Discover. Oncology最新文献

Background: High-grade serous ovarian cancer (HGSOC) exhibits poor prognosis due to late diagnosis, chemoresistance, and limited responses to immune checkpoint inhibitors. Although tumor-infiltrating CD8⁺ T cells correlate with improved survival, current prognostic models remain inadequate. Thus, robust biomarkers linked to CD8⁺ T cell activation are urgently needed to guide clinical management.

Methods: Transcriptomic and clinical profiles from 874 late-stage HGSOC patients were analyzed via single-sample gene set enrichment analysis for immune infiltration and weighted gene co-expression network analysis to identify CD8⁺ T cell-associated genes. An integrative machine learning approach was employed to develop a CD8⁺ T cell-associated immune prognostic signature (CIPS), which was then validated across multiple independent cohorts and benchmarked against 56 published models. CIPS was further characterized using single-cell RNA-seq analysis.

Results: The resulting 10-gene signature independently predicted overall survival in all cohorts and consistently surpassed most clinicopathological variables and comparator models. Low-risk patients exhibited significantly enhanced CD8⁺ T cell and cytotoxic gene scores, correlating with better responses to chemotherapy and immunotherapy. CIPS inversely correlated with tumor-mutation burden, BRCA1/2 mutations and homologous-recombination deficiency. Single-cell analysis localized signature genes to T lymphocyte and myeloid compartments and linked elevated CIPS activity to augmented intercellular communication in platinum-resistant tumors.

Conclusion: CIPS captures a CD8⁺ T cell activation program that powerfully stratifies late-stage HGSOC, forecasts therapeutic benefit and offers a practicable biomarker for personalized immuno-oncology strategies.

Background: Prostate adenocarcinoma (PRAD) is a common malignancy in the male genitourinary system, with growing evidence linking its progression to mitochondrial function and macrophage polarization. This study identifies prognostic genes associated with these factors in PRAD through integrated transcriptomic data analysis and Mendelian randomization (MR).

Methods: This study utilized transcriptome datasets from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD). Candidate genes were selected by integrating mitochondrial-related genes (MRGs), macrophage polarization-related genes (MPRGs), and differentially expressed genes (DEGs). Prognostic genes were subsequently identified through MR and regression analyses, enabling the construction and validation of a risk prediction model. The model underwent independent prognostic assessment and nomogram validation, followed by comprehensive analyses including functional enrichment, immune profiling, and drug sensitivity evaluation comparing high- and low-risk cohorts.

Results: From the overlap of 6,734 DEGs, 5,940 key module genes, and 1,136 MRGs, 103 CGs were identified. MR and regression analyses revealed seven prognostic genes (ABHD11, PTRH2, CAT, NTHL1, SLC25A39, OXR1, GSTZ1), which formed a robust risk prediction model. The model confirmed risk score, prostate-specific antigen, and Gleason score as independent prognostic factors for PRAD. A validated nomogram demonstrated high accuracy in outcome prediction. Functional enrichment analysis highlighted differential E2F target activity between risk groups, while immune profiling identified nine distinct cell populations, including immature dendritic cells. Finally, drug sensitivity analysis showed elevated IC50 values for bexarotene and CCT018159 in high-risk patients.

Conclusion: This study identified seven prognostic genes and provided a new theoretical basis for exploring immune defense mechanisms and targeted therapeutic drugs in PRAD.

Clear cell renal cell carcinoma (ccRCC) is a highly aggressive and metastatic malignancy that poses a serious threat to patient health. While its development involves both genetic and environmental influences, the interactions between genetic susceptibility and endocrine-disrupting chemicals (EDCs) remain poorly understood. In this study, we integrated multi-omics datasets and applied machine learning approaches to identify EDCs and their associated target genes implicated in ccRCC pathogenesis. We analyzed bulk and single-cell RNA sequencing data and evaluated 101 machine learning algorithms to construct a robust prognostic model. This analysis identified 8 EDCs potentially involved in ccRCC: Diethylnitrosamine, Diethylstilbestrol, Resveratrol, 4,4'-diaminodiphenylmethane, Trichloroethylene, Arsenic, Lead, and 2,3,5-(triglutathion-S-yl)hydroquinone. 5 EDC-associated prognostic genes-BIRC5, CCND1, RRM2, CDH1, and TRIB3-were also identified. Pathway enrichment and immune infiltration analyses revealed intricate interactions between these genes and the tumor microenvironment. Furthermore, cell-cell communication analysis revealed distinct signaling patterns among EDC-associated subpopulations, offering new insights into how EDCs may modulate gene expression and contribute to ccRCC progression. This study provides a foundation for future investigations into EDC-driven tumor biology and may guide the development of targeted therapies and preventative strategies against ccRCC.

Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with complex molecular mechanisms underlying its pathogenesis. Understanding the transcriptional landscape and cellular heterogeneity within the tumor microenvironment is crucial for identifying potential therapeutic targets and prognostic biomarkers.

Methods: We performed comprehensive bulk RNA sequencing on lung cancer tissues and adjacent normal samples to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was conducted to identify functionally related gene modules associated with clinical phenotypes. Functional enrichment and pathway analyses were performed to elucidate biological significance. Single-cell RNA sequencing (scRNA-seq) was utilized to characterize cellular heterogeneity and reconstruct pseudotemporal trajectories. RT-qPCR validation was performed on A549 and H1299 lung cancer cell lines to confirm key findings.

Results: Bulk RNA-seq analysis identified extensive transcriptional reprogramming in lung cancer with symmetrical distribution of upregulated and downregulated genes. WGCNA revealed multiple co-expression modules significantly correlated with clinical traits, with turquoise and blue modules showing particularly strong associations. Functional enrichment analysis highlighted dysregulation in cell proliferation, immune response, metabolic reprogramming, and developmental signaling pathways including Wnt, Hedgehog, and estrogen signaling. Single-cell analysis identified distinct cellular populations including epithelial cells, immune cells (B cells, NK cells, dendritic cells), fibroblasts, and proliferating cells. Pseudotemporal trajectory analysis revealed dynamic cellular state transitions and identified five key cell cycle regulators (AURKA, FANCD2, HELLS, RRM2, STMN1) with stage-specific expression patterns. RT-qPCR validation confirmed significant upregulation of all five genes in both A549 (4.2 to 6.3-fold increase) and H1299 cells (3.9 to 5.9-fold increase) compared to normal bronchial epithelial cells (all p < 0.001).

Conclusions: This integrated multi-omics approach reveals the complex transcriptional landscape and cellular heterogeneity in lung cancer.

Background: Glioma is the most common and aggressive primary brain tumor, characterized by high heterogeneity, invasive growth, and poor prognosis. Identifying novel molecular drivers is essential for improving diagnosis, prognosis, and treatment. This study aimed to investigate the expression pattern, clinical significance, and functional role of VAMP5 in glioma.

Methods: This study integrated data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and systematically evaluated VAMP5 expression in glioma, with validation performed via Real-time PCR, immunohistochemistry, and Western blot. Meanwhile, we analysed the prognostic value, clinicopathological features, function enrichment analysis, immune cell infiltration relevance of VAMP5. Subsequently, in vitro and in vivo experiments were conducted to further validate the role and mechanism of VAMP5 in glioma.

Results: VAMP5 was significantly upregulated in glioma tissues compared to normal brain tissue across multiple datasets and clinical samples. Single-cell RNA sequencing (scRNA-seq) further confirmed distinct distribution heterogeneity of VAMP5 within glioma cells. A protein-protein interaction (PPI) network was constructed to identify key hub genes, combined with pathway enrichment analysis and immune infiltration profiling to decipher VAMP5-mediated mechanisms in glioma progression. Finally, functional assays in GBM cell lines were conducted to illustrate the molecular mechanisms underlying VAMP5-mediated promotion of GBM progression. In vitro and in vivo experiments further validated that VAMP5 promotes the proliferation and migration of glioma.

Conclusion: VAMP5 promotes glioma progression and is associated with immune modulation and adverse clinical outcomes. It may serve as a novel biomarker for prognosis and a potential therapeutic target in glioma.