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The genetic transcription profile and underlying molecular mechanisms of ischemic stroke (IS) remain elusive. To address this issue, four mRNA and one miRNA expression profile of rats with middle cerebral artery occlusion (MCAO) were acquired from the Gene Expression Omnibus (GEO) database. A total of 780 differentially expressed genes (DEGs) and 56 miRNAs (DEMs) were screened. Gene set and functional enrichment analysis revealed that a substantial number of immune-inflammation–related pathways were abnormally activated in IS. Through weighted gene co-expression network analysis, the turquoise module was identified as meaningful. By taking the intersection of the turquoise module genes, DEM-target genes, and all DEGs, 354 genes were subsequently obtained as key IS-related genes. Among them, six characteristic genes were identified using the least absolute shrinkage and selection operator. After validation with three external datasets, transforming growth factor beta 1 (Tgfb1) was selected as the hub gene. This finding was further confirmed by gene expression pattern analysis in both the MCAO model rats and clinical IS patients. Moreover, the expression of the hub genes exhibited a negative correlation with the modified Rankin scale score (P < 0.05). Collectively, these results expand our knowledge of the genetic profile and molecular mechanisms involved in IS and suggest that the Tgfb1 gene is a potential biomarker of this disease. [ABSTRACT FROM AUTHOR]

BACKGROUND: Fibromyalgia syndrome, as a common rheumatic disease, is related to central sensitization and immune abnormalities. However, the specific mechanism has not been elucidated, and there is a lack of specific diagnostic markers. Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE: To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods, such as weighted gene co-expression network analysis (WGCNA), and machine learning. METHODS: Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus (GEO) database. The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis. Least absolute shrinkage and selection operator (LASSO) and support vector machine -recursive feature elimination (SVM-RFE) machine learning algorithm were further used to identify hub biomarkers, and draw receiver operating characteristic curve (ROC) to evaluate the accuracy of diagnosing fibromyalgia syndrome. Finally, single sample gene set enrichment analysis (ssGSEA) and gene set enrichment analysis (GSEA) were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION: Eight down-regulated differentially expressed genes (DEGs) were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)| > 0 and P < 0.05. After WGCNA analysis, 497 genes were included in the module (MEdarkviolet) with the highest positive correlation (r=0.22, P=0.04), and 19 genes were included in the module (MEsalmon2) with the highest negative correlation (r=-0.41, P=6×10-5). After intersecting DEGs and the module genes of WGCNA, seven genes were obtained. Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm. After the intersection of the two, three core genes were identified, which were germinal center associated signaling and motility like, integrin beta-8, and carboxypeptidase A3. The areas under the ROC curve of the three core genes were 0.744, 0.739, and 0.734, respectively, indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome. The results of immune infiltration analysis showed that memory B cells, CD56 bright NK cells, and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group (P < 0.05), and were significantly positively correlated with the above three biomarkers (P < 0.05). The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways, mainly related to olfactory transduction pathway, neuroactive ligand- receptor interaction, and infection pathway. The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes, abnormal immune regulation, and multiple pathways imbalance. However, the interactions between these genes and immune cells, as well as their relationships with various pathways need to be further investigated. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is a major global health burden, with biochemical recurrence (BCR) following radical prostatectomy affecting 20–40% of patients and posing significant challenges to prognosis and treatment. Emerging evidence suggests a critical role for differentially expressed in normal and neoplastic cell (DENN) domain-containing genes in oncogenesis; however, their implications in prostate cancer and BCR risk remain underexplored. Methods: This study systematically evaluated 151 single-nucleotide polymorphisms in DENN domain-containing genes in 458 patients with prostate cancer and BCR, followed by validation in an independent cohort of 185 patients. Results: Multivariate Cox regression analyses identified DENND2D rs610261 G>A as significantly associated with improved BCR-free survival in both cohorts (adjusted hazard ratio = 0.39, 95% confidence interval = 0.23–0.66, p = 0.001). Functional analysis revealed rs610261's regulatory potential, with the protective A allele correlating with increased DENND2D expression in various human tissues. Compared to normal prostate tissues, DENND2D expression was reduced in prostate cancer, with higher expression being linked to favorable patient prognosis (p = 0.03). Gene set enrichment analysis revealed an association between DENND2D expression and the negative regulation of MYC target genes, including MAD2L1, ERH, and CLNS1A, which are overexpressed in prostate cancer and associated with poor survival. Furthermore, the elevated DENND2D expression promotes immune infiltration in prostate cancer, supporting its role in immune modulation. Conclusions: DENND2D is a prognostic biomarker for BCR in prostate cancer and offers new avenues for personalized treatment strategies. [ABSTRACT FROM AUTHOR]

ABSTRACT: Breastfeeding by mothers with gestational diabetes mellitus (GDM) has been shown to reduce maternal insulin demands and diminish the risks of diabetes in infants, leading to improved long-term health outcomes. Milk fat globule membrane (MFGM) proteins play a crucial role in influencing the immunity and cognitive development of infants. Understanding the alterations in MFGM proteins in breast milk from mothers with GDM is essential for enhancing their self-efficacy and increase breastfeeding rates. The objective of this study is to investigate and compare MFGM proteins in milk from mothers with and without GDM based on tandem mass tag (TMT) labeling and liquid chromatography–tandem MS techniques. A total of 5,402 proteins were identified, including 4 upregulated proteins and 24 downregulated proteins. These significantly altered proteins were found to be associated with human diseases, cellular processes, and metabolism pathways. Additionally, the oxidative phosphorylation pathway emerged as the predominant pathway through Gene Set Enrichment Analysis involving all genes.

Osteoarthritis (OA) is a comprehensive joint disorder. The specific genes that trigger OA and the strategies for its effective management are not fully understood. This study focuses on identifying key genes linked to iron metabolism that could influence both the diagnosis and therapeutic approaches for OA. Analysis of GEO microarray data and iron metabolism genes identified 15 ferroptosis-related DEGs, enriched in hypoxia and HIF-1 pathways. Ten key hub genes (ATM, GCLC, PSEN1, CYBB, ATG7, MAP1LC3B, PLIN2, GRN, APOC1, SIAH2) were identified. Through stepwise regression, we screened 4 out of the above 10 genes, namely, GCLC, GRN, APOC1, and SIAH2, to obtain the optimal model. AUROCs for diagnosis of OA for the four hub genes were 0.81 and 0.80 of training and validation sets, separately. According to immune infiltration results, OA was related to significantly increased memory B cells, M0 macrophages, regulatory T cells, and resting mast cells but decreased activated dendritic cells. The four hub genes showed a close relation to them. It is anticipated that this model will aid in diagnosing osteoarthritis by assessing the expression of specific genes in blood samples. Moreover, studying these hub genes may further elucidate the pathogenesis of osteoarthritis.

Abstract Background Sarcoptic mange is an emerging and neglected contagious skin disease caused by the mite Sarcoptes scabiei, affecting humans, domestic animals, and wildlife. Mange is the main disease and a major concern for the management and conservation of populations of Iberian ibex (Capra pyrenaica), a medium-sized mountain ungulate endemic to the Iberian Peninsula and Northern Pyrenees. Differences in host-parasite interaction and host immune response determine mange clinical outcome, but little is known about the related differences in gene expression. This study determined blood and skin gene expressions in S. scabiei-experimentally infested Iberian ibexes. Results Infestation with S. scabiei promoted immune and inflammatory genomic responses both in skin and blood, with two different clinical outcomes: either severe infestation or recovery. Sarcoptes scabiei induced local skin immunosuppression to favour its multiplication and establishment of the infestation in the host. Skin gene expression was mostly inflammatory and inefficient to control mange in the severely infected ibexes. Conversely, the immune skin response of the recovered ibexes effectively recognised S. scabiei and activated T-cells, limiting the infestation. Consequently, inflammation-related genes were more expressed in the blood of the severely infested ibexes than in those that recovered. Conclusions The results demonstrate that skin local cellular immune response is key to control sarcoptic mange and prevent the systemic spread of the disease and the associated inflammatory response. These results will be useful to understand the pathogenesis and drivers of the differential outcome of mange at individual scale, and the population and ecological consequences of such variability in Iberian ibex, as well as in other wildlife species, domestic animals, and humans.

