsmethods were applied to identify the potential signaling pathways involved, and a protein-protein interaction (PPI) network was constructed. Weighted gene co-expression network analysis (WGCNA) was used to further investigate the relationship between gene co-expression and different LBD subtypes. Hub genes that are strongly associated with PDD and DLB were obtained from the intersection of DEGs and selected modules by WGCNA. Results: A total of 1,864 DEGs between PDD and DLB were filtered by the online analysis tool GEO2R. We found that the most significant GO- and KEGG-enriched terms are involved in the establishment of the vesicle localization and pathways of neurodegeneration-multiple diseases. Glycerolipid metabolism and viral myocarditis were enriched in the PDD group. A B-cell receptor signaling pathway and one carbon pool by folate correlated with DLB in the results obtained from the GSEA. We found several clusters of co-expressed genes which we designated by colors in our WGCNA analysis. Furthermore, we identified seven upregulated genes, namely, SNAP25,GRIN2A,GABRG2,GABRA1,GRIA1, SLC17A6, and SYN1, which are significantly correlated with PDD. Conclusion: The seven hub genes and the signaling pathways we identified may be involved in the heterogeneous pathogenesis of PDD and DLB. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to identify autophagy-related genes (ARGs) associated with non-obstructive azoospermia and explore the underlying molecular mechanisms. Methods: Two datasets associated with azoospermia were downloaded from the Gene Expression Omnibus database, and ARGs were obtained from the Human Autophagy-dedicated Database. Autophagy-related differentially expressed genes were identified in the azoospermia and control groups. These genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, protein-protein interaction (PPI) network, and functional similarity analyses. After identifying the hub genes, immune infiltration and hub gene-RNA-binding protein (RBP)-transcription factor (TF)-miRNA-drug interactions were analyzed. Results: A total 46 differentially expressed ARGs were identified between the azoospermia and control groups. These genes were enriched in autophagy-associated functions and pathways. Eight hub genes were selected from the PPI network. Functional similarity analysis revealed that HSPA5 may play a key role in azoospermia. Immune cell infiltration analysis revealed that activated dendritic cells were significantly decreased in the azoospermia group compared to those in the control groups. Hub genes, especially ATG3, KIAA0652, MAPK1, and EGFR were strongly correlated with immune cell infiltration. Finally, a hub gene-miRNA-TF-RBP-drug network was constructed. Conclusion: The eight hub genes, including EGFR, HSPA5, ATG3, KIAA0652, and MAPK1, may serve as biomarkers for the diagnosis and treatment of azoospermia. The study findings suggest potential targets and mechanisms for the occurrence and development of this disease. [ABSTRACT FROM AUTHOR]

Asthma is a chronic airway inflammatory disease caused by multiple genetic and environmental factors. This study mainly sought to provide potential therapeutic targets and biomarkers for neutrophilic asthma (NA). Three gene expression profiling datasets were obtained from the Genome Expression Omnibus (GEO) database. GSE45111 and GSE41863 were used to identify hub genes and potential biomarkers, and GSE137268 was used for data verification. We verified the repeatability of intragroup data and identified differentially expressed genes (DEGs). Then, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs, and a protein–protein interaction (PPI) network was constructed to identify the hub genes. Finally, receiver operating characteristic (ROC) analysis was used to verify the ability of the hub genes to differentiate between NA and eosinophilic asthma (EA). In this study, we identified 411 DEGs by comprehensive analysis of NA/EA patients and NA/healthy controls (HCs). Ten hub genes (CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, IL6R, JAK3, CD48, and GNG2) were identified from the PPI network. Finally, based on the ROC analysis, 7 genes showed good diagnostic value for discriminating NA from EA—CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, and IL6R (AUC > 0.7). We identified 7 hub genes that can distinguish NA from EA. The IL-8-mediated signaling may be the primary pathway to determine the NA phenotype in asthma. CXCR1/2 and S100A12 may be the primary genes determining the NA phenotype. CXCR1/2 and S100A12 might be biomarkers and new therapeutic targets for NA. Supplemental data for this article is available online at at [ABSTRACT FROM AUTHOR]

