Chen Yusa, Liang Yumei, Chen Ying, Ouyang Shaxi, Liu Kanghan, Yin Wei
Department of Nephrology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410000, China.
J Oncol. 2021 Sep 27;2021:2042114. doi: 10.1155/2021/2042114. eCollection 2021.
Clear cell renal cell carcinoma (ccRCC) is a cancer with abnormal metabolism. The purpose of this study was to investigate the effect of metabolism-related genes on the prognosis of ccRCC patients.
The data of ccRCC patients were downloaded from the TCGA and the GEO databases and clustered using the nonnegative matrix factorization method. The limma software package was used to analyze differences in gene expression. A random forest model was used to screen for important genes. A novel Riskscore model was established using multivariate regression. The model was evaluated based on the metabolic pathway, immune infiltration, immune checkpoint, and clinical characteristics.
According to metabolism-related genes, kidney clear cell carcinoma (KIRC) datasets downloaded from TCGA were clustered into two groups and showed significant differences in prognosis and immune infiltration. There were 667 differentially expressed genes between the two clusters, of which 408 were screened by univariate analysis. Finally, 12 differentially expressed genes (, , , , , , , , , , , and ) were filtered out using the random forest model. The model of Riskscore was obtained by multiplying the expression levels of these 12 genes with the corresponding coefficients of the multivariate regression. We found that the Riskscore correlated with the expression of these 12 genes; the high Riskscore matched the low survival rate verified in the verification set. The analysis found that the Riskscore model was associated with most of the metabolic processes, immune infiltration of cells such as plasma cells, immune checkpoints such as PD-1, and clinical characteristics such as M stage.
We established a new Riskscore model for the prognosis of ccRCC based on metabolism. The genes in the model provided several novel targets for the study of ccRCC.
肾透明细胞癌（ccRCC）是一种代谢异常的癌症。本研究旨在探讨代谢相关基因对ccRCC患者预后的影响。
从TCGA和GEO数据库下载ccRCC患者的数据，并使用非负矩阵分解方法进行聚类。使用limma软件包分析基因表达差异。使用随机森林模型筛选重要基因。使用多变量回归建立了一个新的风险评分模型。基于代谢途径、免疫浸润、免疫检查点和临床特征对该模型进行评估。
根据代谢相关基因，从TCGA下载的肾透明细胞癌（KIRC）数据集被聚类为两组，在预后和免疫浸润方面存在显著差异。两个聚类之间有667个差异表达基因，其中408个通过单变量分析进行筛选。最后，使用随机森林模型筛选出12个差异表达基因（、、、、、、、、、、和）。通过将这12个基因的表达水平与多变量回归的相应系数相乘，得到风险评分模型。我们发现风险评分与这12个基因的表达相关；高风险评分与验证集中验证的低生存率相匹配。分析发现，风险评分模型与大多数代谢过程、浆细胞等细胞的免疫浸润、PD-1等免疫检查点以及M分期等临床特征相关。
我们基于代谢建立了一种新的ccRCC预后风险评分模型。该模型中的基因提供了几个ccRCC研究的新靶点。
