Cancer Cell International最新文献

Background: Biomechanical features show notable heterogeneity in tumor risk stratification, yet their role in prostate cancer (PCa) progression remains unclear. This study aimed to elucidate the role and underlying mechanism of biomechanical features in PCa progression.

Methods: We integrated transcriptomic data from 1693 PCa patients across ten public cohorts and single-cell RNA sequencing (scRNA-seq) data from 19 PCa samples to define biomechanical subtypes. RT-qPCR was used to assess the impact of mechanical stimulation on malignant phenotypes. Biomechanical regulatory genes (BMRGs) were identified using consensus clustering and Weighted Gene Co-expression Network Analysis (WGCNA). A prognostic index (MRPX) was developed using machine learning. Immune infiltration and drug sensitivity analyses were conducted to assess the clinical utility of MRPX in guiding precision therapy. A co-culture model was employed to assess the impact of COL5A1-positive fibroblasts on the metastatic potential of PCa cells.

Results: Loss of biomechanical features was associated with smooth muscle disruption and PCa progression. In vitro mechanical stimulation suppressed EMT-related gene expression in PC-3 cells. WGCNA identified 137 hub BMRGs, from which MRPX was constructed. MRPX demonstrated strong generalizability in predicting PCa progression and effectively stratified patient responses to both immunotherapy and chemotherapy. Elevated MRPX was associated with smooth muscle disruption, which linked MRPX to extracapsular extension (ECE) and enhanced metastatic potential. Mechanistically, COL5A1 was closely linked to PCa progression, and CellChat analysis indicated that COL5A1⁺ fibroblasts contribute to shaping an aggressive tumor microenvironment. Co-culture experiments confirmed a marked upregulation of COL5A1 in cancer-associated fibroblasts (CAFs). Furthermore, silencing of COL5A1 significantly attenuated the ability of CAFs to promote the metastatic potential of PC-3 cells.

Conclusions: This study establishes MRPX as a robust biomarker for prognostic stratification and therapeutic guidance in PCa, offering new insights into biomechanical regulation of tumor progression and providing a potential avenue for precision oncology.

Background: Glycosylation, as one of the most prevalent forms of post-translational modification, plays a pivotal role in tumor progression through structural alterations of serum N-glycans in hepatocellular carcinoma (HCC). In this study, we systematically investigated these N-glycan profiles as potential prognostic biomarkers for predicting patient survival outcomes.

Methods: This study enrolled a cohort of 150 hepatitis B virus (HBV)-related HCC patients (BCLC stages A-D) who received treatment and follow-up monitoring at Southwest Medical University Hospital from 2022 to 2023. Additionally, 105 chronic hepatitis B (CHB) patients, 50 healthy individuals matched for age and gender were recruited as controls. Serum N-glycan profiles were characterized using capillary gel electrophoresis laser-induced fluorescence detection (CGE-LIF).

Results: The relative intensities (RIs) of Peak 9 (NA3Fb) was specifically elevated in HBV-related HCC patients. This peak emerged as an independent predictor of survival (p = 0.004), strongly correlated with advanced tumor stage (p < 0.01), and it was associated with HBsAg loss following anti-PD-1 therapy. MGAT4A, the gene implicated in regulating Peak 9, is likely significantly involved in critical pathways driving HCC progression.

Conclusions: Serum N-glycan profiling represents a promising noninvasive tool for monitoring prognosis and outcomes in HCC patients. Peak 9 (NA3Fb) was identified as a prognostic biomarker significantly associated with clinical outcomes in HBV-related HCC.

