Since its discovery, clear cell renal cell carcinoma (ccRCC) has been the most prevalent and lethal kidney malignancy. Our research aims to identify possible prognostic genes of ccRCC and to develop efficient prognostic models for ccRCC patients based on multi-omics investigations to shed light on the treatment and prognosis of ccRCC.

To determine a risk score for each patient, we screened out differentially expressed genes using data from tumor samples, and control samples mined from The Cancer Genome Atlas (TCGA) and GTEx datasets. Somatic mutation and copy number variation profiles were analyzed to look for specific genomic changes connected to risk scores. To investigate potential functional relationships of prognostic genes, gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were carried out. We created a prognostic model by fusing risk ratings with other clinical variables. For validation, the 786-O cell line was used to carry out the dual-gRNA approach to knock down CAPN12 and MSC. This was followed by qRT-PCR to verify the knockdown of CAPN12 and MSC.

For ccRCC, seven predictive genes were discovered: PVT1, MSC, ALDH6A1, TRIB3, QRFPR, CYS1, and CAPN12. The most enriched pathways in the GSVA study and GSEA analysis promote tumorigenesis and immune system modulation. The risk score derived from prognostic genes corresponds with immune infiltration cells and helps predict how well a medicine will work. The mutation of numerous oncogenes was also linked to a high-risk score. A prognostic model with a high ROC value was created for the risk score. An in vitro study demonstrates that the suppression of CAPN12 and MSC dramatically reduced the ability of 786-O cells to proliferate in the CCK-8 proliferation assay and plate clonality assays.

A thorough prognostic model with good performance has been developed for ccRCC patients using seven prognostic genes that were discovered to be related to ccRCC prognosis. In ccRCC, CAPN12 and MSC were significant indicators and would make good therapeutic targets.

自发现以来，透明细胞肾细胞癌 (ccRCC) 一直是最普遍和致命的肾脏恶性肿瘤。我们的研究旨在确定 ccRCC 可能的预后基因，并基于多组学研究为 ccRCC 患者开发有效的预后模型，以阐明 ccRCC 的治疗和预后。

为了确定每个患者的风险评分，我们使用肿瘤样本的数据筛选出差异表达的基因，并从癌症基因组图谱 (TCGA) 和 GTEx 数据集中提取对照样本。分析了体细胞突变和拷贝数变异概况，以寻找与风险评分相关的特定基因组变化。为了研究预后基因的潜在功能关系，进行了基因集变异分析（GSVA）和基因集富集分析（GSEA）。我们通过将风险评级与其他临床变量相融合，创建了一个预后模型。为了进行验证，786-O 细胞系用于执行双 gRNA 方法以击倒 CAPN12 和 MSC。随后进行 qRT-PCR 以验证 CAPN12 和 MSC 的击倒。

对于 ccRCC，发现了七个预测基因：PVT1、MSC、ALDH6A1、TRIB3、QRFPR、CYS1 和 CAPN12。GSVA 研究和 GSEA 分析中最丰富的途径促进肿瘤发生和免疫系统调节。来自预后基因的风险评分与免疫浸润细胞相对应，有助于预测药物的疗效。许多致癌基因的突变也与高风险评分有关。为风险评分创建了具有高 ROC 值的预后模型。一个体外研究表明，抑制 CAPN12 和 MSC 会显着降低 786-O 细胞在 CCK-8 增殖测定和平板克隆性测定中的增殖能力。

已经使用发现与 ccRCC 预后相关的七个预后基因为 ccRCC 患者开发了具有良好性能的彻底预后模型。在 ccRCC 中，CAPN12 和 MSC 是重要的指标，将成为良好的治疗靶点。