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Objective To identify the lipid metabolism-related metastasis risk genes for hepatocellular carcinoma (HCC) based on related databases, and to construct a predictive model for the prognosis of HCC patients in combination with other clinical risk factors. Methods R software was used to obtain the differentially expressed genes (DEGs) between the patients with primary HCC and those with metastatic HCC from the GEO database, and the DEGs associated with the prognosis of patients were identified. The HCC patients in TCGA database were divided into two groups based on hierarchical clustering, and the two groups were assessed in terms of epithelial-mesenchymal transition (EMT), lipid metabolism, and prognosis. The data in the ICGC database were used for validation of the above analysis. The LASSO regression model was used to obtain the lipid metabolism-related metastasis risk genes and determine their risk scores, and according to the median of risk scores, HCC patients in both TCGA and ICGC databases were divided into high and low risk groups to analyze the prognosis of patients. Univariate and multivariate Cox regression analyses were used to obtain independent risk factors for the prognosis of HCC patients, and a nomogram prognostic model was constructed. Western blot and oil red O staining were used to detect the lipid metabolism of Huh7 cells after treatment with the lipid metabolism inhibitor Fatostatin, and qPCR was used to measure the expression levels of lipid metabolism-related metastasis risk genes in Huh7 cells. Results A total of 159 DEGs were obtained from the patients with primary HCC and those with metastatic HCC in the GEO database, among which 65 DEGs were significantly associated with the overall survival (OS) of HCC patients. Based on the EMT score, the two groups of HCC patients obtained by clustering from the TCGA database were defined as high and low metastasis risk groups, respectively, and the patients in the high metastasis risk group tended to have a higher lipid metabolism score and a shorter OS. The validation results in the ICGC database were consistent with the results based on the TCGA database. The LASSO regression model was used to identify the lipid metabolism-related metastasis risk genes, and the high-risk group had a shorter OS. The lipid metabolism-related metastasis risk genes were combined with the independent risk factors for the prognosis of patients with HCC to construct a nomogram prognostic model. Cell experiments confirmed that after the treatment with Fatostatin, there were reductions in the expression of fatty acid synthase and the content of lipid droplets in Huh7 cells, as well as changes in the expression of a variety of lipid metabolism-related metastasis risk genes. Conclusion A total of 13 lipid metabolism-related metastasis risk genes are obtained based on related databases, which are combined with the clinical risk factors to construct a prognostic predictive model for HCC patients, and cell experiments are conducted to confirm that the lipid metabolism-related metastasis risk genes are closely associated with lipid metabolism.

Objective To establish a prognostic risk scoring model for patients with pancreatic ductal adenocarcinoma (PDAC) based on cell ferroptosis-related genes, and to investigate its application value. Methods The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus database, and Genotype-Tissue Expression database were used to obtain the transcriptome sequencing data of PDAC tissue and normal pancreatic tissues and the clinical data of PDAC patients including overall survival (OS), and the GeneCards database was used to obtain the data on cell ferroptosis-related genes. R software was used to identify the differentially expressed genes (DEGs) between PDAC tissue and normal pancreatic tissue. The univariate Cox regression analysis and LASSO regression were used to establish a prognostic risk scoring model for PDAC patients, and based on this risk scoring model, the PDAC cases in TCGA database were divided into low- and high-risk groups. The Kaplan-Meier survival curves were plotted to compare OS between the two groups. CCK-8 assay and Western blotting were used to further validate the association between the key genes of cell ferroptosis in the risk scoring model and cell ferroptosis in PDAC. Results A prognostic risk scoring model for PDAC patients was successfully established based on seven cell ferroptosis-related genes, i.e., SLC6A14, DKK1, KRT19, AURKA, EMP1, ANXA2, and LGALS3, and the Kaplan-Meier survival curve showed that the low-risk group had a significantly longer OS than the high-risk group (P

Objective To investigate the expression of the growth factor receptor-bound protein-2 (GRB2) gene in renal carcinoma and its influence on prognosis. Methods The ONCOMINE database was used to obtain the data on the expression of the GRB2 gene in renal carcinoma, and the TCGA database was used to analyze the difference in the expression of the GRB2 gene between renal carcinoma tissue and normal tissue, as well as the association between the expression of the GRB2 gene and the prognosis of patients. The STRING database was used to construct the protein-protein interaction network of the GRB2 gene. Quantitative real-time PCR was used to measure the relative expression level of the GRB2 gene in six renal carcinoma tissue samples and corresponding adjacent normal tissue samples. Results The expression of the GRB2 gene in renal carcinoma tissue was significantly higher than that in normal renal tissue (t=8.21,P

Background: This study performs a detailed bioinformatics and machine learning analysis to investigate the genetic foundations of membranous nephropathy (MN) in lung adenocarcinoma (LUAD)., Methods: In this study, the gene expression profiles of MN microarray datasets (GSE99339) and LUAD dataset (GSE43767) 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 with R Cluster Profiler package according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Machine learning algorithms, including LASSO regression, support vector machine (SVM), Random Forest, and Boruta analysis, were applied to identify hubgenes linked to LUAD-associated MN. These genes' prognostic values were evaluated in the TCGA-LUAD cohort and validated through immunohistochemistry on renal biopsy specimens., Results: A total of 36 DEGs in common were identified for downstream analyses. Functional enrichment analysis highlighted the involvement of the Toll-like receptor 4 pathway and several immune recognition pathways in LUAD-associated MN. COL3A1, PSENEN, RACGAP1, and TNFRSF10B were identified as hub genes in LUAD-associated MN using machine learning algorithms. ROC analysis demonstrated their effective discrimination of MN with high accuracy. Survival analysis showed that lung adenocarcinoma patients with higher expression of these genes had significantly reduced overall survival. In patients with lung adenocarcinoma-associated MN, RACGAP1, COL3A1, PSENEN, and TNFRSF10B were higher expressed in the glomerular, especially RACGAP1, indicating an important role in the pathogenesis of LUAD-associated membranous nephropathy., Conclusions: Our study underscores the critical role of RACGAP1, COL3A1, PSENEN, and TNFRSF10B in the development of LUAD-associated MN, providing important insights for future research and the development of potential therapeutic strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Ischemic cardiomyopathy (IC) is primarily due to long-term ischemia/hypoxia of the coronary arteries, leading to impaired cardiac contractile or diastolic function. A new form of cell death induced by copper, called "cuproptosis" is related to the development and progression of multiple diseases. The cuproptosis-related gene (CuGs) plays an important role in acute myocardial infarction, while the specific mechanisms of CuGs in ischemic cardiomyopathy remain unclear., Methods: The expressions of CuGs and their immune characteristics were analyzed with the IC datasets obtained from the Gene Expression Omnibus, namely GSE5406 and GSE57338, identifying core genes associated with IC development. By comparing RF, SVM, GLM and XGB models, the optimal machine learning model was selected. The expression of marker genes was validated based on the GSE57345, GSE48166 and GSE42955 datasets. Construct a CeRNA network based on core genes. Therapeutic chemiacals targeting core genes were acquired using the CTD database, and molecular docking was performed using Autodock vina software. By ligating the left anterior descending (LAD) coronary artery, an IC mouse model is established, and core genes were experimentally validated using Western blot (WB) and immunohistochemistry (IHC) methods., Results: We identified 14 CuGs closely associated with the onset of IC. The SVM model exhibited superior discriminative power (AUC = 0.914), with core genes being DLST, ATP7B, FDX1, SLC31A1 and DLAT. Core genes were validated on the GSE42955, GSE48166 and GSE57345 datasets, showing excellent performance (AUC = 0.943, AUC = 0.800, and AUC = 0.932). The CeRNA network consists of 218 nodes and 264 lines, including 5 core diagnostic genes, 52 miRNAs, and 161 lncRNAs. Chemicals predictions indicated 8 chemicals have therapeutic effects on the core diagnostic genes, with benzo(a)pyrene molecular docking showing the highest affinity (-11.3 kcal/mol). Compared to the normal group, the IC group,which was established by LAD ligation, showed a significant decrease in LVEF as indicated by cardiac ultrasound, and increased fibrosis as shown by MASSON staining, WB results suggest increased expression of DLST and ATP7B, and decreased expression of FDX1, SLC31A1 and DLAT in the myocardial ischemic area (p < 0.05), which was also confirmed by IHC in tissue sections., Conclusion: In summary, this study comprehensively revealed that DLST, ATP7B, FDX1, SLC31A1 and DLAT could be identified as potential immunological biomarkers in IC, and validated through an IC mouse model, providing valuable insights for future research into the mechanisms of CuGs and its diagnostic value to IC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Diagnosing individuals with complex genetic diseases is a challenging task. Computational methodologies exploit information at the genotype level by taking into account single nucleotide polymorphisms (SNPs) leveraging the results of genome-wide association studies analysis to assign a statistical significance to each SNP. Recent methodologies extend such an approach by aggregating SNP significance at the genetic level to identify genes that are related to the condition under study. However, such methodologies still suffer from the initial SNP analysis limitations. Here, we present DiGAS, a tool for diagnosing genetic conditions by computing significance, by means of SNP information, directly at the complex level of genetic regions. Such an approach is based on a generalized notion of allele spectrum, which evaluates the complete genetic alterations of the SNP set belonging to a genetic region at the population level. The statistical significance of a region is then evaluated through a differential allele spectrum analysis between the conditions of individuals belonging to the population. Tests, performed on well-established datasets regarding Alzheimer's disease, show that DiGAS outperforms the state of the art in distinguishing between sick and healthy subjects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Anesthesia serves as a pivotal tool in modern medicine, creating a transient state of sensory deprivation to ensure a pain-free surgical or medical intervention. While proficient in alleviating pain, anesthesia significantly modulates brain dynamics, metabolic processes, and neural signaling, thereby impairing typical cognitive functions. Furthermore, anesthesia can induce notable impacts such as memory impairment, decreased cognitive function, and diminished intelligence, emphasizing the imperative need to explore the concealed repercussions of anesthesia on individuals. In this investigation, we aggregated gene expression profiles (GSE64617, GSE141242, GSE161322, GSE175894, and GSE178995) from public repositories following second-generation sequencing analysis of various anesthetics. Through scrutinizing post-anesthesia brain tissue gene expression utilizing Gene Set Enrichment Analysis (GSEA), Robust Rank Aggregation (RRA), and Weighted Gene Co-expression Network Analysis (WGCNA), this research aims to pinpoint pivotal genes, pathways, and regulatory networks linked to anesthesia. This undertaking not only enhances comprehension of the physiological changes brought about by anesthesia but also lays the groundwork for future investigations, cultivating new insights and innovative perspectives in medical practice., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024. Published by Elsevier Ltd.)