Polyploid giant cancer cells (PGCCs) are a distinct subpopulation of tumor cells characterized by enlarged morphology, increased nuclear content, and stem cell-like plasticity. Once considered senescent or non-functional, PGCCs are now recognized as critical drivers of tumor progression, metastasis, therapeutic resistance, and relapse. Their formation can be triggered by various stresses, including chemotherapy, radiotherapy, targeted therapies, as well as by other conditions such as endoplasmic reticulum (ER) stress or hypoxia. Mechanistically, PGCCs arise through processes such as endoreplication, mitotic slippage, cell fusion, and failed cytokinesis, which enable cells to escape mitotic catastrophe and transition into a polyploid state. Under therapeutic stress, PGCCs can persist by adopting a dormant or quiescent phenotype and later resume proliferation through neosis, characterized by asymmetric cytokinesis, generating daughter cells with enhanced migratory, invasive, and tumor-initiating capabilities. These progenies, along with the PGCCs themselves, frequently exhibit cancer stem cell (CSC)-like traits and undergo epithelial-mesenchymal transition (EMT), contributing to tumor heterogeneity and plasticity. Key signaling pathways implicated in PGCC biology include IL-6/IL-6R signaling, unfolded protein response (UPR), impaired p53 pathway, Aurora kinase B (AURKB) inhibition, and activation of the PLK4/CDC25C axis. PGCCs have also been shown to promote angiogenesis, induce therapy resistance, and evade immune surveillance. Clinically, elevated PGCC levels correlate with poor prognosis and resistance across multiple cancer types, including breast, colorectal, lung, ovarian, and so on. Given their unique properties and clinical relevance, PGCCs represent a promising frontier in cancer biology with the potential to overcome therapeutic resistance and prevent tumor recurrence through targeted interventions. This review seeks to elucidate the role of PGCCs across multiple cancer types and highlights their emerging potential as novel targets for future cancer therapies.

Non-small cell lung cancer (NSCLC) constitutes a significant proportion of lung cancers and poses a serious threat to human health. Osimertinib is the first-line drug for treating NSCLC, but long-term use can lead to drug resistance. Exploring the mechanism of drug resistance and effectively selecting treatment plans based on the mechanism of resistance are urgent issues to be addressed. In this study, dryness characteristics were evaluated by measuring cell activity, cell spheroid formation and cloning conditions, and the levels of stem cell marker molecules. The sensitivity of SPP1 to osimertinib was also assessed in mice. The results showed that SPP1 regulates cancer stem cells (CSCs) by interacting with CD44, thereby generating osimertinib resistance. These findings provide a basis for clinical research.

Breast cancer progression is facilitated by the epithelial to mesenchymal transition (EMT), generating cancer cells with enhanced metastatic capacity and resistance to chemotherapeutics. The fungus-derived sesterterpenoid natural produce compound, ophiobolin A (OpA), possesses nanomolar cytotoxic activity and a high therapeutic index, although its molecular targets and mechanism of action are not well characterized. Herein, we utilized a model of mammary epithelial cells and breast cancer cell lines with and without EMT features to characterize the mechanism of selectivity towards EMT(+) cells by OpA. Proteins interacting with OpA in EMT(+) cells, including mitochondrial glutathione transporter SLC25A40, were identified through via mass spectrometry. We utilized trans-mitochondrial cybrids to determine that mitochondria mediate sensitivity to OpA. Furthermore, we report effects on glycolysis, oxidative metabolism, and disruption of metabolite abundance in the TCA cycle. Antioxidant mechanisms are activated by OpA in EMT(+) cells via the NRF2-ARE pathway, verified by decreased cytotoxicity in EMT(+) cells pretreated with the NRF2 activator CDDO. Collectively, we conclude that OpA selectivity toward EMT is mediated by the mitochondria, and at sub-cytotoxic levels, generates a metabolic shift leading to cell death countered by antioxidant mechanisms.

Background: Gastric cancer (GC) remains a major global health challenge, characterized by high morbidity and mortality rates. Early diagnosis is essential for improving patient outcome. This study aims to develop a diagnostic model based on specific signature genes by investigating the association between double-negative (DN) T cells and GC.

Methods: A bidirectional Mendelian randomization (MR) analysis was conducted to assess the causal relationship between immune cell phenotypes and GC pathogenesis. Three machine learning (ML) algorithms, combined with logistic regression, were employed to identify featured genes. Real-world cohorts and animal experiments were applied to validate the expression levels of DN T cells and selected model genes. Virtual screening was further performed to identify potential therapeutic candidates.

Results: DN T cells were identified as significant risk factors for GC. A diagnostic model incorporating four genes-EML4, IL32, FXYD5, and TTC39C-was constructed using ML algorithms and demonstrated high predictive accuracy across multiple clinical cohorts. External validation and experimental analyses confirmed elevated DN T cell levels and increased expression of all model genes in GC tissues, correlating with poor prognosis. Virtual screening identified potential therapeutic compounds with strong binding affinity to target proteins, indicating their potential for GC treatment.

Conclusions: The study established a novel diagnostic model for GC based on DN T cell signature genes, which shows robust predictive performance and significant clinical benefit. The findings underscore the important role of DN T cells and model genes in GC, providing new insights into early diagnosis and potential therapeutic targets for effective management of GC.