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
New insights are provided into the differential expression and pathogenesis of LACTB2 in digestive tract tumours, particularly its prognostic and diagnostic value in GC.
Abstract
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.
Background and aim Lactylation is a novel histone modification driven by lactate accumulation, which has been implicated in clear cell renal cell carcinoma (ccRCC) progression. However, its comprehensive molecular mechanism and clinical relevance remain poorly understood. This study aimed to investigate lactylation-related molecular mechanisms and therapeutic responses in ccRCC using integrated single-cell analysis and machine learning algorithms. Methods We integrated single-cell RNA sequencing and bulk transcriptomic data from patients with ccRCC. Transcriptional signatures of lactylation-related genes were evaluated using four gene set scoring algorithms. Key lactylation-related genes were identified through weighted gene co-expression network analysis and differential expression analysis. A prognostic model was constructed using 10 machine learning algorithms and subsequently validated in independent cohorts. The functional role of the core gene, CDT1, was validated through in vitro and in vivo experiments. Results We established a prognostic model comprising 13 lactylation-related genes. The model robustly stratified patients into high- and low-risk groups with distinct survival outcomes, immune microenvironment features, and immunotherapy responses. Functional assays revealed that CDT1, the core gene of the signature, promoted ccRCC cell proliferation, migration, and invasion. Moreover, modulation of CDT1 altered intracellular L-lactate levels. However, whether this effect reflects a direct metabolic–epigenetic regulatory mechanism or is secondary to proliferative changes remains to be elucidated. Conclusions This study delineates the molecular heterogeneity associated with lactylation-related gene expression in ccRCC and presents a validated prognostic model. It identifies CDT1 as a novel oncogene and potential biomarker, providing insights into metabolic–epigenetic crosstalk and a foundation for future therapeutic development.
Yue Zhang, Hang Zhou, Bihui Zhang et al.· Frontiers in Endocrinology· 0 citations
BACKGROUND
Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer. Brain metastasis (BrM) is a major cause of mortality in patients with LUAD. The tumor microenvironment (TME) of LUAD BrM remains incompletely characterized, and there is an urgent need to identify biologically relevant biomarkers and potential therapeutic vulnerabilities.
METHODS
Single-cell transcriptomic data from LUAD BrM samples were processed through dimensionality reduction, clustering, and cell annotation. BrM-associated core genes were identified from BrM scRNA-seq datasets using inferCNV and hdWGCNA. A prognostic model for LUAD was then constructed using 101 combinatorial approaches derived from 10 machine learning algorithms and validated in LUAD cohorts. We further performed supportive analyses including enrichment, pseudotime trajectory, cell-cell communication, immune infiltration, and single-cell-based drug sensitivity prediction to provide additional biological context. Key findings were validated by immunohistochemistry (IHC) in human tissues and by qRT-PCR and IHC in a mouse BrM model. Functional assays were performed in PC9-BrM cells to assess the roles of SEC61G and CFL1 in LUAD BrM.
RESULTS
Our study unveiled a comprehensive single-cell atlas of the BrM TME and established a robust 12-gene BrM-informed prognostic signature for LUAD, validated across multiple LUAD survival cohorts. Functional analysis revealed dysregulated cell cycle control in BrM progression. Immune profiling demonstrated significantly reduced CD4+ and CD8+ T-cell infiltration in high-risk subgroups. Cell-cell interaction analysis highlighted the MIF-CD74/CXCR4 and MIF-CD74/CD44 signaling axes as key regulators of TME remodeling. IHC in human tissues and validation using a mouse BrM model supported the biological relevance of SEC61G and CFL1 in LUAD BrM. qRT-PCR further showed elevated expression of SEC61G and CFL1 mRNA in brain-metastatic cells. Knockdown of SEC61G or CFL1 significantly inhibited PC9-BrM cell viability, proliferation, and migration.
CONCLUSIONS
This study identified genes associated with LUAD BrM and established a machine-learning-based prognostic signature for LUAD. Further experimental validation supports SEC61G and CFL1 as candidate biomarkers with potential therapeutic relevance.
Jing-Xiang Pang, Wentian Wu, Peiwen Zhu et al.· Cancer Cell International· 0 citations
BACKGROUND
Heterogeneity in cancer-associated fibroblast (CAF) infiltration within the tumor microenvironment is closely associated with gastric cancer progression and immune evasion; however, precise CAF-related diagnostic markers and actionable therapeutic targets remain scarce.
METHODS
Weighted gene coexpression network analysis (WGCNA) was employed to screen CAF-related coexpression modules. Gastric cancer samples were stratified into molecular subtypes via nonnegative matrix factorization (NMF). Core genes were screened using LASSO regression, SVM-RFE, and RF. Functional assays (Western blot, CCK-8, colony formation, Transwell, tube formation, ELISA, and flow cytometry) were conducted to assess THY1's role in CAF activation and effects on tumor/immune cells.
RESULTS
WGCNA identified modules correlated with CAF abundance, yielding 417 hub genes. NMF classified gastric cancer into two molecular subtypes with distinct differences in immune microenvironment, drug sensitivity, and pathway activity. Four core genes (THY1, VASH1, CHSY3, and CDH11) were consistently identified. The nomogram model demonstrated strong diagnostic performance for discriminating gastric cancer from normal tissue (AUC = 0.907 in the discovery cohort and > 0.85 in the validation cohort), with calibration and decision curve analysis (DCA) supporting clinical utility. THY1 knockdown in CAFs downregulated activation markers. Conditioned medium of THY1-deficient CAFs reduced cell proliferation and invasion, inhibited HUVEC tube formation, enhanced CD8+ T cell-mediated tumor killing, and elevated IFN-γ secretion.
