Guangyang Cheng, Zhaokai Zhou, Shiqi Li, Zhuo Ye, Yan Wang, Jianguo Wen, Chuanchuan Ren
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Ultimately, the validation of protein-coding genes was confirmed by utilizing an online database and implementing quantitative real-time PCR (qRT-PCR). <b>Results:</b> The patients were divided into two risk categories based on prognostic proteins, and notable disparities in both overall survival (OS) and progression free interval (PFI) were observed between the two groups. The OS was more unfavorable in the high-risk group, and there was a noteworthy disparity in the level of immune infiltration observed between the two groups. In addition, the nomogram showed high accuracy in predicting survival in KIRC patients. <b>Conclusion:</b> In this research, we elucidated the core proteins associated with prognosis in terms of survival prediction, immunotherapeutic response, somatic mutation, and immune microenvironment. 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引用次数: 0
摘要
背景:在了解癌症相关生物学过程或分子功能的研究中,蛋白质信息往往被 RNA 数据所取代,而对肾透明细胞癌(KIRC)预后有重要意义的蛋白质仍有待挖掘。研究方法利用癌症基因组图谱计划(TCGA)数据筛选对肾透明细胞癌(KIRC)有预后意义的蛋白质。利用机器学习算法建立蛋白质预后模型。此外,还分析了不同蛋白质风险亚组的免疫浸润丰度、体细胞突变差异和免疫治疗反应。最后,利用在线数据库和定量实时 PCR(qRT-PCR)对蛋白编码基因进行了验证。结果:根据预后蛋白将患者分为两个风险类别,并观察到两组患者的总生存期(OS)和无进展间期(PFI)存在显著差异。高风险组的 OS 更差,两组患者的免疫浸润程度也有显著差异。此外,提名图在预测 KIRC 患者的生存率方面显示出较高的准确性。结论在这项研究中,我们从生存预测、免疫治疗反应、体细胞突变和免疫微环境等方面阐明了与预后相关的核心蛋白。此外,我们还建立了一个具有出色预测能力的可靠预后模型。
Integration of proteomics and transcriptomics to construct a prognostic signature of renal clear cell carcinoma.
Background: Protein information is often replaced by RNA data in studies to understand cancer-related biological processes or molecular functions, and proteins of prognostic significance in Kidney clear cell carcinoma (KIRC) remain to be mined. Methods: The cancer genome atlas program (TCGA) data was utilized to screen for proteins that are prognostically significant in KIRC. Machine learning algorithms were employed to develop protein prognostic models. Additionally, immune infiltration abundance, somatic mutation differences, and immunotherapeutic responses were analyzed in various protein risk subgroups. Ultimately, the validation of protein-coding genes was confirmed by utilizing an online database and implementing quantitative real-time PCR (qRT-PCR). Results: The patients were divided into two risk categories based on prognostic proteins, and notable disparities in both overall survival (OS) and progression free interval (PFI) were observed between the two groups. The OS was more unfavorable in the high-risk group, and there was a noteworthy disparity in the level of immune infiltration observed between the two groups. In addition, the nomogram showed high accuracy in predicting survival in KIRC patients. Conclusion: In this research, we elucidated the core proteins associated with prognosis in terms of survival prediction, immunotherapeutic response, somatic mutation, and immune microenvironment. Additionally, we have developed a reliable prognostic model with excellent predictive capabilities.
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