Dataset Information

Establishment of an immune-related gene pair model to predict colon adenocarcinoma prognosis.

ABSTRACT: BACKGROUND:Colon cancer is the most common type of gastrointestinal cancer and has high morbidity and mortality. Colon adenocarcinoma (COAD) is the main pathological type of colon cancer, and much evidence has supported the correlation between the prognosis of COAD and the immune system. The current study aimed to develop a robust prognostic immune-related gene pair (IRGP) model to estimate the overall survival of patients with COAD. METHODS:The gene expression profiles and clinical information of patients with colon adenocarcinoma were obtained from the TCGA and GEO databases and were divided into training and validation cohorts. Immune genes were selected that showed a significant association with prognosis. RESULTS:Among 1647 immune genes, a model with 17 IRGPs was built that was significantly associated with OS in the training cohort. In the training and validation datasets, the IRGP model divided patients into the high-risk group and low-risk group, and the prognosis of the high-risk group was significantly worse (P<0.001). Univariate and multivariate Cox proportional hazard analyses confirmed the feasibility of this model. Functional analysis confirmed that multiple tumor progression and stem cell growth-related pathways were upregulated in the high-risk groups. Regulatory T cells and macrophages M0 were significantly highly expressed in the high-risk group. CONCLUSION:We successfully constructed an IRGP model that can predict the prognosis of COAD, providing new insights into the treatment strategy of COAD.

SUBMITTER: Luo J

PROVIDER: S-EPMC7654612 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Establishment of an immune-related gene pair model to predict colon adenocarcinoma prognosis.

Luo Jihang J Liu Puyu P Wang Leibo L Huang Yi Y Wang Yuanyan Y Geng Wenjing W Chen Duo D Bai Yuju Y Yang Ze Z

BMC cancer 20201109 1

<h4>Background</h4>Colon cancer is the most common type of gastrointestinal cancer and has high morbidity and mortality. Colon adenocarcinoma (COAD) is the main pathological type of colon cancer, and much evidence has supported the correlation between the prognosis of COAD and the immune system. The current study aimed to develop a robust prognostic immune-related gene pair (IRGP) model to estimate the overall survival of patients with COAD.<h4>Methods</h4>The gene expression profiles and clinic ...[more]

PMID: 33167940

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Project description:A robust and accurate gene expression signature is essential to assist oncologists to determine which subset of patients at similar Tumor-Lymph Node-Metastasis (TNM) stage has high recurrence risk and could benefit from adjuvant therapies. Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD) prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA). The predictive performance of the 12-gene signature was validated with two independent gene expression microarray datasets: GSE39582 includes 566 COAD cases for the development of six molecular subtypes with distinct clinical, molecular and survival characteristics; GSE17538 is a dataset containing 232 colon cancer patients for the generation of a metastasis gene expression profile to predict recurrence and death in COAD patients. The signature could effectively separate the poor prognosis patients from good prognosis group (disease specific survival (DSS): Kaplan Meier (KM) Log Rank p = 0.0034; overall survival (OS): KM Log Rank p = 0.0336) in GSE17538. For patients with proficient mismatch repair system (pMMR) in GSE39582, the signature could also effectively distinguish high risk group from low risk group (OS: KM Log Rank p = 0.005; Relapse free survival (RFS): KM Log Rank p = 0.022). Interestingly, advanced stage patients were significantly enriched in high 12-gene score group (Fisher's exact test p = 0.0003). After stage stratification, the signature could still distinguish poor prognosis patients in GSE17538 from good prognosis within stage II (Log Rank p = 0.01) and stage II & III (Log Rank p = 0.017) in the outcome of DFS. Within stage III or II/III pMMR patients treated with Adjuvant Chemotherapies (ACT) and patients with higher 12-gene score showed poorer prognosis (III, OS: KM Log Rank p = 0.046; III & II, OS: KM Log Rank p = 0.041). Among stage II/III pMMR patients with lower 12-gene scores in GSE39582, the subgroup receiving ACT showed significantly longer OS time compared with those who received no ACT (Log Rank p = 0.021), while there is no obvious difference between counterparts among patients with higher 12-gene scores (Log Rank p = 0.12). Besides COAD, our 12-gene signature is multifunctional in several other cancer types including kidney cancer, lung cancer, uveal and skin melanoma, brain cancer, and pancreatic cancer. Functional classification showed that seven of the twelve genes are involved in immune system function and regulation, so our 12-gene signature could potentially be used to guide decisions about adjuvant therapy for patients with stage II/III and pMMR COAD.

