Identification and Validation of the Prognostic Stemness Biomarkers in Bladder Cancer Bone Metastasis - Kang, Yao; Zhu, Xiaojun; Wang, Xijun; Liao, Shiyao; Jin, Mengran; Zhang, Li; Wu, Xiangyang; Zhao, Tingxiao; Zhang, Jun; Lv, Jun; Zhu, Danjie (Frontiers in Oncology, 2021)
Background Bladder urothelial carcinoma (BLCA) is one of the most common urinary system malignancies with a high metastasis rate. Cancer stem cells (CSCs) play important roles in its occurrence and progression, however, its roles in bone metastasis and prognostic stemness biomarkers have not been identified in BLCA. Method The RNA sequencing data of BLCA patients were retrieved from The Cancer Genome Atlas (TCGA) database. The mRNA expression-based stemness index (mRNAsi) and differential expressed genes (DEGs) were identified, along with their associations with tumorigenesis, bone metastasis, clinical stage and prognosis. The key prognostic stemness-related genes (PSRGs) were screened by Lasso regression, and based on them, the predict model was constructed. Its accuracy was tested by the area under the curve (AUC) of receiver operator characteristic (ROC) curve. The relationship among differentially expressed TFs, PSRGs and hallmarks of cancer were explored by Pearson correlation analysis. The identified key TFs and PSRGs were validated by immunohistochemistry. The potential mechanism was evaluated by ATAC-seq. Results A total of 8,647 DEGs were identified between 411 primary BLCAs and 19 normal solid tissue samples, and 2,383 stage-, 3,680 stemness- and 716 bone metastasis-associated DEGs were uncovered, respectively. Compared with normal tissue, mRNAsi was significantly upregulated in primary BLCA and associated with prognosis (P=0.016), bone metastasis (P<0.001) and AJCC clinical stage (P<0.001). Twenty key PSRGs were further identified and based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.699). In the regulatory network, we found 2 key TFs (EPO, ARID3A), 4 key PRSGs (CACNA1E, LINC01356, CGA and SSX3) and 5 key hallmarks of cancer gene sets (DNA repair, myc targets, E2F targets, mTORC1 signaling and unfolded protein response). The tissue microarray revealed high expression of key TFs (EPO, ARID3A) and PRSGs (CGA, SSX3) and the ATAC-seq data revealed their accessible peaks. Conclusion Our study identifies mRNAsi as a reliable index in predicting the tumorigenesis, bone metastasis and prognosis of BLCA and provides a well-applied model for BLCA patients. Besides, we identified the potential regulatory network between key PSRGs and cancer gene sets in mediating BLCA bone metastasis.