4.5 Article

Identifying the role of transient receptor potential channels (TRPs) in kidney renal clear cell carcinoma and their potential therapeutic significances using genomic and transcriptome analyses

期刊

BMC MEDICAL GENOMICS
卷 15, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12920-022-01312-x

关键词

Transient receptor potential channels; Kidney renal clear cell carcinoma; Prognisis; Bioinformatics

资金

  1. Liaoning Province [2021BS-209]
  2. Dalian Youth Science and Technology Star [2021RQ010]

向作者/读者索取更多资源

This study investigated the role of TRPs in KIRC and identified a prognostic signature with superior survival prediction. The study also found differences in immune checkpoint gene expression, tumor mutation burden, and immune subtypes and response between low-risk and high-risk subgroups, and identified potential drugs for different risk populations.
Kidney renal clear cell carcinoma (KIRC) is among the major causes of cancer-caused mortality around the world. Transient receptor potential channels (TRPs), due to their role in various human diseases, might become potential drug targets in cancer. The mRNA expression, copy number variation, single-nucleotide variation, prognostic values, drug sensitivity, and pathway regulation of TRPs were studied across cancer types. The ArrayExpress and The Cancer Genome Atlas (TCGA) databases were used to retrieve KIRC samples. Simultaneously, training, internal, and external cohorts were grouped. In KIRC, a prognostic signature with superior survival prediction in contrast with other well-established signatures was created after a stepwise screening of optimized genes linked to TRPs using univariate Cox, weighted gene co-expression network analysis, multivariate Cox, and least absolute shrinkage and selection operator regression analyses. Subsequent to the determination of risk levels, the variations in the expression of immune checkpoint genes, tumor mutation burden, and immune subtypes and response between low-risk and high-risk subgroups were studied using a variety of bioinformatics algorithms, including ESTIMATE, XCELL, EPIC, CIBERSORT-ABS, CIBERSORT, MCPCOUNTER, TIMER, and QUANTISEQ. Gene set enrichment analysis helped in the identification of abnormal pathways across the low- and high-risk subgroups. Besides, high-risk KIRC patients might benefit from ABT888, AZD6244, AZD7762, Bosutinib, Camptothecin, CI1040, JNK inhibitor VIII, KU55933, Lenalidomide, Nilotinib, PLX4720, RO3306, Vinblastine, and ZM.447439; however, low-risk populations might benefit from Bicalutamide, FH535, and OSI906. Finally, calibration curves were used to validate the nomogram with a satisfactory predictive survival probability. In conclusion, this research provides useful insight that can aid and guide clinical practice and scientific research.

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