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.
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 1,647 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.
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. There is a lot of evidence describing the correlation between the prognosis of COAD and the immune system. The objective of the current study was the development of a robust prognostic immune-related gene pairs (IRGPs) model for estimating overall survival of COAD. Methods. The gene expression profiles and clinical information of patients with colon adenocarcinoma come from TCGA and GEO databases and are divided into training and validation cohorts. Immune genes were selected which show significantly association with prognosis. Results. Among 1647 immune genes, a 17 IRGPs model was built which was significantly associated with OS in the training cohort. In the training and validation data set, the IRGPs model divided patients into high-risk groups and low-risk groups, and the prognosis of the high-risk group was significantly worse(P<0.001). Univariate and multivariate Cox proportional hazard analysis confirmed the feasibility of this model. Functional analysis confirmed that multiple tumor progression and stem cell growth-related pathways in high-risk groups were up-regulated. T cells regulatory and Macrophage M0 were significantly highly expressed in the high-risk group. Conclusion. We successfully constructed an IRGPs model that can predict the prognosis of COAD, which provides new insights into the treatment strategy of COAD.
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. There is a lot of evidence describing the correlation between the prognosis of COAD and the immune system. The objective of the current study was the development of a robust prognostic immune-related gene pairs (IRGPs) model for estimating overall survival of COAD. Methods The gene expression profiles and clinical information of patients with colon adenocarcinoma come from TCGA and GEO databases and are divided into training and validation cohorts. Immune genes were selected which show significantly association with prognosis. Results Among 1647 immune genes, a 17 IRGPs model was built which was significantly associated with OS in the training cohort. In the training and validation data set, the IRGPs model divided patients into high-risk groups and low-risk groups, and the prognosis of the high-risk group was significantly worse( P <0.001). Univariate and multivariate Cox proportional hazard analysis confirmed the feasibility of this model. Functional analysis confirmed that multiple tumor progression and stem cell growth-related pathways in high-risk groups were up-regulated. T cells regulatory and Macrophage M0 were significantly highly expressed in the high-risk group. Conclusion We successfully constructed an IRGPs model that can predict the prognosis of COAD, which provides new insights into the treatment strategy of COAD.
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