Prediction Of Survival In Patients With Kidney Renal Clear Cell Carcinoma Based On Gene Expression And DNA Methylation

Renal cell carcinoma(RCC),which ranks tenth in adult malignancies,the most common type being kidney renal clear cell carcinoma(KIRC).Thanks to the large amount of data generated by high-throughput sequencing technology,studies on kidney renal clear cell carcinoma have proved that the change of gene expression is directly affected by DNA methylation,and the change of gene expression is closely related to the occurrence and development of kidney renal clear cell carcinoma.However,there are few studies on whether mRNA and LncRNA can be used as biomarkers of diseases after combination,and whether changes in gene expression and DNA methylation have the same effect on the prognosis of KIRC patients.This article hopes to find gene transcripts related to the prognosis of patients with renal clear cell carcinoma,and to determine the biological functions of these RNAs,and to understand the difference in gene expression changes on the prognosis of KIRC.Firstly,detailed gene expression,DNA methylation and clinical data from TCGAdatabase were downloaded.Aiming at differentially expressed genes,COX proportional hazard model and random survival forest model were used to study the relationship between the expression of all these genes and survival of KIRC patients.A prognostic model consisting of 6 mRNA and 3 LncRNA was established,and KIRC patients were divided into two groups.The Kaplan-Meier survival analysis of patients at high and low risk showed that the survival rates of patients in the two groups were significantly different(P value = 6.2e-14).The performance of the model was assessed by the receiver operating characteristic curve(ROC curve),and the final area under the curve(AUC)was 0.841,representing a high performance.Secondly,weighted gene co-expression network analysis(WGCNA)was use differentially expressed genes into different gene expression patterns,and 21 modules were obtained.The survival analysis of the module combined with the distribution of genes in each module in the prognostic model indicated the genes in which the green-yellow,blue and brown modules have a clear correlation with the prognosis of patients with KIRC.The GO function analysis of the genes in these modules proves that the most reliable(minimum P value)functions are all distributed in the items related to cell division.At the same time,it was found that the gene with greater differential expression changed more closely with the prognosis of KIRC patients.A joint analysis of the various modules and clinical features separated a potential pathological target CDCA8.Finally,to explore whether gene expression and DNA methylation characteristics have different effects on the prognosis of KIRC patients in differentage groups,four-step method was used to study.Results show that the young(< 50)and elderly(> 70)KIRC risk in patients were higher than in the middle ages(50 ~ 70)the risk of patients,and different characteristics,such as DNAm age,in patient outcomes in the patients of different ages with different functions.There is a clear relationship between hypomethylation and age groups,and hypermethylation often leads to poor prognosis in patients with KIRC.These conclusions may have some significance for clinical diagnosis of KIRC or disease development research.