Background
Bladder cancer is the fifth most common type of cancer worldwide, with high recurrence and progression rates. Although considerable progress has been made in the treatment of bladder cancer through accurate typing of molecular characteristics, little is known about the various genetic and epigenetic changes that have evolved in stem and progenitor cells. Thus, we developed a novel stem cell typing method to fill this gap.
Methods
Based on six published genomic data sets, we used 26 stem cell gene sets to classify each data set. Unsupervised and supervised machine learning methods were used to perform the classification.
Results
We classified BLCA into three subtypes—high stem cell enrichment (SCE_H), medium stem cell enrichment (SCE_M), and low stem cell enrichment (SCE_L)—based on multiple cross-platform data sets. The stability and reliability of the classification were verified. The stemness index obtained from the one-class logistic regression machine learning method showed that the degree of tumor stem cell enrichment was not proportional to the stemness index. Compared with the other subtypes, SCE_H showed the highest degree of cancer stem cell concentration, lowest stemness index, and highest level of immune cell infiltration and was the most sensitive to predicted immune checkpoint inhibitor treatment. However, this group showed the worst prognosis. Comparison of gene set enrichment analysis results for pathway enrichment of various subtypes revealed that the SCE_H subtype activates some important pathways regulating cancer occurrence, development, and even poor prognosis, including epithelial mesenchymal transition, hypoxia, angiogenesis, KRAS signal up-regulation, the interleukin 6-mediated Jak-STAT signaling pathway, and inflammatory response. Two identified pairs of transcription factors, GRHL2 and GATA6 and IRF5 and GATA3 , likely have opposite regulatory effects on SCE_H and SCE_L, respectively.
Conclusions
The identification of BLCA subtypes based on cancer stem cell gene sets revealed the complex mechanism of carcinogenesis causing BLCA and provides a new direction for the diagnosis and treatment of BLCA.