Abstract Our group has developed an extra-uterine environment for newborn development (EXTEND) using an ovine model, that aims to mimic the womb to improve short and long-term health outcomes associated with prematurity. This study’s objective was to determine the histologic and transcriptomic consequences of EXTEND on the brain. Histology and RNA-sequencing was conducted on brain tissue from three cohorts of lambs: control pre-term (106–107 days), control late pre-term (127 days), and EXTEND lambs who were born pre-term and supported on EXTEND until late pre-term age (125–128 days). Bioinformatic analysis determined differential gene expression among the three cohorts and across four different brain tissue sections: basal ganglia, cerebellum, hippocampus, and motor cortex. There were no clinically relevant histological differences between the control late pre-term and EXTEND ovine brain tissues. RNA-sequencing demonstrated that there was greater differential gene expression between the control pre-term lambs and EXTEND lambs than between the control late pre-term lambs and EXTEND lambs (Supplemental Figs. 1 and 2). Our study demonstrates that the use of EXTEND to support pre-term lambs until they reach late pre-term gestational age results in brain tissue gene expression that more closely resembles that of the lambs who reached late pre-term gestation within their maternal sheep’s womb than that of the lambs who were born prematurely.

Background: Advanced age is a primary risk factor for adverse COVID-19 outcomes, potentially attributed to immunosenescence and dysregulated inflammatory responses. In the post-pandemic era, with containment measures lifted, the elderly remain particularly susceptible, highlighting the need for intensified focus on immune health management. Methods: A total of 281 elderly patients were enrolled in this study and categorized based on their clinical status at the time of admission into three groups: non-severe (n = 212), severe survivors (n = 49), and severe non-survivors (n = 20). Binary logistic regression analysis was employed to identify independent risk factors associated with disease severity and in-hospital outcomes. The diagnostic performance of risk factors was assessed using the receiver operating characteristic (ROC) curves. Kaplan-Meier survival analysis and log-rank test were utilized to compare the 30-day survival rates. Furthermore, the transcriptomic data of CD4+ T cells were extracted from Gene Expression Omnibus (GEO) database. Gene Set Enrichment Analysis (GSEA) was applied to reveal biological processes and pathways involved. Results: In the comparison between severe and non-severe COVID-19 cases, significant elevations were observed in the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and Serum Amyloid A (SAA) levels, concurrent with a notable reduction in CD8+ T cells, CD4+ T cells, natural killer (NK) cells, and monocytes (all p < 0.05). CD4+ T cells (OR: 0.997 [0.995-1.000], p <0.05) and NLR (OR: 1.03 [1.001-1.060], p <0.05) were independent risk factors affecting disease severity. The diagnostic accuracy for COVID-19 severity, as measured by the area under the curve (AUC) for CD4+ T cells and NLR, was 0.715 (95% CI: 0.645-0.784) and 0.741 (95% CI: 0.675-0.807), respectively. Moreover, patients with elevated NLR or IL-6 levels at admission exhibited significantly shorter survival times. Gene Set Enrichment Analysis (GSEA) revealed several biological pathways that are implicated in the regulation of immune responses and metabolic processes. Conclusions: Lymphocytopenia and the cytokine storm onset are significant predictors of an unfavorable prognosis in elderly patients. The decrease in CD4+ T cells among elderly patients is detrimental to disease recovery, and the biological pathways regulated by these cells could potentially heighten vulnerability to SARS-CoV-2 infection, thereby exacerbating the development of associated complications. [ABSTRACT FROM AUTHOR]

Plant diseases diminish crop yields and put the world's food supply at risk. Plant elicitor peptides (Peps) are innate danger signals inducing defense responses both naturally and after external application onto plants. Pep-triggered defense networks are compatible with pattern-triggered immunity (PTI). Nevertheless, in complex regulatory pathways, there is crosstalk among different signaling pathways, involving noncoding RNAs in the natural response to pathogen attack. Here, we used Prunus persica, PpPep2 and a miRNA-Seq approach to show for the first time that Peps regulate, in parallel with a set of protein-coding genes, a set of plant miRNAs (~15%). Some PpPep2-regulated miRNAs have been described to participate in the response to pathogens in various plant–pathogen systems. In addition, numerous predicted target mRNAs of PpPep2-regulated miRNAs are themselves regulated by PpPep2 in peach trees. As an example, peach miRNA156 and miRNA390 probably have a role in plant development regulation under stress conditions, while others, such as miRNA482 and miRNA395, would be involved in the regulation of resistance (R) genes and sulfate-mediated protection against oxygen free radicals, respectively. This adds to the established role of Peps in triggering plant defense systems by incorporating the miRNA regulatory network and to the possible use of Peps as sustainable phytosanitary products. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the therapeutic, clinicopathological and biological relevancy of CREB3L4 expression in primary prostate cancer (PCa) and to determine the mechanisms underlying the deregulation of CREB3L4 expression in PCa. Methods: The therapeutic, clinicopathological and biological significance of CREB3L4 expressions in two cohorts of PCa, and the mechanisms of deregulation of CREB3L4 expression using TCGA data were determined using integrative computational analyses of the clinico-genomic data of the cancer genome atlas (TCGA) and Deutsches Krebsforschungszentrum (DFKZ). Result: Gene set enrichment analyses (GSEA) demonstrated enrichment of gene sets that predict biological responses to a range of approved inhibitors in the PCa subsets with low CREB3L4 expression, and at nominal and false discovery rates of p < 0.05 and p < 0.25, respectively. In addition, lower CREB3L4 expression in TCGA PCa cohort showed poorer outcomes following androgen deprivation therapy. Furthermore, GSEA demonstrated that cell proliferation, epithelial-mesenchymal transition, angiogenesis, inflammatory response and apoptosis gene sets were enriched in PCa subsets with low CREB3L4 expressions. Low CREB3L4 expression was associated with adverse clinicopathological features of PCa at adjusted p < 0.05. Multiple regression analysis of the methylation, microRNA expression and copy number data of CREB3L4 identified the methylation loci and miRNA expression which independently predicted the expression of CREB3L4 in PCa. Conclusion: This study demonstrates the potential therapeutic relevance and clinico-biological significance of CREB3L4 expression in primary PCa. [ABSTRACT FROM AUTHOR]

