Meta

Gastric cancer (GC) remains a leading cause of global cancer mortality, necessitating deeper insights into its molecular mechanisms. This meta-analysis and systematic review integrated transcriptomic data from 28 studies (14 RNA-seq, 13 microarray) to identify critical genes and pathways driving GC progression. Leveraging AI-driven approaches for data harmonization and batch effect correction, we standardized raw datasets from public repositories (GEO, SRA, TCGA) and performed rigorous quality control. Differential expression analysis using edgeR and LIMMA identified 1,163 differentially expressed genes (DEGs), including CST1 (most up-regulated) and PGA3 (most down-regulated). Pathway enrichment revealed tumor proliferation (E2F targets, G2-M checkpoint), ECM remodeling (collagens, MMPs), immune evasion (CXCL chemokines), and metabolic reprogramming as key processes. Protein-protein interaction (PPI) network analysis highlighted hub genes such as AURKA, COL1A1, and IL6, while AI-enhanced clustering delineated functional modules linked to metastasis and prognosis. Survival and immune infiltration analyses underscored the clinical relevance of identified genes. Notably, ERBB4 down-regulation and collagen family up-regulation were mechanistically tied to apoptosis resistance and microenvironment stiffening. AI algorithms further aided in resolving dataset heterogeneity and prioritizing high-confidence biomarkers. This study provides a comprehensive transcriptomic landscape of GC, emphasizing the interplay between genetic drivers, tumor microenvironment, and immune evasion. The integration of AI methodologies enhanced robustness in cross-study data synthesis, offering novel therapeutic targets and underscoring the potential of computational strategies in advancing GC research. These findings illuminate pathways for precision oncology and underscore the need for multi-omics approaches to unravel GC complexity.

Apr 9, 2025

Triple-negative breast cancer (TNBC), characterized by its aggressive behavior and lack of hormone receptor expression, remains a therapeutic challenge. This study integrates multi-omics data and AI-driven approaches to dissect the molecular mechanisms driving TNBC progression. Through a meta-analysis of 49 transcriptomic studies (2013–2024), we identified 2,101 differentially expressed genes (DEGs), including 68 consistently dysregulated protein-coding genes, with CXCL10 (↑4.01-fold) and ADH1B (↓4.8-fold) as the most significantly altered. Pathway enrichment revealed upregulated genes associated with cell proliferation, immune evasion, and metabolic reprogramming, while downregulated genes implicated hormonal signaling suppression and extracellular matrix remodeling. Gene Ontology analysis highlighted mitotic regulation and immune dysregulation as central processes. AI-based clustering of protein-protein interaction networks identified five functional modules (Tumor Growth, Invasion & Metastasis, Metabolism, Immune & Inflammation, Hormonal & Stress Response), with hub genes like CDK1 and CXCL8 driving tumor proliferation and immune escape. Notably, machine learning algorithms enhanced data integration and cluster identification, revealing FOXM1 as a key regulator of mitotic pathways (p = 6.189E-07) and JUN as a mediator of stromal-epithelial interactions despite its downregulation.

Apr 9, 2025

Endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) pathway play pivotal roles in cancer progression and therapy resistance, yet their pan-cancer dynamics and clinical implications remain poorly understood. This study presents a comprehensive analysis of ER stress and UPR pathway activity across 32 cancer types using The Cancer Genome Atlas (TCGA) data. By integrating gene-centric and pathway-centric approaches, including single-sample Gene Set Enrichment Analysis (ssGSEA), we characterized the expression landscape, tumor microenvironment interactions, and clinical relevance of UPR signaling. Our results revealed coordinated ER stress gene expression patterns in primary tumors, with UPR pathway activity significantly elevated in most cancers compared to adjacent normal tissues. Tumor purity inversely correlated with ER stress activity, underscoring microenvironmental influences. Differential expression analysis identified 61 UPR-related genes dysregulated across cancers, with IRE1 and PERK branches predominantly upregulated. Clinically, elevated UPR activity correlated with poor prognosis, advanced tumor stages, and resistance to therapies targeting EGFR, chromatin remodeling, and DNA repair. Co-expression networks highlighted UPR interactions with DNA repair and extracellular matrix pathways, while hallmark pathway analysis linked UPR to mTORC1 signaling, hypoxia, and epithelial-mesenchymal transition. Immune profiling revealed UPR-associated shifts in cytotoxic T cells and macrophages, suggesting microenvironmental modulation. Drug response analysis demonstrated UPR-mediated resistance to EGFR inhibitors and PARP inhibitors, implicating IRE1 as a key contributor. This study establishes the UPR as a central regulator of cancer progression, offering insights into its dual roles in tumor survival and therapy resistance. Our findings advocate for UPR pathway inhibition as a promising strategy to enhance treatment efficacy, particularly in lung, gastrointestinal, and kidney cancers.

