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Analyzing and Validating the Prognostic Value and Mechanism Of Wang et al. J Transl Med (2020) 18:324 https://doi.org/10.1186/s12967-020-02491-w Journal of Translational Medicine RESEARCH Open Access Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment Xinyi Wang1,2†, Jinzhong Duanmu1†, Xiaorui Fu1,2, Taiyuan Li1 and Qunguang Jiang1* Abstract Background: Colon cancer is a disease with high malignancy and incidence in the world. Tumor immune micro- environment (TIM) and tumor mutational burden (TMB) have been proved to play crucial roles in predicting clinical outcomes and therapeutic efcacy, but the correlation between them and the underlying mechanism were not completely understood in colon cancer. Methods: In this study, we used Single-Sample Gene Set Enrichment Analysis (ssGSEA) and unsupervised consensus clustering analysis to divide patients from the TCGA cohort into three immune subgroups. Then we validated their diferences in immune cell infltration, overall survival outcomes, clinical phenotypes and expression levels of HLA and checkpoint genes by Mann–Whitney tests. We performed weighted correlation network analysis (WGCNA) to obtain immunity-related module and hub genes. Then we explored the underlying mechanism of hub genes by gene set enrichment analysis (GSEA) and gene set evaluation analysis (GSVA). Finally, we gave an overall view of gene variants and verifed the correlation between TIM and TMB by comparing microsatellite instability (MSI) and gene mutations among three immune subgroups. Results: The colon cancer patients were clustered into low immunity, median immunity and high immunity groups. The median immunity group had a favorable survival probability compared with that of the low and high immunity groups. Three groups had signifcant diferences in immune cell infltration, tumor stage, living state and T classifca- tion. We got 8 hub genes (CCDC69, CLMP, FAM110B, FAM129A, GUCY1B3, PALLD, PLEKHO1 and STY11) and predicted that immunity may correlated with infammatory response, KRAS signaling pathway and T cell infltration. With higher immunity, the TMB was higher. The most frequent mutations in low and median immunity groups were APC, TP53 and KRAS, while TTN and MUC16 showed higher mutational frequency in high immunity group. Conclusions: We performed a comprehensive evaluation of the immune microenvironment landscape of colon can- cer and demonstrated the positive correlation between immunity and TMB. The hub genes and frequently mutated genes were strongly related to immunity and may give suggestion for immunotherapy in the future. Keywords: Colon cancer, Tumor immune microenvironment, Tumor mutational burden, Weighted correlation network analysis Background Colon cancer is one of the most malignant tumors *Correspondence: [email protected] †Xinyi Wang and Jinzhong Duanmu contributed equally to this work worldwide [1]. Tanks to the progression in systemically 1 Department of Gastrointestinal Surgery, The First Afliated Hospital medical treatment and surgical techniques, prognosis of of Nanchang University, Nanchang, Jiangxi, People’s Republic of China patients with colon cancer has dramatically improved Full list of author information is available at the end of the article if they are diagnosed at early stage [2]. Prognostic © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Wang et al. J Transl Med (2020) 18:324 Page 2 of 14 prediction of patients with colon cancer mainly relies targets [14]. KRAS and BRAF mutational status have on the TNM staging system, histopathological criteria, been considered as prognostic factors in colon cancers molecular markers and tumor-cell diferentiation [3]. with MSI and may give clues for adjuvant therapy in Nowadays, accumulated studies have demonstrated the the future [15–17]. Lin et al. have reported that acti- role of gene mutation status, gene expression levels and vation of STAT3 plays a signifcant role in increasing signaling pathway changes in tumor progression and infltration with CD8+ lymphocytes and inhibiting the malignization, but it is still a challenge to fnd out prog- recruitment of T-regs that enhance colon tumor pro- nostic factors which can also provide targets for therapy gression and immune escape [18]. Tere were several [4, 5]. Te viewpoint that the immune system can infu- researches which explored the characteristics of TIM ence the progression of cancer has been the hotspot for in pan-cancer and evaluate the correlation between the study over a century. Recently, numerous evidences indi- landscape of TIM and prognosis of patients, but they cate that the tumor immune microenvironment (TIM) were focused on comparing diferent cancer types in is of great value in predicting prognosis and evaluat- immune cell infltrating. As a result, further exploration ing therapeutic efcacy factors [6]. TIM is composed of of TIM in genetic level is of great signifcance. immune cells, immune-related pathways and cytokines In this study, by applying unsupervised consen- that secreted by immune cells. In colon cancer, there sus clustering analysis, we divided patients from the has been studies showed that adaptive immune reaction TCGA cohort into three groups (high, median and low is strongly correlated with survival outcomes and recur- immunity) according to ssGSEA scores. Furthermore, rence, and the infltration of diferent kinds of immune we validated their diferences in immune cell infltra- cells might construct a favorable or unfavorable environ- tion, overall survival outcomes, clinical phenotypes ment for tumor cells to proliferate and metastasize [7]. and expression levels of HLA and checkpoint genes. Since immune checkpoint-inhibiting agents, such In order to screen out essential genes for construct- as programmed death-1 receptor (PD1) and cytotoxic ing colon cancer immune microenvironment, we per- T-lymphocyte antigen 4 (CTLA-4) inhibitors, have formed WGCNA and got 8 hub genes which were in been developed as antitumor drugs, immunotherapy the module correlated with immune capacity. Finally, has become a promising feld of cancer treatment and we explored the underlying mechanism of hub genes by demonstrated its impressive clinical value in patients GSEA and GSVA, and verifed the correlation between across multiple types of solid tumors [8, 9]. Lympho- TIM and TMB to give ideas for immunotherapy of cyte activation gene-3 (LAG3) is another immunother- patients with colon cancer. apy target in the clinic, whose up-regulation is required to prevent the onset of autoimmunity. Sustained anti- gen exposure in the TIM leads to up-regulated LAG3 Methods expression, resulting in exhaustion of immune cell pro- Data download liferation and cytokine production [10]. Recently, many Te transcriptome data, somatic mutation data and clini- studies proved that the expression levels of Indoleam- cal information of colon cancer patients were obtained ine 2, 3-dioxygenase 1 (IDO1) plays an important from the TCGA database via the GDC data portal (https role in engender immune tolerance and pathogenic ://porta l.gdc.cance r.gov/repos itory ). We downloaded infammatory processes, which highlights its strong RNA-seq (level 3, HTSeq-FPKM data) of 445 colon can- association with T-cell infltration [11]. Te essence cer patients [445primarytumortissueand41solidnormalt- of tumor immunotherapy is to arouse and strengthen issue] with complete clinical information from the TCGA the immune system to kill tumor cells in various ways. database. Te clinical information of patients from Tumor mutational burden (TMB) was defned as the TCGA database are summarized in Table 1. We down- total amount of coding errors of somatic genes, base loaded “Masked Somatic Mutation” subtype of somatic substitutions, insertions or deletions detected across mutation data and used the VarScan software to process per million bases [12]. If TMB is larger, the cancer cell it. We used a R package called “maftools” [19] to analyze is more mutated, and it is easier for immune cells to and visualize the Mutation Annotation Format (MAF) recognize and kill it [13]. And tumors which respond of somatic variants. Human Protein Atlas (https ://www. to immune checkpoint-inhibiting agents have a higher prote inatl as.org) was used to validate expression levels level of immune cell infltration and exhibit a T-cell of hub genes by immunohistochemistry. MSI informa- infamed phenotype. Tere is a certain correlation tion (MSI-H, MSI-L or MSS) for each TCGA samples between TIM and TMB, and exploring this correla- were obtained
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