Project description:In recent years, tumor immunotherapy has become an important treatment program and popular research focus. However, the use of immune checkpoint inhibitors (ICI) in the treatment of colorectal cancer still has limitations due to the current markers only being able to predict the prognosis of a small number of patients. As the chemokine signaling pathway can promote the anti-tumor response of the immune system by recruiting immune cells, we explored the relationship between mutations in the chemokine signaling pathway and the prognosis of colon adenocarcinoma (COAD) patients receiving ICI treatment. To analyze the relationship between chemokine mutation status and the prognosis of patients receiving ICI treatment, clinical and mutation data, with immunotherapy, for a COAD cohort was obtained from "cbioportal." Then, combining this with COAD cohort data from The Cancer Genome Atlas (TCGA) database, the panorama of gene mutation, immunogenicity, and difference in tumor microenvironment (TME) of chemokine pathways with different mutation statuses were analyzed. High-mut status has been proved to be a prognostic indicator of COAD patients receiving ICI treatment by Univariate and Multivariate Cox regression analysis. CIBERSORT analysis showed that the infiltration degree of M1 macrophages, neutrophils, and activated natural killer (NK) cells was higher in those with high-mut status. Immunogenicity of the high-mut group was also significantly increased, with the mutation number of tumor mutation burden (TMB), neoantigen load (NAL), DNA damage repair (DDR) pathway and microsatellite instability biomarker (MSI-H) being significantly higher. In this study, we found that the mutation state of chemokine pathways is closely associated with the prognosis of COAD patients undergoing ICI treatment. The higher number of TMB, NAL, and DDR mutations and inflammatory TME, may be the mechanism of behind a better prognosis. This discovery provides a possible idea for ICI therapy of COAD.

Project description:BackgroundPyroptosis has been demonstrated to be an inflammatory form of programmed cell death recently. However, the expression of pyroptosis-related genes (PRGs) in colon adenocarcinoma (COAD) and their correlations with prognosis remain unclear.MethodsData of COAD patients were obtained from The Cancer Genome Atlas (TCGA) database to screen differentially expressed genes (DEGs). Univariate Cox regression analysis and the LASSO Cox regression analysis were applied to construct a gene signature. All COAD patients in TCGA cohort were separated into low-risk subgroup or high-risk subgroup via the risk score. Kaplan-Meier survival analysis and receiver operator characteristic (ROC) curves were adopted to assess its prognostic efficiency. COAD data from the GSE17537 datasets was used for validation. A prognostic nomogram was established to predict individual survival. The correlation between PRGs and immune cell infiltration in COAD was verified based on TIMER database. CIBERSORT analysis was utilized on risk subgroup as defined by model. The protein and mRNA expression level of PRGs were verified by HPA database and qPCR.ResultsA total of 51 differentially expressed PRGs were identified in TCGA cohort. Through univariate Cox regression analysis and LASSO Cox regression analysis, a prognostic model containing 7 PRGs was constructed. Kaplan-Meier survival analysis indicated that patients in the low-risk subgroup exhibited better prognosis compared to those in the high-risk subgroup. Additionally, the area under the curve (AUC) of ROC is 0.60, 0.63, and 0.73 for 1-, 3-, and 5-year survival in TCGA cohort and 0.63, 0.65, and 0.64 in validation set. TIMER database showed a strong correlation between 7 PRGs and tumor microenvironment in COAD. Moreover, CIBERSORT showed significant differences in the infiltration of plasma cells, M0 macrophages, resting dendritic cells, and eosinophils between low-risk subgroup and high-risk subgroup. HPA database showed that protein expression level of SDHB, GZMA, BTK, EEF2K, and NR1H2 was higher in normal tissues. And the transcriptional level of CASP5, BTK, SDHB, GZMA, and RIPK3 was high in normal tissues.ConclusionsOur study identified a novel PRGs signature that could be used to predict the prognosis of COAD patients, which might provide a new therapeutic target for the treatment of COAD patients.

Project description:ObjectiveLung cancer is the most common malignancy worldwide and exhibits both high morbidity and mortality. In recent years, scientists have made substantial breakthroughs in the early diagnosis and treatment of lung adenocarcinoma (LUAD), however, patient prognosis still shows vast individual differences. In this study, bioinformatics methods were used to identify and analyze ferroptosis-related genes to establish an effective signature for predicting prognosis in LUAD patients.MethodsThe gene expression profiles of LUAD patients with complete clinical and follow-up information were downloaded from two public databases, univariate Cox regression and multivariate Cox regression analysis were used to obtain ferroptosis-related genes for constructing the prognos tic risk model, AUC and calibration plot were used to evaluate the predictive accuracy of the FRGS and nomogram.ResultsA total of 74 ferroptosis-related differentially expressed genes (DEGs) were identi fied between LUAD and normal tissues from The Cancer Genome Atlas (TCGA) database. A five-gene panel for prediction of LUAD prognosis was established by multivariate regression and was verified using the GSE68465 cohort from the Gene Expression Omnibus (GEO) database. Patients were divided into two different risk groups according to the median risk score of the five genes. Based on Kaplan-Meier (KM) analysi, the OS rate of the high-risk group was markedly worse than that of the low-risk group. We also found that risk score was an independent prognostic indicator. The receiver operating characteristic ROC curve showed that the proposed model had good prediction ability. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses indicated that risk score was prominently enriched in ferroptosis processes. Moreover, at the score of immune-associated gene sets, significant differences were found between the two risk groups.ConclusionsThis study demonstrated that ferroptosis-related gene signatures can be used as a potential predictor for the prognosis of LUAD, thus providing a novel strategy for individualized treatment in LUAD patients.

Dataset Information

Establishment of an immune-related gene pair model to predict colon adenocarcinoma prognosis.

Publications

Establishment of an immune-related gene pair model to predict colon adenocarcinoma prognosis.

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