Background: Ductal carcinoma in-situ (DCIS) is a pre-invasive form of invasive breast cancer (IBC). Due to improved breast cancer screening, it now accounts for ~ 25% of all breast cancers. While the treatment success rates are over 90%, this comes at the cost of considerable morbidity, considering that the majority of DCIS never become invasive and our understanding of the molecular changes occurring in DCIS that predispose to invasive disease is limited. The aim of this study is to characterize molecular changes that occur in DCIS, with the goal of improving DCIS risk stratification. Methods: We identified and obtained a total of 197 breast tissue samples from 5 institutions (93 DCIS progressors, 93 DCIS non-progressors, and 11 adjacent normal breast tissues) that had at least 10-year follow-up. We isolated DNA and RNA from archival tissue blocks and characterized genome-wide mRNA expression, DNA methylation, DNA copy number variation, and RNA splicing variation. Results: We obtained all four genomic data sets in 122 of the 197 samples. Our intrinsic expression subtype-stratified analyses identified multiple molecular differences both between DCIS subtypes and between DCIS and IBC. While there was heterogeneity in molecular signatures and outcomes within intrinsic subtypes, several gene sets that differed significantly between progressing and non-progressing DCIS were identified by Gene Set Enrichment Analysis. Conclusion: DCIS is a molecularly highly heterogenous disease with variable outcomes, and the molecular events determining DCIS disease progression remain poorly defined. Our genome-wide multi-omic survey documents DCIS-associated alterations and reveals molecular heterogeneity within the intrinsic DCIS subtypes. Further studies investigating intrinsic subtype-stratified characteristics and molecular signatures are needed to determine if these may be exploitable for risk assessment and mitigation of DCIS progression. The highly significant associations of specific gene sets with IBC progression revealed by our Gene Set Enrichment Analysis may lend themselves to the development of a prognostic molecular score, to be validated on independent DCIS cohorts. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a comprehensive joint disorder. The specific genes that trigger OA and the strategies for its effective management are not fully understood. This study focuses on identifying key genes linked to iron metabolism that could influence both the diagnosis and therapeutic approaches for OA. Analysis of GEO microarray data and iron metabolism genes identified 15 ferroptosis-related DEGs, enriched in hypoxia and HIF-1 pathways. Ten key hub genes (ATM, GCLC, PSEN1, CYBB, ATG7, MAP1LC3B, PLIN2, GRN, APOC1, SIAH2) were identified. Through stepwise regression, we screened 4 out of the above 10 genes, namely, GCLC, GRN, APOC1, and SIAH2, to obtain the optimal model. AUROCs for diagnosis of OA for the four hub genes were 0.81 and 0.80 of training and validation sets, separately. According to immune infiltration results, OA was related to significantly increased memory B cells, M0 macrophages, regulatory T cells, and resting mast cells but decreased activated dendritic cells. The four hub genes showed a close relation to them. It is anticipated that this model will aid in diagnosing osteoarthritis by assessing the expression of specific genes in blood samples. Moreover, studying these hub genes may further elucidate the pathogenesis of osteoarthritis. [ABSTRACT FROM AUTHOR]

Background: Sarcoptic mange is an emerging and neglected contagious skin disease caused by the mite Sarcoptes scabiei, affecting humans, domestic animals, and wildlife. Mange is the main disease and a major concern for the management and conservation of populations of Iberian ibex (Capra pyrenaica), a medium-sized mountain ungulate endemic to the Iberian Peninsula and Northern Pyrenees. Differences in host-parasite interaction and host immune response determine mange clinical outcome, but little is known about the related differences in gene expression. This study determined blood and skin gene expressions in S. scabiei-experimentally infested Iberian ibexes. Results: Infestation with S. scabiei promoted immune and inflammatory genomic responses both in skin and blood, with two different clinical outcomes: either severe infestation or recovery. Sarcoptes scabiei induced local skin immunosuppression to favour its multiplication and establishment of the infestation in the host. Skin gene expression was mostly inflammatory and inefficient to control mange in the severely infected ibexes. Conversely, the immune skin response of the recovered ibexes effectively recognised S. scabiei and activated T-cells, limiting the infestation. Consequently, inflammation-related genes were more expressed in the blood of the severely infested ibexes than in those that recovered. Conclusions: The results demonstrate that skin local cellular immune response is key to control sarcoptic mange and prevent the systemic spread of the disease and the associated inflammatory response. These results will be useful to understand the pathogenesis and drivers of the differential outcome of mange at individual scale, and the population and ecological consequences of such variability in Iberian ibex, as well as in other wildlife species, domestic animals, and humans. [ABSTRACT FROM AUTHOR]

Background/Objectives: PRKACA alterations have clear diagnostic and biological roles in the fibrolamellar variant of hepatocellular carcinoma and a potential predictive role in that cancer type. However, the roles of PRKACA have not been comprehensively examined in gastric and colorectal cancers (GC and CRC). This study, therefore, sought to investigate the roles of PRKACA expression in GC and CRC. Methods: The clinico-genomic data of 441 GC and 629 CRC cases were analyzed for therapeutic, clinicopathological, and biological correlates using appropriate bioinformatics and statistical tools. Furthermore, the deregulation of PRKACA expression in GC and CRC was investigated using correlative and regression analyses. Results: The results showed that PRKACA expression subsets were enriched for gene targets of chemotherapeutics, tyrosine kinase, and β-adrenergic inhibitors. Moreover, high PRKACA expression was associated with adverse clinicopathological and genomic features of GC and CRC. Gene Ontology Enrichment Analysis also showed that PRKACA-high subsets of the GI cancers were enriched for the biological and molecular functions that are associated with cell motility, invasion, and metastasis but not cell proliferation. Finally, multiple regression analyses identified multiple methylation loci, transcription factors, miRNA species, and PRKACA copy number changes that deregulated PRKACA expression in GC and CRC. Conclusions: This study has identified potential predictive and clinicopathological roles for PRKACA expression in GI cancers and has added to the growing body of knowledge on the deregulation of PRKACA in cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Migration–proliferation dichotomy (MPD) is a characteristic that has been observed in cancer cells growing in artificial culture media. The MPD principle states that when cells begin to grow in number (cell proliferation), their ability to migrate from one point to another (cell migration) is paused, and vice versa. This phenomenon has been used to study cancer advancement in cancer cells growing in culture, and is proposed to be responsible for the adverse therapeutic outcomes observed in patients with cancer. However, MPD has not been comprehensively investigated in cancer tissues obtained directly from patients. This study investigated MPD in stomach and bowel cancers using gene expression signatures of natural cancers. The overall findings confirm that cell proliferation and migration occur in the reverse direction in cancer cells, which is in keeping with the features that have long been observed in artificially grown cancer cells. These results also provide a basis for further investigation of MPD in natural cancers. The migration–proliferation dichotomy (MPD) has long been observed in cultured cancer cells. This phenomenon is not only relevant to tumour progression but may also have therapeutic significance in clinical cancer. However, MPD has rarely been investigated in primary cancer. This study aimed to either confirm or disprove the existence of MPD in primary cancer. Using primary gastric, colorectal and prostate cancer (GC, CRC and PCa) cohorts from the Cancer Genome Atlas and Memorial Sloan Kettering Cancer Center, this study interrogated the MPD phenomenon by utilising RNA–Seq-based proliferation (CIN70 signature) and migration (epithelial-mesenchymal transition) indices, as well as gene set enrichment analyses (GSEA). Alternative hypothetical migration–proliferation models—The simultaneous migration–proliferation (SMP) and phenotype–refractory (PR) models—were compared to the MPD model by probing the migration–proliferation relationships within cancer stages and between early- and late-stage diseases using chi-square and independent T tests, z-score statistics and GSEA. The results revealed an inverse relationship between migration and proliferation signatures overall in the GC, CRC and PCa cohorts, as well as in early- and late-stage diseases. Additionally, a shift in proliferation- to migration dominance was observed from early- to late-stage diseases in the GC and CRC cohorts but not in the PCa cohorts, which showed enhanced proliferation dominance in metastatic tumours compared to primary cancers. The above features exhibited by the cancer cohorts are in keeping with the MPD model of the migration–proliferation relationship at the cellular level and exclude the SMP and PR migration–proliferation models. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Distinctive heterogeneity characterizes diffuse large B‐cell lymphoma (DLBCL), one of the most frequent types of non‐Hodgkin's lymphoma. Mitochondria have been demonstrated to be closely involved in tumorigenesis and progression, particularly in DLBCL. Objective The purposes of this study were to identify the prognostic mitochondria‐related genes (MRGs) in DLBCL, and to develop a risk model based on MRGs and machine learning algorithms. Methods Transcriptome profiles and clinical information were obtained from the Gene Expression Omnibus (GEO) database. The risk model was defined using Least Absolute Shrinkage and Selection Operator (Lasso) regression algorithm, and its prognostic value was further examined in independent datasets. Patients were stratified into two clusters based on the risk scores, additionally a nomogram was generated based on the risk score and clinical characteristics. Gene pathway level, microenvironment, expression of targeted therapy‐associated genes, response to immunotherapy, drug sensitivity, and somatic mutation status were compared between clusters. Results Eighteen prognostic MRGs (DNM1L, PUSL1, CHCHD4, COX7A1, CPT1A, CYP27A1, POLDIP2, PCK2, MRPL2, PDK3, PDK4, MARC2, ACSM3, COA7, THNSL1, ATAD3B, C15orf48, TOMM70A) were identified to construct the risk model. Remarkable discrepancies were observed between groups. The high‐risk group had shorter overall survival, less immune infiltration, lower CD20 and higher PD‐L1 expression than the low‐risk group. Distinct immune microenvironment, responses to immunotherapy and predictive drug IC50 values were found between groups. Conclusions We established a novel prognostic mitochondria‐related signature by machine learning algorithm, which also demonstrated outstanding predictive value in tumor microenvironment and responses to therapies.

