Construction and evaluation of a prognostic model for metabolism-related genes in kidney renal clear cell carcinoma using TCGA database DOI

Jingteng He,

M Z Du, Xiao-Jun Bi

et al.

American Journal of Clinical and Experimental Urology, Journal Year: 2024, Volume and Issue: 12(6), P. 352 - 366

Published: Jan. 1, 2024

To investigate the expression of metabolism-related genes (MRGs) in kidney renal clear cell carcinoma (KIRC) and their association with patient prognosis, to identify potential targets for intervention. Bioinformatics methods were employed mine KIRC transcriptome data The Cancer Genome Atlas Program (TCGA) database order MRGs that are aberrantly expressed cancerous tissues. Subsequently, a prognostic risk score model was constructed its predictive capacity evaluated. Finally, prognostically relevant validated using external datasets clinical samples. A total 789 differentially associated screened, which 465 upregulated 324 downregulated, finally 23 screened establish model. We found AUCs predicting patients' 1-, 3- 5-year overall survival (OS) 0.804, 0.766 0.802, respectively. These findings suggest has good ability. multifactorial Cox analysis revealed significantly patients, could therefore be used as an independent factor prognosis patients (HR = 3.495, P < 0.001). Meanwhile, Kaplan-Meier analyses high-risk low-risk groups indicated group exhibited markedly inferior prognosis. validation samples from four sets (GSE36895, GSE40435, GSE53757 GSE66272) demonstrated KCNN4 PLK1 highly KIRC, whereas TEK, PLG, ANGPTL3, TFAP2A, ANK3, ATP1A1 UCN low KIRC. Several we can effectively predict provide new foundation personalised diagnosis treatment

Language: Английский

Construction and evaluation of a prognostic model for metabolism-related genes in kidney renal clear cell carcinoma using TCGA database DOI

Jingteng He,

M Z Du, Xiao-Jun Bi

et al.

American Journal of Clinical and Experimental Urology, Journal Year: 2024, Volume and Issue: 12(6), P. 352 - 366

Published: Jan. 1, 2024

To investigate the expression of metabolism-related genes (MRGs) in kidney renal clear cell carcinoma (KIRC) and their association with patient prognosis, to identify potential targets for intervention. Bioinformatics methods were employed mine KIRC transcriptome data The Cancer Genome Atlas Program (TCGA) database order MRGs that are aberrantly expressed cancerous tissues. Subsequently, a prognostic risk score model was constructed its predictive capacity evaluated. Finally, prognostically relevant validated using external datasets clinical samples. A total 789 differentially associated screened, which 465 upregulated 324 downregulated, finally 23 screened establish model. We found AUCs predicting patients' 1-, 3- 5-year overall survival (OS) 0.804, 0.766 0.802, respectively. These findings suggest has good ability. multifactorial Cox analysis revealed significantly patients, could therefore be used as an independent factor prognosis patients (HR = 3.495, P < 0.001). Meanwhile, Kaplan-Meier analyses high-risk low-risk groups indicated group exhibited markedly inferior prognosis. validation samples from four sets (GSE36895, GSE40435, GSE53757 GSE66272) demonstrated KCNN4 PLK1 highly KIRC, whereas TEK, PLG, ANGPTL3, TFAP2A, ANK3, ATP1A1 UCN low KIRC. Several we can effectively predict provide new foundation personalised diagnosis treatment

Language: Английский

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