Identification of a polyamine-related signature and six novel prognostic biomarkers in oral squamous cell carcinoma - Tang, Jiezhang; Wu, Xuechen; Cheng, Bo; Lu, Yajie (Frontiers in Molecular Biosciences, 2023)
Elevated polyamine levels are required for tumor transformation and development; however, expression patterns of polyamines and their diagnostic potential have not been investigated in oral squamous cell carcinoma (OSCC), its impact on prognosis has yet to be determined. A total of 440 OSCC samples and clinical data were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Consensus clustering was conducted to classify OSCC patients into two subgroups based on the expression of the 17 polyamine regulators. Polyamine-related differentially expressed genes (PARDEGs) among distinct polyamine clusters were determined. To create a prognostic model, PARDEGs were examined in the training cohorts using univariate-Lasso-multivariate Cox regression analyses. Six prognostic genes ‘CKS2’, ‘RIMS3’, ‘TRAC’, ‘FMOD’, ‘CALML5’ and ‘SPINK7’ were identified and applied to develop a predictive model for OSCC. According to the median risk score, the patients were split into high-risk and low-risk groups. The predictive performance of the six-gene model was proven by ROC curve analysis of the training and validation cohorts. Kaplan-Meier curves revealed that the high-risk group had a poorer prognosis. Furthermore, the low-risk group was more susceptible to four chemotherapy drugs according to the IC50 of the samples computed by ‘pRRophetic’ package. The correlation between the risk scores and the proportion of immune cells was calculated. Meanwhile, the tumor mutational burden (TMB) value of the high-risk group was higher. Real-time quantitative PCR was applied to verify the genes constructing the model. The possible connections of the six genes with various immune cell infiltration and therapeutic markers were anticipated. In conclusion, we identified a polyamine-related prognostic signature, and 6 novel biomarkers in OSCC, which may provide insights to identify new treatment targets for OSCC.