Discover. Oncology最新文献

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

Methods: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

Results: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

Conclusions: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

Background: Previous studies have indicated potential associations between some drugs and cancer risk, but the causal relationship with gastric cancer remains unclear. This study aimed to explore the causality between 23 medications and gastric cancer using Mendelian randomization (MR) analysis.

Methods: A two-sample MR analysis was conducted to assess the causal effects of medications on gastric cancer risk. Primary causal estimates were derived using the inverse variance weighted method, complemented by weighted median, MR-Egger, simple mode, and weighted mode methods. Sensitivity analyses (MR-PRESSO, MR-Egger regression, Cochran's Q test, leave-one-out analysis, Steiger filtering) validated the robustness of significant findings. Furthermore, enrichment analysis was used to analyze the biological functions of medication-related genes.

Results: After Bonferroni correction, anti-migraine formulations (OR, 0.83 [95% CI, 0.70-0.98]; p = 0.0253), drugs acting on the renin-angiotensin system (OR, 0.83 [95% CI, 0.73-0.94]; p = 0.0035), and adrenergic drugs/inhalants (OR, 0.86 [95% CI, 0.76-0.98]; p = 0.0212) were found to be suggestively associated with a reduced risk of gastric cancer in European populations. Sensitivity analyses revealed no significant heterogeneity or horizontal pleiotropy (all p > 0.05), and the Steiger filtering excluded reverse causality-related bias, collectively supporting the robustness of the primary findings. No significant causal relationship was found between the remaining 20 medications and gastric cancer risk. The causal association between 23 medications and gastric cancer differed between East Asian and European populations, indicating that the applicability of the causalities to non-European populations remains limited.

Conclusion: This study revealed negative genetically proxied associations related to drug targets of anti-migraine formulations, drugs acting on the renin-angiotensin system, and adrenergic drugs/inhalants with gastric cancer risk. These hypothesis-generating findings may help inform future mechanistic and clinical investigations.

Objective: Triple-negative breast cancer (TNBC) exhibits pronounced intratumoral heterogeneity, and cancer stem cells (CSCs) are thought to play a pivotal role in this process. However, the molecular regulatory mechanisms linking CSC-associated stemness features to tumor progression remain insufficiently elucidated.

Methods: We integrated single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk transcriptomic data to identify high-stemness cell populations using the inferCNV and CytoTRACE algorithms. Stemness-related genes were evaluated for feature importance through hdWGCNA combined with machine learning approaches, and an XGBoost-based risk prediction model was constructed. Cellular differentiation trajectories were inferred using Monocle3 and scTour, while the effects of core genes on stemness pathways and malignant biological behaviors were assessed via CellChat analysis, SHAP attribution, and scTenifoldKnk-based virtual knockdown experiments.

Results: We successfully established a predictive model comprising five core stemness-related genes (CALD1, ANP32B, FIS1, CD82, and APLP2), with the high-stemness score group exhibiting poorer prognosis and enhanced immune evasion. Trajectory analysis confirmed that the high-stemness subpopulation resided at the initiation stage of differentiation. Enrichment analyses revealed highly active Notch signaling communication, and virtual knockdown of hub genes effectively suppressed the expression of stemness markers such as NOTCH1. In addition, drug sensitivity analysis identified BI.2536 and related compounds as exhibiting higher therapeutic sensitivity in the high-risk group.

Conclusion: Our predictive model offers a novel perspective on the stemness landscape of TNBC. These core genes play key roles in maintaining stemness and also serve as potential molecular targets for personalized therapies aimed at TNBC stem-like cells.

Introduction: Bladder cancer (BLCA) is a malignant tumour that occurs on the mucosa of the bladder. It accounts for the first place in the incidence of genitourinary tumours in China. BLCA is characterized by high recurrence rate and poor survival rate. There is still a research gap regarding super-enhancer-related genes (SERGs) in BLCA.

Methods: The The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA-BLCA) and GSE31684 were subjected into this study. In addition, the Super-Enhancer Archive database was used to identify SERGs. Differential expression analysis was used to analyse the differentially expressed genes (DEGs) between the BLCA and control groups. The DEGs were overlapped with SERGs to get candidate genes in TCGA-BLCA, which were analyzed for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Univariate Cox, Least Absolute Shrinkage and Selection Operator (Lasso) regression and multivariate Cox regression analyses were used to build the risk model for BLCA. Survival analyses and validation of the model were performed by Kaplan-Meier (K-M) curves and Receiver Operating Characteristic (ROC) curve, respectively. In addition, using the estimating relative subsets of RNA transcripts (CIBERSORT) algorithm, 22 immune cell proportions were calculated. The drug sensitivity was also analyzed in this study.

Results: First of all, based on the TCGA-BLCA, 70 DE-SERGs were yielded. A prognosis model based on MXRA7, PLEKHG4B and ATP2B4 was finally constructed. ROC curves revealed that the prognosis model was a good predictor of BLCA outcomes. Immune infiltration analysis revealed that risk score was positively associated with T cells CD4 memory resting, Mast cells resting and Macrophages M2 and negatively associated with Dendritic cells activated and T cells CD8. Besides, AZD8186, BMS-754,807, JQ1, KRAS (G12C) Inhibitor and NU7441 were the top five sensitivity drugs for BLCA.

Conclusion: Three genes (MXRA7, PLEKHG4B and ATP2B4) were identified to construct a SERG-related model in BLCA, which provides a basis for understanding BLCA pathogenesis and new insights into BLCA treatment.