Arteriovenous fistula (AVF) is the most widely used hemodialysis vascular access in China. However, stenosis of the AVF limits its use. The mechanism of AVF stenosis is currently unknown. Therefore, the purpose of our study was to explore the mechanisms of AVF stenosis. In this study, we identified the differentially expressed genes (DEGs) based on the Gene Expression Omnibus (GEO) dataset (GSE39488) between venous segments of AVF and normal veins. A protein–protein interaction (PPI) network was constructed to identify hub genes of AVF stenosis. Finally, six hub genes (FOS, NR4A2, EGR2, CXCR4, ATF3, and SERPINE1) were found. Combined with the results of the PPI network analysis and literature search, FOS and NR4A2 were selected as the target genes for further investigation. We validated the bioinformatic results via reverse transcription PCR (RT-PCR) and Western blot analyses on human and rat samples. The expression levels of the mRNA and protein of FOS and NR4A2 were upregulated in both human and rat samples. In summary, we found that FOS may play an important role in AVF stenosis, which could be a potential therapeutic target of AVF stenosis. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) affects reproductive-age women. This condition causes infertility, insulin resistance, obesity, and heart difficulties. The molecular basis and mechanism of PCOS might potentially generate effective treatments. Long non-coding RNAs (lncRNAs) show control over multifactorial disorders' growth and incidence. Numerous studies have emphasized its significance and alterations in PCOS. We used bioinformatic methods to find novel dysregulated lncRNAs in PCOS. To achieve this objective, the gene expression profile of GSE48301, comprising PCOS patients and normal control tissue samples, was evaluated using the R limma package with the following cut-off criterion: p-value < 0.05. Firstly, weighted gene co-expression network analysis (WGCNA) was used to determine the co-expression genes of lncRNAs; subsequently, hub gene identification and pathway enrichment analysis were used. With the defined criteria, nine novel dysregulated lncRNAs were identified. In WGCNA, different colors represent different modules. In the current study, WGCNA resulted in turquoise, gray, blue, and black co-expression modules with dysregulated lncRNAs. The pathway enrichment analysis of these co-expressed modules revealed enrichment in PCOS-associated pathways, including gene expression, signal transduction, metabolism, and apoptosis. In addition, CCT7, EFTUD2, ESR1, JUN, NDUFAB1, CTTNB1, GRB2, and CTNNB1 were identified as hub genes, and some of them have been investigated in PCOS. This study uncovered nine novel PCOS-related lncRNAs. To confirm how these lncRNAs control translational modification in PCOS, functional studies are required. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) is not only a common complication in patients with end-stage renal disease (ESRD) but also a major cause of death. Although clinical studies have shown that there is a close relationship between them, the mechanism of its occurrence is unclear. The aim of this study is to explore the molecular mechanisms between HF and ESRD through comprehensive bioinformatics analysis, providing a new perspective on the crosstalk between these two diseases. Methods: The HF and ESRD datasets were downloaded from the Gene Expression Omnibus (GEO) database; we identified and analyzed common differentially expressed genes (DEGs). First, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analyses (GSVA) were applied to explore the potential biological functions and construct protein−protein interaction (PPI) networks. Also, four algorithms, namely, random forest (RF), Boruta algorithm, logical regression of the selection operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE), were used to identify the candidate genes. Subsequently, the diagnostic efficacy of hub genes for HF and ESRD was evaluated using eXtreme Gradient Boosting (XGBoost) algorithm. CIBERSORT was used to analyze the infiltration of immune cells. Thereafter, we predicted target microRNAs (miRNAs) using databases (miRTarBase, TarBase, and ENOCRI), and transcription factors (TFs) were identified using the ChEA3 database. Cytoscape software was applied to construct mRNA−miRNA−TF regulatory networks. Finally, the Drug Signatures Database (DSigDB) was used to identify potential drug candidates. Results: A total of 68 common DEGs were identified. The enrichment analysis results suggest that immune response and inflammatory factors may be common features of the pathophysiology of HF and ESRD. A total of four hub genes (BCL6, CCL5, CNN1, and PCNT) were validated using RF, LASSO, Boruta, and SVM-RFE algorithms. Their AUC values were all greater than 0.8. Immune infiltration analysis showed that immune cells such as macrophages, neutrophils, and NK cells were altered in HF myocardial tissue, while neutrophils were significantly correlated with all four hub genes. Finally, 11 target miRNAs and 10 TFs were obtained, and miRNA−mRNA−TF regulatory network construction was performed. In addition, 10 gene-targeted drugs were discovered. Conclusion: Our study revealed important crosstalk between HF and ESRD. These common pathways and pivotal genes may provide new ideas for further clinical treatment and experimental studies. [ABSTRACT FROM AUTHOR]

Grape (Vitis vinifera L.) transcriptomes have been extensively profiled in different tissues, at various developmental stages, under biotic or abiotic stresses, and other conditions. However, there is still no user-friendly tool to explore these precious data. A total of 1 019 microarray sample data were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database (NCBI GEO). Weighted gene correlation network analysis (WGCNA) was conducted. Forty-one gene co-expression modules were identified. Module functional annotation suggested that these modules performed different functions and were associated with experimental conditions ／ phenotypes. Hub genes, which bear important functions, were screened based on intramodule connectivity. Module genes with unknown functions were annotated according to the "guilt-by-association" rationale. Finally, a web tool VitisMod was developed as a new resource for grape gene function investigation. VitisMod is freely available at http: ／ ／ bioinformatics.fafu. edu.cn ／ grape. [ABSTRACT FROM AUTHOR]

