Construction of a Prognostic Model for Predicting Colorectal Cancer Prognosis and Response to Immunotherapy Based on Cuproptosis-Associated lncRNAs

Yi Yang; Xiaoli Wang; Jin Lu; Zhiyong Dong; Ruixiang Hu; Wenhui Chen; Songhao Hu; Guanhua Lu; Biao Huang; Shiliang Dong; Lu Wang; Cunchuan Wang

doi:10.1155/2023/2733232

Construction of a Prognostic Model for Predicting Colorectal Cancer Prognosis and Response to Immunotherapy Based on Cuproptosis-Associated lncRNAs

J Oncol. 2023 Mar 15:2023:2733232. doi: 10.1155/2023/2733232. eCollection 2023.

Authors

Yi Yang^{1

2}, Xiaoli Wang³, Jin Lu⁴, Zhiyong Dong¹, Ruixiang Hu¹, Wenhui Chen¹, Songhao Hu¹, Guanhua Lu¹, Biao Huang¹, Shiliang Dong¹, Lu Wang⁵, Cunchuan Wang^{1

2}

Affiliations

¹ Department of Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
² Jinan University Institute of Obesity and Metabolic Disorders, Guangzhou, Guangdong, China.
³ West General Department, The First Affiliated Hospital of Jinan University, Guangzhou, China.
⁴ Key Laboratory of Computational Medicine and Intelligent Health of Anhui Higher Education Institutes, Bengbu Medical College, Bengbu, Anhui, China.
⁵ Institute of Precision Cancer Medicine and Pathology, Jinan University Medical College, Guangzhou, Guangdong, China.

Abstract

Colorectal cancer (CRC) is a common and highly lethal gastrointestinal malignancy. Immunotherapy has shown positive efficacy in the treatment of CRC; however, only a minority of patients benefit from immunotherapy. The aim of this study is to construct a cuproptosis-related lncRNA (CRLs) risk score model to predict the prognosis and immune infiltration of CRC patients. Firstly, we synthetically analyzed 19 cuproptosis-related genes (CRGs) from CRC samples derived from the TCGA and obtained 33 CRLs that were significantly associated with prognosis. Next, we defined three cuproptosis modification patterns via consensus clustering analysis (C1, C2, and C3). Further analysis showed that there were significant differences in the abundance of B cells, NK cells, fibroblasts, monocytes, CD8⁺ cells, bone marrow dendritic cells, and cytotoxic lymphocytes in different clusters. In addition, the LASSO regression screened out 6 individual CRLs (AC009315.1, PLS3-AS1, ZEB1-AS1, AC007608.3, AC010789.2, and AC010207.1) closely related to the prognosis of CRC. We found that the low-risk group had better survival prognoses in patients. Furthermore, the high-risk group had lower immune scores and exhibited lower CD8⁺ T cell infiltration. Moreover, the low-risk group had lower immune exclusion, immune dysfunction and TIDE scores than the high-risk group. Interestingly, the lncRNAs in our risk model were positively associated with most immune checkpoints. CD274 (PD-L1), CTLA4, and HAVCR2 (TIM3) were positively correlated with risk scores. Moreover, MSI-H patients had lower risk scores than MSI-L patients, and IPS scores were significantly higher in the low CRLs score group. In conclusion, we constructed a novel risk score model with6 lncRNAs related to cuproptosis, which may be a potential biomarker for evaluating the prognosis and immune treatment for CRC.