Baolin Wang, Jun Yang, Jiexin Wu, Xiaoming Hu, Jun Zhu, Jiang Fang, Bo Han, Bo Zhou
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The inherent biological process differences between risk groups were explored by Gene Ontology (GO) and gene set enrichment analysis (GSEA). ESTIMATE and single-sample GSEA (ssGSEA) algorithms were used to analyze the correlation between tumor microenvironment (TME) and immune checkpoint and risk score. The semi-inhibitory concentration (IC<sub>50</sub>) values of chemotherapeutic drugs between risk groups were calculated to evaluate the sensitivity of immunotherapy.</p><p><strong>Results: </strong>The pathway analysis showed that the ERS risk model was relevant to biosynthesis and metabolism. Consistent clustering based on the ERS-related differentially expressed genes (DEGs) demonstrated that the samples divided into three clusters had significant clinicopathological differences. A risk model consisting of six ERS-related genes was established. The model was verified on GSE39582 and GSE17536 testing datasets. 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引用次数: 0
摘要
背景：内质网应激（ERS）相关基因与肿瘤生长、转移和免疫治疗反应有关。本文试图找出与结肠癌免疫治疗相关的ERS相关基因：方法：从分子特征数据库（MSigDB）和基因卡片网站下载ERS相关基因。结肠正常样本和肿瘤样本来自癌症基因组图谱（TCGA）、基因型-组织表达项目（GTEx）和基因表达总库（GEO）数据库。利用最小绝对收缩和选择算子（LASSO）回归法构建了基于基因系数的风险模型。通过基因本体（GO）和基因组富集分析（GSEA）探讨了风险组之间固有的生物过程差异。ESTIMATE和单样本GSEA（ssGSEA）算法用于分析肿瘤微环境（TME）和免疫检查点与风险评分之间的相关性。计算了风险组间化疗药物的半抑制浓度（IC50）值，以评估免疫疗法的敏感性：通路分析表明，ERS风险模型与生物合成和代谢有关。基于ERS相关差异表达基因（DEGs）的一致聚类表明，分为三个聚类的样本具有显著的临床病理学差异。由六个 ERS 相关基因组成的风险模型得以建立。该模型在 GSE39582 和 GSE17536 测试数据集上得到了验证。结果表明，ERS风险模型与TME和免疫检查点显著相关，这些基因增强了结肠癌的免疫治疗能力：结论：我们建立了一个与ERS相关基因（PMM2、STC2、EIF2AK1、HSPA1A、SLC8A1、KCNQ1）的风险模型，这些基因提高了结肠癌免疫治疗的敏感性。这些都可能为结肠癌的治疗提供新的视角。
Identification and validation of endoplasmic reticulum stress-related genes that enhance immunotherapy in colon cancer.
Background: Endoplasmic reticulum stress (ERS)-related genes are related to tumor growth, metastasis, and immunotherapy response. In this paper, we tried to identify ERS-related genes related to immunotherapy in colon cancer.
Methods: ERS-related genes were downloaded from the Molecular Signatures Database (MSigDB) and GeneCards websites. Normal and tumor samples of the colon were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), and Gene Expression Omnibus (GEO) databases. A risk model based on gene coefficients was constructed by using the least absolute shrinkage and selection operator (LASSO) regression. The inherent biological process differences between risk groups were explored by Gene Ontology (GO) and gene set enrichment analysis (GSEA). ESTIMATE and single-sample GSEA (ssGSEA) algorithms were used to analyze the correlation between tumor microenvironment (TME) and immune checkpoint and risk score. The semi-inhibitory concentration (IC50) values of chemotherapeutic drugs between risk groups were calculated to evaluate the sensitivity of immunotherapy.
Results: The pathway analysis showed that the ERS risk model was relevant to biosynthesis and metabolism. Consistent clustering based on the ERS-related differentially expressed genes (DEGs) demonstrated that the samples divided into three clusters had significant clinicopathological differences. A risk model consisting of six ERS-related genes was established. The model was verified on GSE39582 and GSE17536 testing datasets. The results showed that ERS risk model was significantly related to TME and immune checkpoint, and these genes enhanced the immunotherapy ability of colon cancer.
Conclusions: We established a risk model with ERS-related genes (PMM2, STC2, EIF2AK1, HSPA1A, SLC8A1, KCNQ1), which enhance the sensitivity of immunotherapy for colon cancer. These may provide a new perspective for the treatment of colon cancer.
期刊介绍：
Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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