Background. Post-renal transplant patients have a high likelihood of developing renal cancer. However, the underlying biological mechanisms behind the development of renal cancer in post-kidney transplant patients remain to be elucidated. Therefore, this study aimed to investigate the underlying biological mechanism behind the development of renal cell carcinoma in post-renal transplant patients. Methods. Next-generation sequencing data and corresponding clinical information of patients with clear cell renal cell carcinoma (ccRCC) were obtained from The Cancer Genome Atlas Program (TCGA) database. The microarray data of kidney transplant patients with or without rejection response was obtained from the Gene Expression Omnibus (GEO) database. In addition, statistical analysis was conducted in R software. Results. We identified 55 upregulated genes in the transplant patients with rejection from the GEO datasets (GSE48581, GSE36059, and GSE98320). Furthermore, we conducted bioinformatics analyses, which showed that all of these genes were upregulated in ccRCC tissue. Moreover, a prognosis model was constructed based on four rejection-related genes, including PLAC8, CSTA, AIM2, and LYZ. The prognosis model showed excellent performance in prognosis prediction in a ccRCC cohort. In addition, the machine learning algorithms identified 19 rejection-related genes, including PLAC8, involved in ccRCC occurrence. Finally, the PLAC8 was selected for further research, including its clinical and biological role. Conclusion. In all, our study provides novel insight into the transition from the rejection of renal transplant to renal cancer. Meanwhile, PLAC8 could be a potential biomarker for ccRCC diagnosis and prognosis in post-kidney transplant patients.

中文翻译：

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肾移植排斥与肾癌之间潜在生物学关联和​​靶点的研究

背景。肾移植后患者患肾癌的可能性很高。然而，肾移植后患者发生肾癌的潜在生物学机制仍有待阐明。因此，本研究旨在探讨肾移植后患者发生肾细胞癌的潜在生物学机制。方法. 透明细胞肾细胞癌（ccRCC）患者的下一代测序数据和相应的临床信息来自癌症基因组图谱计划（TCGA）数据库。有或没有排斥反应的肾移植患者的微阵列数据是从基因表达综合 (GEO) 数据库中获得的。此外，在R软件中进行统计分析。结果。我们从 GEO 数据集（GSE48581、GSE36059 和 GSE98320）中识别出移植排斥反应患者中的 55 个上调基因。此外，我们进行了生物信息学分析，结果表明所有这些基因在 ccRCC 组织中都被上调。此外，基于四种排斥相关基因构建了预后模型，包括PLAC8，CSTA、AIM2和LYZ。预后模型在 ccRCC 队列的预后预测中表现出优异的性能。此外，机器学习算法确定了 19 个排斥相关基因，包括PLAC8，参与 ccRCC 的发生。最后，选择PLAC8进行进一步研究，包括其临床和生物学作用。结论。总之，我们的研究为从肾移植排斥到肾癌的转变提供了新的见解。同时，PLAC8可能成为肾移植后患者 ccRCC 诊断和预后的潜在生物标志物。