Prognostic risk analysis related to radioresistance genes in colorectal cancer

Haoren Qin; Heng Zhang; Haipeng Li; Qiong Xu; Wanjun Sun; Shiwu Zhang; Xipeng Zhang; Siwei Zhu; Hui Wang

doi:10.3389/fonc.2022.1100481

Prognostic risk analysis related to radioresistance genes in colorectal cancer

Front Oncol. 2023 Jan 18:12:1100481. doi: 10.3389/fonc.2022.1100481. eCollection 2022.

Authors

Haoren Qin^{1

2}, Heng Zhang¹, Haipeng Li³, Qiong Xu³, Wanjun Sun¹, Shiwu Zhang⁴, Xipeng Zhang⁵, Siwei Zhu¹, Hui Wang¹

Affiliations

¹ Department of Oncology, Tianjin Union Medical Center, Nankai University, Tianjin, China.
² School of Medicine, Nankai University, Tianjin, China.
³ School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
⁴ Department of Pathology, Tianjin Union Medical Center, Nankai University, Tianjin, China.
⁵ Department of Colorectal Surgery, Tianjin Union Medical Center, Nankai University, Tianjin, China.

Abstract

Background: Radiotherapy (RT) is one of the most important treatments for patients with colorectal cancer (CRC). Radioresistance is the crucial cause of poor therapeutic outcomes in colorectal cancer. However, the underlying mechanism of radioresistance in colorectal cancer is still poorly defined. Herein we established a radioresistant colorectal cancer cell line and performed transcriptomics analyses to search for the underlying genes that contribute to radioresistance and investigate its association with the prognosis of CRC patients.

Methods: The radioresistant cell line was developed from the parental HCT116 cell by a stepwise increased dose of irradiation. Differential gene analysis was performed using cellular transcriptome data to identify genes associated with radioresistance, from which extracellular matrix (ECM) and cell adhesion-related genes were screened. Survival data from a CRC cohort in the TCGA database were used for further model gene screening and validation. The correlation between the risk score model and tumor microenvironment, clinical phenotype, drug treatment sensitivity, and tumor mutation status were also investigated.

Results: A total of 493 different expression genes were identified from the radioresistant and wild-type cell line, of which 94 genes were associated with ECM and cell adhesion-related genes. The five model genes TNFRSF13C, CD36, ANGPTL4, LAMB3, and SERPINA1 were identified for CRC radioresistance via screening using the best model. A ROC curve indicated that the AUC of the resulting prognostic model (based on the 5-gene risk score and other clinical parameters, including age, sex, and tumor stages) was 0.79, 0.77, and 0.78 at 1, 2, and 3 years, respectively. The calibration curve showed high agreement between the risk score prediction and actual survival probability. The immune microenvironment, drug treatment sensitivity, and tumor mutation status significantly differed between the high- and low-risk groups.

Conclusions: The risk score model built with five radioresistance genes in this study, including TNFRSF13C, CD36, ANGPTL4, LAMB3, and SERPINA1, showed favorable performance in prognosis prediction after radiotherapy for CRC.

Keywords: colorectal cancer; prognostic analysis; radioresistance; radiotherapy; tumor microenvironment.

Grants and funding

National Natural Science Foundation of China (grant number 81972847). China Postdoctoral Science Foundation (grant number 2022M722377). Tianjin Key Medical Discipline (Specialty) Construction Project (grant number TJYXZDXK-053B). Natural Science Foundation of Tianjin Municipal Science and Technology Commission (grant numbers 21JCQNJC01900 and 21JCYBJC01830).