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Characterization of glycometabolism and tumor immune microenvironment for predicting clinical outcomes in gastric cancer.

ABSTRACT: Recent evidence demonstrates that the reprogramming of energy metabolism can interact with the tumor immune microenvironment, thereby participating in the progression of cancer. In this study, multi-omics data of 2471 gastric cancer samples were used to identify tumor glycometabolism and its correlation with tumor immune microenvironment. A series of bioinformatic approaches were performed to establish a scoring system to predict the survival and response of chemotherapy and immunotherapy. Three glycometabolic subtypes and two immune clustering subgroups of gastric cancer were determined. We further established a Gluco-Immune Scoring system to quantify the cancer glycometabolic status and immune infiltration of individual patients. Patients with low Gluco-Immune Score were sensitive to adjuvant chemotherapy, while patients with high Gluco-Immune Score may benefit from immunotherapy. Our results indicate that in gastric cancer, the assessment of tumor glucose metabolism and immune microenvironment has application value for the prediction of curative effects and the formulation of combined treatment strategies.

SUBMITTER: Shi J

PROVIDER: S-EPMC10006618 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Characterization of glycometabolism and tumor immune microenvironment for predicting clinical outcomes in gastric cancer.

Shi Jinyao J Wu Zhouqiao Z Wu Xiaolong X Huangfu Longtao L Guo Ting T Cheng Xiaojing X Han Jing J Li Ziyu Z Xing Xiaofang X Ji Jiafu J

iScience 20230216 3

Recent evidence demonstrates that the reprogramming of energy metabolism can interact with the tumor immune microenvironment, thereby participating in the progression of cancer. In this study, multi-omics data of 2471 gastric cancer samples were used to identify tumor glycometabolism and its correlation with tumor immune microenvironment. A series of bioinformatic approaches were performed to establish a scoring system to predict the survival and response of chemotherapy and immunotherapy. Three ...[more]

PMID: 36915686

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Project description:Objective: Because of the modest immunotherapeutic response among ovarian carcinoma (OC) patients, it is significant to evaluate antitumor immune response and develop more effective precision immunotherapeutic regimens. Here, this study aimed to determine diverse immune subtypes of OC. Methods: This study curated the expression profiles of prognostic immunologically relevant genes and conducted consensus clustering analyses for determining immune subtypes among OC patients in TCGA cohort. With Boruta algorithm, characteristic genes were screened for conducting an immune scoring system through principal component analysis algorithm. The single-sample gene set enrichment analysis and ESTIAMTE methods were adopted for quantifying the immune infiltrates and responses to chemotherapeutic agents were estimated with pRRophetic algorithm. Two immunotherapeutic cohorts were used for investigating the efficacy of immune score in predicting therapeutic benefits. Results: Two immune subtypes were conducted among 377 OC patients. Immune subtype 2 was characterized by worse clinical prognosis, more frequent genetic variations and mutations, enhanced immune infiltrates, and increased expression of MHC molecules and programmed cell death protein 1/programmed death ligand 1 (PD-1/PD-L1). In total, 30 prognosis-relevant characteristic immune subtype-derived genes were identified for constructing the immune score of OC patients. High immune score was linked with more dismal prognosis, decreased immune infiltrations, and expression of MHC molecules. High immune score presented favorable sensitivity to doxorubicin and vinorelbine and reduced sensitivity to cisplatin. In addition, immune score possessed the potential in predicting benefits from anti-PD-1/PD-L1 therapy. Conclusion: Collectively, our findings propose two complex and diverse immune subtypes of OC. Quantitative assessment of immune subtypes in individual patients strengthens the understanding of tumor microenvironment features and promotes more effective immunotherapeutic regimens.

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Dataset Information

Characterization of glycometabolism and tumor immune microenvironment for predicting clinical outcomes in gastric cancer.

Publications

Characterization of glycometabolism and tumor immune microenvironment for predicting clinical outcomes in gastric cancer.

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