Objectives: To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value., Methods: Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored using Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts., Results: Fourteen DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion., Conclusion: A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment.

The incidence of Alzheimer's disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis‑related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

Sepsis is a highly lethal disease that poses a serious threat to human health. Increasing evidence indicates that neutrophil extracellular traps (NETs) are key factors in the pathological progression of sepsis. This study aims to screen potential biomarkers for sepsis and delve into their regulatory function in the pathogenesis. We downloaded 6 microarray datasets from the Gene Expression Omnibus (GEO) database, with 4 as the training sets and 2 as the validation sets. NETs-related genes (NRGs) were obtained from relevant literature. Differential expression analysis was performed on four training sets separately. We intersected differentially expressed genes (DEGs) from the four training sets and NRGs, finally resulting in 19 NETs-related sepsis genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) unearthed that NETs-related sepsis genes were majorly abundant in functions and pathways such as defense response to bacterium and Neutrophil extracellular trap formation. Using the PPI network, the MCC algorithm, and the MCODE algorithm in the CytoHubba plugin, 7 sepsis hub genes (ELANE, TLR4, MPO, PADI4, CTSG, MMP9, S100A12) were identified. ROC curve for each Hub gene in the training and validation sets were plotted, which revealed that the Area Under Curve (AUC) values are all greater than 0.6, indicating good classification ability. A total of 349 miRNAs targeting Hub genes were predicted in the mirDIP database, and 620 lncRNAs targeting miRNAs were predicted in the ENCORI database. The ceRNA regulatory network was constructed using Cytoscape software. Finally, we employed the cMAP database to predict small molecular complexes as potentially effective drugs for the treatment of sepsis, such as chloroquine, harpagoside, and PD-123319. In conclusion, this project successfully identified 7 core genes, which may serve as promising candidates for novel sepsis biomarkers. Meanwhile, we constructed a related ceRNA network and predicted potential targeted drugs, providing potential therapeutic targets and treatment strategies for sepsis patients., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024. Published by Elsevier Inc.)