Aldosterone‐producing adenoma (APA) is a common cause of secondary hypertension. This study aimed to explore the lncRNA‐miRNA‐mRNA competitive endogenous RNA (ceRNA) network to uncover molecular mechanism underlying APA. The mRNA and lncRNA expression data of APA and adjacent adrenal gland (AAG) from GSE60044, GSE64957 and GSE101894 were obtained from the Gene Expression Omnibus (GEO) database to analyse differentially expressed genes (DEGs) and lncRNAs (DElncs). Hub genes were identified by robust rank aggregation (RRA) and protein–protein interaction (PPI) network analysis. The miRcode and miRWalk network tools were used to construct the ceRNA network. 1526 upregulated and 1512 downregulated DEGs were identified, which are mainly enriched in extracellular matrix and Ca2+‐related GO terms. In the KEGG pathway analysis, Ca2+ signalling and the aldosterone synthesis and secretion pathways were enriched. ceRNA network included 2 lncRNAs, 9 miRNAs, and 13 mRNAs. The lncRNAs are MEG3 and LINC00115. The mRNAs included CCND1, TP53, GPRC5B, BMI1, COMMD3‐BMI1, ADAMTS15, STAT3, MMP2, SCN2B, CXCL12, HGF, FOS, and THBS1. Overall, this study conducted a ceRNA regulatory network analysis and identified that 2 lncRNAs and 13 mRNAs may contribute to the development of APA. These findings may provide novel diagnostic and intervention targets for APA. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) and Parkinson's disease (PD) are on the rise and greatly impact the quality of individuals' lives. Although accumulating evidence indicates a relationship between OA and PD, the particular interactions connecting the two diseases have not been thoroughly examined. Therefore, this study explored the association through genetic characterization and functional enrichment. Four datasets (GSE55235, GSE12021, GSE7621, and GSE42966) were chosen for assessment and validation from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was implemented to determine the most relevant genes for clinical features. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to explore the biological processes of common genes, and to display the interrelationships between common genes, the STRING database and the application Molecular Complex Detection Algorithm (MCODE) of Cytoscape software were leveraged to get hub genes. By intersecting the common genes with the differentially expressed genes (DEGs) acquired from GSE12021 and GSE42966, the hub genes were identified. Finally, we validated the diagnostic efficacy of hub genes and explored their correlation with 22 immune infiltrating cells. As a consequence, we discovered 71 common genes, most of which were functionally enriched in antigen processing and presentation, mitochondrial translation, the mRNA surveillance pathway, and nucleocytoplasmic transport. Furthermore, WDR43 was found by intersecting eight hub genes with 28 DEGs from the two validation datasets. Receiver Operating Characteristic (ROC) implied the diagnostic role of WDR43 in OA and PD. Immune infiltration research revealed that T-cell regulatory (Tregs), monocytes, and mast cells resting were associated with the pathogenesis of OA and PD. WDR43 may provide key insights into the relationship between OA and PD. [ABSTRACT FROM AUTHOR]

Spinal cord injury (SCI) often leads to sensory and motor dysfunction. Two major factors that hinder spinal cord repair are local inflammation and glial scar formation after SCI, and thus appropriate immunotherapy may alleviate damage. To characterize changes in gene expression that occur during SCI and thereby identify putative targets for immunotherapy, here we analyzed the dataset GSE5296 (containing one control group and six SCI groups at different timepoints) to identify differentially‐expressed genes. Functional enrichment analysis was performed and a protein–protein interaction network was created to identify possible hub genes. Finally, we performed quantitative PCR to verify changes in gene expression. The CIBERSORT algorithm was used to analyze innate immune cell infiltration patterns. The dataset GSE162610 (containing one control group and three SCI groups at different timepoints) was analyzed to evaluate innate immune cell infiltration at the single‐cell level. The dataset GSE151371 (containing one control group [n = 10] and an SCI group [n = 38]) was used to detect the expression of hub genes in the blood from SCI patients. Differentially‐expressed innate immune‐related genes at each timepoint were identified, and the functions and related signaling pathways of these genes were examined. Six hub genes were identified and verified. We then analyzed the expression characteristics of these hub genes and characteristics of innate immune infiltration in SCI; finally, we examined ligand expression in the context of the CCL signaling pathway and COMPLEMENT signaling pathway networks. This study reveals the characteristics of innate immune cell infiltration and temporal expression patterns of hub genes, and may aid in the development of immunotherapies for SCI. [ABSTRACT FROM AUTHOR]