Abstract Knee osteoarthritis (KOA) is a common chronic joint disease globally. Synovial inflammation plays a pivotal role in its pathogenesis, preceding cartilage damage. Identifying biomarkers in osteoarthritic synovial tissues holds promise for early diagnosis and targeted interventions. Gene expression profiles were obtained from the Gene Expression Omnibus database. Subsequent analyses included differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) on the combined datasets. We performed functional enrichment analysis on the overlapping genes between DEGs and module genes and constructed a protein–protein interaction network. Using Cytoscape software, we identified hub genes related to the disease and conducted gene set enrichment analysis on these hub genes. The CIBERSORT algorithm was employed to evaluate the correlation between hub genes and the abundance of immune cells within tissues. Finally, Mendelian randomization analysis was utilized to assess the potential of these hub genes as biomarkers. We identified 46 differentially expressed genes (DEGs), comprising 20 upregulated and 26 downregulated genes. Using WGCNA, we constructed a gene co-expression network and selected the most relevant modules, resulting in 24 intersecting genes with the DEGs. KEGG enrichment analysis of the intersecting genes identified the IL-17 signaling pathway, associated with inflammation, as the most significant pathway. Cytoscape software was utilized to rank the candidate genes, with JUN, ATF3, FOSB, NR4A2, and IL6 emerging as the top five based on the Degree algorithm. A nomogram model incorporating these five genes, supported by ROC curve analysis, validated their diagnostic efficacy. Immune infiltration and correlation analysis revealed that macrophages were significantly associated with JUN (p

Abstract 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.

Abstract Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P

This study aims to address the substantive issue of lacking reliable prognostic biomarkers in hepatocellular carcinoma (HCC) by investigating the relationship between TP53-inducible glycolysis and apoptosis regulator (TIGAR) and HCC prognosis using The Cancer Genome Atlas database.

Knee osteoarthritis (KOA) is a common chronic joint disease globally. Synovial inflammation plays a pivotal role in its pathogenesis, preceding cartilage damage. Identifying biomarkers in osteoarthritic synovial tissues holds promise for early diagnosis and targeted interventions. Gene expression profiles were obtained from the Gene Expression Omnibus database. Subsequent analyses included differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) on the combined datasets. We performed functional enrichment analysis on the overlapping genes between DEGs and module genes and constructed a protein–protein interaction network. Using Cytoscape software, we identified hub genes related to the disease and conducted gene set enrichment analysis on these hub genes. The CIBERSORT algorithm was employed to evaluate the correlation between hub genes and the abundance of immune cells within tissues. Finally, Mendelian randomization analysis was utilized to assess the potential of these hub genes as biomarkers. We identified 46 differentially expressed genes (DEGs), comprising 20 upregulated and 26 downregulated genes. Using WGCNA, we constructed a gene co-expression network and selected the most relevant modules, resulting in 24 intersecting genes with the DEGs. KEGG enrichment analysis of the intersecting genes identified the IL-17 signaling pathway, associated with inflammation, as the most significant pathway. Cytoscape software was utilized to rank the candidate genes, with JUN, ATF3, FOSB, NR4A2, and IL6 emerging as the top five based on the Degree algorithm. A nomogram model incorporating these five genes, supported by ROC curve analysis, validated their diagnostic efficacy. Immune infiltration and correlation analysis revealed that macrophages were significantly associated with JUN (p < 0.01), FOSB (p < 0.01), and NR4A2 (p < 0.05). Additionally, T follicular helper cells showed significant associations with ATF3 (p < 0.05), FOSB (p < 0.05), and JUN (p < 0.05). Mendelian randomization analysis provided strong evidence linking JUN (IVW: OR = 0.910, p = 0.005) and IL6 (IVW: OR = 1.024, p = 0.026) with KOA. Through the utilization of various bioinformatics analysis methods, we have pinpointed key hub genes relevant to knee osteoarthritis. These findings hold promise for advancing pre-symptomatic diagnostic strategies and enhancing our understanding of the biological underpinnings behind knee osteoarthritis susceptibility genes. [ABSTRACT FROM AUTHOR]

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD. [ABSTRACT FROM AUTHOR]

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High‐throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although, there have been many prior explorations in miRNA‐mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA‐target gene prediction and data driven methods for enrichment and network analysis for miRnome–targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high‐throughput data. This review explains how to apply the existing methods to analyse miRNA‐mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA‐mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi‐source data to identify molecular signatures of various human diseases. [ABSTRACT FROM AUTHOR]

Given the importance of peroxisome proliferator-activated receptor (PPAR)-gamma in epidermal inflammation and carcinogenesis, we analyzed the transcriptomic changes observed in epidermal PPARγ-deficient mice (Pparg-/-epi). A gene set enrichment analysis revealed a close association with epithelial malignancy, inflammatory cell chemotaxis, and cell survival. Single-cell sequencing of Pparg-/-epi mice verified changes to the stromal compartment, including increased inflammatory cell infiltrates, particularly neutrophils, and an increase in fibroblasts expressing myofibroblast marker genes. A comparison of transcriptomic data from Pparg-/-epi and publicly available human and/or mouse actinic keratoses (AKs) and cutaneous squamous cell carcinomas (SCCs) revealed a strong correlation between the datasets. Importantly, PPAR signaling was the top common inhibited canonical pathway in AKs and SCCs. Both AKs and SCCs also had significantly reduced PPARG expression and PPARγ activity z-scores. Smaller reductions in PPARA expression and PPARα activity and increased PPARD expression but reduced PPARδ activation were also observed. Reduced PPAR activity was also associated with reduced PPARα/RXRα activity, while LPS/IL1-mediated inhibition of RXR activity was significantly activated in the tumor datasets. Notably, these changes were not observed in normal sun-exposed skin relative to non-exposed skin. Finally, Ppara and Pparg were heavily expressed in sebocytes, while Ppard was highly expressed in myofibroblasts, suggesting that PPARδ has a role in myofibroblast differentiation. In conclusion, these data provide strong evidence that PPARγ and possibly PPARα represent key tumor suppressors by acting as master inhibitors of the inflammatory changes found in AKs and SCCs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective To explore the mechanism of the differential osteogenesis capacity between dental pulp stem cells (DPSCs) and periodontal ligament stem cells (PDLSCs). Methods Human pulp stem cells and periodontal stem cells were cultured，the differences in the ability of the two stem cells to induce osteogenic differentiation were compared，and the transcriptional expression differences of key transcription factors in the process of osteogenic differentiation were detected by real-time PCR. Meanwhile，we analyzed single cell sequencing data of human teeth to uncover the characteristic of dental pulp stem cells and periodontal ligament stem cells by using dataset (GSE161267_RAW) downloaded from official website of GEO by comparing the difference in the proportion of stem cell subsets，the expression of genes regulating osteogenic differentiation and the enrichment of biological functions. Results The results of osteogenic induction of differentiation showed that the alizarin red staining in the DPSCs group was darker than that in the PDLSCs group，and the key transcription factors of osteogenic differentiation were detected by RT-PCR，Osteocalcin (OC)，Runt-related transcription factor 2 (RunX2)， alkaline phosphatase (ALP)，Osteoprotegerin (OPG) and bone morphogenetic protein-2 (BMP-2). The results showed that compared with the PDLSCs group，the transcription levels of Osteocalin，ALP，RunX2，OPG and BMP-2 genes were significantly increased in the DPSCs group (**P＜0.01，*P＜0.05). The results of single-cell sequencing data analysis showed that the proportion of stem cells in periodontal and pulp tissues was different，with MSC2 and MSC3 subsets accounting for more in periodontal tissues and MSC1 subsets accounting for more in pulp tissues. The expression of genes regulating osteogenic differentiation in MSC1 was significantly higher than that in MSC2 and MSC3. The results of biological enrichment of differential genes showed that MSC1 subsets mainly showed osteogenic differentiation and signaling pathways involved in osteogenic differentiation. Conclusion DPSCs have higher osteogenic differentiation function than PDLSCs，because of the cell subsets of DPSCs shows strong osteogenic differentiation potential. [ABSTRACT FROM AUTHOR]

