Dataset Information

The Immune Microenvironment in Gastric Cancer: Prognostic Prediction.

ABSTRACT: Although therapeutic methods have been developed, gastric cancer (GC) still leads to high rates of mortality and morbidity and is the fourth leading cause of cancer-associated death and the fifth most common cancer worldwide. To understand the factors associated with the prognostic prediction of GC and to discover efficient therapeutic targets, previous studies on tumour pathogenesis have mainly focused on the cancer cells themselves; in recent years, a large number of studies have shown that cancer invasion and metastasis are the results of coevolution between cancer cells and the microenvironment. It seems that studies on the tumour microenvironment could help in prognostic prediction and identify potential targets for treating GC. In this review, we mainly introduce the research progress for prognostic prediction and the immune microenvironment in GC in recent years, focusing on cancer-associated fibroblasts (CAFs), tumour-associated macrophages (TAMs), and tumour-infiltrating lymphocytes (TILs) in GC, and discuss the possibility of new therapeutic targets for GC.

SUBMITTER: Ma M

PROVIDER: S-EPMC9096124 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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The Immune Microenvironment in Gastric Cancer: Prognostic Prediction.

Ma Mingwei M Sun Juan J Liu Zhen Z Ouyang Siwen S Zhang Zimu Z Zeng Ziyang Z Li Jie J Kang Weiming W

Frontiers in oncology 20220428

Although therapeutic methods have been developed, gastric cancer (GC) still leads to high rates of mortality and morbidity and is the fourth leading cause of cancer-associated death and the fifth most common cancer worldwide. To understand the factors associated with the prognostic prediction of GC and to discover efficient therapeutic targets, previous studies on tumour pathogenesis have mainly focused on the cancer cells themselves; in recent years, a large number of studies have shown that ca ...[more]

PMID: 35574386

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Project description:BackgroundGastric cancer (GC) is one of the most common causes of cancer-related death worldwide. As a novel form of programmed cell death, disulfidptosis is characterized by excessive cysteine accumulation, disulfide stress and actin destruction. There is evidence that targeting disulfidptosis is a promising anticancer strategy. Further improvement of GC risk stratification based on disulfidptosis has positive clinical significance.MethodsWe analyzed the expression levels of disulfidptosis-associated genes (DPAGs) in normal and GC tissues and characterized the molecular subtypes of GC patients. Based on the characteristics of DPAG subtypes, differentially expressed prognosis-related genes were selected by LASSO-univariate Cox analysis and multivariate Cox analysis analyzed to establish a prognostic model. Using single-cell sequencing analysis reveals the cell subpopulation for GC. The function of the selected target in GC was verified by in vitro experimental means, including siRNA, qRT-PCR, Western blot, CCK-8, and Transwell assay.ResultsDPAG score was verified to be an independent prognostic factor of GC and was significantly associated with poor prognosis of gastric cancer. Subsequent studies on subgroup immunoinfiltration characteristics, drug sensitivity analysis, immunotherapy response and somatic mutation characteristics of DPAG score comprehensively confirmed the potential guiding significance of DPAG score for individualized treatment of gastric cancer patients. Single-cell sequencing analysis revealed the expression characteristics of DPAG-related prognostic signatures across cell subpopulations. In vitro experiments showed APC11, as one of the selected DPAGs, was highly expressed in gastric cancer, and knockdown of APC11 could significantly inhibit the proliferation and migration of GC cells, demonstrating the reliability of bioinformatics results.ConclusionThe results of this study provide a new perspective for exploring the role of disulfidptosis in the occurrence and development of GC.

Project description:Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study. Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score. Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways. Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.

Dataset Information

The Immune Microenvironment in Gastric Cancer: Prognostic Prediction.

Publications

The Immune Microenvironment in Gastric Cancer: Prognostic Prediction.

Similar Datasets

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