Supplementary Material for: Construction and validation of a mutation-related model in papillary renal cell carcinoma and associated immune infiltration

Background: To improve the clinical evaluation of the prognosis of papillary renal cell carcinoma (PRCC), we screened a model to predict the survival of patients with mutations in related genes. Methods: We downloaded RNA sequencing information from all patients with PRCC in TCGA. We first analyzed the differences in genes and the enrichment of these differences. Then, by selecting mutant genes, constructing a protein–protein interaction network, lasso regression, and multivariable Cox regression, a prognosis model was constructed. Additionally, the model was validated using external data sets. We analyzed the immune infiltration of PRCC and the correlation between the model and popular targets. Finally, we performed tissue microarray analysis and immunohistochemistry to verify the expression levels of the three genes. Results: We constructed a three-gene (NEK2, CENPA, and GINS2) model. The verification results indicated that the model had a good prediction effect. We also developed a visual nomogram. Enrichment analysis revealed the major pathways involved in muscle system processes. Immunoassays showed that the expression level of CENPA was positively correlated with PD-1 and CTLA4 expression levels. Immunohistochemical and tissue microarray results showed that these three genes were highly expressed in PRCC, which was consistent with the predicted results in the database. Conclusion: We constructed and verified a three-gene model to predict the patient survival. The results show that the model has a good prediction effect.背景：为优化乳头状肾细胞癌（papillary renal cell carcinoma, PRCC）患者预后的临床评估，本研究旨在筛选可预测相关基因突变患者生存结局的模型。方法：本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）中下载所有乳头状肾细胞癌患者的RNA测序数据。首先分析基因表达差异及差异基因的富集情况；随后通过筛选突变基因、构建蛋白质-蛋白质相互作用网络，联合Lasso回归与多变量Cox回归分析，构建预后预测模型。此外，利用外部数据集对该模型进行验证。本研究还分析了乳头状肾细胞癌的免疫浸润特征，以及该模型与热门免疫治疗靶点的相关性；最后通过组织微阵列分析与免疫组化实验，验证三个目标基因的表达水平。结果：本研究构建了包含NEK2、CENPA与GINS2三个基因的预后预测模型。验证结果显示，该模型具备良好的预测效能，同时本研究还构建了可视化列线图。富集分析结果表明，该模型主要涉及肌肉系统进程相关通路。免疫检测结果显示，CENPA的表达水平与程序性死亡受体1（programmed death 1, PD-1）及细胞毒性T淋巴细胞相关抗原4（cytotoxic T-lymphocyte-associated protein 4, CTLA4）的表达水平呈正相关。免疫组化与组织微阵列分析结果证实，这三个基因在乳头状肾细胞癌组织中呈高表达，与数据库中的预测结果一致。结论：本研究构建并验证了可预测患者生存情况的三基因预后模型，结果表明该模型具有良好的预测效能。