{"title":"Investigation of Underlying Biological Association and Targets between Rejection of Renal Transplant and Renal Cancer","authors":"Yinwei Chen, Zhanpeng Liu, Qian Yu, Xu Sun, Shuai Wang, Qingyi Zhu, Jian Yang, Rongjiang Jiang","doi":"10.1155/2023/5542233","DOIUrl":null,"url":null,"abstract":"<div>\n <p><i>Background</i>. 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. <i>Methods</i>. 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. <i>Results</i>. 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 <i>PLAC8</i>, <i>CSTA</i>, <i>AIM2</i>, and <i>LYZ</i>. 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 <i>PLAC8</i>, involved in ccRCC occurrence. Finally, the <i>PLAC8</i> was selected for further research, including its clinical and biological role. <i>Conclusion</i>. In all, our study provides novel insight into the transition from the rejection of renal transplant to renal cancer. Meanwhile, <i>PLAC8</i> could be a potential biomarker for ccRCC diagnosis and prognosis in post-kidney transplant patients.</p>\n </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2023 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229252/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Comparative and Functional Genomics","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2023/5542233","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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肾细胞癌发生的潜在生物学机制。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The Cancer Genome Atlas Program (TCGA)数据库。有或无排斥反应的肾移植患者的微阵列数据来自基因表达综合数据库(Gene Expression Omnibus, GEO)。并在R软件中进行统计分析。结果。我们从GEO数据集(GSE48581、GSE36059和GSE98320)中鉴定出移植排斥患者中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组织中都上调。此外,基于PLAC8、CSTA、AIM2、LYZ四个排斥相关基因构建预后模型。该预后模型在ccRCC队列中表现出良好的预后预测效果。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,我们选择PLAC8进行进一步的研究,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。