Background: This study aims to investigate cell-free DNA (cfDNA) methylation of genes involved in some immune system targets as biomarkers of radioresistance in patients with non-metastatic rectal cancer., Methods: Gene expression (GSE68204, GPL6480, and GSE15781) and DNA methylation profiles (GSE75548 and GSE139404) of rectal cancer patients were obtained from the Gene Expression Omnibus (GEO) database. GEO2R and FunRich software were first used to identify genes with significant expression differences. Enricher softwer was then used to analyze Gene Ontology and detect pathway enrichment of hub genes. Blood samples were then taken from 43 rectal cancer patients. After cfDNA extraction from samples, it was treated with bisulfite and analyzed by methylation-specific PCR., Results: 1088 genes with high and 629 with low expression were identified by GEO2R and FunRich software. A total of five high-expression hub genes, including CDH24, FGF18, CCND1, IFITM1, UBE2V1, and three low-expression hub genes, including CBLN2, VIPR2, and IRF4, were identified from UALCAN and DNMIVD databases. Methylation-specific PCR indicated a significant difference in hub gene methylation between cancerous and non-cancerous individuals. Radiochemotherapy significantly affected hub gene methylation. There was a considerable difference in the methylation rate of hub genes between patients who responded to radiochemotherapy and those who did not., Conclusions: Evaluating gene methylation patterns might be an appropriate diagnostic tool to predict radiochemotherapy response and develop targeted therapeutic agents., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Endometrial carcinoma is one of most common malignant tumors in women, and ferroptosis is closely related to the development and treatment of endometrial carcinoma. The aim of this study was to screen ferroptosis-related genes associated with endometrial carcinoma and predict targeted drugs through bioinformatics. 761 differentially expressed genes were obtained by the dataset GSE63678 from the GEO database, and most of the genes were enriched in the KEGG&#95;CELL&#95;CYCLE and KEGG&#95;OOCYTE&#95;MEIOSIS signaling pathways. 22 ferroptosis-differentially expressed genes were obtained by intersection with the FerrDb database. These genes were involved in biological processes including macromolecular complex assembly and others, and involved in signal pathways including glutathione metabolism, p53 signaling pathway and others. CDKN2A, IDH1, NRAS, TFRC and GOT1 were obtained as hub genes by PPI network analysis. GEPIA showed that CDKN2A, IDH1, NRAS and TFRC were significantly expressed in endometrial carcinoma. Immunohistochemical results showed that CDKN2A, NRAS and TFRC were significantly expressed in endometrial carcinoma clinical tissue samples. The ROC constructed by TCGA database showed that CDKN2A, NRAS and TFRC had significant value in the diagnosis of endometrial carcinoma, and all had prognostic efficacy. 136,572-09-3 BOSS and others were identified as potential targeted drugs for endometrial carcinoma targeting ferroptosis. Our study has shown that ferroptosis-related genes CDKN2A, NRAS and TFRC are diagnostic markers of endometrial carcinoma, and 136,572-09-3 BOSS, methyprylon BOSS, daunorubicin CTD 00005752, nitroglycerin BOSS and dUTP BOSS, IRON BOSS, Imatinib mesylate BOSS, 2-Butanone BOSS, water BOSS, and L-thyroxine BOSS may be potential therapeutic drugs., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Angiogenesis is mainly regulated by the delivery of VEGF-dependent signaling to cells. However, the angiogenesis mechanism regulated by VEGF-induced miRNA is still not understood. After VEGF treatment in HUVECs, we screened the changed miRNAs through small-RNA sequencing and found VEGF-induced miR-4701-3p. Furthermore, the GFP reporter gene was used to reveal that TOB2 expression was regulated by miR-4701-3p, and it was found that TOB2 and miR-4701-3p modulation could cause angiogenesis in an in-vitro angiogenic assay. Through the luciferase assay, it was confirmed that the activation of the angiogenic transcription factor MEF2 was regulated by the suppression and overexpression of TOB2 and miR-4701-3p. As a result, MEF2 downstream gene mRNAs that induce angiogenic function were regulated. We used the NCBI GEO datasets to reveal that the expression of TOB2 and MEF2 was significantly changed in cardiovascular disease. Finally, it was confirmed that the expression of circulating miR-4701-3p in the blood of myocardial infarction patients was remarkably increased. In patients with myocardial infarction, circulating miR-4701-3p was increased regardless of age, BMI, and sex, and showed high AUC levels in specificity and sensitivity analysis (AUROC) (AUC = 0.8451, 95 % CI 0.78-0.90). Our data showed TOB2-mediated modulation of MEF2 and its angiogenesis by VEGF-induced miR-4701-3p in vascular endothelial cells. In addition, through bioinformatics analysis using GEO data, changes in TOB2 and MEF2 were revealed in cardiovascular disease. We suggest that circulating miR-4701-3p has high potential as a biomarker for myocardial infarction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Heat stroke (HS) is a severe medical condition characterized by a systemic inflammatory response that may precipitate multi-organ dysfunction, with a particular predilection for inducing profound central nervous system impairments. We aim to employ bioinformatics techniques for the retrieval and analysis of genes associated with heat stroke-induced neurological damage. We performed a comprehensive analysis of the GSE64778 dataset from the Sequence Read Archive, resulting in the identification of 1178 significantly differentially expressed genes (DEGs). We retrieved 2914 genes associated with heat stroke from the GeneCards database and 2377 genes associated with heat stroke from the Comparative Toxicogenomics Database (CTD). The intersection of the top 300 DEGs in the GSE64778 dataset intersected with the search results of GeneCards and CTD, yielding 25 final candidates for DEGs associated with heat stroke. Gene Ontology functional annotation results indicated that the target genes were mainly involved in apoptosis, stress response, and negative regulation of cellular processes and function in processes such as protein dimerization and protein binding. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed a predominant enrichment of candidate target genes within the PI3K-AKT signaling pathway. Subsequent protein-protein interaction network analysis highlighted HSP90aa1 as a central gene, indicating its pivotal role by possessing the highest number of edges among the genes enriched in the PI3K-AKT signaling pathway. Quantitative reverse transcription-polymerase chain reaction analysis performed on blood samples from patients validated the expression of Hsp90aa1 in individuals exhibiting early neurological damage in HS, consistent with the findings from the mRNA bioinformatics analysis. Additionally, the bioinformatics analysis of the upstream microRNAs (miRNAs) regulating HSP90aa1 and the target miRNAs associated with candidate long non-coding RNAs (lncRNAs) identified three lncRNAs, eight miRNAs, and one mRNA in the regulatory network. The DIANA Tools database and algorithms were employed for pathway enrichment and correlation analysis, revealing a significant association between LOC102547734 and MIR-206-3p, with the latter being identified as a target binding site Moreover, the analysis unveiled a correlation between MIR-206-3p and HSP90aa1, implicating the latter as a potential target binding site within the regulatory network., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Comprehensive information on genetic alterations in salivary gland cancer (SGC) is limited. This study aimed to elucidate the genetic and clinical characteristics of patients with SGC using the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database, a Japanese national genomic database. We analyzed data of 776 patients with SGC registered in the C-CAT database between June 1, 2019, and June 30, 2023. Adenoid cystic carcinoma was the most common histologic type, followed by salivary duct carcinoma (SDC) and adenocarcinoma not otherwise specified. Genetic data of 681 patients receiving FoundationOne® CDx were analyzed. We identified specific features of the combination of TP53 and CDKN2A alterations among the histological types. Specific LYN amplification was mainly detected in carcinoma ex pleomorphic adenoma and myoepithelial carcinoma. For SDC, the frequency of ERBB2 and BRAF alterations were higher in cases with metastatic lesions than in those with primary lesions. Although 28.6% patients were offered recommended treatment options, only 6.8% received the recommended treatments. This study highlights the differences in genetic alterations among the histological types of SGC, with comprehensive genomic profiling tests revealing lower drug accessibility. These findings could contribute to the development of personalized treatment for patients with SGC., (© 2024 UICC.)

The objective of the study was to explore the relationship and potential mechanism between Parkinson's disease (PD) and diabetic retinopathy (DR) using bioinformatics methods. We first examined the causal relationship between PD and DR by Mendelian randomization (MR) analysis. The datasets of PD and DR patients from the Gene Expression Omnibus database were used to identify differentially expressed genes (DEGs). Then, we performed the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and immune infiltration analysis. We also constructed a protein-protein interaction network and receiver operating characteristic (ROC) curve. Finally, an online website was used for drug prediction. The MR analysis demonstrated a causal relationship between DR and PD (odds ratio [OR] = 0.86; 95% confidence interval [CI] 0.79-0.93; p = 3.24E - 04), in which DR acted as a protective factor against PD. There were 81 DEGs identified from the PD and DR datasets, of which 29 genes had protein interaction relationships, and enrichment analysis showed that these genes were mainly related to immune pathways. As indicated by immune cell infiltration analysis, the expression of immune cells between PD and the control group was significantly different. ROC curve results showed five genes had diagnostic value, and several potential chemical compounds were predicted to target the genes. Our findings demonstrate a reduced risk of PD in patients with DR. We also found that PD and DR are closely related in terms of inflammation, which provides clues for further exploring the common mechanisms and interaction of these two diseases., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Naïve CD4 + T cells and their differentiated counterparts play a significant regulatory role in the tumor immune microenvironment, yet their effects on lung adenocarcinoma (LUAD) are not fully understood., Methods: We utilized Mendelian randomization to assess the causal association between naïve CD4 + T cells and LUAD. Employing a modified single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm with The Cancer Genome Atlas (TCGA) database, we determined the infiltration levels of naïve CD4 + T cells and their differentiation subtypes and investigated their correlation with clinical characteristics. Potential regulatory pathways of T helper cells were identified through Mantel tests and Kyoto Encyclopedia of Genes and Genomes (KEGG) database enrichment analysis., Results: Mendelian randomization analysis revealed an inhibitory effect of naïve CD4 + T cells on LUAD (false discovery rate < 0.05), which was corroborated by observational experiments using TCGA database. Specifically, T helper cell type 2 demonstrated a promotive effect on LUAD in terms of overall, disease-free, and progression-free survival (p < 0.05). Moreover, regulatory T cells exhibited a protective effect on LUAD in terms of disease-specific survival (p < 0.01). Concurrently, we explored the overall impact of naïve CD4 + T cell differentiation subtypes on LUAD, revealing upregulation in pathways such as neutrophil degranulation, MAPK family signaling pathways, and platelet activation, signaling, and aggregation., Conclusion: Naïve CD4 + T cells and their differentiated counterparts play essential regulatory roles in the tumor immune microenvironment, demonstrating bidirectionality in their effects.Thus, elucidating the mechanisms and developing novel cell differentiation-inducing agents will benefit anti-cancer therapy., (© 2024. The Author(s).)

The growing number of genes identified in relation to epilepsy represents a major breakthrough in diagnosis and treatment, but experts face the challenge of efficiently accessing and consolidating the vast amount of genetic data available. Therefore, we present the process of transforming data from different sources and formats into an Entity-Attribute-Value (EAV) model database. Combined with the use of standard coding systems, this approach will provide a scalable and adaptable database to present the data in a comprehensive way to experts via a dashboard.