Renal cell carcinoma (RCC) is characterized by high mortality and morbidity rates. Vav guanine nucleotide exchange factors (VAVs), crucial for signal transduction between cell membrane receptors and intracellular mediators, have been implicated in carcinogenesis. However, their potential prognostic value in RCC remains unclear. The impact of 150 common VAV polymorphisms on RCC risk and survival was investigated in a cohort of 630 individuals. Publicly available gene expression datasets were utilized to analyze VAV gene expression in relation to patient outcomes. The VAV3 rs17019888 polymorphism was significantly associated with RCC risk and overall survival after adjusting for false discovery rates. Expression quantitative trait loci analysis revealed that the risk allele of rs17019888 is linked to reduced VAV3 expression. Analysis of 19 kidney cancer gene expression datasets revealed lower VAV3 expression in RCC tissues compared to normal tissues, with higher expression correlating with better prognosis. Gene set enrichment analysis demonstrated that VAV3 negatively regulates the ubiquitin–proteasome system, extracellular matrix and membrane receptors, inflammatory responses, matrix metalloproteinases, and cell cycle pathways. Furthermore, elevated VAV3 expression was associated with increased infiltration of B cells, macrophages, and neutrophils into the RCC tumor microenvironment. Our findings suggest that VAV3 gene variants influence RCC risk and survival, contributing to a favorable prognosis in RCC. [ABSTRACT FROM AUTHOR]

Background: Obstructive sleep apnea (OSA) syndrome and nonalcoholic fatty liver disease (NAFLD) have been shown to have a close association in previous studies, but their pathogeneses are unclear. This study explores the molecular mechanisms associated with the pathogenesis of OSA and NAFLD and identifies key predictive genes. Methods: Using the Gene Expression Omnibus (GEO) database, we obtained gene expression profiles GSE38792 for OSA and GSE89632 for NAFLD and related clinical characteristics. Mitochondrial unfolded protein response-related genes (UPRmtRGs) were acquired by collating and collecting UPRmtRGs from the GeneCards database and relevant literature from PubMed. The differentially expressed genes (DEGs) associated with OSA and NAFLD were identified using differential expression analysis. Gene Set Enrichment Analysis (GSEA) was conducted for signaling pathway enrichment analysis of related disease genes. Based on the STRING database, protein-protein interaction (PPI) analysis was performed on differentially co-expressed genes (Co-DEGs), and the Cytoscape software (version 3.9.1) was used to visualize the PPI network model. In addition, the GeneMANIA website was used to predict and construct the functional similar genes of the selected Co-DEGs. Key predictor genes were analyzed using the receiver operating characteristic (ROC) curve. Results: The intersection of differentially expressed genes shared between OSA and NAFLD-related gene expression profiles with UPRmtRGs yielded four Co-DEGs: ASS1, HDAC2, SIRT3, and VEGFA. GSEA obtained the relevant enrichment signaling pathways for OSA and NAFLD. PPI network results showed that all four Co-DEGs interacted (except for ASS1 and HDAC2). Ultimately, key predictor genes were selected in the ROC curve, including HDAC2 (OSA: AUC = 0.812; NAFLD: AUC = 0.729), SIRT3 (OSA: AUC = 0.775; NAFLD: AUC = 0.750), and VEGFA (OSA: AUC = 0.812; NAFLD: AUC = 0.861) (they have a high degree of accuracy in predicting whether a subject will develop two diseases). Conclusion: In this study, four co-expression differential genes for OSA and NAFLD were obtained, and they can predict the occurrence of both diseases. Transcriptional mechanisms involved in OSA and NAFLD interactions may be better understood by exploring these key genes. Simultaneously, this study provides potential diagnostic and therapeutic markers for patients with OSA and NAFLD. [ABSTRACT FROM AUTHOR]

Objective: Based on gene expression omnibus （GEO）， differential expressed genes， gene set enrichment analysis （GSEA） and immune cell infiltration analysis were performed on microarray data of chronic spontaneous urticaria （CSU） expression profile， to gain more insight into the pathogenesis of CSU. Methods: The GSE72541 raw data were obtained from the GEO. Differential expressed genes were screened using R software. String database were used to construct the the protein-protein interaction （PPI） net-work. Gene ontology （GO） and Kyoto encyclopedia of gene and genomes （KEGG） enrichment analysis were performed using GSEA software. The ssGSEA method was used to analyze the infiltration of immune cells in the expression profile. Results: Genes closely related to platelet activation and its function were up-regulated in CSU serum， while genes related to Th1 cell chemotaxis were downregulated in CSU serum. Biological processes and signal pathways related to coagulation cascade reaction， regulation of vascular per-meability， immune and inflammatory reactions， and mood-modulating were up-regulated in CSU group. Immunized cell infiltration analysis showed that activated B cells， immature B cells， follicular helper T cells， and Th2 cells were down-regulated in the CSU serum. Conclusion: Platelet activation， coagulation cascade reaction and the imbalance of Th1/Th2 immunity play important roles in the pathogenesis of CSU. [ABSTRACT FROM AUTHOR]

Ischemic stroke followed by reperfusion (IR) leads to extensive cerebrovascular injury characterized by neuroinflammation and brain cell death. Inhibition of matrix metalloproteinase-3 (MMP-3) emerges as a promising therapeutic approach to mitigate IR-induced stroke injury. We employed middle cerebral artery occlusion with subsequent reperfusion (MCAO/R) to model ischemic stroke in adult mice. Specifically, we investigated the impact of MMP-3 knockout (KO) on stroke pathophysiology using RNA sequencing (RNA-seq) of stroke brains harvested 48 h post-MCAO. MMP-3 KO significantly reduced brain infarct size following stroke. Notably, RNA-seq analysis showed that MMP-3 KO altered expression of 333 genes (252 downregulated) in male stroke brains and 3768 genes (889 downregulated) in female stroke brains. Functional pathway analysis revealed that inflammation, integrin cell surface signaling, endothelial- and epithelial-mesenchymal transition (EndMT/EMT), and apoptosis gene signatures were decreased in MMP-3 KO stroke brains. Intriguingly, MMP-3 KO downregulated gene signatures more profoundly in females than in males, as indicated by greater negative enrichment scores. Our study underscores MMP-3 inhibition as a promising therapeutic strategy, impacting multiple cellular pathways following stroke. [ABSTRACT FROM AUTHOR]

