Juan Chenxia, Zhu Ye, Zhou Yan, Zhu Weiwei, Wang Xufang, He Weiming, Chen Yan
Department of Nephrology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Department of Nephrology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
J Oncol. 2022 Feb 8;2022:6100187. doi: 10.1155/2022/6100187. eCollection 2022.
Kidney renal clear cell carcinoma (KIRC) has a poor prognosis and a high death rate globally. Cancer prognosis is strongly linked to immune-related genes (IRGs), according to numerous research. We utilized KIRC RNA-seq data from the TCGA database to build a prognostic model incorporating seven immune-related (IR) lncRNAs, and we constructed the model using LASSO regression. Additionally, we calculated a risk score for each patient using a prognostic model that divided patients into high-risk and low-risk groups. The ESTIMATE and CIBERSORT methodologies were then used to analyze the differences in the tumor microenvironment of the two groups of patients. Finally, we predicted three small molecule drugs that may have potential therapeutic effects for high-risk patients. We combined the acute kidney injury dataset to obtain differential genes that may serve standard biological functions with two risk groups. Our study shows that the model we constructed for IR-lncRNAs has reliable predictive efficacy for patients with KIRC.
肾透明细胞癌（KIRC）在全球范围内预后较差且死亡率较高。众多研究表明，癌症预后与免疫相关基因（IRGs）密切相关。我们利用来自TCGA数据库的KIRC RNA测序数据构建了一个包含7个免疫相关（IR）长链非编码RNA的预后模型，并使用LASSO回归构建该模型。此外，我们使用预后模型为每位患者计算风险评分，将患者分为高风险组和低风险组。然后使用ESTIMATE和CIBERSORT方法分析两组患者肿瘤微环境的差异。最后，我们预测了三种可能对高风险患者具有潜在治疗作用的小分子药物。我们结合急性肾损伤数据集，获得了可能具有标准生物学功能的差异基因以及两个风险组。我们的研究表明，我们构建的IR-长链非编码RNA模型对KIRC患者具有可靠的预测效力。