MicroRNAs are key players involved in stress responses in plants and reports are available on the role of miRNAs in drought stress response in rice. This work reports the development of a database, RiceMetaSys: Drought-miR, based on the meta-analysis of publicly available sRNA datasets. From 28 drought stress-specific sRNA datasets, we identified 216 drought-responsive miRNAs (DRMs). The major features of the database include genotype-, tissue- and miRNA ID-specific search options and comparison of genotypes to identify common miRNAs. Co-localization of the DRMs with the known quantitative trait loci (QTLs), i.e., meta-QTL regions governing drought tolerance in rice pertaining to different drought adaptive traits, narrowed down this to 37 promising DRMs. To identify the high confidence target genes of DRMs under drought stress, degradome datasets and web resource on drought-responsive genes (RiceMetaSys: DRG) were used. Out of the 216 unique DRMs, only 193 had targets with high stringent parameters. Out of the 1081 target genes identified by Degradome datasets, 730 showed differential expression under drought stress in at least one accession. To retrieve complete information on the target genes, the database has been linked with RiceMetaSys: DRG. Further, we updated the RiceMetaSys: DRGv1 developed earlier with the addition of DRGs identified from RNA-seq datasets from five rice genotypes. We also identified 759 putative novel miRNAs and their target genes employing stringent criteria. Novel miRNA search has all the search options of known miRNAs and additionally, it gives information on their in silico validation features. Simple sequence repeat markers for both the miRNAs and their target genes have also been designed and made available in the database. Network analysis of the target genes identified 60 hub genes which primarily act through abscisic acid pathway and jasmonic acid pathway. Co-localization of the hub genes with the meta-QTL regions governing drought tolerance narrowed down this to 16 most promising DRGs. Database URL: http://14.139.229.201/RiceMetaSys&#95;miRNA Updated database of RiceMetaSys URL: http://14.139.229.201/RiceMetaSysA/Drought/., (© The Author(s) 2024. Published by Oxford University Press.)

Background: The availability of transcriptomic data for species without a reference genome enables the construction of de novo transcriptome assemblies as alternative reference resources from RNA-Seq data. A transcriptome provides direct information about a species' protein-coding genes under specific experimental conditions. The de novo assembly process produces a unigenes file in FASTA format, subsequently targeted for the annotation. Homology-based annotation, a method to infer the function of sequences by estimating similarity with other sequences in a reference database, is a computationally demanding procedure., Results: To mitigate the computational burden, we introduce HPC-T-Annotator, a tool for de novo transcriptome homology annotation on high performance computing (HPC) infrastructures, designed for straightforward configuration via a Web interface. Once the configuration data are given, the entire parallel computing software for annotation is automatically generated and can be launched on a supercomputer using a simple command line. The output data can then be easily viewed using post-processing utilities in the form of Python notebooks integrated in the proposed software., Conclusions: HPC-T-Annotator expedites homology-based annotation in de novo transcriptome assemblies. Its efficient parallelization strategy on HPC infrastructures significantly reduces computational load and execution times, enabling large-scale transcriptome analysis and comparison projects, while its intuitive graphical interface extends accessibility to users without IT skills., (© 2024. The Author(s).)

Idiopathic orbital inflammation, formerly known as NSOI (nonspecific orbital inflammation), is characterized as a spectrum disorder distinguished by the polymorphic infiltration of lymphoid tissue, presenting a complex and poorly understood etiology. Recent advancements have shed light on the HLF (Human lactoferrin), proposing its critical involvement in the regulation of hematopoiesis and the maintenance of innate mucosal immunity. This revelation has generated significant interest in exploring HLF's utility as a biomarker for NSOI, despite the existing gaps in our understanding of its biosynthetic pathways and operational mechanisms. Intersecting multi-omic datasets-specifically, common differentially expressed genes between GSE58331 and GSE105149 from the Gene Expression Omnibus and immune-related gene compendiums from the ImmPort database-we employed sophisticated analytical methodologies, including Lasso regression and support vector machine-recursive feature elimination, to identify HLF. Gene set enrichment analysis and gene set variation analysis disclosed significant immune pathway enrichment within gene sets linked to HLF. The intricate relationship between HLF expression and immunological processes was further dissected through the utilization of CIBERSORT and ESTIMATE algorithms, which assess characteristics of the immune microenvironment, highlighting a noteworthy association between increased HLF expression and enhanced immune cell infiltration. The expression levels of HLF were corroborated using data from the GSE58331 dataset, reinforcing the validity of our findings. Analysis of 218 HLF-related differentially expressed genes revealed statistically significant discrepancies. Fifteen hub genes were distilled using LASSO and SVM-RFE algorithms. Biological functions connected with HLF, such as leukocyte migration, ossification, and the negative regulation of immune processes, were illuminated. Immune cell analysis depicted a positive correlation between HLF and various cells, including resting mast cells, activated NK cells, plasma cells, and CD8 T cells. Conversely, a negative association was observed with gamma delta T cells, naive B cells, M0 and M1 macrophages, and activated mast cells. Diagnostic assessments of HLF in distinguishing NSOI showed promising accuracy. Our investigation delineates HLF as intricately associated with NSOI, casting light on novel biomarkers for diagnosis and progression monitoring of this perplexing condition., (© 2024. The Author(s).)

Background: Delayed cerebral ischemia (DCI) is a common and serious complication of subarachnoid hemorrhage (SAH). Its pathogenesis is not fully understood. Here, we developed a predictive model based on peripheral blood biomarkers and validated the model using several bioinformatic multi-analysis methods., Methods: Six datasets were obtained from the GEO database. Characteristic genes were screened using weighted correlation network analysis (WGCNA) and differentially expressed genes. Three machine learning algorithms, elastic networks-LASSO, support vector machines (SVM-RFE) and random forests (RF), were also used to construct diagnostic prediction models for key genes. To further evaluate the performance and predictive value of the diagnostic models, nomogram model were constructed, and the clinical value of the models was assessed using Decision Curve Analysis (DCA), Area Under the Check Curve (AUC), Clinical Impact Curve (CIC), and validated in the mouse single-cell RNA-seq dataset. Mendelian randomization(MR) analysis explored the causal relationship between SAH and stroke, and the intermediate influencing factors. We validated this by retrospectively analyzing the qPCR levels of the most relevant genes in SAH and SAH-DCI patients. This experiment demonstrated a statistically significant difference between SAH and SAH-DCI and normal group controls. Finally, potential small molecule compounds interacting with the selected features were screened from the Comparative Toxicogenomics Database (CTD)., Results: The fGSEA results showed that activation of Toll-like receptor signaling and leukocyte transendothelial cell migration pathways were positively correlated with the DCI phenotype, whereas cytokine signaling pathways and natural killer cell-mediated cytotoxicity were negatively correlated. Consensus feature selection of DEG genes using WGCNA and three machine learning algorithms resulted in the identification of six genes (SPOCK2, TRRAP, CIB1, BCL11B, PDZD8 and LAT), which were used to predict DCI diagnosis with high accuracy. Three external datasets and the mouse single-cell dataset showed high accuracy of the diagnostic model, in addition to high performance and predictive value of the diagnostic model in DCA and CIC. MR analysis looked at stroke after SAH independent of SAH, but associated with multiple intermediate factors including Hypertensive diseases, Total triglycerides levels in medium HDL and Platelet count. qPCR confirmed that significant differences in DCI signature genes were observed between the SAH and SAH-DCI groups. Finally, valproic acid became a potential therapeutic agent for DCI based on the results of target prediction and molecular docking of the characterized genes., Conclusion: This diagnostic model can identify SAH patients at high risk for DCI and may provide potential mechanisms and therapeutic targets for DCI. Valproic acid may be an important future drug for the treatment of DCI., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Genetically engineered mouse models (GEMMs) are vital for elucidating gene function and disease mechanisms. An overwhelming number of GEMM lines have been generated, but endeavors to collect and organize the information of these GEMMs are seriously lagging behind. Only a few databases are developed for the information of current GEMMs, and these databases lack biological descriptions of allele compositions, which poses a challenge for nonexperts in mouse genetics to interpret the genetic information of these mice. Moreover, these databases usually do not provide information on human diseases related to the GEMM, which hinders the dissemination of the insights the GEMM provides as a human disease model. To address these issues, we developed an algorithm to annotate all the allele compositions that have been reported with Python programming and have developed the genetically modified mice information database (GMMID; http://www.gmmid.cn), a user-friendly database that integrates information on GEMMs and related diseases from various databases, including National Center for Biotechnology Information, Mouse Genome Informatics, Online Mendelian Inheritance in Man, International Mouse Phenotyping Consortium, and Jax lab. GMMID provides comprehensive genetic information on >70 055 alleles, 65 520 allele compositions, and ∼4000 diseases, along with biologically meaningful descriptions of alleles and allele combinations. Furthermore, it provides spatiotemporal visualization of anatomical tissues mentioned in these descriptions, shown alongside the allele compositions. Compared to existing mouse databases, GMMID considers the needs of researchers across different disciplines and presents obscure genetic information in an intuitive and easy-to-understand format. It facilitates users in obtaining complete genetic information more efficiently, making it an essential resource for cross-disciplinary researchers. Database URL: http://www.gmmid.cn., (© The Author(s) 2024. Published by Oxford University Press.)

