Identification of subgroups of bladder cancer based on immune gene signature

Immunotherapy has become a new frontier in bladder cancer (BC) treatment. In this study, we utilized bioinformatic tools to identify an immune signature for BC. RNA-seq data of BC samples was downloaded from The Cancer Genome Atlas (TCGA) database, and GSE31684, GSE32894 and GSE77952 chip expression data were downloaded from the Gene Expression Omnibus (GEO) database. The immune related genes (IRGs) dataset was extracted from the ImmPort database. Unsupervised clustering method was used to determine genes with significant association with the prognosis of BC. Macrophages were enriched in C1/C2 subtypes whereas infiltration of B cells ware prominent in C3 subtype. The frequency of FGFR3 mutation was high in C3 subtypes whereas RB1 mutations were high in C1/C2 subtypes. The immune modules gSig1and gSig2 were associated with poor prognosis. gSig3 was associated with good prognosis. gSig1 was associated with activation and degranulation of neutrophils. On the other hand, gSig2 was associated with steroid hormone mediated signaling pathway whereas gSig3 was associated with secretion of extracellular matrix and muscle cell proliferation. gSig1 and gSig2 were positively correlated with expression of immune checkpoint genes (ICGs) in two different cohorts, whereas gSig3 scores was negatively correlated with expression of ICGs. Expression profile of immune-related genes could subclassify BC into three molecular subtypes with distinct histological characteristics, genetic and transcriptional changes. Our study have provided a novel insight into the immune-related state of BC and shed light on the prognostication of BC patient, all of them are of potential clinical implications.