Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer

Mengjia Huang; Yuqing Ye; Yi Chen; Junkai Zhu; Li Xu; Wenxuan Cheng; Xiaofan Lu; Fangrong Yan

doi:10.3389/fgene.2022.955240

Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer

Front Genet. 2022 Sep 30:13:955240. doi: 10.3389/fgene.2022.955240. eCollection 2022.

Authors

Mengjia Huang¹, Yuqing Ye¹, Yi Chen¹, Junkai Zhu¹, Li Xu¹, Wenxuan Cheng¹, Xiaofan Lu¹, Fangrong Yan¹

Affiliation

¹ State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.

Abstract

Background: Colorectal cancer is the fourth most deadly cancer worldwide. Although current treatment regimens have prolonged the survival of patients, the prognosis is still unsatisfactory. Inflammation and lncRNAs are closely related to tumor occurrence and development in CRC. Therefore, it is necessary to establish a new prognostic signature based on inflammation-related lncRNAs to improve the prognosis of patients with CRC. Methods: LASSO-penalized Cox analysis was performed to construct a prognostic signature. Kaplan-Meier curves were used for survival analysis and ROC curves were used to measure the performance of the signature. Functional enrichment analysis was conducted to reveal the biological significance of the signature. The R package "maftool" and GISTIC2.0 algorithm were performed for analysis and visualization of genomic variations. The R package "pRRophetic", CMap analysis and submap analysis were performed to predict response to chemotherapy and immunotherapy. Results: An effective and independent prognostic signature, IRLncSig, was constructed based on sixteen inflammation-related lncRNAs. The IRLncSig was proved to be an independent prognostic indicator in CRC and was superior to clinical variables and the other four published signatures. The nomograms were constructed based on inflammation-related lncRNAs and detected by calibration curves. All samples were classified into two groups according to the median value, and we found frequent mutations of the TP53 gene in the high-risk group. We also found some significantly amplificated regions in the high-risk group, 8q24.3, 20q12, 8q22.3, and 20q13.2, which may regulate the inflammatory activity of cancer cells in CRC. Finally, we identified chemotherapeutic agents for high-risk patients and found that these patients were more likely to respond to immunotherapy, especially anti-CTLA4 therapy. Conclusion: In short, we constructed a new signature based on sixteen inflammation-related lncRNAs to improve the outcomes of patients in CRC. Our findings have proved that the IRLncSig can be used as an effective and independent marker for predicting the survival of patients with CRC.

Keywords: colorectal cancer; immunotherapy; inflammation; lncRNAs; prognosis.