Background: Ulcerative colitis (UC) is a common chronic disease associated with inflammation and oxidative stress. This study aimed to construct a long noncoding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) network based on bioinformatics analysis and to explore oxidative stress-related genes underlying the pathogenesis of UC., Methods: The GSE75214, GSE48959, and GSE114603 datasets were downloaded from the Gene Expression Omnibus database. Following differentially expressed (DE) analysis, the regulatory relationships among these DERNAs were identified through miRDB, miRTarBase, and TargetScan; then, the lncRNA-miRNA-mRNA network was established. The Molecular Signatures Database (MSigDB) was used to search oxidative stress-related genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for functional annotation and enrichment analyses. Based on the drug gene interaction database DGIdb, drugs that interact with oxidative stress-associated genes were explored. A dextran sulfate sodium (DSS)-induced UC mouse model was used for experimental validation., Results: A total of 30 DE-lncRNAs, 3 DE-miRNAs, and 19 DE-mRNAs were used to construct a lncRNA-miRNA-mRNA network. By comparing these 19 DE-mRNAs with oxidative stress-related genes in MSigDB, three oxidative stress-related genes ( CAV1, SLC7A11 , and SLC7A5 ) were found in the 19 DEM sets, which were all negatively associated with miR-194. GO and KEGG analyses showed that CAV1, SLC7A11 , and SLC7A5 were associated with immune inflammation and steroid hormone synthesis. In animal experiments, the results showed that dexamethasone, a well-known glucocorticoid drug, could significantly decrease the expression of CAV1, SLC7A11 , and SLC7A5 as well as improve UC histology, restore antioxidant activities, inhibit inflammation, and decrease myeloperoxidase activity., Conclusion: SLC7A5 was identified as a representative gene associated with glucocorticoid therapy resistance and thus may be a new therapeutic target for the treatment of UC in the clinic., Competing Interests: The authors declare that they have no competing interests., (© 2024 Li et al.)

Background: Heart failure (HF), which is caused by cardiac overload and injury, is linked to significant mortality. Writers of RNA modification (WRMs) play a crucial role in the regulation of epigenetic processes involved in immune response and cardiovascular disease. However, the potential roles of these writers in the immunological milieu of HF remain unknown., Methods: We comprehensively characterized the expressions of 28 WRMs using datasets GSE145154 and GSE141910 to map the cardiac immunological microenvironment in HF patients. Based on the expression of WRMs, the immunological cells in the datasets were scored., Results: Single-cell transcriptomics analysis (GSE145154) revealed immunological dysregulation in HF as well as differential expression of WRMs in immunological cells from HF and non-HF (NHF) samples. WRM-scored immunological cells were positively correlated with the immunological response, and the high WRM score group exhibited elevated immunological cell infiltration. WRMs are involved in the differentiation of T cells and myeloid cells. WRM scores of T cell and myeloid cell subtypes were significantly reduced in the HF group compared to the NHF group. We identified a myogenesis-related resident macrophage population in the heart, Macro-MYL2, that was characterized by an increased expression of cardiomyocyte structural genes (MYL2, TNNI3, TNNC1, TCAP, and TNNT2) and was regulated by TRMT10C. Based on the WRM expression pattern, the transcriptomics data (GSE141910) identified two distinct clusters of HF samples, each with distinct functional enrichments and immunological characteristics., Conclusion: Our study demonstrated a significant relationship between the WRMs and immunological microenvironment in HF, as well as a novel resident macrophage population, Macro-MYL2, characterized by myogenesis. These results provide a novel perspective on the underlying mechanisms and therapeutic targets for HF. Further experiments are required to validate the regulation of WRMs and Macro-MYL2 macrophage subtype in the cardiac immunological milieu., (© 2024. The Author(s).)

Background: Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration., Methods: We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results., Results: WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings., Conclusions: HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration., (© 2024. The Author(s).)

Endometrial cancer (UCEC) is one of three major malignant tumors in women. The HOX gene regulates tumor development. However, the potential roles of HOX in the expression mechanism of multiple cell types and in the development and progression of tumor microenvironment (TME) cell infiltration in UCEC remain unknown. In this study, we utilized both the The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database to analyze transcriptome data of 529 patients with UCEC based on 39 HOX genes, combing clinical information, we discovered HOX gene were a pivotal factor in the development and progression of UCEC and in the formation of TME diversity and complexity. Here, a new scoring system was developed to quantify individual HOX patterns in UCEC. Our study found that patients in the low HOX score group had abundant anti-tumor immune cell infiltration, good tumor differentiation, and better prognoses. In contrast, a high HOX score was associated with blockade of immune checkpoints, which enhances the response to immunotherapy. The Real-Time quantitative PCR (RT-qPCR) and Immunohistochemistry (IHC) exhibited a higher expression of the HOX gene in the tumor patients. We revealed that the significant upregulation of the HOX gene in the epithelial cells can activate signaling pathway associated with tumour invasion and metastasis through single-cell RNA sequencing (scRNA-seq), such as nucleotide metabolic proce and so on. Finally, a risk prognostic model established by the positive relationship between HOX scores and cancer-associated fibroblasts (CAFs) can predict the prognosis of individual patients by scRNA-seq and transcriptome data sets. In sum, HOX gene may serve as a potential biomarker for the diagnosis and prediction of UCEC and to develop more effective therapeutic strategies., (© 2024. The Author(s).)

Cancer stemness plays an important role in cancer initiation and progression, and is the major cause of tumor invasion, metastasis, recurrence, and poor prognosis. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play a critical role in regulating cancer stemness. Here, we developed the ncStem database to record manually curated and predicted ncRNAs associated with cancer stemness. In total, ncStem contains 645 experimentally verified entries, including 159 long non-coding RNAs (lncRNAs), 254 microRNAs (miRNAs), 39 circular RNAs (circRNAs), and 5 other ncRNAs. The detailed information of each entry includes the ncRNA name, ncRNA identifier, disease, reference, expression direction, tissue, species, and so on. In addition, ncStem also provides computationally predicted cancer stemness-associated ncRNAs for 33 TCGA cancers, which were prioritized using the random walk with restart (RWR) algorithm based on regulatory and co-expression networks. The total predicted cancer stemness-associated ncRNAs included 11 132 lncRNAs and 972 miRNAs. Moreover, ncStem provides tools for functional enrichment analysis, survival analysis, and cell location interrogation for cancer stemness-associated ncRNAs. In summary, ncStem provides a platform to retrieve cancer stemness-associated ncRNAs, which may facilitate research on cancer stemness and offer potential targets for cancer treatment. Database URL: http://www.nidmarker-db.cn/ncStem/index.html., (© The Author(s) 2024. Published by Oxford University Press.)

