Exploration of the Associations of lncRNA Expression Patterns with Tumor Mutation Burden and Prognosis in Colon Cancer

• Published in: OncoTargets and therapy Vol. 14; pp. 2893 - 2909
• Main Authors: Ding, Chengsheng, Shan, Zezhi, Li, Mengcheng, Xia, Yang, Jin, Zhiming
• Format: Journal Article
• Language: English
• Published: New Zealand Taylor & Francis Ltd 01.01.2021; Dove
• Subjects: Biomarkers; Colon cancer; Colorectal cancer; CTLA-4 protein; Cytolytic activity; Gene expression; Genomes; Immune checkpoint; Immune response; Immune system; Immunotherapy; Major histocompatibility complex; Medical prognosis; Metastases; Mortality; Mutation; Nomograms; Non-coding RNA; Original Research; Patients; PD-1 protein; PD-L1 protein; Polymerase chain reaction; Software; γ-Interferon; long non-coding RNA; tumor mutation burden; prognosis; colon cancer; immunotherapy
• ISSN: 1178-6930; 1178-6930
• DOI: 10.2147/OTT.S300095

Tumor mutation burden (TMB) is emerging as a new biomarker to monitor the response of cancer patients to immunotherapy. Long non-coding RNAs (lncRNAs) are critical in regulating gene expression and play a significant role in cancer-associated immune responses. However, the association between lncRNA expression patterns and TMB levels and survival outcomes remains unknown in colon cancer. In colon cancer patients from The Cancer Genome Atlas Program (TCGA), a multi-lncRNAs based classifier for predicting TMB levels was established using the least absolute shrinkage and selection operator (LASSO) method. The association between classifier index and immune-related characteristics of patients was also investigated. Quantitative polymerase chain reaction (qPCR) was used to verify the expression levels of these lncRNAs in normal and CRC cell lines. The multi-lncRNAs based classifier had ability to predict TMB level of patients with accuracy (AUC= 0.70), and the general applicability of this classifier was proved in the validation set (AUC= 0.71) and the pooled set (AUC= 0.70). The classifier index was related to three immune checkpoints (PD1, PD-L1, and CTLA-4), the infiltration level of immune cells, and immune response-related score (IFN-γ score, gene expression profiles (GEP) score, cytolytic activity (CYT) score and MHC score). A nomogram, which integrates classifier and some common clinical information, was able to predict the overall survival of colon cancer patients accurately. LncRNA expression patterns are associated with TMB, which may serve as a classifier to predict the TMB in colon cancer patients. The nomogram could potentially evaluate survival outcomes and provide a reference to better manage colon cancer patients.