CONCLUSION
CAF-derived THY1 promoted malignant phenotypes and immune evasion by modulating CAF function and paracrine cross talk with tumor cells, endothelial cells, and CD8+ T cells, offering new insights for tumor microenvironment-targeted therapy in gastric cancer.
Sihui Hou, Mengzhu Sang, Yan Wang et al.· Journal of Gastroenterology...· 0 citations
Background As the leading histological form of lung cancer, lung adenocarcinoma (LUAD) displays considerable intratumoral heterogeneity, frequent therapeutic resistance, and an unfavorable clinical outcome. Although rewiring of mitochondrial energy metabolism is known to drive tumor progression and treatment failure, a comprehensive understanding of its dual role in LUAD drug resistance and prognosis has yet to be established. Here, we built a robust predictive signature that integrates mitochondrial metabolism with drug resistance through multi-omics integration and machine learning frameworks. Methods We explored single-cell RNA sequencing profiles together with TCGA-LUAD transcriptomic data. Weighted gene co-expression network analysis (WGCNA) was applied to extract gene modules linked to mitochondrial-related genes (MRGs) and drug resistance-related genes (DRGs). From these, a five-gene (KLF4, KLF10, CAT, ALDOA, HLA-DRA) prognostic classifier, designated MDrisk, was formulated using LASSO-Cox regression and 101 combinations of 10 machine learning algorithms. Results The MDrisk model demonstrated reliable and precise prognostic capacity across training, internal test, and external GEO cohorts, serving as an independent risk factor. Elevated MDrisk scores correlated with an immunosuppressive microenvironment, higher tumor mutational burden, distinct copy-number alteration profiles, and decreased drug sensitivity in computational predictions. In vitro experiments further validated that silencing ALDOA—a central component of the signature—suppressed the proliferation, migration, and invasive capacity of LUAD cells. Conclusion The MDrisk signature derived from mitochondrial energy metabolism and drug resistance may be useful for distinguishing prognosis, immune contexture, and computationally inferred drug susceptibility in LUAD. It may offer a tool for further exploration of individualized therapy and sheds light on the interplay between metabolic dysregulation and antitumor immunity.
Chengyang Wu, Rui Jiao, Han Yan et al.· Frontiers in Medicine· 0 citations
Gallbladder cancer (GBC) is a malignant digestive system tumor, and patients are often only diagnosed at an advanced stage, resulting in high mortality and poor prognosis. Insufficiently specific and sensitive biomarkers impede early screening and diagnosis, highlighting the need to improve early diagnostic capabilities and identify reliable molecular biomarkers. Two GBC datasets and 3,252 acetylation-related genes were compiled from GeneCards and PubMed. Key genes were then identified using four machine learning algorithms, and their diagnostic performance and correlations with immune cell infiltration were evaluated. There were 848 identified differentially expressed genes (DEGs), of which 203 were acetylation-related DEGs. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed associations with biological processes. Machine learning approaches identified seven key genes that were used to develop a diagnostic model. The model’s performance was assessed using receiver operating characteristic curves and decision curve analysis. Immune infiltration analysis revealed links between the principal genes and immune cell populations, with NCOA1 showing the strongest positive correlation with activated natural killer cells. This study systematically characterizes acetylation-related genes in GBC, develops a high-accuracy diagnostic model, and provides preliminary insights into potential links between epigenetic regulation and the immune microenvironment.
BACKGROUND
Colorectal cancer (CRC) exhibits pronounced biological diversity, a feature increasingly attributed to alterations in cellular metabolic reprograms. Serine metabolism supports nucleotide synthesis, redox balance, and epigenetic regulation via one-carbon metabolism, yet its role in shaping the tumor cellular interactions and immune landscape at single-cell level remains unclear.
METHODS
Single-cell transcriptomic profiles (GSE284449) were jointly analyzed with bulk expression datasets (GSE271719), together with Mendelian randomization (MR)-based inference, to dissect serine metabolism in CRC. High-serine-metabolism (HSM) cell populations were identified using scMetabolism, and robust marker genes were selected through Lasso, random forest, XGBoost, and SVM-RFE machine learning approaches. MR was applied to evaluate causal associations with CRC risk. Functional validation included IHC, qRT-PCR, western blot, and LC-MS metabolomics, while CellChat analysis characterized HSM cell interactions with immune and stromal cells.
RESULTS
ZBTB21 and TRPM2 were identified as core regulators of HSM cells, with ZBTB21 predominantly expressed in monocytes and pro-B cells. MR analysis suggested a potential inverse association between genetically predicted ZBTB21 expression and CRC risk, indicating that ZBTB21 may exert context-dependent effects at the population level. Cell-cell communication analysis suggested that HSM monocytes may interact with fibroblasts through signaling pathways including the MIF-SPP1 axis; however, these interactions are computationally inferred and require further experimental validation. Functional assays showed that ZBTB21 overexpression upregulated PHGDH and SHMT1, increased intracellular NADPH/NADP⁺ ratios, and influenced monocyte-related phenotypes. ZBTB21 levels were markedly increased in CRC samples relative to matched non-tumorous mucosal tissues.
CONCLUSIONS
At single-cell resolution, ZBTB21 emerges as a metabolic regulator that strengthens serine biosynthesis and redox homeostasis. While integrative analyses suggest a potential link between ZBTB21-associated metabolic states and immune interactions, further experimental validation is required to establish causal relationships. This integrative framework connects genetic causality, metabolism, and immune interactions, providing mechanistic insights and potential strategies for metabolic-targeted therapies in CRC.
TRIAL REGISTRATION
Not applicable.
Yi-Mei Jiang, Haiyan Huang, Haoran Feng et al.· Cancer Immunology and Immuno...· 0 citations