International Journal of Genomics最新文献

Background: Mismatch repair (MMR) genes are implicated in stomach adenocarcinoma (STAD). This study assessed their causal role in STAD, prognostic value, and developed a histopathology-based model to predict MutS homolog 2 (MSH2) expression.

Methods: Using data from the IEU OpenGWAS database, five Mendelian randomization (MR) models evaluated causal links between MMR genes and gastric cancer (GC). Prognostic relevance was assessed via survival analysis. A random forest model using TCGA hematoxylin and eosin-stained images was trained to predict MSH2 expression. Biological insights were explored via pathomics score, gene set enrichment analysis (GSEA), immune infiltration, and tumor mutational burden (TMB).

Results: MR analysis identified MLH1 and PMS2 as risk genes, while MSH2 had a protective effect. Cox regression confirmed MSH2 as an independent protective factor (HR = 0.690, 95% CI: 0.487-0.977, p < 0.05). The pathomics model predicted MSH2 expression with an AUC of 0.811. A comparison of high- and low-survival-probability (SP) groups showed differentially expressed genes, including SFRP4. The high-SP group had elevated TMB and TP53 mutation frequency.

Conclusion: MMR genes, especially MSH2, are critical in STAD development and prognosis. The image-based model effectively predicts MSH2 expression, supporting the integration of genomic and histopathologic data for personalized GC care.

Osteosarcoma is a highly aggressive bone tumor with a complex tumor microenvironment (TME) that contributes to its progression and therapeutic resistance. In this study, we integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq datasets to characterize the TME and identify key prognostic genes in osteosarcoma. Using scRNA-seq data from 16 osteosarcoma samples, we defined eight major cell types within the TME and performed functional enrichment analyses. Through weighted gene co-expression network analysis (WGCNA) focused on an inflammation-related gene signature, we identified the yellow module as the most correlated with inflammation. By intersecting tumor-upregulated genes with WGCNA-derived genes, we identified BNIP3 as the only significant prognostic gene associated with poor survival in both the TARGET and GSE21257 cohorts. Functional annotation revealed that high BNIP3 expression is negatively correlated with immune-related pathways and immune cell infiltration, including T cells, B cells, NK cells, and neutrophils. Additionally, BNIP3-high patients exhibited a reduced sensitivity to several potential therapeutic agents. Our findings highlight BNIP3 as a hazardous gene in osteosarcoma, with important roles in immune evasion and prognosis, suggesting its potential as a therapeutic target.

Background: Repair and remodeling following myocardial infarction (MI) are complex processes with a wide array of cellular and molecular mechanisms; however, the cell source mediating repair is still poorly understood in terms of heterogeneity and temporal dynamics.

Methods: We performed a single-cell RNA sequencing (scRNA-seq) analysis of cardiac tissues from different time points post-MI, as well as in gene knockout (ChrisKO) and health control groups. The data were mined by UMAP and t-SNE dimension reduction visualization, pseudotime trajectory analysis, cell communication network analysis, and gene expression pattern cluster.

Results: A collection of cell types contributing to cardiac repair was identified, including fibroblasts, macrophages, endothelial cells, and cardiomyocytes that each expressed gene markers and showed temporal distributions associated with distinct injury phases. Pseudotime trajectory analysis identified a continuous change in cellular state from inflammatory to reparative phase, with immune cells in early stages and tissue repair cells at latter stages. The activation of macrophage migration inhibitory factor (MIF) signaling pathway is highly involved in repair after MI, where chemokine-secreting cells and cardiac fibroblasts act as major MIF signal sources. Network analysis of the intercellular communication revealed that macrophages are key orchestrators of repair. When analyzing branch-specific gene expression, we found that several important regulatory factors including Atpdv1h, Lypla1, Mrpl15, Tcea1, Apoa, Cldn1, Dpep1, and Map had changing trends at different phases during regeneration.

Conclusion: Our study profiled a panoramic landscape of cellular and molecular dynamics after MI at single-cell resolution, demonstrating key cell communication networks and regulatory genes that present novel targets for developing therapeutic strategy toward cardiac repair.

Background: Yogurt is reported to maintain the balance of gut microbiota and prevent disease, but the causal relationship remains unclear.

Methods: We selected data from UK Biobank and MiBioGen to perform Mendelian randomization analysis. MR Egger, inverse variance weighted, and so forth were employed to assess the causality between yogurt intake, low-fat and full-fat yogurt, and 196 taxa of gut microbiota. Parallelly, low-fat and full-fat yogurt were integrated to perform multivariable Mendelian randomization. Then, we summarized preliminary results according to microbiotic taxonomy.

