Dephosphorylation-related signature predicts the prognosis of papillary renal cell carcinoma.

BACKGROUND

Phosphorylation-dephosphorylation is one of the most common and critical cellular activities. It is essential for cell cycle control and leads to large changes in protein conformation, which can alter protein function and coordinate multiple functions such as cell metabolism, gene transcription and translation, signaling, growth, differentiation, and apoptosis. Alterations in the phosphorylated proteome have been shown in many cancers. Many phosphatases that catalyze dephosphorylation have been described as oncogenes and tumor suppressors. Papillary renal cell carcinoma (PRCC) is the second most common subtype of kidney cancer, in which most patients diagnosed with PRCC are already in advanced stages with a poor prognosis. It is necessary to identify reliable predictors associated with early diagnosis and prognosis of PRCC. The study used PRCC patients data from The Cancer Genome Atlas (TCGA) database to evaluate dephosphorylation-related genes and build a panel of prognostic gene signatures which predicts accurately the outcome of PRCC patients.

METHODS

The mutation data, and the fragments per kilobase of exon model per million mapped fragments (FPKM) data together with the corresponding clinical information were downloaded from TCGA database for 288 PRCC patients. Lasso regression algorithm (LASSO) and multivariate Cox regression analysis were performed to produce a panel of risk-related genetic signatures.

RESULTS

We analyzed 417 dephosphorylation-associated genes and, finally, identified 9 genes () and constructed a panel of signatures associated with prognosis. The area under the receiver operating characteristic curve (AUC) value was 0.833 for the prognostic risk score signature. It was confirmed that the risk score was an independent predictor of prognosis [hazard ratio (HR) =1.013, 95% confidence interval (CI): 1.002-1.024, P=0.02].

CONCLUSIONS

We identified 9 genes associated with dephosphorylation differentially expressed in PRCC tumor tissues and established the first prognostic model based on dephosphorylation-associated genes in PRCC patients. It was shown to be a valid and reliable prognostic indicator that could predict the prognosis of PRCC patients accurately. This study has a lot of potential value for future studies.