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Updated: Nov 11, 2025
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
MicroRNA related prognosis biomarkers from high throughput sequencing data of kidney renal papillary cell carcinoma
1Hunan Provincial Key Laboratory for Synthetic Biology of Traditional Chinese Medicine, Hunan University of Medicine, Huaihua, Hunan. P. R. China.yuanwu_liu@126.com.
Objective:
Renal cell carcinoma (RCC) is the most common type of kidney cancer which could be mainly classified as kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP). KIRP ranks second in terms of morbidity rate which comprised 10%-15% of patients. Till now, there were few biomarkers could forecast the outcomes of KIRP. The aim of this study was to identify novel prognostic biomarkers to predict clinical outcomes for KIRP.
Materials And Methods:
In this study, we firstly downloaded 326 miRNAs (35 controls vs. 291 patients), 321 mRNAs (33 controls vs. 288 patients) data and their corresponding clinical information from The Cancer Genome Atlas database. Then, we used DESeq2 analysis, univariate and multivariate Cox regression analysis, pathologic MNT correlation analysis, and specific prognostic model analysis to identify the potential prognosis biomarkers.
Results:
We found 25 differential expression miRNAs (DEMs) and 7 differential expression genes (DEGs) were associated with the overall survival rates of KIRP patients. After multivariate Cox regression analysis, we established 2 prognostic prediction models and calculated the area under the 1-, 3-, and 5-year curve (AUC) values of DEMs and DEGs respectively. Among them, the prognostic index (PI) of DEMs and DEGs showed good predictive ability which was 0.8293/0.7205, 0.8148/0.7301 and 0.7776/0.6810 respectively.
Conclusions:
In this study, we found that 3 DEMs and 2 DEGs could be used as prognostic biomarkers to predict the outcome for KIRP. Our study was just a primary analysis based on high-throughput sequencing and clinical information.
