Xuegang Wang, Weiquan Li, Ning Lou, Weiwei Han, Bo Hai, Wen Xiao, Xiaoping Zhang
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The quantitative real-time polymerase chain reaction (qRT-PCR) detected the DNTTIP1 expression in renal cancer tissues, and the Office of Cancer Clinical Proteomics Research (CPTAC) verified the protein expression of DNTTIP1. Moreover, nomogram predicted the role of DNTTIP1 in ccRCC patient. Single-sample Gene Set Enrichment Analysis (SsGSEA) and GSEA evaluated the pathogenesis role of DNTTIP1 in TCGA-KIRC.</p><p><strong>Results: </strong>DNTTIP1 expression was higher in ccRCC tumor tissues. High expression of DNTTIP1 was associated with poor OS (HR = 1.618, P < 0.0001), and poor DFS (HR = 1.789, P < 0.0001). SsGSEA and GSEA showed DNTTIP1 was associated with hypoxia, epithelial-mesenchymal transition (EMT), angiogenesis, G2M checkpoint. DNTTIP1 had a positive correlation with EMT biomarkers in ccRCC, and might be an effective target for ccRCC.</p><p><strong>Conclusion: </strong>This study provided that higher expression of DNTTIP1 predicted poor prognosis in ccRCC, and DNTTIP1 might be a novel detection biomarker and therapeutic target of tumor malignant in the future.</p>","PeriodicalId":56015,"journal":{"name":"Pharmacogenomics & Personalized Medicine","volume":"16 ","pages":"1-14"},"PeriodicalIF":1.8000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/82/80/pgpm-16-1.PMC9831534.pdf","citationCount":"0","resultStr":"{\"title\":\"High Expression of DNTTIP1 Predicts Poor Prognosis in Clear Cell Renal Cell Carcinoma.\",\"authors\":\"Xuegang Wang, Weiquan Li, Ning Lou, Weiwei Han, Bo Hai, Wen Xiao, Xiaoping Zhang\",\"doi\":\"10.2147/PGPM.S382843\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Invasion and metastasis led to poor prognosis and death of clear cell renal cell carcinoma (ccRCC) patients. The deoxynucleotidyl transferase terminal interacting protein 1 (DNTTIP1) was reported to promote multiple tumor progression. However, there is no research about DNTTIP1 in ccRCC.</p><p><strong>Methods: </strong>Kaplan-Meier survival analysis, multivariate analysis demonstrated the prognostic indicator in overall survival (OS) and disease-free survival (DFS) of ccRCC with DNTTIP1 expression in the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC). Receiver operator characteristic (ROC) curve analyzed diagnostic ability of DNTTIP1 in TCGA-KIRC and validation dataset. The quantitative real-time polymerase chain reaction (qRT-PCR) detected the DNTTIP1 expression in renal cancer tissues, and the Office of Cancer Clinical Proteomics Research (CPTAC) verified the protein expression of DNTTIP1. Moreover, nomogram predicted the role of DNTTIP1 in ccRCC patient. Single-sample Gene Set Enrichment Analysis (SsGSEA) and GSEA evaluated the pathogenesis role of DNTTIP1 in TCGA-KIRC.</p><p><strong>Results: </strong>DNTTIP1 expression was higher in ccRCC tumor tissues. High expression of DNTTIP1 was associated with poor OS (HR = 1.618, P < 0.0001), and poor DFS (HR = 1.789, P < 0.0001). SsGSEA and GSEA showed DNTTIP1 was associated with hypoxia, epithelial-mesenchymal transition (EMT), angiogenesis, G2M checkpoint. 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引用次数: 0
摘要
