Construction Of A Prognostic Model Of Transcription Factor-related Genes In Early-onset Renal Cancer Based On Bioinformatics

Objective:Early-onset renal cancer may be associated with a higher risk of metastasis and genetic factors,and a number of genes encoding transcription factor E2 F cell cycle-related genes have been identified to be strongly associated with tumour development;to date there is still no relevant genetic prediction model in early-onset renal cancer.The aim of this study was to develop a model for predicting healing in early-onset renal cancer based on transcription factor-associated genes.Methods:The Cancer Genome Atlas(TCGA)database(https://portal.gdc.cancer.gov/repository?facet Tab=fifiles)was first used to download gene expression data and clinical data related to early-onset renal cancer patients and apply one-way COX regression The transcription factor-associated differentially expressed genes were screened using one-way COX regression analysis and Lasso regression analysis.The results of the multifactor COX regression were used to construct the column line graphs.The predictive performance of the column line graphs was evaluated using the subject operating curve(ROC)curves,C-index and fitted curves,and the tumour mutation genes were statistically and mathematically analysed.Differential gene-related enrichment pathways were derived based on differential genes.Results:There were 12 transcription factor E2F-associated differential genes(AURKB,BIRC5,CCNB2,CDC20,CDCA3,CDCA8,HMMR,KIF4 A,PLK1,PTTG1,RAD51AP1,TOP2A)and patients with high risk scores had a worse prognosis than those with low risk scores in the TCGA clinical dataset.Tumour mutation load(TMB)analysis showed higher mutations in genetically related genes such as VHL and TTN in low and high risk patients.GO and KEGG enrichment analysis showed that differential genes were enriched in pathways such as humoral immunity.Finally,we constructed a prediction model with risk score,AJCC staging,pathological type and stage,and its predictive performance was quite excellent.Conclusion:We successfully constructed a novel prognostic model of transcription factor-related genes in early-onset renal cancer that can accurately predict patient prognosis.