Dyslipidemia and hepatic steatosis are the characteristics of the initial stage of nonalcohol fatty liver disease (NAFLD), which can be reversed by lifestyle intervention, including dietary supplementation. However, such commercial dietary supplements with solid scientific evidence and in particular clear mechanistic elucidation are scarce. Here, the health benefits of MHP, a commercial mulberry and Hippophae‐based solid beverage, were evaluated in NAFLD rat model and the underlying molecular mechanisms were investigated. Histopathologic examination of liver and white adipose tissue found that MHP supplementation reduced hepatic lipid accumulation and adipocyte hypertrophy. Serum biochemical results confirmed that MHP effectively ameliorated dyslipidemia and decreased circulation‐free fatty acid level. RNA‐Seq‐based transcriptomic analysis showed that MHP‐regulated genes are involved in the inhibition of lipolysis of adipose tissue and thus may contribute to the reduction of hepatic ectopic lipid deposition. Furthermore, MHP upregulated ACSL1–CPT1a–CPT2 pathway, a canonical pathway that regulated mitochondrial fatty acid metabolism, and promoted liver and adipose tissue fatty acid β‐oxidation. These results suggest that adipose tissue–liver crosstalk may play a key role in maintaining glucose and lipid metabolic hemostasis. In addition, MHP can also ameliorate chronic inflammation through regulating the secretion of adipokines. Our study demonstrates that MHP is able to improve dyslipidemia and hepatic steatosis through crosstalk between adipose tissue and liver and also presents transcriptomic evidence to support the underlying mechanisms of action, providing solid evidence for its health claims. [ABSTRACT FROM AUTHOR]

High-throughput quantification techniques provide considerable amounts of data. Making sense of such data requires not only thorough statistical analysis but a logical approach to data visualisation. DGE-ontology is software that has been primarily designed for transcriptomics, however it may be utilised for any data that express fold change of relative or absolute quantity measures of multiple entities, such as transcripts, proteins or metabolites. The software integrates results of differential and functional analyses in order to produce a single circular, highly informative and visually appealing chart. The chart simultaneously depicts numbers of quantified entities, their assignment to functional categories, singles out statistically over-represented categories, and visualises quantity fold change values. The presented approach to data visualisation considerably facilitates communication of experimental results as well as inference from large omic data sets.

BackgroundAdvanced age is a primary risk factor for adverse COVID-19 outcomes, potentially attributed to immunosenescence and dysregulated inflammatory responses. In the post-pandemic era, with containment measures lifted, the elderly remain particularly susceptible, highlighting the need for intensified focus on immune health management.MethodsA total of 281 elderly patients were enrolled in this study and categorized based on their clinical status at the time of admission into three groups: non-severe (n = 212), severe survivors (n = 49), and severe non-survivors (n = 20). Binary logistic regression analysis was employed to identify independent risk factors associated with disease severity and in-hospital outcomes. The diagnostic performance of risk factors was assessed using the receiver operating characteristic (ROC) curves. Kaplan-Meier survival analysis and log-rank test were utilized to compare the 30-day survival rates. Furthermore, the transcriptomic data of CD4+ T cells were extracted from Gene Expression Omnibus (GEO) database. Gene Set Enrichment Analysis (GSEA) was applied to reveal biological processes and pathways involved.ResultsIn the comparison between severe and non-severe COVID-19 cases, significant elevations were observed in the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and Serum Amyloid A (SAA) levels, concurrent with a notable reduction in CD8+ T cells, CD4+ T cells, natural killer (NK) cells, and monocytes (all p < 0.05). CD4+ T cells (OR: 0.997 [0.995-1.000], p

Background: Colorectal cancer (CRC) liver metastasis (CRLM) is a clinical challenge, and optimizing treatment strategies is crucial for improving patient outcomes. This study aimed to identify gene expression signatures associated with CRLM for early diagnosis and improved treatment outcomes. Methods: We obtained RNA-seq data of 34 samples (17 colorectal tumor samples from metastasized liver and 17 samples from normal surrounding colonic epithelia) from the Gene Expression Omnibus (GEO) with accession number GSE50760 and analysed them using next-generation knowledge discovery (NGKD) tools such as GEO2R and web based gene set enrichment analysis (WebGestalt). Results: A total of 18808 genes were identified in the initial analysis which were further reduced to 2490 differentially expressed genes (DEGs) after applying different parameters using GEO2R tools. Furthermore, in the gene set enrichment analysis (GSEA), we analysed the biological processes, cellular components, and molecular functions. We analysed four pathways: KEGG, panther, reactome, and wikipathway cancer. In each analysis, we ascertained the most important top expressed and downregulated genes. Conclusions: We identified various gene sets that could be used as important prognostic and therapeutic markers, particularly in patients with advanced CRLM. Future studies in larger cohorts may further our research findings to establish the best prognostic biomarkers for early diagnosis and overall survival.

Although sows do not directly enter the market, they play an important role in piglet breeding on farms. They consume large amounts of feed, resulting in a significant environmental burden. Pig farms can increase their income and reduce environmental pollution by increasing the litter size (LS) of swine. PCR-RFLP/SSCP and GWAS are common methods to evaluate single-nucleotide polymorphisms (SNPs) in candidate genes. We conducted a systematic meta-analysis of the effect of SNPs on pig LS. We collected and analysed data published over the past 30 years using traditional and network meta-analyses. Trial sequential analysis (TSA) was used to analyse population data. Gene set enrichment analysis and protein–protein interaction network analysis were used to analyse the GWAS dataset. The results showed that the candidate genes were positively correlated with LS, and defects in PCR-RFLP/SSCP affected the reliability of candidate gene results. However, the genotypes with high and low LSs did not have a significant advantage. Current breeding and management practices for sows should consider increasing the LS while reducing lactation length and minimizing the sows’ non-pregnancy period as much as possible.

Abstract Background Identifying modifiable risk factors of autism spectrum disorders (ASDs) may inform interventions to reduce financial burden. The infant/toddler gut microbiome is one such feature that has been associated with social behaviors, but results vary between cohorts. We aimed to identify consistent overall and sex-specific associations between the early-life gut microbiome and autism-related behaviors. Methods Utilizing the Environmental influences on Children Health Outcomes (ECHO) consortium of United States (U.S.) pediatric cohorts, we gathered data on 304 participants with fecal metagenomic sequencing between 6-weeks to 2-years postpartum (481 samples). ASD-related social development was assessed with the Social Responsiveness Scale (SRS-2). Linear regression, PERMANOVA, and Microbiome Multivariable Association with Linear Models (MaAsLin2) were adjusted for sociodemographic factors. Stratified models estimated sex-specific effects. Results Genes encoding pathways for synthesis of short-chain fatty acids were associated with higher SRS-2 scores, indicative of ASDs. Fecal concentrations of butyrate were also positively associated with ASD-related SRS-2 scores, some of which may be explained by formula use. Limitations The distribution of age at outcome assessment differed in the cohorts included, potentially limiting comparability between cohorts. Stool sample collection methods also differed between cohorts. Our study population reflects the general U.S. population, and thus includes few participants who met the criteria for being at high risk of developing ASD. Conclusions Our study is among the first multicenter studies in the U.S. to describe prospective microbiome development from infancy in relation to neurodevelopment associated with ASDs. Our work contributes to clarifying which microbial features associate with subsequent diagnosis of neuropsychiatric outcomes. This will allow for future interventional research targeting the microbiome to change neurodevelopmental trajectories.

Abstract Background Dendrobium huoshanense, a traditional medicinal and food plant, has a rich history of use. Recently, its genome was decoded, offering valuable insights into gene function. However, there is no comprehensive gene functional analysis platform for D. huoshanense. Result To address this, we created a platform for gene function analysis and comparison in D. huoshanense (DhuFAP). Using 69 RNA-seq samples, we constructed a gene co-expression network and annotated D. huoshanense genes by aligning sequences with public protein databases. Our platform contained tools like Blast, gene set enrichment analysis, heatmap analysis, sequence extraction, and JBrowse. Analysis revealed co-expression of transcription factors (C2H2, GRAS, NAC) with genes encoding key enzymes in alkaloid biosynthesis. We also showcased the reliability and applicability of our platform using Chalcone synthases (CHS). Conclusion DhuFAP ( www.gzybioinformatics.cn/DhuFAP ) and its suite of tools represent an accessible and invaluable resource for researchers, enabling the exploration of functional information pertaining to D. huoshanense genes. This platform stands poised to facilitate significant biological discoveries in this domain.

