Comorbidity-based Analysis On Prostate Cancer Evolution

Prostate cancer(PCa)is the most popular urological malignancy diagnosed in western countries.Recent studies showed that the incidence and mortality of prostate cancer in Asian countries have increased yearly.Most published studies of prostate cancer are focus on PCa phenotype diagnosis and treatment.In this study,we explored the pathogenetic association between prostate cancer and its' comorbidity,including cardiovascular disease(CVD),cerebrovascular disease(CEVD),diabetes mellitus(DM)and bladder cancer(BCa).Based on systems biology theories,this study analyzed the underlying role of comorbidities in PCa occurrence and progression from molecular,network and pathway angles.At the molecular level,we selected the gene expression data and identified differentially expressed(DE)genes using Student's t test for further functional analysis.At the network level,weighted co-expression networks were constructed based on WGCNA package in R.The co-expressed modules associated with disease evolution were then extracted and evaluated through functional enrichment analysis.Compared with gene expression and co-expressed network signatures between PCa at different stages and four comorbidities,we found that the number of genes and pathway/gene ontology items shared by four comorbidities and metastatic PCa was higher.And literature validation convinced that these pathways played important roles in prostate cancer and its four comorbidities.Considering comorbidities,BCa had the closest relationship with PCa,following by DM,CVD and CEVD.From the perspective of PCa,DM showed the rapid change during PCa primary and metastatic periods,the change of BCa in primary stage and metastatic stage of PCa was consistent with PCa itself,and CVD and CEVD only related to the metastatic stage of PCa.Comorbidity plays an important role in the evolution of PCa.This study explored the relationship between PCa and four comorbidities based on systems biology analysis,and provided new ideas and methods for PCa pathogenesis investigation and personalized treatment.