A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

Fengxia Qin; Houxi Xu; Guoli Wei; Yi Ji; Jialin Yu; Canhong Hu; Chunyi Yuan; Yuzhu Ma; Jun Qian; Lingchang Li; Jiege Huo

doi:10.3389/fgene.2021.658736

A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

Front Genet. 2021 Apr 20:12:658736. doi: 10.3389/fgene.2021.658736. eCollection 2021.

Authors

Fengxia Qin¹, Houxi Xu², Guoli Wei¹, Yi Ji¹, Jialin Yu¹, Canhong Hu¹, Chunyi Yuan³, Yuzhu Ma¹, Jun Qian⁴, Lingchang Li¹, Jiege Huo¹

Affiliations

¹ Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
² Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing, China.
³ Department of Oncology, Ganyu District Hospital of Traditional Chinese Medicine, Lianyungang, China.
⁴ School of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China.

Abstract

Background: Colorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.

Methods: The clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.

Results: A total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.

Conclusion: Eight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.

Keywords: TCGA; bioinformatics; colorectal cancer; immune-related lncRNA; prognostic index.