Whole-tissue transcriptomic analyses have been helpful to characterize molecular subtypes of hepatocellular carcinoma (HCC). Metabolic subtypes of human HCC have been defined, yet whether these different metabolic classes are clinically relevant or derive in actionable cancer vulnerabilities is still an unanswered question. Publicly available gene sets or gene signatures have been used to infer functional changes through gene set enrichment methods. However, metabolism-related gene signatures are poorly co-expressed when applied to a biological context. Here, we apply a simple method to infer highly consistent signatures using graph-based statistics. Using the Cancer Genome Atlas Liver Hepatocellular cohort (LIHC), we describe the main metabolic clusters and their relationship with commonly used molecular classes, and with the presence of TP53 or CTNNB1 driver mutations. We find similar results in our validation cohort, the LIRI-JP cohort. We describe how previously described metabolic subtypes could not have therapeutic relevance due to their overall downregulation when compared to non-tumoral liver, and identify N-glycan, mevalonate and sphingolipid biosynthetic pathways as the hallmark of the oncogenic shift of the use of acetyl-coenzyme A in HCC metabolism. Finally, using DepMap data, we demonstrate metabolic vulnerabilities in HCC cell lines. [ABSTRACT FROM AUTHOR]

Background: Identifying modifiable risk factors of autism spectrum disorders (ASDs) may inform interventions to reduce financial burden. The infant/toddler gut microbiome is one such feature that has been associated with social behaviors, but results vary between cohorts. We aimed to identify consistent overall and sex-specific associations between the early-life gut microbiome and autism-related behaviors. Methods: Utilizing the Environmental influences on Children Health Outcomes (ECHO) consortium of United States (U.S.) pediatric cohorts, we gathered data on 304 participants with fecal metagenomic sequencing between 6-weeks to 2-years postpartum (481 samples). ASD-related social development was assessed with the Social Responsiveness Scale (SRS-2). Linear regression, PERMANOVA, and Microbiome Multivariable Association with Linear Models (MaAsLin2) were adjusted for sociodemographic factors. Stratified models estimated sex-specific effects. Results: Genes encoding pathways for synthesis of short-chain fatty acids were associated with higher SRS-2 scores, indicative of ASDs. Fecal concentrations of butyrate were also positively associated with ASD-related SRS-2 scores, some of which may be explained by formula use. Limitations: The distribution of age at outcome assessment differed in the cohorts included, potentially limiting comparability between cohorts. Stool sample collection methods also differed between cohorts. Our study population reflects the general U.S. population, and thus includes few participants who met the criteria for being at high risk of developing ASD. Conclusions: Our study is among the first multicenter studies in the U.S. to describe prospective microbiome development from infancy in relation to neurodevelopment associated with ASDs. Our work contributes to clarifying which microbial features associate with subsequent diagnosis of neuropsychiatric outcomes. This will allow for future interventional research targeting the microbiome to change neurodevelopmental trajectories. [ABSTRACT FROM AUTHOR]

Bayesian multiple regression models are often used for genomic selection, where all markers are adjusted simultaneously as random effects to reduce the false-discovery rate. The purpose of this research was to identify individual candidate genes for some reproductive traits in Holstein cattle through genome wide association studies (GWAS) using a Bayesian method in combination with post-GWAS analysis. Reproductive traits included: days open (DO), pregnancy rate (PR), calving interval (CI) and age at first calving (AFC). The animals were genotyped using single-nucleotide polymorphism (SNP) panels of different densities imputed to a 50 K SNP density. After quality control, we included 2400 genotyped animals. According to the Bayesian analysis, there were 19 windows with an explained additive genetic variance of >0.1 percent for CI, DO, AFC and PR in Holstein cattle, which were 3, 3, 6 and 7, respectively. Using Bayesian analysis, 79 genes were located within or nearby (250-kb) 19 significant SNPs/windows in the Bos taurus autosomes. Among these genes, we identified 25 candidate genes for reproductive traits, namely CHD7, CLVS1, EVX2, MAT2B, NUDCD2, GPR39, NCKAP5, LYPD1, HOXD13, SEMA5B, CCNG1, SEMA5A, BRF1, PSEN2, CACHD1, SUGTA, ELF1, SNORA70, AKT1, TM2D1, SLF1, MCTPA, PAB2A, MTRF1 and ADCY2. Additionally, another nine candidate genes (CLVS1, GPR39, CENPF, AMOT, ARF1, CCDC186, ADCY2, BDP1 and AMOTL1) were identified in the network cluster analysis as hub genes for reproductive traits. The results of gene set enrichment analysis (GSEA) and pathway analysis, suggest that the most important gene ontology term involving cellular metabolic process was related to the AFC trait. To summarize, Bayesian methods were used to identify SNPs and candidate genes that could be useful in genomic selection to improve reproductive traits of Holstein dairy cattle. [ABSTRACT FROM AUTHOR]

Background: Dendrobium huoshanense, a traditional medicinal and food plant, has a rich history of use. Recently, its genome was decoded, offering valuable insights into gene function. However, there is no comprehensive gene functional analysis platform for D. huoshanense. Result: To address this, we created a platform for gene function analysis and comparison in D. huoshanense (DhuFAP). Using 69 RNA-seq samples, we constructed a gene co-expression network and annotated D. huoshanense genes by aligning sequences with public protein databases. Our platform contained tools like Blast, gene set enrichment analysis, heatmap analysis, sequence extraction, and JBrowse. Analysis revealed co-expression of transcription factors (C2H2, GRAS, NAC) with genes encoding key enzymes in alkaloid biosynthesis. We also showcased the reliability and applicability of our platform using Chalcone synthases (CHS). Conclusion: DhuFAP (www.gzybioinformatics.cn/DhuFAP) and its suite of tools represent an accessible and invaluable resource for researchers, enabling the exploration of functional information pertaining to D. huoshanense genes. This platform stands poised to facilitate significant biological discoveries in this domain. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a challenging disease to evaluate in terms of prognosis, requiring close attention to the prognosis of HCC patients. Exosomes have been shown to play an important role in HCC development and have significant potential in managing HCC patient prognosis, as they are detectable in patients' blood. By using small extracellular vesicular RNA, liquid biopsies can reflect the underlying physiological and pathological status of the originating cells, providing a valuable assessment of human health. No study has explored the diagnostic value of mRNA expression changes in exosomes for liver cancer. The present study investigated establishing a risk prognosis model based on mRNA expression levels in exosomes from blood samples of liver cancer patients and evaluated its diagnostic and prognostic value, providing new targets for liver cancer detection. We obtained mRNA data from HCC patients and normal controls from the TCGA and exoRBase 2.0 databases and established a risk prognostic assessment model using exosomes-related risk genes selected through prognostic analysis and Lasso Cox analysis. The patients were divided into high-risk and low-risk groups based on median risk score values to validate the independence and evaluability of the risk score. The clinical value of the model was further analyzed using a nomograph model, and the efficacy of immunotherapy and cell-origin types of prognostic risk genes were further assessed in the high- and low-risk groups by immune checkpoint and single-cell sequencing. A total of 44 genes were found to be significantly associated with the prognosis of HCC patients. From this group, we selected six genes (CLEC3B, CYP2C9, GNA14, NQO1, NT5DC2, and S100A9) as exosomal risk genes and used them as a basis for the risk prognosis model. The clinical information of HCC patients from the TCGA and ICGC databases demonstrated that the risk prognostic score of the model established in this study was an independent prognostic factor with good robustness. When pathological stage and risk prognostic score were incorporated into the model to predict clinical outcomes, the nomograph model had the best clinical benefit. Furthermore, immune checkpoint assays and single-cell sequencing analysis suggested that exosomal risk genes were derived from different cell types and that immunotherapy in the high-risk groups could be beneficial. Our study demonstrated that the prognostic scoring model based on exosomal mRNA was highly effective. The six genes selected using the scoring model have been previously reported to be associated with the occurrence and development of liver cancer. However, this study is the first to confirm that these related genes existed in the blood exosomes, which could be used for liquid biopsy of patients with liver cancer, thereby avoiding the need for puncture diagnosis. This approach has a high value in clinical application. Through single-cell sequencing, we found that the six genes in the risk model originate from multiple cell types. This finding suggests that the exosomal characteristic molecules secreted by different types of cells in the microenvironment of liver cancer may serve as diagnostic markers. [ABSTRACT FROM AUTHOR]

