Metabolic responses to the occurrence and chemotherapy of pancreatic cancer: biomarker identification and prognosis prediction
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ABSTRACT:
SUBMITTER: Teng T
PROVIDER: S-EPMC10960848 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Mitophagy is a conserved cellular process that plays a vital role in maintaining cellular homeostasis by selectively removing dysfunctional mitochondria. Notwithstanding that growing evidence suggests that mitophagy is implicated in pancreatic tumorigenesis, the effect of mitophagy-related genes on pancreatic cancer (PC) prognosis and therapeutic response remains largely unknown. In this study, we sought to construct a mitophagy-related gene signature and assessed its ability to predict the survival, immune activity, mutation status, and chemotherapy response of PC patients. During the screening process, we identified three mitophagy-related genes (PRKN, SRC, VDAC1) from The Cancer Genome Atlas (TCGA) cohort and a 3-gene signature was established. The prognostic model was validated using an International Cancer Genome Consortium (ICGC) cohort and two Gene Expression Omnibus (GEO) cohorts. According to the median risk score, PC patients were divided into high and low-risk groups, and the high-risk group correlated with worse survival in the four cohorts. The risk score was then identified as an independent prognostic predictor, and a predictive nomogram was constructed to guide clinical decision-making. Remarkably, enhanced immunosuppressive levels and higher mutation rates were observed in patients from the high-risk group, which may account for their poor survival. Furthermore, we found that high-risk patients were more sensitive to paclitaxel and erlotinib. In conclusion, a mitophagy-related gene signature is a novel prognostic model that can be used as a predictive indicator and allows prognostic stratification of PC patients.
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Project description:Objective: Necroptosis represents a new target for cancer immunotherapy and is considered a form of cell death that overcomes apoptosis resistance and enhances tumor immunogenicity. Herein, we aimed to determine necroptosis subtypes and investigate the roles of necroptosis in pancreatic cancer therapy. Methods: Based on the expression of prognostic necroptosis genes in pancreatic cancer samples from TCGA and ICGC cohorts, a consensus clustering approach was implemented for robustly identifying necroptosis subtypes. Immunogenic features were evaluated according to immune cell infiltrations, immune checkpoints, HLA molecules, and cancer-immunity cycle. The sensitivity to chemotherapy agents was estimated using the pRRophetic package. A necroptosis-relevant risk model was developed with a multivariate Cox regression analysis. Results: Five necroptosis subtypes were determined for pancreatic cancer (C1∼C5) with diverse prognosis, immunogenic features, and chemosensitivity. In particular, C4 and C5 presented favorable prognosis and weakened immunogenicity; C2 had high immunogenicity; C1 had undesirable prognosis and high genetic mutations. C5 was the most sensitive to known chemotherapy agents (cisplatin, gemcitabine, docetaxel, and paclitaxel), while C4 displayed resistance to aforementioned agents. The necroptosis-relevant risk model could accurately predict prognosis, immunogenicity, and chemosensitivity. Conclusion: Our findings provided a conceptual framework for comprehending necroptosis in pancreatic cancer biology. Future work is required for evaluating its relevance in the design of combined therapeutic regimens and guiding the best choice for immuno- and chemotherapy.
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Project description:Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein-protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.
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Project description:A subset of pancreatic ductal adenocarcinomas (PDACs) is highly resistant to systemic chemotherapy, but no markers are available in clinical settings to identify this subset. We found that chemotherapy-resistant PDACs, but not chemotherapy-sensitive PDACs, express a glycan biomarker called sTRA. We confirmed the use of this marker to identify treatment-resistant PDAC in multiple systems: sets of 24 cell lines, 27 organoids, and isogenic cell lines; gene-expression signatures in primary tumors; tissue microarrays containing primary tumors; and blood plasma from cohorts of human subjects. These findings establish the use of tissue or plasma assays to identify the PDACs that are resistant to chemotherapy, which could improve the precision of systemic treatments and facilitate biomarker-guided trials targeting resistant PDAC.
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