Huang Yanpeng, Zhou Jinming, Zhong Haibin, Xie Ning, Zhang Fei-Ran, Zhang Zhanmin
Department of General Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
GenePlus-Beijing, Beijing, China.
Front Genet. 2022 Sep 6;13:989327. doi: 10.3389/fgene.2022.989327. eCollection 2022.
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 ( = 3.36e-07) in the TCGA cohort and the validationdatasets ( = 4.03e-05). Analysis of immune infiltration identified multiple T cells were associated with better prognosis in the low-risk group, including Th2 cells ( = 0.0208), regulatory T cells ( = 0.0425), and gammadelta T cells ( = 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.
结直肠癌（CRC）是全球常见的恶性肿瘤。脂质代谢是癌细胞生长、增殖和侵袭的先决条件。然而，脂质代谢相关基因特征及其潜在分子机制仍不清楚。本研究旨在建立一种用于预测CRC生存的脂质代谢特征风险模型，并研究基因特征对免疫微环境的影响。从分子特征数据库中获取脂质代谢介导基因（LMGs）。基于LMGs和癌症基因组图谱（TCGA）数据，使用“ConsensusClusterPlus”建立共识分子亚型。利用TCGA数据库通过单变量和多变量Cox回归建立风险模型，并在国际癌症基因组联盟（ICGC）数据集中进行独立验证。使用CIBERSORT和xCell分析评估风险模型中的免疫浸润情况。从共识分子亚型中确定了1型和2型之间总共267个差异表达基因（DEGs），其中包括153个上调的DEGs和114个下调的DEGs。通过单变量Cox回归分析选择了21个与总生存期（OS）相关的DEGs。此外，利用11基因特征的风险系数和基因表达构建了预后风险模型。在TCGA队列和验证数据集中，高风险评分患者的OS比低风险评分组患者更差（P = 3.36e - 07）（P = 4.03e - 05）。免疫浸润分析表明，低风险组中多种T细胞与较好的预后相关，包括Th2细胞（P = 0.0208）、调节性T细胞（P = 0.0425）和γδT细胞（P = 0.0112）。进一步开发了一个整合风险模型和临床特征的列线图，以预测CRC患者的预后。总之，我们的研究表明脂质代谢基因的表达与免疫微环境相关。11基因特征可能有助于预测CRC患者的预后。
