Background: Post-surgical recurrence of the metastatic colorectal cancer (mCRC) remains a challenge, even with adjuvant therapy. Moreover, patients show variable outcomes. Here, we set to identify gene models based on the perspectives of intrinsic cell activities and extrinsic immune microenvironment to predict the recurrence of mCRC and guide the adjuvant therapy. Methods: An RNA-based gene expression analysis of CRC samples (total = 998, including mCRCs = 344, non-mCRCs = 654) was performed. A metastasis-evaluation model (MEM) for mCRCs was developed using the Cox survival model based on the prognostic differentially expressed genes between mCRCs and non-mCRCs. This model separated the mCRC samples into high- and low-recurrence risk clusters that were tested using machine learning to predict recurrence. Further, an immune prognostic model (IPM) was built using the COX survival model with the prognostic differentially expressed immune-related genes between the two MEM risk clusters. The ability of MEM and IPM to predict prognosis was analyzed and validated. Moreover, the IPM was utilized to evaluate its relationship with the immune microenvironment and response to immuno-/chemotherapy. Finally, the dysregulation cause of IPM three genes was analyzed in bioinformatics. Results: A high post-operative recurrence risk was observed owing to the downregulation of the immune response, which was influenced by MEM genes (BAMBI, F13A1, LCN2) and their related IPM genes (SLIT2, CDKN2A, CLU). The MEM and IPM were developed and validated through mCRC samples to differentiate between low- and high-recurrence risk in a real-world cohort. The functional enrichment analysis suggested pathways related to immune response and immune system diseases as the major functional pathways related to the IPM genes. The IPM high-risk group (IPM-high) showed higher fractions of regulatory T cells (Tregs) and smaller fractions of resting memory CD4+ T cells than the IPM-low group. Moreover, the stroma and immune cells in the IPM-high samples were scant. Further, the IPM-high group showed downregulation of MHC class II molecules. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and GDSC analysis suggested the IPM-low as a promising responder to anti-CTLA-4 therapy and the common FDA-targeted drugs, while the IPM-high was non-responsive to these treatments. However, treatment using anti-CDKN2A agents, along with the activation of major histocompatibility complex (MHC) class-II response might sensitize this refractory mCRC subgroup. The dysfunction of MEIS1 might be the reason for the dysregulation of IPM genes. Conclusions: The IPM could identify subgroups of mCRC with a distinct risk of recurrence and stratify the patients sensitive to immuno-/chemotherapy. Further, for the first time, our study highlights the importance of MHC class-II molecules in the treatment of mCRCs using immunotherapy.
Keywords: bioinformatics; disease recurrence; immune prognostic model; immunotherapy; metastatic colorectal cancer; real-world cohort.
Copyright © 2021 Luo, Chen, Zhang, Huang, Zhao, Zhao, Li, Zhou, Liu, Cai and Bi.