Apr 7, 2025

The aim of this study was to understand the interactions between tumor-associated mesenchymal stem cells (TA-MSCs) and triple-negative breast cancer (TNBC) cells, which appear to be necessary for developing effective therapies. The findings of the present study revealed a complex interplay between TA-MSCs and TNBC cells that affects tumor growth and metastasis. Preclinical results indicate that intratumoral IL-12 immunotherapy shows promise in overcoming TA-MSC-promoted tumor growth and metastasis.

Jan 11, 2025

The balanced fatty acid profile of olive oil not only enhances its stability but also contributes to its positive effects on health, making it a valuable dietary choice. Olive oil's high content of unsaturated fatty acids and low content of saturated fatty acids contribute to its beneficial effects on cardiovascular diseases and cancer. The quantities of these fatty acids in olive oil may fluctuate due to various factors, with genotype being a crucial determinant of the oil's quality. This study investigated the genetic basis of oil quality by comparing the transcriptome of two Iranian cultivars with contrasting oil profiles; Mari, known for its high oleic acid content, and Shengeh, characterized by high linoleic acid at Jaén index four. Gas chromatography confirmed a significant difference in fatty acid composition between the two cultivars. Mari exhibited significantly higher oleic acid content (78.48%) compared to Shengeh (48.05%), while linoleic acid content was significantly lower in Mari (4.76%) than in Shengeh (26.69%). Using RNA sequencing at Jaén index four, we analyzed genes involved in fatty acid biosynthesis. Differential expression analysis identified 2775 genes showing statistically significant differences between the cultivars. Investigating these genes across nine fundamental pathways involved in oil quality led to the identification of 25 effective genes. Further analysis revealed 78 transcription factors and 95 transcription binding sites involved in oil quality, with BPC6 and RGA emerging as unique factors. This research provides a comprehensive understanding of the genetic and molecular mechanisms underlying oil quality in olive cultivars. The findings have practical implications for olive breeders and producers, potentially streamlining cultivar selection processes and contributing to the production of high-quality olive oil.

Oct 1, 2024

The Cancers Meta-analysis Projects integrate comprehensive transcriptome, genome, and epigenome datasets to advance precision oncology through rigorous meta-analysis and cutting-edge AI models. Focused on high-impact cancers—including gastric, triple-negative breast cancer (TNBC), colorectal, lung, glioblastoma, and pancreatic cancers.

Jan 26, 2024

A deep search of RNA-seq published data shed light on thirty-nine experiments associated with the olive transcriptome, four of these proved to be ideal for meta-analysis. Meta-analysis confirmed the genes identified in previous studies and released new genes, which were not identified before. According to the IDR index, the meta-analysis had good power to identify new differentially expressed genes. The key genes were investigated in the metabolic pathways and were grouped into four classes based on the biosynthetic cycle of fatty acids and factors that affect oil quality. Galactose metabolism, glycolysis pathway, pyruvate metabolism, fatty acid biosynthesis, glycerolipid metabolism, and terpenoid backbone biosynthesis were the main pathways in olive oil quality. In galactose metabolism, raffinose is a suitable source of carbon along with other available sources for carbon in fruit development. The results showed that the biosynthesis of acetyl-CoA in glycolysis and pyruvate metabolism is a stable pathway to begin the biosynthesis of fatty acids. Key genes in oleic acid production as an indicator of oil quality and critical genes that played an important role in production of triacylglycerols were identified in different developmental stages. In the minor compound, the terpenoid backbone biosynthesis was investigated and important enzymes were identified as an interconnected network that produces important precursors for the synthesis of a monoterpene, diterpene, triterpene, tetraterpene, and sesquiterpene biosynthesis.

Aug 22, 2023