Infiltrating T-cell abundance combined with EMT-related gene expression as a prognostic factor of colon cancer

Bioengineered. 2021 Dec;12(1):2688-2701. doi: 10.1080/21655979.2021.1939618.

Abstract

EMT-related gene expression reportedly exhibits correlation with the anti-tumor immunity of T cells. In the present study, we explored the factors that might affect the efficacy of immunotherapy in colon cancer with treatment. In this regard, RNA-seq and clinical data of 469 colon cancer samples derived from the Cancer Genome Atlas (TCGA) database were used to calculate infiltrating T-cell abundance (ITA), to illustrate a pathway enrichment analysis, and to construct Cox proportional hazards (CPH) regression models. Subsequently, the RNA-seq and clinical data of 177 colon cancer samples derived from the GSE17536 cohort were used to validate the CPH regression models. We found that ITA showed correlation with EMT-related gene expression, and that it was not an independent prognostic factor for colon cancer. However, upon comparison of two groups with the same ITA, higher EMT expression helped predicted a worse prognosis, whereas a higher ITA could help predict a better prognosis upon comparison of two groups with the same EMT. Additionally, seven genes were found to be statistically related to the prognosis of patients with colon cancer. These results suggest that the balance between ITA and EMT-related gene expression is conducive to the prognosis of patients with colon cancer, and TPM1 is necessary to further explore the common target genes of immune checkpoint blockade.

Keywords: Colon cancer; EMT; T-cells; immune checkpoint blockade; survival.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor / genetics
  • Biomarkers, Tumor / metabolism
  • Colonic Neoplasms* / genetics
  • Colonic Neoplasms* / immunology
  • Colonic Neoplasms* / metabolism
  • Colonic Neoplasms* / mortality
  • Epithelial-Mesenchymal Transition / genetics*
  • Gene Expression Regulation, Neoplastic / genetics
  • Humans
  • Lymphocytes, Tumor-Infiltrating / metabolism
  • Prognosis
  • T-Lymphocytes / metabolism*
  • Transcriptome / genetics

Substances

  • Biomarkers, Tumor

Grants and funding

This work was supported by the National Natural Science Foundation of China under grant number 81602658, Zhejiang Province Public Welfare Technology Application Research Project under grant number 2021KY782, and Wenzhou Municipal Science and Technology Bureau under grant numbers Y2020151 and Y20190423; National Natural Science Foundation of China [81602658];Wenzhou Municipal Science and Technology Bureau [Y20190423];Wenzhou Municipal Science and Technology Bureau [Y2020151].