Uncovering the full list of human ciliary genes holds enormous promise for the diagnosis of cilia-related human diseases, collectively known as ciliopathies. Currently, genetic diagnoses of many ciliopathies remain incomplete (1-3). While various independent approaches theoretically have the potential to reveal the entire list of ciliary genes, approximately 30% of the genes on the ciliary gene list still stand as ciliary candidates (4,5). These methods, however, have mainly relied on a single strategy to uncover ciliary candidate genes, making the categorization challenging due to variations in quality and distinct capabilities demonstrated by different methodologies. Here, we develop a method called CilioGenics that combines several methodologies (single-cell RNA sequencing, protein-protein interactions (PPIs), comparative genomics, transcription factor (TF) network analysis, and text mining) to predict the ciliary capacity of each human gene. Our combined approach provides a CilioGenics score for every human gene that represents the probability that it will become a ciliary gene. Compared to methods that rely on a single method, CilioGenics performs better in its capacity to predict ciliary genes. Our top 500 gene list includes 258 new ciliary candidates, with 31 validated experimentally by us and others. Users may explore the whole list of human genes and CilioGenics scores on the CilioGenics database (https://ciliogenics.com/)., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Non-alcoholic fatty liver disease (NAFLD) is a chronic hepatic disease. The incidence and prevalence of NAFLD have increased greatly in recent years, and there is still a lack of effective drugs. Autophagy plays an important role in promoting liver metabolism and maintaining liver homeostasis, and defects in autophagy levels are considered to be related to the development of NAFLD. However, the molecular mechanisms of autophagy in NAFLD still remain unknown. In this study, we identified 6 autophagy-associated hub genes using gene expression profiles obtained from the GSE48452 and GSE89632 datasets. Biomarkers were screened according to gene significance (GS) and module membership (MM) using weighted gene co-expression network analysis (WGCNA), and the immune infiltration landscape of the liver in NAFLD patients was explored using the CIBERSORT algorithm. Subsequently, we analyzed the relationship between liver non-parenchymal cells and autophagy-related hub genes using scRNA-seq data (GSE129516). Finally, we separated the NAFLD patients into two groups based on 6 hub genes by consensus clustering and screened 10 potential autophagy-related small molecules based on the cMAP database.

Background: Large-scale sequencing plays important roles in revealing the genomic map of ccRCC and predicting prognosis and therapeutic response to targeted drugs. However, the relevant clinical data is still sparse in Chinese population., Methods: Fresh tumor specimens were collected from 66 Chinese ccRCC patients, then the genomic RNAs were subjected to whole transcriptome sequencing (WTS). We comprehensively analyzed the frequently mutated genes from our hospital's cohort as well as TCGA-KIRC cohort., Results: VHL gene is the most frequently mutated gene in ccRCC. In our cohort, BAP1 and PTEN are significantly associated with a higher tumor grade and DNM2 is significantly associated with a lower tumor grade. The mutant type (MT) groups of BAP1 or PTEN, BAP1 or SETD2, BAP1 or TP53, BAP1 or MTOR, BAP1 or FAT1 and BAP1 or AR had a significantly correlation with higher tumor grade in our cohort. Moreover, we identified HMCN1 was a hub mutant gene which was closely related to worse prognosis and may enhance anti-tumor immune responses., Conclusions: In this preliminary research, we comprehensively analyzed the frequently mutated genes in the Chinese population and TCGA database, which may bring new insights to the diagnosis and medical treatment of ccRCC., (© 2024. The Author(s).)

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social and communication difficulties, along with repetitive behaviors. While genetic factors play a significant role in ASD, the precise genetic landscape remains complex and not fully understood, particularly in non-syndromic cases. The study performed an in silico comparison of three genetic databases. ClinVar, SFARI Gene, and AutDB were utilized to identify relevant gene subset and genetic variations associated with non-syndromic ASD. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis were conducted to elucidate the biological significance of the identified genes. The integrity of ASD-related gene subset and the distribution of their variations were statistically assessed. A subset of twenty overlapping genes potentially specific for non-syndromic ASD was identified. GSEA revealed enrichment of biological processes related to neuronal development and differentiation, synaptic function, and social skills, highlighting their importance in ASD pathogenesis. PPI network analysis demonstrated functional relationships among the identified genes. Analysis of genetic variations showed predominance of rare variants and database-specific distribution patterns. The results provide valuable insights into the genetic landscape of ASD and outline the genes and biological processes involved in the condition, while taking into account that the study relied exclusively on in silico analyses, which may be subject to biases inherent to database methodologies. Further research incorporating multi-omics data and experimental validation is warranted to enhance our understanding of non-syndromic ASD genetics and facilitate the development of targeted research, interventions and therapies., (© 2024. The Author(s).)

Background: Type 2 Diabetes Mellitus (T2D) and Osteoarthritis (OA) are both prevalent diseases that significantly impact the health of patients. Increasing evidence suggests that there is a big correlation between T2D and OA, but the molecular mechanisms remain elusive. The aims of this study are to investigate the shared biomarkers and potential molecular mechanisms in T2D combined with OA., Methods: T2D and OA-related differentially expressed genes (DEGs) were identified via bioinformatic analysis on Gene Expression Omnibus (GEO) datasets GSE26168 and GSE114007 respectively. Subsequently, extensive target prediction and network analysis were finished with Gene Ontology (GO), protein-protein interaction (PPI), and pathway enrichment with DEGs. The transcription factors (TFs) and miRNAs coupled in co-expressed DEGs involved in T2D and OA were predicted as well. The key genes expressed both in the clinical tissues of T2D and OA were detected with western blot and qRT-PCR assay. Finally, the most promising candidate compounds were predicted with the Drug-Gene Interaction Database (DGIdb) and molecular docking., Results: In this study, 209 shared DEGs between T2D and OA were identified. Functional analysis disclosed that these DEGs are predominantly related to ossification, regulation of leukocyte migration, extracellular matrix (ECM) structural constituents, PI3K/AKT, and Wnt signaling pathways. Further analysis via Protein-Protein Interaction (PPI) analysis and validation with external datasets emphasized MMP9 and ANGPTL4 as crucial genes in both T2D and OA. Our findings were validated through qRT-PCR and Western blot analyses, which indicated high expression levels of these pivotal genes in T2D, OA, and T2D combined with OA cases. Additionally, the analysis of Transcription Factors (TFs)-miRNA interactions identified 7 TFs and one miRNA that jointly regulate these important genes. The Receiver Operating characteristic (ROC) analysis demonstrated the significant diagnostic potential of MMP9 and ANGPTL4.Moreover, we identified raloxifene, ezetimibe, and S-3304 as promising agents for patients with both T2D and OA., Conclusion: This study uncovers the shared signaling pathways, biomarkers, potential therapeutics, and diagnostic models for individuals suffering from both T2D and OA. These findings not only present novel perspectives on the complex interplay between T2D and OA but also hold significant promise for improving the clinical management and prognosis of patients with this concurrent condition., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Mao, Xu, Wan, Wang, Xu, Zhang, Li, Zhang, Lai, Lan and Liu.)

A structural alteration in copper/zinc superoxide dismutase (SOD1) is one of the common features caused by amyotrophic lateral sclerosis (ALS)-linked mutations. Although a large number of SOD1 variants have been reported in ALS patients, the detailed structural properties of each variant are not well summarized. We present SoDCoD, a database of superoxide dismutase conformational diversity, collecting our comprehensive biochemical analyses of the structural changes in SOD1 caused by ALS-linked gene mutations and other perturbations. SoDCoD version 1.0 contains information about the properties of 188 types of SOD1 mutants, including structural changes and their binding to Derlin-1, as well as a set of genes contributing to the proteostasis of mutant-like wild-type SOD1. This database provides valuable insights into the diagnosis and treatment of ALS, particularly by targeting conformational alterations in SOD1. Database URL: https://fujisawagroup.github.io/SoDCoDweb/., (© The Author(s) 2024. Published by Oxford University Press.)

