Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

Yanpeng Huang; Jinming Zhou; Haibin Zhong; Ning Xie; Fei-Ran Zhang; Zhanmin Zhang

doi:10.3389/fgene.2022.989327

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

Front Genet. 2022 Sep 6:13:989327. doi: 10.3389/fgene.2022.989327. eCollection 2022.

Authors

Yanpeng Huang¹, Jinming Zhou², Haibin Zhong¹, Ning Xie³, Fei-Ran Zhang¹, Zhanmin Zhang³

Affiliations

¹ Department of General Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
² GenePlus-Beijing, Beijing, China.
³ Department of Cancer Center, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.

Abstract

Colorectal cancer (CRC) is a common malignant tumor worldwide. Lipid metabolism is a prerequisite for the growth, proliferation and invasion of cancer cells. However, the lipid metabolism-related gene signature and its underlying molecular mechanisms remain unclear. The aim of this study was to establish a lipid metabolism signature risk model for survival prediction in CRC and to investigate the effect of gene signature on the immune microenvironment. Lipid metabolism-mediated genes (LMGs) were obtained from the Molecular Signatures Database. The consensus molecular subtypes were established using "ConsensusClusterPlus" based on LMGs and the cancer genome atlas (TCGA) data. The risk model was established using univariate and multivariate Cox regression with TCGA database and independently validated in the international cancer genome consortium (ICGC) datasets. Immune infiltration in the risk model was developed using CIBERSORT and xCell analyses. A total of 267 differentially expressed genes (DEGs) were identified between subtype 1 and subtype 2 from consensus molecular subtypes, including 153 upregulated DEGs and 114 downregulated DEGs. 21 DEGs associated with overall survival (OS) were selected using univariate Cox regression analysis. Furthermore, a prognostic risk model was constructed using the risk coefficients and gene expression of eleven-gene signature. Patients with a high-risk score had poorer OS compared with patients in the low-risk score group (p = 3.36e-07) in the TCGA cohort and the validationdatasets (p = 4.03e-05). Analysis of immune infiltration identified multiple T cells were associated with better prognosis in the low-risk group, including Th2 cells (p = 0.0208), regulatory T cells (p = 0.0425), and gammadelta T cells (p = 0.0112). A nomogram integrating the risk model and clinical characteristics was further developed to predict the prognosis of patients with CRC. In conclusion, our study revealed that the expression of lipid-metabolism genes were correlated with the immune microenvironment. The eleven-gene signature might be useful for prediction the prognosis of CRC patients.

Keywords: biomarkers; colorectal cancer; lipid metabolism; prognosis; signature.