Results: Statistics hinted at the implicit associations between yogurt intake and Haemophilus (OR = 2.08), Clostridium sensu stricto_1 (OR = 1.84), Peptostreptococcaceae (OR = 1.53), Betaproteobacteria (OR = 0.70), Bilophila (OR = 0.58), and Ruminococcaceae UCG-011 (OR = 0.40), along with the associations between low-fat yogurt and Eubacterium ruminantium (OR = 2.48), Methanobacteriaceae (OR = 3.06). The findings were causal and consistent, albeit with some false positive rates.

Conclusions: Yogurt intake suggestively increased the abundance of Haemophilus, Clostridium sensu stricto_1, and Peptostreptococcaceae and decreased the abundance of Ruminococcaceae UCG-011, Betaproteobacteria and Bilophila; low-fat yogurt suggestively increased the abundance of Eubacterium ruminantium and Methanobacteriaceae.

Background: Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults and exhibits a high propensity for liver metastasis, often leading to poor prognosis. However, effective prognostic biomarkers and therapeutic strategies for metastatic UVM remain limited.

Methods: We comprehensively analyzed transcriptomic data from both single-cell and bulk RNA sequencing cohorts, integrating data from TCGA and GEO (GSE139829, GSE22138, and GSE84976). After batch effect correction and cell type annotation, differentially expressed genes (DEGs) between primary and metastatic malignant cells were identified. These were intersected with 900 prognosis-related genes from TCGA, and 11 key prognostic genes were selected via least absolute shrinkage and selection operator (LASSO) regression to construct a risk prediction model. Model performance was evaluated across multiple cohorts. Furthermore, immune infiltration was assessed using CIBERSORT, and drug sensitivity was predicted based on chemotherapeutic IC50 values.

Results: The 11-gene risk model effectively stratified UVM patients into high-risk and low-risk groups with distinct survival outcomes. High-risk patients exhibited a more immunosuppressive tumor microenvironment and were associated with altered sensitivity to multiple chemotherapeutic agents. Immune checkpoint gene expression also varied significantly between risk groups, indicating potential implications for immunotherapy response.

Conclusion: This study identifies critical molecular features underlying UVM metastasis and immune remodeling, providing novel prognostic markers and potential therapeutic targets for clinical management of UVM.

Background: Epigenetic medication, such as RNA 5-methylcytosine (m5C), is well-recognized as a key regulator in hepatic metabolism and immune responses. However, m5C regulatory mechanisms in NASH pathogenesis have not yet been clearly elucidated.

Methods: By utilizing three bulk profiles of NASH patients acquired from GEO and integrative bioinformatic pipelines, such as Limma framework, consensus clustering, and machine learning, we first identified m5C-related molecular subgroups and hub genes for NASH patients. Besides, diagnostic performance and biological characteristics of m5C-related hub gene were estimated at bulk level. Indeed, the heterogeneity of m5C-related hub gene for NASH patients was deciphered in single-cell transcriptomic profiles at temporal and spatial manners, especially in artificial intelligence (AI)-driven virtual cells. Furthermore, potential therapeutic agents targeting m5C-associated hub genes for the treatment of NASH were enriched by AI-driven drug enrichment framework (DrugReflector) based on NASH bulk profile and then validated by molecular docking. Finally, in vitro studies quantified the expression of m5C-associated hub genes compared to normal control.

Results: m5C can divide NASH patients into two various consensus groups with different molecular and immune patterns. Furthermore, ERCC2 and FOXC2 can be considered two upregulated m5C-associated hub genes involved in NASH pathogenesis, which were mainly distributed at cholangiocyte. BRD-K93672499 can be considered a multitarget therapeutic strategy targeting ERCC2 and FOXC2 for the treatment of NASH.

Conclusion: Our study first deciphered the m5C in predictive and therapeutic potential for NASH patients, which gains more insight into their personalized and precision medicine.

The 5-year overall survival rate for hepatocellular carcinoma (HCC) patients remains below 20%. Alterations in the extracellular matrix (ECM) are increasingly recognized as central drivers of HCC initiation and progression. This study applied a system biology framework integrating omics data and machine learning to analyze gene expression and regulatory networks in HCC using The Cancer Genome Atlas. Eight ECM-associated genes (CSPG4, CD34, C1orf35, ESM1, MAPT, PLXDC1, STC2, and THBS4) were identified as upregulated diagnostic biomarkers with strong discriminatory power. Among them, MAPT, PLXDC1, and STC2 showed significant associations with poor overall survival, defining a prognostic subset. Validation in the GSE104310 and GSE144269 datasets confirmed consistent expression patterns across cohorts. Functional enrichment linked these genes to tissue remodeling and angiogenesis. Single-cell RNA sequencing revealed MAPT upregulation in T cells, PLXDC1 enrichment in cancer-associated fibroblasts, and mild STC2 elevation in tumor-associated macrophages and endothelial cells. These findings identify key ECM-based biomarkers with potential for early detection, prognosis, and therapeutic targeting in HCC.