Coronary microcirculation dysfunction (CMD) is one of the main causes of cardiovascular disease. Traditional treatment methods lack specificity, making it difficult to fully consider the differences in patient conditions and achieve effective treatment and intervention. The complexity and diversity of CMD require more standardized diagnosis and treatment plans to clarify the best treatment strategy and long-term outcomes. The existing treatment measures mainly focus on symptom management, including medication treatment, lifestyle intervention, and psychological therapy. However, the efficacy of these methods is not consistent for all patients, and the long-term efficacy is not yet clear. GSEA is a bioinformatics method used to interpret gene expression data, particularly for identifying the enrichment of predefined gene sets in gene expression data. In order to achieve personalized treatment and improve the quality and effectiveness of interventions, this article combined GSEA (Gene Set Enrichment Analysis) technology to conduct in-depth research on potential drug targets and their interaction networks in coronary microcirculation dysfunctions. This article first utilized the Coremine medical database, GeneCards, and DrugBank public databases to collect gene data. Then, filtering methods were used to preprocess the data, and GSEA was used to analyze the preprocessed gene expression data to identify and calculate pathways and enrichment scores related to CMD. Finally, protein sequence features were extracted through the calculation of autocorrelation features. To verify the effectiveness of GSEA, this article conducted experimental analysis from four aspects: precision, receiver operating characteristic (ROC) curve, correlation, and potential drug targets, and compared them with Gene Regulatory Networks (GRN) and Random Forest (RF) methods. The results showed that compared to the GRN and RF methods, the average precision of GSEA improved by 0.11. The conclusion indicated that GSEA helped identify and explore potential drug targets and their interaction networks, providing new ideas for personalized quality of CMD.

Abstract Osteosarcoma (OS) is a highly malignant tumor, and its dysregulated lipid metabolism is associated with tumorigenesis and unfavorable prognosis. Interestingly, long noncoding RNAs (lncRNAs) have emerged as pivotal regulators of lipid metabolism, exerting notable impacts on tumor proliferation. Nevertheless, the involvement of RPARP-AS1, a novel lipid metabolism-associated lncRNA, remains unexplored in the context of OS. This study aims to identify functionally relevant lncRNAs impacting OS proliferation and lipid metabolism and seeks to shed light on the upstream regulatory mechanisms governing lipogenic enzyme activity. Based on comprehensive bioinformatic analysis and the establishment of a risk model, we identified seven lncRNAs significantly associated with clinical characteristics and lipid metabolism-related genes in patients with OS. Among these, RPARP-AS1 was selected for in-depth investigation regarding its roles in OS proliferation and lipid metabolism. Experimental techniques including RT-qPCR, Western blot, cell viability assay, assessment, and quantification of free fatty acids (FFAs) and triglycerides (TGs) were utilized to elucidate the functional significance of RPARP-AS1 in OS cells and validate its effects on lipid metabolism. Manipulation of RPARP-AS1 expression via ectopic expression or siRNA-mediated knockdown led to alterations in epithelial-mesenchymal transition (EMT) and expression of apoptosis-associated proteins, thereby influencing OS cell proliferation and apoptosis. Mechanistically, RPARP-AS1 was found to augment the expression of key lipogenic enzymes (FABP4, MAGL, and SCD1) and potentially modulate the Akt/mTOR pathway, thereby contributing to lipid metabolism (involving alterations in FFA and TG levels) in OS cells. Collectively, our findings establish RPARP-AS1 as a novel oncogene in OS cells and suggest its role in fostering tumor growth through the enhancement of lipid metabolism.

Abstract Background Treatment failure following androgen deprivation therapy (ADT) presents a significant challenge in the management of advanced prostate cancer. Thus, understanding the genetic factors influencing this process could facilitate the development of personalized treatments and innovative therapeutic strategies. The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway plays a pivotal role in controlling cell growth and tumorigenesis. We hypothesized that genetic variants within this pathway may affect the clinical outcomes of patients undergoing ADT for prostate cancer. Methods We genotyped 399 single-nucleotide polymorphisms (SNPs) across 28 core PI3K/AKT pathway genes in a cohort of 630 patients with prostate cancer undergoing ADT. We assessed the potential association of the SNPs with patient survival. Functional analyses of the implicated genes were also performed to evaluate their effects on prostate cancer. Results After multivariate Cox regression analysis and multiple testing correction, GABRB3 rs12591845 exhibited the most significant association with both overall and cancer-specific survivals (P

Background and Aims: Ulcerative colitis (UC) and Crohn’s disease (CD) are chronic inflammatory bowel diseases (IBDs) with an incompletely understood etiology and pathogenesis. Identification of suitable drug targets and assessment of disease severity are crucial for optimal management. Methods: Using RNA sequencing, we investigated differential gene expression in peripheral blood samples from IBD patients and non-inflamed controls, analyzed pathway enrichment, and identified genes whose expression correlated with endoscopic disease severity. Results: Neutrophil degranulation emerged as the most significant pathway across all IBD sample types. Signaling by interleukins was prominent in patients with active intestinal inflammation but also enriched in CD and UC patients without intestinal inflammation. Nevertheless, genes correlated to endoscopic disease severity implicated the primary cilium in CD patients and translation and focal adhesion in UC patients. Moreover, several of these genes were located in genome-wide associated loci linked to IBD, cholesterol levels, blood cell counts, and levels of markers assessing liver and kidney function. These genes also suggested connections to intestinal epithelial barrier dysfunction, contemporary IBD drug treatment, and new actionable drug targets. A large number of genes associated with endoscopic disease severity corresponded to noncoding RNAs. Conclusion: This study revealed biological pathways associated with IBD disease state and endoscopic disease severity, thus providing insights into the underlying mechanisms of IBD pathogenesis as well as identifying potential biomarkers and therapies. Peripheral blood might constitute a suitable noninvasive diagnostic sample type, in which gene expression profiles might serve as indicators of ongoing mucosal inflammation, and thus guide personalized treatment decisions.

Background: Prostate cancer is a major global health burden, with biochemical recurrence (BCR) following radical prostatectomy affecting 20–40% of patients and posing significant challenges to prognosis and treatment. Emerging evidence suggests a critical role for differentially expressed in normal and neoplastic cell (DENN) domain-containing genes in oncogenesis; however, their implications in prostate cancer and BCR risk remain underexplored. Methods: This study systematically evaluated 151 single-nucleotide polymorphisms in DENN domain-containing genes in 458 patients with prostate cancer and BCR, followed by validation in an independent cohort of 185 patients. Results: Multivariate Cox regression analyses identified DENND2D rs610261 G>A as significantly associated with improved BCR-free survival in both cohorts (adjusted hazard ratio = 0.39, 95% confidence interval = 0.23–0.66, p = 0.001). Functional analysis revealed rs610261’s regulatory potential, with the protective A allele correlating with increased DENND2D expression in various human tissues. Compared to normal prostate tissues, DENND2D expression was reduced in prostate cancer, with higher expression being linked to favorable patient prognosis (p = 0.03). Gene set enrichment analysis revealed an association between DENND2D expression and the negative regulation of MYC target genes, including MAD2L1, ERH, and CLNS1A, which are overexpressed in prostate cancer and associated with poor survival. Furthermore, the elevated DENND2D expression promotes immune infiltration in prostate cancer, supporting its role in immune modulation. Conclusions: DENND2D is a prognostic biomarker for BCR in prostate cancer and offers new avenues for personalized treatment strategies.