Zhang Jiawei, Wu Yinan, Mu Jiayi, Xin Dijia, Wang Luyao, Fan Yili, Zhang Suzhan, Xu Yang
Department of Hematology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Zhejiang University Cancer Institute, Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Front Oncol. 2022 Sep 20;12:954226. doi: 10.3389/fonc.2022.954226. eCollection 2022.
Colon adenocarcinoma (COAD) is the most common type of colorectal cancer (CRC) and is associated with poor prognosis. Emerging evidence has demonstrated that glycosylation by long noncoding RNAs (lncRNAs) was associated with COAD progression. To date, however, the prognostic values of glycosyltransferase (GT)-related lncRNAs in COAD are still largely unknown.
We obtained the expression matrix of mRNAs and lncRNAs in COAD from The Cancer Genome Atlas (TCGA) database. Then, the univariate Cox regression analysis was conducted to identify 33 prognostic GT-related lncRNAs. Subsequently, LASSO and multivariate Cox regression analysis were performed, and 7 of 33 GT-related lncRNAs were selected to conduct a risk model. Gene set enrichment analysis (GSEA) was used to analyze gene signaling pathway enrichment of the risk model. ImmuCellAI, an online tool for estimating the abundance of immune cells, and correlation analysis were used to explore the tumor-infiltrating immune cells in COAD. Finally, the expression levels of seven lncRNAs were detected in colorectal cancer cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).
A total of 1,140 GT-related lncRNAs were identified, and 7 COAD-specific GT-related lncRNAs (LINC02381, MIR210HG, AC009237.14, AC105219.1, ZEB1-AS1, AC002310.1, and AC020558.2) were selected to conduct a risk model. Patients were divided into high- and low-risk groups based on the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, indicating the good reliability and specificity of our risk model. Additionally, a nomogram based on the risk score and clinical traits was built to help clinical decisions. GSEA showed that the risk model was significantly enriched in metabolism-related pathways. Immune infiltration analysis revealed that five types of immune cells were significantly different between groups, and two types of immune cells were negatively correlated with the risk score. Besides, we found that the expression levels of these seven lncRNAs in tumor cells were significantly higher than those in normal cells, which verified the feasibility of the risk model.
The efficient risk model based on seven GT-related lncRNAs has prognostic potential for COAD, which may be novel biomarkers and therapeutic targets for COAD patients.
结肠腺癌（COAD）是结直肠癌（CRC）最常见的类型，且预后较差。新出现的证据表明，长链非编码RNA（lncRNA）介导的糖基化与COAD进展相关。然而，迄今为止，糖基转移酶（GT）相关lncRNA在COAD中的预后价值仍 largely未知。
我们从癌症基因组图谱（TCGA）数据库中获取了COAD中mRNA和lncRNA的表达矩阵。然后，进行单变量Cox回归分析以鉴定33个预后相关的GT lncRNA。随后，进行LASSO和多变量Cox回归分析，从33个GT相关lncRNA中选择7个构建风险模型。基因集富集分析（GSEA）用于分析风险模型的基因信号通路富集情况。使用ImmuCellAI（一种用于估计免疫细胞丰度的在线工具）和相关性分析来探索COAD中的肿瘤浸润免疫细胞。最后，通过逆转录定量聚合酶链反应（RT-qPCR）检测结肠癌细胞系中7种lncRNA的表达水平。
共鉴定出1140个GT相关lncRNA，并选择7个COAD特异性GT相关lncRNA（LINC02381、MIR210HG、AC009237.14'、AC105219.1、ZEB1-AS1、AC002310.1和AC020558.2）构建风险模型。根据风险评分中位数将患者分为高风险组和低风险组。高风险组的预后比低风险组差，表明我们的风险模型具有良好的可靠性和特异性。此外，基于风险评分和临床特征构建了列线图以帮助临床决策。GSEA显示风险模型在代谢相关通路中显著富集。免疫浸润分析显示，两组之间五种免疫细胞存在显著差异，两种免疫细胞与风险评分呈负相关。此外，我们发现这7种lncRNA在肿瘤细胞中的表达水平显著高于正常细胞，这验证了风险模型的可行性。
基于7个GT相关lncRNA的有效风险模型对COAD具有预后潜力，可能是COAD患者的新型生物标志物和治疗靶点。
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