Establishment And Validation Of Inflammation-related Gene Prognostic Risk Model For Prostate Cancer Based On The TCGA Database

Objectives Prostate cancer is one of the common malignant tumors of the male genitourinary system.Inflammation plays a key role in the development and progression of prostate cancer.This study aimed to construct an inflammation-related gene prognostic risk model to predict the prognosis of prostate cancer patients based on inflammation-related genes.Materials and Methods In this study,clinical data and m RNA sequencing data of prostate cancer patients(TCGA-RPAD cohort)were downloaded from The Cancer Genome Atlas(TCGA),and inflammation-related pathway gene set was downloaded from the Molecular Signature Database(MSig DB).Inflammation-related genes associated with prognosis were screened by univariate Cox regression.LASSO regression analysis was used to construct an inflammation-related gene prognostic risk model.Independent prognostic factors for prostate cancer were identified by univariate Cox and multifactorial Cox regression analysis for the construction of prognostic nomogram to quantify the risk of recurrence of prostate cancer.The inflammationrelated gene prognostic risk model and prognostic nomogram were evaluated using the receiver operating characteristic curve(ROC),C-index,and Kaplan-Meier survival curve.GEO validation cohort was used for validation of inflammation-related genes prognostic risk model.The inflammation scores were calculated based on the prognostic risk model of inflammation-related genes,patients in the TCGA-RPAD cohort were divided into two groups with high-and low-inflammation scores.Differentially expressed genes(DEGs)between the two groups with high and low inflammation scores were identified,and DEGs were subjected to gene ontolog(GO),kyoto encyclopedia of genes and genomes(KEGG)and HALLMARK gene set enrichment analysis.Protein-protein interaction network(PPI)of DEGs was mapped to investigate the potential molecular mechanisms of inflammation-related genes in the prognosis of prostate cancer.Verification of SPHK1 expression levels in prostate cancer tissue microarrays by immunohistochemical(IHC)staining.Results1.Univariate Cox regression and LASSO regression analyses were performed to screen19 inflammation-related genes(CD14,GABBR1,IRF7,MSR1,OSM,PIK3R5,RELA,SCARF1,SPHK1,STAB1,AQP9,ATP2C1,CXCL6,DCBLD2,IFNAR1,LPAR1(PCDH7 and P2RY2 and NDP),the inflammation-related genes prognostic risk model of prostate cancer was constructed.Among the model construction genes CD14,PIK3R5,GABBR1,RELA,IRF7,SCARF1,MSR1,SPHK1,OSM and STAB1 were risk genes,and AQP9,LPAR1,ATP2C1,NDP,CXCL6,P2RY2,DCBLD2,PCDH7 and IFNAR1 were protective genes.2.The inflammation scores were calculated based on the prognostic risk model.Patients in the TCGA-RPAD cohort were divided into two groups with high-and lowinflammation score.Kaplan-Meier survival curves showed that patients in the high inflammation score group had a significantly shorter recurrence-free survival compared with those in the low inflammation score group(P < 0.001).The area under the curve of ROC curve for 1,3,and 5 years was 0.796,0.756,and 0.793,respectively.GEO validation cohort also confirmed the excellent predictive power of the prognostic risk model constructed in this study.3.The results of univariate Cox regression and multivariate Cox regression indicated that inflammation score based on prognostic risk model was an independent prognostic factor for prostate cancer patients in the TCGA-PRAD cohort and GEO validation cohort.Prognostic nomogram was constructed using inflammation scores and other clinicopathological factors as covariates to quantify the risk of recurrence at 1,3,and 5years in patients.Patients in the TCGA-RPAD cohort were divided into high and low score groups by the total score calculated from the prognostic nomogram.Kaplan-Meier curves showed that patients in the low score group had a significantly better prognosis compared with those in the high score group(P < 0.001).It was confirmed that the constructed prognostic nomogram had excellent discrimination and calibration ability based on the results of the C-index,time-dependent ROC curve,and calibration curves.4.The results of GO enrichment analysis showed that cell-cell adhesion,immune cell proliferation,activation and regulation were associated with the prognosis of prostate cancer.The results of KEGG enrichment analysis showed the activation of cytokinecytokine receptor interaction,chemokine signaling pathway,cell adhesion molecules and other pathways.The results of HALLMARK gene set enrichment analysis showed the activation of epithelial-mesenchymal transition,G2 M checkpoint,inflammatory response,IL6 /JAK/STAT3 signaling and angiogenesis were activated.5.Compared with normal tissues,SPHK1 expression was elevated in prostate cancer tissues with increasing Gleason score.the significant difference in expression score of SPHK1 among four groups of normal,Gleason 6,Gleason 7,and Gleason 8 tissues(P=0.0369).The association between SPHK1 expression and envelope invasion(P=0.0312),the greater the SPHK1 expression,the more likely the envelope was invaded by the tumor(P =0.0042).Conclusion We established and validated an inflammation-related gene signature prognostic risk model of prostate cancer based on the TCGA database.Prognostic nomogram was constructed at the same time to specifically quantify the risk of recurrence in prostate cancer patients.The inflammation score based on the prognostic risk model was an independent prognostic factor of prostate cancer.These inflammation-associated genes may be biomarkers and potential therapeutic targets for prostate cancer.