Lung adenocarcinoma is the most common primary lung cancer seen in the world, and identifying genetic markers is essential for predicting the prognosis of lung adenocarcinoma and improving treatment outcomes. It is well known that alterations in circadian rhythms are associated with a higher risk of cancer. Moreover, circadian rhythms play a regulatory role in the human body. Therefore, studying the changes in circadian rhythms in cancer patients is crucial for optimizing treatment. The gene expression data and clinical data were sourced from TCGA database, and we identified the circadian clock-related genes. We used the obtained TCGA-LUAD data set to build the model, and the other 647 lung adenocarcinoma patients' data were collected from two GEO data sets for external verification. A risk score model for circadian clock-related genes was constructed, based on the identification of 8 genetically significant genes. Based on ROC analyses, the risk model demonstrated a high level of accuracy in predicting the overall survival times of lung adenocarcinoma patients in training folds, as well as external data sets. This study has successfully constructed a risk model for lung adenocarcinoma prognosis, utilizing circadian rhythm as its foundation. This model demonstrates a dependable capacity to forecast the outcome of the disease, which can further guide the relevant mechanism of lung adenocarcinoma and combine behavioral therapy with treatment to optimize treatment decision-making., (© 2024. The Author(s).)

Several studies reveal that allergic rhinitis (AR) is a significant risk factor of systemic lupus erythematosus (SLE). However, studies investigating the common pathogenesis linking AR and SLE are lacking. Our study aims to search for the shared biomarkers and mechanisms that may provide new therapeutic targets for preventing AR from developing SLE. GSE50223 for AR and GSE103760 for SLE were downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). The Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to explore the functions of shared DEGs. Hub genes were screened by cytoHubba (a plugin of Cytoscape) and validated in another two datasets. Gene set enrichment analysis (GSEA) and single-sample Gene set enrichment analysis (ssGSEA) algorithm were applied to understand the functions of hub gene. ENTPD1 was validated as a hub gene between AR and SLE. GSEA results revealed that ENTPD1 was associated with KRAS&#95;SIGNALING&#95;UP pathway in AR and related to HYPOXIA, TGF&#95;BETA&#95;SIGNALING and TNFA&#95;SIGNALING&#95;VIA&#95;NFKB pathways in SLE. The expression of ENTPD1 was positively correlated with activated CD8 T cell in both diseases. Thus, ENTPD1 may be a novel therapeutic target for preventing AR from developing SLE., (© 2024. The Author(s).)

MicroRNAs (miRNAs) play important roles in post-transcriptional processes and regulate major cellular functions. The abnormal regulation of expression of miRNAs has been linked to numerous human diseases such as respiratory diseases, cancer, and neurodegenerative diseases. Latest miRNA-disease associations are predominantly found in unstructured biomedical literature. Retrieving these associations manually can be cumbersome and time-consuming due to the continuously expanding number of publications. We propose a deep learning-based text mining approach that extracts normalized miRNA-disease associations from biomedical literature. To train the deep learning models, we build a new training corpus that is extended by distant supervision utilizing multiple external databases. A quantitative evaluation shows that the workflow achieves an area under receiver operator characteristic curve of 98% on a holdout test set for the detection of miRNA-disease associations. We demonstrate the applicability of the approach by extracting new miRNA-disease associations from biomedical literature (PubMed and PubMed Central). We have shown through quantitative analysis and evaluation on three different neurodegenerative diseases that our approach can effectively extract miRNA-disease associations not yet available in public databases. Database URL: https://zenodo.org/records/10523046., (© The Author(s) 2024. Published by Oxford University Press.)

Background: Acute myocardial infarction (AMI) is a leading cause of death worldwide. Mitochondrial dysfunction is a key determinant of cell death post-AMI. Preventing mitochondrial dysfunction is thus a key therapeutic strategy. This study aimed to explore key genes and target compounds related to mitochondrial dysfunction in AMI patients and their association with major adverse cardiovascular events (MACE)., Methods: Differentially expressed genes in AMI were identified from the Gene Expression Omnibus (GEO) datasets (GSE166780 and GSE24519), and mitochondria-related genes were obtained from MitoCarta3.0 database. By intersection of the two gene groups, mitochondria-related genes in AMI were identified. Next, the identified genes related to mitochondria were subject to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Protein-protein interaction (PPI) network was constructed, and key genes were screened. Then, targeted drug screening and molecular docking were performed. Blood samples from AMI patients and healthy volunteers were analyzed for the key genes expressions using quantitative real time polymerase chain reaction (qRT-PCR). Later, receiver operating characteristic (ROC) curves assessed the diagnostic value of key genes, and univariate and multivariate COX analyses identified risk factors and protective factors for MACE in AMI patients., Results: After screening and identification, 138 mitochondria-related genes were identified, mainly enriched in the processes and pathways of cellular respiration, redox, mitochondrial metabolism, apoptosis, amino acid and fatty acid metabolism. According to the PPI network, 5 key mitochondria-related genes in AMI were obtained: translational activator of cytochrome c oxidase I (TACO1), cytochrome c oxidase subunit Va (COX5A), PTEN-induced putative kinase 1 (PINK1), SURF1, and NDUFA11. Molecular docking showed that Cholic Acid, N-Formylmethionine interacted with COX5A, nicotinamide adenine dinucleotide + hydrogen (NADH) and NDUFA11. Subsequent basic experiments revealed that COX5A and NDUFA11 expressions were significantly lower in the blood of patients with AMI than those in the corresponding healthy volunteers; also, AMI patients with MACE had lower COX5A and NDUFA11 expressions in the blood than those without MACE (P < 0.01). ROC analysis also showed high diagnostic value for COX5A and NDUFA11 [area under the curve (AUC) > 0.85]. In terms of COX results, COX5A, NDUFA11 and left ventricular ejection fraction (LVEF) were protective factors for MACE in AMI, while C-reactive protein (CRP) was a risk factor., Conclusion: COX5A and NDUFA11, key mitochondria-related genes in AMI, may be used as biomarkers to diagnose AMI and predict MACE., (© 2024. The Author(s).)

FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/., (© The Author(s) 2024. Published by Oxford University Press.)

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. However, the 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other nearly-universal prokaryotic marker genes as the number of gene sequences increases in reference databases. This trend was strongly correlated with how represented a taxonomic group was in the database and indicates that, at the gene-level, the boundaries between many species might be fuzzy. Through our study, we argue that any approach that relies on a single marker to distinguish bacterial taxa is fraught even if some markers appear to be discriminative in current databases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Background: Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there is a paucity of studies that reflect the available biomarkers from separate gene expression profiles in PAH., Methods: The GSE131793 and GSE113439 datasets were combined for subsequent analyses, and batch effects were removed. Bioinformatic analysis was then performed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and a protein-protein interaction (PPI) network analysis were then used to further filter the hub genes. Functional enrichment analysis of the intersection genes was performed using Gene Ontology (GO), Disease Ontology (DO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA). The expression level and diagnostic value of hub gene expression in pulmonary arterial hypertension (PAH) patients were also analyzed in the validation datasets GSE53408 and GSE22356. In addition, target gene expression was validated in the lungs of a monocrotaline (MCT)-induced pulmonary hypertension (PH) rat model and in the serum of PAH patients., Results: A total of 914 differentially expressed genes (DEGs) were identified, with 722 upregulated and 192 downregulated genes. The key module relevant to PAH was selected using WGCNA. By combining the DEGs and the key module of WGCNA, 807 genes were selected. Furthermore, protein-protein interaction (PPI) network analysis identified HSP90AA1, CD8A, HIF1A, CXCL8, EPRS1, POLR2B, TFRC, and PTGS2 as hub genes. The GSE53408 and GSE22356 datasets were used to evaluate the expression of TFRC, which also showed robust diagnostic value. According to GSEA enrichment analysis, PAH-relevant biological functions and pathways were enriched in patients with high TFRC levels. Furthermore, TFRC expression was found to be upregulated in the lung tissues of our experimental PH rat model compared to those of the controls, and the same conclusion was reached in the serum of the PAH patients., Conclusions: According to our bioinformatics analysis, the observed increase of TFRC in the lung tissue of human PAH patients, as indicated by transcriptomic data, is consistent with the alterations observed in PAH patients and rodent models. These data suggest that TFRC may serve as a potential biomarker for PAH., (© 2024. The Author(s).)

