{"title":"肾移植排斥反应与肾癌的潜在生物学关联及靶点研究。","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":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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>","PeriodicalId":13988,"journal":{"name":"International Journal of Genomics","volume":"2023 ","pages":"5542233"},"PeriodicalIF":2.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229252/pdf/","citationCount":"0","resultStr":"{\"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\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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>\",\"PeriodicalId\":13988,\"journal\":{\"name\":\"International Journal of Genomics\",\"volume\":\"2023 \",\"pages\":\"5542233\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-01-01\",\"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\":\"International Journal of Genomics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1155/2023/5542233\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Genomics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1155/2023/5542233","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Investigation of Underlying Biological Association and Targets between Rejection of Renal Transplant and Renal Cancer.
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.
期刊介绍:
International Journal of Genomics is a peer-reviewed, Open Access journal that publishes research articles as well as review articles in all areas of genome-scale analysis. Topics covered by the journal include, but are not limited to: bioinformatics, clinical genomics, disease genomics, epigenomics, evolutionary genomics, functional genomics, genome engineering, and synthetic genomics.