Abstract

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.

Highlights

  • Colorectal cancer is among the most commonly diagnosed cancers and a leading cause of cancer-related deaths globally

  • The gene expression profile matrix files from GSE72968 and GSE72969 based on GPL570 (22 M0 and 102 M1 samples), GSE39582 based on GPL570-55999 (376 M0 and 54 M1 samples), GSE41258 based on GPL96 (125 M0 and 88 M1 samples), GSE81558 based on GPL15207 (5 M0 and 18 M1 samples), and GSE71222 based on GPL570 platform (126 M0 and 26 M1 samples) were downloaded from the Gene Expression Omnibus (GEO) database to analyze the different colorectal cancer samples

  • In the training cohort (N = 102), with three prognostic metastasis-related genes, BAMBI, F13A1, and LCN2 (Figures 1B–D), MEM was used for stratifying the metastatic colorectal cancer (mCRC) into high- and low-recurrence risk clusters with a forum, MEM level = (0.2613∗ normalized expression level of BAMBI) + (−0.3311∗ normalized expression level of F13A1) + (0.2836 ∗ normalized expression level of LCN2)

Read more

Summary

Introduction

Colorectal cancer is among the most commonly diagnosed cancers and a leading cause of cancer-related deaths globally. It is known that the understanding that malignant phenotype of cancer cells is determined by their intrinsic activities, surroundings, and the recruitment and activation of immune cells in the tumorrelated microenvironment has increased (Ben-Baruch, 2003; Zhang et al, 2016; Xiong et al, 2018), existing prediction models consider the role of intrinsic factors only. It is unclear whether these models would comprehensively represent the malignancy of mCRC from the perspective of extrinsic factors. 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
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call