Xiao-Bo Ma1, Yuan-Yuan Xu2, Meng-Xuan Zhu3, Lu Wang4. 1. Department of General Surgery, The First Hospital of Shanxi Medical University, Taiyuan, China. 2. Department of Day Surgery Centre, The First Hospital of Shanxi Medical University, Taiyuan, China. 3. Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China. 4. Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Abstract
BACKGROUND: The immunosuppressive microenvironment is closely related to tumorigenesis and cancer development, including colorectal cancer (CRC). The aim of the current study was to identify new immune biomarkers for the diagnosis and treatment of CRC. MATERIALS AND METHODS: CRC data were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Sequences of immune-related genes (IRGs) were obtained from the ImmPort and InnateDB databases. Gene set enrichment analysis (GSEA) and transcription factor regulation analysis were used to explore potential mechanisms. An immune-related classifier for CRC prognosis was conducted using weighted gene co-expression network analysis (WGCNA), Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis. ESTIMATE and CIBERSORT algorithms were used to explore the tumor microenvironment and immune infiltration in the high-risk CRC group and the low-risk CRC group. RESULTS: By analyzing the IRGs that were significantly associated with CRC in the module, a set of 13 genes (CXCL1, F2RL1, LTB4R, GPR44, ANGPTL5, BMP5, RETNLB, MC1R, PPARGC1A, PRKDC, CEBPB, SYP, and GAB1) related to the prognosis of CRC were identified. An IRG-based prognostic signature that can be used as an independent potentially prognostic indicator was generated. The ROC curve analysis showed acceptable discrimination with AUCs of 0.68, 0.68, and 0.74 at 1-, 3-, and 5- year follow-up respectively. The predictive performance was validated in the train set. The potential mechanisms and functions of prognostic IRGs were analyzed, i.e., NOD-like receptor signaling, and transforming growth factor beta (TGFβ) signaling. Besides, the stromal score and immune score were significantly different in high-risk group and low-risk group (p=4.6982e-07, p=0.0107). Besides, the proportions of resting memory CD4+ T cells was significantly higher in the high-risk groups. CONCLUSIONS: The IRG-based classifier exhibited strong predictive capacity with regard to CRC. The survival difference between the high-risk and low-risk groups was associated with tumor microenvironment and immune infiltration of CRC. Innovative biomarkers for the prediction of CRC prognosis and response to immunological therapy were identified in the present study.
BACKGROUND: The immunosuppressive microenvironment is closely related to tumorigenesis and cancer development, including colorectal cancer (CRC). The aim of the current study was to identify new immune biomarkers for the diagnosis and treatment of CRC. MATERIALS AND METHODS: CRC data were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Sequences of immune-related genes (IRGs) were obtained from the ImmPort and InnateDB databases. Gene set enrichment analysis (GSEA) and transcription factor regulation analysis were used to explore potential mechanisms. An immune-related classifier for CRC prognosis was conducted using weighted gene co-expression network analysis (WGCNA), Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis. ESTIMATE and CIBERSORT algorithms were used to explore the tumor microenvironment and immune infiltration in the high-risk CRC group and the low-risk CRC group. RESULTS: By analyzing the IRGs that were significantly associated with CRC in the module, a set of 13 genes (CXCL1, F2RL1, LTB4R, GPR44, ANGPTL5, BMP5, RETNLB, MC1R, PPARGC1A, PRKDC, CEBPB, SYP, and GAB1) related to the prognosis of CRC were identified. An IRG-based prognostic signature that can be used as an independent potentially prognostic indicator was generated. The ROC curve analysis showed acceptable discrimination with AUCs of 0.68, 0.68, and 0.74 at 1-, 3-, and 5- year follow-up respectively. The predictive performance was validated in the train set. The potential mechanisms and functions of prognostic IRGs were analyzed, i.e., NOD-like receptor signaling, and transforming growth factor beta (TGFβ) signaling. Besides, the stromal score and immune score were significantly different in high-risk group and low-risk group (p=4.6982e-07, p=0.0107). Besides, the proportions of resting memory CD4+ T cells was significantly higher in the high-risk groups. CONCLUSIONS: The IRG-based classifier exhibited strong predictive capacity with regard to CRC. The survival difference between the high-risk and low-risk groups was associated with tumor microenvironment and immune infiltration of CRC. Innovative biomarkers for the prediction of CRC prognosis and response to immunological therapy were identified in the present study.
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