Lung Squamous Cell Carcinoma is characterised by significant alterations in RNA expression patterns, and a lack of early symptoms and diagnosis results in poor survival rates. Our study aimed to identify the hub genes involved in LUSC by differential expression analysis and their influence on overall survival rates in patients. Thus, identifying genes with the potential to serve as biomarkers and therapeutic targets. RNA sequence data for LUSC was obtained from TCGA and analysed using R Studio. Survival analysis was performed on DE genes. PPI network and hub gene analysis was performed on survival-relevant genes. Enrichment analysis was conducted on the PPI network to elucidate the functional roles of hub genes. Our analysis identified 2774 DEGs in LUSC patient datasets. Survival analysis revealed 511 genes with a significant impact on patient survival. Among these, 20 hub genes-FN1, ACTB, HGF, PDGFRB, PTEN, SNAI1, TGFBR1, ESR1, SERPINE1, THBS1, PDGFRA, VWF, BMP2, LEP, VTN, PXN, ABL1, ITGA3 and ANXA5-were found to have lower expression levels associated with better patient survival, whereas high expression of SOX2 correlated with longer survival. Enrichment analysis indicated that these hub genes are involved in critical cellular and cancer-related pathways. Our study has identified six key hub genes that are differentially expressed and exhibit significant influence over LUSC patient survival outcomes. Further, in vitro and in vivo studies must be conducted on the key genes for their utilisation as therapeutic targets and biomarkers in LUSC., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study aimed to perform exhaustive bioinformatic analysis by using GSE29221 micro-array maps obtained from healthy controls and Type 2 Diabetes (T2DM) patients. Raw data are downloaded from the Gene Expression Omnibus database and processed by the limma package in R software to identify Differentially Expressed Genes (DEGs). Gene ontology functional analysis and Kyoto Gene Encyclopedia and Genome Pathway analysis are performed to determine the biological functions and pathways of DEGs. A protein interaction network is constructed using the STRING database and Cytoscape software to identify key genes. Finally, immune infiltration analysis is performed using the Cibersort method. This study has implications for understanding the underlying molecular mechanism of T2DM and provides potential targets for further research., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 HCFMUSP. Published by Elsevier España, S.L.U. All rights reserved.)

Hepatocellular carcinoma (HCC) is a malignant tumor. It is estimated that approximately 50-80% of HCC cases worldwide are caused by hepatitis b virus (HBV) infection, and other pathogenic factors have been shown to promote the development of HCC when coexisting with HBV. Understanding the molecular mechanisms of HBV-induced hepatocellular carcinoma (HBV-HCC) is crucial for the prevention, diagnosis, and treatment of the disease. In this study, we analyzed the molecular mechanisms of HBV-induced HCC by combining bioinformatics and deep learning methods. Firstly, we collected a gene set related to HBV-HCC from the GEO database, performed differential analysis and WGCNA analysis to identify genes with abnormal expression in tumors and high relevance to tumors. We used three deep learning methods, Lasso, random forest, and SVM, to identify key genes RACGAP1, ECT2, and NDC80. By establishing a diagnostic model, we determined the accuracy of key genes in diagnosing HBV-HCC. In the training set, RACGAP1(AUC:0.976), ECT2(AUC:0.969), and NDC80 (AUC: 0.976) showed high accuracy. They also exhibited good accuracy in the validation set: RACGAP1(AUC:0.878), ECT2(AUC:0.731), and NDC80(AUC:0.915). The key genes were found to be highly expressed in liver cancer tissues compared to normal liver tissues, and survival analysis indicated that high expression of key genes was associated with poor prognosis in liver cancer patients. This suggests a close relationship between key genes RACGAP1, ECT2, and NDC80 and the occurrence and progression of HBV-HCC. Molecular docking results showed that the key genes could spontaneously bind to the anti-hepatocellular carcinoma drugs Lenvatinib, Regorafenib, and Sorafenib with strong binding activity. Therefore, ECT2, NDC80, and RACGAP1 may serve as potential biomarkers for the diagnosis of HBV-HCC and as targets for the development of targeted therapeutic drugs., (© 2024. The Author(s).)

Background: Atherosclerosis and metabolic syndrome are the main causes of cardiovascular events, but their underlying mechanisms are not clear. In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases., Methods: Transcriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RStudio software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.Furthermore, the hub gene was screened by Cytoscape software, and the immune infiltration of hub gens was analyzed. Finally, relevant clinical blood samples were collected for qRT-PCR verification of the three most important hub genes., Results: A total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895. Then 23 up-regulated genes and 11 down-regulated genes were screened by venn diagram. Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases. Through the construction of the Protein-Protein Interaction(PPI) network and Cytoscape software analysis, we finally screened 10 hub genes. The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05). Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets. The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05). There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05). Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples., Conclusion: We have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. Through the method of bioinformatics, we finally obtained 10 hub genes in As and MS, and selected 3 of the most significant genes (CX3CR1, IL32, TLR5) for blood PCR verification. This may be helpful to provide new research ideas for the diagnosis and treatment of AS complicated with MS., (© 2024. The Author(s).)

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene-gene interaction. The main purpose of this study is to develop extraction methods for gene-gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene-gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene-gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene-gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene-gene interactions. In this way, the results of this study are expected to help achieve precision medicine., (© 2024. The Author(s).)

This study aimed to identify novel biomarkers associated with cuproptosis in human nonobstructive azoospermia (NOA). We obtained 4 NOA microarray datasets (GSE145467, GSE9210, GSE108886, and GSE45885) from the NCBI Gene Expression Omnibus database and merged them into training set. Another NOA dataset (GSE45887) was used as validation set. Differentially expressed cuproptosis-related genes were identified from training set. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted. Least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination were used to identify hub cuproptosis-related genes. We calculated the expression of the hub cuproptosis-related genes in both validation set and patients with NOA. Gene set variation analysis was used to explore their potential biological functions. The risk prediction model was built by logistic regression analysis and was evaluated in the validation set. Finally, we constructed a competing endogenous RNA network. The training set included 29 patents in the control group and 92 in the NOA group, and 10 cuproptosis-related differentially expressed genes were identified. Subsequently, we screened 6 hub cuproptosis-related genes (DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1) by least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination. GCSH, NFE2L2, NLRP3, and SLC31A1 expressed higher in NOA group than in control group (P < .05) in the validation set (4 patients in control and 16 in NOA groups), while the expression levels of GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 were higher in NOA group than in control group (P < .05) in our patients (3 patients in control and 4 in NOA groups). The model based on the 6-gene signature showed superior performance with an AUC value of 0.970 in training set, while 1.0 in validation set. Gene set variation analysis revealed a higher enrichment score of "homologous recombination" in the high expression groups of the 6 hub genes. Finally, we constructed a competing endogenous RNA network and found hsa-miR-335-3p and hsa-miR-1-3p were the most frequently related to the 6 hub genes. DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 may serve as predictors of cuproptosis and play important roles in the NOA pathogenesis., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Patients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers., Methods: Gene expression profiles for AF and stable CAD were downloaded from the Gene Expression Omnibus database. Overlapping genes related to both diseases were identified using weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Hub genes were then identified using the machine learning algorithm. Immune cell infiltration and correlations with hub genes were explored, followed by drug predictions. Hub gene expression in AF and CAD patients was validated by real-time qPCR., Results: We obtained 28 common overlapping genes in AF and stable CAD, mainly enriched in the PI3K-Akt, ECM-receptor interaction, and relaxin signaling pathway. Two hub genes, COL6A3 and FKBP10, were positively correlated with the abundance of MDSC, plasmacytoid dendritic cells, and regulatory T cells in AF and negatively correlated with the abundance of CD56dim natural killer cells in CAD. The AUCs of COL6A3 and FKBP10 were all above or close to 0.7. Drug prediction suggested that collagenase clostridium histolyticum and ocriplasmin, which target COL6A3, may be potential drugs for AF and stable CAD. Additionally, COL6A3 and FKBP10 were upregulated in patients with AF and CAD., Conclusion: COL6A3 and FKBP10 may be key biomarkers for AF and CAD, providing new insights into the diagnosis and treatment of this disease., (© 2024. The Author(s).)