Integrated Elementomics–Genomics–Metabolomics Analysis Reveals Plasma Biomarker Networks and Diagnostic Potential for Gastric Cancer

Pharmacogenomic characterization of a uric acid metabolism-related signature associated with prognosis and drug sensitivity in gastric cancer.

A comprehensive study based on large-sample multi-omics integration and machine learning to decode mitochondria-associated genes: from digestive tract tumours to gastric cancer

Background Mitochondria-related genes play a crucial role in driving tumour cell progression, but little is known about their molecular mechanisms and biological pathways. This study conducted a comprehensive analysis of the mitochondrial key gene LACTB2 in digestive tract tumours and explored a novel early blood-based diagnostic model for gastric cancer (GC). Methods This study analyzed LACTB2 expression, biological pathways, and immune regulation in a large cohort of 10,581 samples. IHC staining was performed using 236 internal GC samples. Multi-omics data were integrated for comprehensive analysis of LACTB2 in GC. A combination of extensive clinical samples and multiple machine learning models enabled the construction of prognostic and blood-based diagnostic models (n = 14,219). Results LACTB2 overexpression is associated with clinical metastasis and the activation of pro-cancer pathways, and LACTB2 may mediate immune suppression and immune evasion through various methods. There is a significant transcriptional regulatory network upstream of LACTB2. LACTB2 overexpression may drive malignant transformation of GC epithelial cells through pro-cancer metabolic signalling networks, and the related mechanisms were spatially validated. Dysregulated expression of LACTB2 can affect the prognosis of GC patients, and Afatinib and Ulixertinib may play a significant role in targeting LACTB2 in the treatment of GC patients. An excellent early blood diagnostic model for GC was constructed based on the upstream miRNA of LACTB2. Conclusion Our study provides new insights into the differential expression and pathogenesis of LACTB2 in digestive tract tumours, particularly its prognostic and diagnostic value in GC.

STAT1 as a potential diagnostic biomarker for gastric cancer

Introduction: Gastric cancer is a major contributor to cancer‑related mortality across the globe. The discovery of novel molecular biomarkers is critical for achieving early diagnosis and developing targeted therapeutic strategies for this disease. The role of STAT1 in gastric cancer has not been fully elucidated. Objective: This research was designed to analyze the expression profile of STAT1 in gastric cancer and explore its feasibility as a diagnostic biomarker. Methods: We screened differentially expressed genes (DEGs) between gastric cancer specimens and normal controls using GSE63089 and GSE2685. Common upregulated DEGs were identified using Venn diagram analysis and further evaluated in GSE49515. STAT1 expression was examined using the Gene Expression Profiling Interactive Analysis (GEPIA) database and validated in clinical gastric cancer samples and paired adjacent normal tissues. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic value of STAT1. Results: A total of 8,058 DEGs were detected in GSE63089 and 2,139 DEGs in GSE2685. Venn diagram analysis revealed that 94 common upregulated DEGs were shared between the two datasets. Analysis of GSE49515 identified STAT1 as the only consistently upregulated gene among these common DEGs. Bioinformatics analysis confirmed that STAT1 levels were notably higher in gastric cancer, especially in diffuse‑type samples, than in normal samples. Validation using clinical samples confirmed significant upregulation of STAT1 in gastric cancer blood samples compared with normal controls. ROC curve analysis showed an area under the curve of 0.8645, indicating favorable diagnostic performance. Conclusion: These findings indicate that STAT1 has potential as a diagnostic biomarker for gastric cancer.

An Integrated Multi-Omics and Causal Inference Study Identifies a DNA Repair-Related Prognostic Biomarker in Gastric Cancer.

OBJECTIVE This study aims to identify biologically relevant genes associated with DNA repair pathways in gastric cancer (GC) by integrating multi-omics analyses with causal inference approaches. METHODS Public GC datasets were integrated to identify consensus differentially expressed genes (DEGs). Candidate genes were screened using WGCNA and intersected with DEGs. Key genes were selected via the machine learning algorithm Lasso+plsRglm and validated by the Area Under the Receiver Operating Characteristic Curve (AUC) analysis. The Protein-Protein Interaction (PPI) network determines the hub genes associated with GC. Single-cell RNA sequencing characterized the cell-type-specific gene expression. Kaplan-Meier analysis assessed the prognostic relevance. Immune infiltration was evaluated using CIBERSORT and ESTIMATE algorithms. Mendelian Randomization (MR) examined causal relationships among BRCA1, NADPH, and GC risk. Experimental validation was performed using qRT-PCR and Western blot in GC cell lines and clinical samples. RESULTS Five hub genes, i.e., BRCA1, CCNA2, CHEK1, KIF14, and KIF15, predominantly enriched in mesenchymal stem cells and fibroblasts, were identified. BRCA1 was consistently overexpressed in GC and associated with improved survival and enhanced antitumor immune activity. MR analysis suggested indirect associations between BRCA1, NAD(P)H metabolism, and GC risk, without a direct causal effect. Experimental results confirmed significant overexpression of BRCA1 at both mRNA and protein levels in GC. DISCUSSION These findings suggest that BRCA1 is not an independent prognostic factor but reflects broader tumor biological processes, particularly DNA repair and redox regulation. Its upregulation likely represents a compensatory response to genomic instability. The identified BRCA1-NAD(P)H axis highlights a potential mechanistic link between DNA repair and metabolic regulation, underscoring its relevance in tumor progression and therapeutic targeting. CONCLUSION This study reveals a regulatory link between DNA repair, redox metabolism and GC progression, positioning BRCA1 as a key component of tumor biology and a potential target for precision oncology strategies.

Systematic Investigation of Key Drivers of Lung Adenocarcinoma: A Focus on Genes, Pathways, and miRNAs.

Lung cancer remains a leading cause of cancer-related mortality worldwide, largely due to its asymptomatic progression in early stages and the development of drug resistance. Non-small cell lung cancer (NSCLC) accounts for 80% of all lung cancer cases, and lung adenocarcinoma (LUAD) is the most prevalent subtype. Despite advancements in treatment, the 5-year survival rate for LUAD remains low. A comprehensive literature review across various databases was conducted to curate a robust set of LUAD-associated genes. These genes were used to construct a weighted network based on KEGG pathway similarity, followed by clustering, hub gene detection, and gene ontology analysis. In parallel, a protein-protein interaction (PPI) network and a gene-miRNA regulatory network were established to provide additional layers of molecular insight. Our analysis identified 48 genes as central to LUAD pathogenesis. Several of these genes, along with their corresponding miRNAs, exhibited significant dysregulation in LUAD tissues. The hub genes PIK3CA, BRAF, EGFR, ERBB2, FGFR3, MTOR, and TP53, along with KRAS, MET, and FGFR2, emerged as potential biomarkers. Additionally, miR-17-5p and miR-27a-3p were highlighted as novel biomarker candidates with strong implications in LUAD biology. In conclusion, this study provides a refined list of genes and miRNAs with high relevance for LUAD, alongside key signaling pathways central to disease progression. These findings establish a foundation for future studies and support the development of targeted therapeutic strategies aimed at improving clinical outcomes in LUAD.

Resveratrol Modulates Lung Cancer-Associated Hub Genes Identified by Integrated Bioinformatics, Single-Cell Analysis, Molecular Docking, and Experimental Validation.