Identification of BCL11A, NTN5, and OGN as diagnosis biomarker of papillary renal cell carcinomas by bioinformatic analysis

Abstract The prevalence of papillary renal cell carcinomas is estimated to be between 10% and 15. At this time, there is no effective therapeutic approach available for patients with advanced PRCCs. The molecular biomarkers associated with PRCC diagnoses have been rarely studied compared to renal clear cell carcinomas, therefore it is imperative that novel molecular biomarkers be identified to aid in the early identification of this disease. Bioinformatics and artificial intelligence technologies have become increasingly important in the search for diagnostic biomarkers for early cancer detection. In this study, three genes, BCL11A, NTN5, and OGN, were identified as diagnostic biomarkers using the TCGA database and deep learning techniques. To identify differentially expressed genes (DEGs), RNA expression profiles of PRCC patients were analyzed using a machine learning approach. A number of molecular pathways and co-expressions of DEGs have been analyzed, and a correlation between DEGs and clinical data has been determined. Diagnostic markers were then determined via machine learning analysis. The 10 genes selected with the highest Variable Importance value (more than 0.9) were further investigated and six of them were upregulated (BCL11A, NTN5, SEL1L3, SKA3, TAPBP, SEMA6A) and four were downregulated (OGN, ADCY4, SMOC2, CCL23). A combined ROC curve analysis revealed that the BCL11A-NTN5-OGN genes, which have specificity and sensitivity values of 0.968 and 0.901 respectively, can be used as a diagnostic biomarker for PRCC. In general, the genes introduced in this study may be able to be used as diagnostic biomarkers for the early diagnosis of PRCC and thus provide the possibility of early treatment and preventing the progression of the disease.