The m7G-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumour Immune Infiltration in Colon Cancer

Li Liu; Yukang Wu; Wenzheng Chen; Yebei Li; Jiahe Yu; Guoyang Zhang; Pengcheng Fu; Liu Huang; Jianbo Xiong; Zhigang Jie

doi:10.3389/fgene.2022.892589

The m7G-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumour Immune Infiltration in Colon Cancer

Front Genet. 2022 Jun 29:13:892589. doi: 10.3389/fgene.2022.892589. eCollection 2022.

Authors

Li Liu¹, Yukang Wu¹, Wenzheng Chen¹, Yebei Li², Jiahe Yu³, Guoyang Zhang¹, Pengcheng Fu¹, Liu Huang¹, Jianbo Xiong¹, Zhigang Jie¹

Affiliations

¹ Department of Gastrointestinal Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China.
² > Department of Renal Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
³ College of Clinical Medicine, Hainan Vocational University of Science and Technology, Hainan, China.

Abstract

With high morbidity and mortality, colon cancer (CC) is considered as one of the most often diagnosed cancers around the world. M7G-related lncRNA may provide a regulatory function in the formation of CC, but the principle of regulation is still unclear. The purpose of this research was to establish a novel signature that may be used to predict survival and tumour immunity in CC patients. Data about CC in TCGA was collected for analysis, coexpression analysis and univariate Cox analysis were used to screen prognostic m7G-related lncRNAs. A consensus clustering analysis based on prognostic m7G-related lncRNAs was applied, and a prognosis model based on least absolute shrinkage and selection operator (LASSO) regression analysis was established. Independent prognostic analysis, nomogram, PCA, clinicopathological correlation analysis, TMB, survival analysis, immune correlation analysis, qRT-PCR and clinical therapeutic compound prediction were also applied. 90 prognostic m7G-related lncRNAs were found, GO and KEGG analysis showed that prognostic m7G-related lncRNAs were mainly related to cell transcription and translation. The results of the consensus clustering analysis revealed substantial disparities in survival prognosis and tumour immune infiltration between two clusters. We built a risk model with 21 signature m7G-related lncRNAs, patients in the high-risk group had a considerably poorer prognosis than those in the low-risk group. Independent prognostic analysis confirmed that patients' prognosis was linked to their tumour stage and risk score. PCA, subgroups with distinct clinicopathological characteristics were studied for survival, multi-index ROC curve, c-index curve, the survival analysis of TMB, and model comparison tested the reliability of risk model. A tumour immunoassay revealed a substantial difference in immune infiltration between high-risk and low-risk individuals. Five chemicals were eliminated, and qRT-PCR indicated that the four lncRNAs were expressed differently. Overall, m7G-related lncRNA is closely related to colon cancer and the 21 signature lncRNAs risk model can efficiently evaluate the prognosis of CC patients, which has a possible positive consequence for the future diagnosis and therapy of CC.

Keywords: bioinformatics; colon cancer; lncRNA; m7G; tumour immunity.