Objective. We aimed at identifying the key genes of prognostic value in clear cell renal cell carcinoma (ccRCC) microenvironment and construct a risk score prognostic model. Materials and Methods. Immune and stromal scores were calculated using the ESTIMATE algorithm. A total of 539 ccRCC cases were divided into high- and low-score groups. The differentially expressed genes in immune and stromal cells for the prognosis of ccRCC were screened. The relationship between survival outcome and gene expression was evaluated using univariate and multivariate Cox proportional hazard regression analyses. A risk score prognostic model was constructed based on the immune/stromal scores. Results. The median survival time of the low immune score group was longer than that of the high immune score group (). Ten tumor microenvironment-related genes were selected by screening, and a predictive model was established, based on which patients were divided into high- and low-risk groups with markedly different overall survival (). Multivariate Cox analyses showed that the risk score prognostic model was independently associated with overall survival, with a hazard ratio of 1.0437 (confidence interval: 1.0237–1.0641, ). Conclusions. Low immune scores were associated with extended survival time compared to high immune scores. The novel risk predictive model based on tumor microenvironment-related genes may be an independent prognostic biomarker in ccRCC.

中文翻译：

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透明细胞肾细胞癌微环境中具有预后价值的关键基因的鉴定和风险评分预后模型。

客观。我们旨在确定透明细胞肾细胞癌（ccRCC）微环境中具有预后价值的关键基因，并构建风险评分预后模型。材料和方法。使用 ESTIMATE 算法计算免疫和基质评分。共有 539 例 ccRCC 病例被分为高分和低分组。筛选免疫细胞和基质细胞中与ccRCC预后相关的差异表达基因。使用单变量和多变量 Cox 比例风险回归分析评估生存结果和基因表达之间的关系。基于免疫/基质评分构建风险评分预后模型。结果. 免疫评分低组的中位生存时间长于免疫评分高组（）。通过筛选筛选出10个肿瘤微环境相关基因，建立预测模型，将患者分为总生存期显着不同的高危组和低危组。）。多变量 Cox 分析表明，风险评分预后模型与总生存期独立相关，风险比为 1.0437（置信区间：1.0237-1.0641，）。 结论。与高免疫评分相比，低免疫评分与延长生存时间相关。基于肿瘤微环境相关基因的新型风险预测模型可能是ccRCC中独立的预后生物标志物。