背景:透明细胞肾细胞癌(ccRCC)患者的侵袭和转移导致预后不良和死亡。据报道,脱氧核苷酸转移酶末端相互作用蛋白1 (DNTTIP1)可促进多发性肿瘤的进展。然而,目前还没有关于DNTTIP1在ccRCC中的研究。方法:通过Kaplan-Meier生存分析、多因素分析,验证了肿瘤基因组图谱肾透明细胞癌(TCGA-KIRC)中DNTTIP1表达的ccRCC的预后指标总生存期(OS)和无病生存期(DFS)。ROC曲线分析DNTTIP1在TCGA-KIRC和验证数据集中的诊断能力。采用实时定量聚合酶链反应(qRT-PCR)检测DNTTIP1在肾癌组织中的表达,并由美国癌症临床蛋白质组学研究办公室(Office of cancer Clinical Proteomics Research, CPTAC)验证DNTTIP1的蛋白表达。此外,nomogram预测了DNTTIP1在ccRCC患者中的作用。单样本基因集富集分析(SsGSEA)和GSEA评估了DNTTIP1在TCGA-KIRC中的发病机制。结果:DNTTIP1在ccRCC肿瘤组织中表达较高。DNTTIP1高表达与不良OS (HR = 1.618, P < 0.0001)和不良DFS (HR = 1.789, P < 0.0001)相关。SsGSEA和GSEA显示DNTTIP1与缺氧、上皮-间质转化(EMT)、血管生成、G2M检查点相关。DNTTIP1与ccRCC中EMT生物标志物呈正相关,可能是ccRCC的有效靶点。结论:本研究提示DNTTIP1的高表达预示着ccRCC的不良预后,DNTTIP1可能是未来肿瘤恶性的新的检测生物标志物和治疗靶点。
High Expression of DNTTIP1 Predicts Poor Prognosis in Clear Cell Renal Cell Carcinoma.
Background: Invasion and metastasis led to poor prognosis and death of clear cell renal cell carcinoma (ccRCC) patients. The deoxynucleotidyl transferase terminal interacting protein 1 (DNTTIP1) was reported to promote multiple tumor progression. However, there is no research about DNTTIP1 in ccRCC.
Methods: Kaplan-Meier survival analysis, multivariate analysis demonstrated the prognostic indicator in overall survival (OS) and disease-free survival (DFS) of ccRCC with DNTTIP1 expression in the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC). Receiver operator characteristic (ROC) curve analyzed diagnostic ability of DNTTIP1 in TCGA-KIRC and validation dataset. The quantitative real-time polymerase chain reaction (qRT-PCR) detected the DNTTIP1 expression in renal cancer tissues, and the Office of Cancer Clinical Proteomics Research (CPTAC) verified the protein expression of DNTTIP1. Moreover, nomogram predicted the role of DNTTIP1 in ccRCC patient. Single-sample Gene Set Enrichment Analysis (SsGSEA) and GSEA evaluated the pathogenesis role of DNTTIP1 in TCGA-KIRC.
Results: DNTTIP1 expression was higher in ccRCC tumor tissues. High expression of DNTTIP1 was associated with poor OS (HR = 1.618, P < 0.0001), and poor DFS (HR = 1.789, P < 0.0001). SsGSEA and GSEA showed DNTTIP1 was associated with hypoxia, epithelial-mesenchymal transition (EMT), angiogenesis, G2M checkpoint. DNTTIP1 had a positive correlation with EMT biomarkers in ccRCC, and might be an effective target for ccRCC.
Conclusion: This study provided that higher expression of DNTTIP1 predicted poor prognosis in ccRCC, and DNTTIP1 might be a novel detection biomarker and therapeutic target of tumor malignant in the future.
期刊介绍:
Pharmacogenomics and Personalized Medicine is an international, peer-reviewed, open-access journal characterizing the influence of genotype on pharmacology leading to the development of personalized treatment programs and individualized drug selection for improved safety, efficacy and sustainability.
In particular, emphasis will be given to:
Genomic and proteomic profiling
Genetics and drug metabolism
Targeted drug identification and discovery
Optimizing drug selection & dosage based on patient''s genetic profile
Drug related morbidity & mortality intervention
Advanced disease screening and targeted therapeutic intervention
Genetic based vaccine development
Patient satisfaction and preference
Health economic evaluations
Practical and organizational issues in the development and implementation of personalized medicine programs.