Background and Aims: This study aimed to develop a prognostic model based on DNA methylation-driven genes for patients with early-stage gastric cancer and to examine immune infiltration and function across varying risk levels. Methods: We analyzed data from stage I/II gastric cancer patients in The Cancer Genome Atlas which included clinical details, mRNA expression profiles, and level 3 DNA methylation array data. Using the empirical Bayes method of the limma package, we identified differentially expressed genes (DEGs), and the MethylMix package facilitated the identification of DNA methylation-driven genes (DMGs). Univariate Cox regression and LASSO (least absolute shrinkage and selector operation) analyses were utilized to pinpoint critical genes. A risk score prediction model was formulated using two genes that demonstrated the most significant hazard ratios (HRs). Model performance was evaluated within the initial cohort and verified in the GSE84437 cohort; a nomogram was also constructed based on these genes. We further examined 50 methylation sites associated with three CpG islands in C1orf35 and 14 methylation sites linked to one CpG island in FAAH. The CIBERSORT package was employed to identify immune cell clusters in the prediction model. Results: A total of 176 DNA methylation-driven genes were refined down to a four-gene signature (ZC3H12A was hypermethylated; GATA3, C1orf35, and FAAH were hypomethylated), which exhibited a significant correlation with overall survival (OS), as evidenced by p -values below 0.05 following univariate Cox regression and LASSO analysis. Specifically, for the risk score prediction model, C1orf35, which had the highest hazard ratio (HR = 2.035, p = 0.028), and FAAH, with the lowest hazard ratio (HR = 0.656, p = 0.012), were selected. The Kaplan–Meier analysis demonstrated distinct survival outcomes between the high-risk and low-risk score groups. The model’s predictive accuracy was confirmed with an area under the curve (AUC) of 0.611 for 3-year survival and 0.564 for 5-year survival. Notably, the hypomethylation of the three CpG islands in C1orf35 and the single CpG island in FAAH was significantly different in stage I/II gastric cancer patients compared to normal tissues. Additionally, the high-risk score group showed a notable association with resting CD4 memory T cells. Conclusion: Promoter hypomethylation of C1orf35 and FAAH in early-stage gastric cancer underscores their potential as biomarkers for accurate diagnosis and treatment. The developed predictive model employing genes affected by DNA methylation serves as a crucial independent prognostic factor in early-stage gastric cancer.

Background Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) are prevalent metabolic disorders with overlapping pathophysiological mechanisms. A comprehensive understanding of the shared molecular pathways involved in these conditions can advance the development of effective therapeutic interventions. Methods We used two datasets sourced from the Gene Expression Omnibus (GEO) database to identify common differentially expressed genes (DEGs) between T2D and NAFLD. Subsequently, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify the enriched biological processes and signaling pathways. In addition, we performed a protein-protein interaction (PPI) network analysis to identify hub genes with pivotal roles. To validate our findings, we established a type 2 diabetic mouse model with NAFLD. Results Our analysis identified 53 DEGs shared between T2D and NAFLD. Enrichment analysis revealed their involvement in signal transduction, transcriptional regulation, and cell proliferation as well as in the ferroptosis signaling pathways. PPI network analysis identified ten hub genes, namely CD44, CASP3, FYN, KLF4, HNRNPM, HNRNPU, FUBP1, RUNX1, NOTCH3, and ANXA2. We validated the differential expression of FYN, HNRNPU, and FUBP1 in liver tissues of a type 2 diabetic mouse model with NAFLD. Conclusions Our study offers valuable insights into the shared molecular mechanisms underlying T2D and NAFLD. The identified hub genes and pathways present promising prospects as therapeutic targets to address these prevalent metabolic disorders.

Full Papers; Authors: R. Ding, D. Liang, S. Huang, X. Huang, B. Wei, S. Wan, H. Zhang, Z. Wan

Objective: Systemic lupus erythematosus (SLE) patients are at risk during the COVID‐19 pandemic, yet the underlying molecular mechanisms remain incompletely understood. This study sought to analyze the potential molecular connections between COVID‐19 and SLE, employing a bioinformatics approach to identify effective drugs for both conditions.Methods: The data sets GSE100163 and GSE183071 were utilized to determine share differentially expressed genes (DEGs). These DEGs were later analyzed by various bioinformatic methods, including functional enrichment, protein–protein interaction (PPI) network analysis, regulatory network construction, and gene–drug interaction construction.Results: A total of 50 common DEGs were found between COVID‐19 and SLE. Gene ontology (GO) functional annotation revealed that “immune response,” “innate immune response,” “plasma membrane,” and “protein binding” were most enriched in. Additionally, the pathways that were enriched include “Th1 and Th2 cell differentiation.” The study identified 48 genes/nodes enriched with 292 edges in the PPI network, of which the top 10 hub genes were CD4, IL7R, CD3E, CD5, CD247, KLRB1, CD40LG, CD7, CR2, and GZMK. Furthermore, the study found 48 transcription factors and 8 microRNAs regulating these hub genes. Finally, four drugs namely ibalizumab (targeted to CD4), blinatumomab (targeted to CD3E), muromonab‐CD3 (targeted to CD3E), and catumaxomab (targeted to CD3E) were found in gene–drug interaction.Conclusion: Four possible drugs that targeted two specific genes, which may be beneficial for COVID‐19 patients with SLE.

Background: Osteoporosis is a progressive bone disease characterized by reduced bone density and deterioration of bone microarchitecture, predominantly affecting the elderly population. The ongoing COVID‐19 pandemic has introduced additional challenges in osteoporosis management, potentially due to systemic inflammation and direct viral impacts on bone metabolism. This study aims to identify common differentially expressed genes (DEGs) and key molecular pathways shared between osteoporosis and COVID‐19, with the goal of uncovering potential therapeutic targets through bioinformatics analysis. Methods: Publicly available gene expression datasets GSE164805 (osteoporosis) and GSE230665 (COVID‐19) were analyzed to identify overlapping DEGs. Functional enrichment analysis using Gene Ontology (GO), pathway analysis, protein–protein interaction (PPI) network construction, and transcription factor (TF)–hub gene regulatory network analysis were performed to explore the biological significance and regulatory mechanisms of these DEGs. Results: A total of 325 common DEGs were identified between osteoporosis and COVID‐19. GO enrichment analysis revealed significant involvement in signal transduction and plasma membrane components. Pathway analysis highlighted the “cytokine–cytokine receptor interaction” pathway as a central player. PPI network analysis identified a module of 193 genes with 397 interactions, from which 10 key hub genes were prioritized: ACTB, CDH1, RPS8, IFNG, RPL17, UBC, RPL36, RPS4Y1, GSK3B, and FGF13. Furthermore, 76 TFs were found to regulate these hub genes, and 15 existing drugs targeting four of these hub genes were identified. Conclusion: This integrative bioinformatics study reveals 15 candidate therapeutic agents that target key regulatory genes shared between osteoporosis and COVID‐19, offering promising treatment strategies for osteoporotic patients, especially those impacted by or at risk of SARS‐CoV‐2 infection.