Li Chenyang, Liu Qian, Song Yiran, Wang Wenxin, Zhang Xiaolan
The Department of Gastroenterology and Hepatology, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Front Oncol. 2022 Sep 29;12:944476. doi: 10.3389/fonc.2022.944476. eCollection 2022.
Many studies have shown that metabolism-related lncRNAs may play an important role in the pathogenesis of colon cancer. In this study, a prognostic model for colon cancer patients was constructed based on metabolism-related lncRNAs.
Both transcriptome data and clinical data of colon cancer patients were downloaded from the TCGA database, and metabolism-related genes were downloaded from the GSEA database. Through differential expression analysis and Pearson correlation analysis, long non-coding RNAs (lncRNAs) related to colon cancer metabolism were obtained. CRC patients were divided into training set and verification set at the ratio of 2:1. Based on the training set, univariate Cox regression analysis was utilized to determine the prognostic differential expression of metabolic-related lncRNAs. The Optimal lncRNAs were obtain by Lasso regression analysis, and a risk model was built to predict the prognosis of CRC patients. Meanwhile, patients were divided into high-risk and low-risk groups and a survival curve was drawn accordingly to determine whether the survival rate differs between the two groups. At the same time, subgroup analysis evaluated the predictive performance of the model. We combined clinical indicators with independent prognostic significance and risk scores to construct a nomogram. C index and the calibration curve, DCA clinical decision curve and ROC curve were obtained as well. The above results were all verified using the validation set. Finally, based on the CIBERSORT analysis method, the correlation between lncRNAs and 22 tumor-infiltrated lymphocytes was explored.
By difference analysis, 2491 differential lncRNAs were obtained, of which 226 were metabolic-related lncRNAs. Based on Cox regression analysis and Lasso results, a multi-factor prognostic risk prediction model with 13 lncRNAs was constructed. Survival curve results suggested that patients with high scores and have a poorer prognosis than patients with low scores (P<0.05). The area under the ROC curve (AUC) for the 3-year survival and 5-year survival were 0.768 and 0.735, respectively. Cox regression analysis showed that age, distant metastasis and risk scores can be used as independent prognostic factors. Then, a nomogram including age, distant metastasis and risk scores was built. The C index was 0.743, and the ROC curve was drawn to obtain the AUC of the 3-year survival and the 5-year survival, which were 0.802 and 0.832, respectively. The above results indicated that the nomogram has a good predictive effect. Enrichment analysis of KEGG pathway revealed that differential lncRNAs may be related to chemokines, amino acid and sugar metabolism, NOD-like receptor and Toll-like receptor activation as well as other pathways. Finally, the analysis results based on the CIBERSORT algorithm showed that the lncRNAs used to construct the model had a strong polarized correlation with B cells, CD8+T cells and M0 macrophages.
13 metabolic-related lncRNAs affecting the prognosis of CRC were screened by bioinformatics methods, and a prognostic risk model was constructed, laying a solid foundation for the research of metabolic-related lncRNAs in CRC.
许多研究表明，代谢相关的长链非编码RNA（lncRNA）可能在结肠癌的发病机制中起重要作用。在本研究中，基于代谢相关lncRNA构建了结肠癌患者的预后模型。
从TCGA数据库下载结肠癌患者的转录组数据和临床数据，从GSEA数据库下载代谢相关基因。通过差异表达分析和Pearson相关性分析，获得与结肠癌代谢相关的长链非编码RNA（lncRNA）。将结直肠癌患者按2:1的比例分为训练集和验证集。基于训练集，采用单因素Cox回归分析确定代谢相关lncRNA的预后差异表达。通过Lasso回归分析获得最佳lncRNA，并建立风险模型以预测结直肠癌患者的预后。同时，将患者分为高风险组和低风险组，并据此绘制生存曲线，以确定两组之间的生存率是否存在差异。同时，亚组分析评估了模型的预测性能。我们将具有独立预后意义的临床指标与风险评分相结合，构建了列线图。还获得了C指数、校准曲线、DCA临床决策曲线和ROC曲线。上述结果均在验证集中得到验证。最后，基于CIBERSORT分析方法，探讨lncRNA与22种肿瘤浸润淋巴细胞之间的相关性。
通过差异分析，获得2491个差异lncRNA，其中226个为代谢相关lncRNA。基于Cox回归分析和Lasso结果，构建了一个包含13个lncRNA的多因素预后风险预测模型。生存曲线结果表明，高分患者的预后比低分患者差（P<0.05）。3年生存率和5年生存率的ROC曲线下面积（AUC）分别为0.768和0.735。Cox回归分析表明，年龄、远处转移和风险评分可作为独立的预后因素。然后，构建了一个包含年龄、远处转移和风险评分的列线图。C指数为0.743，并绘制ROC曲线，获得3年生存率和5年生存率的AUC，分别为0.802和0.832。上述结果表明列线图具有良好的预测效果。KEGG通路富集分析表明，差异lncRNA可能与趋化因子、氨基酸和糖代谢、NOD样受体和Toll样受体激活以及其他通路有关。最后，基于CIBERSORT算法的分析结果表明，用于构建模型的lncRNA与B细胞、CD8+T细胞和M0巨噬细胞具有强极化相关性。
通过生物信息学方法筛选出13个影响结直肠癌预后的代谢相关lncRNA，并构建了预后风险模型，为结直肠癌中代谢相关lncRNA的研究奠定了坚实基础。
