Characterization of the stem cell landscape and identification of a stemness-associated prognostic signature in bladder cancer.
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ABSTRACT:
SUBMITTER: Lin G
PROVIDER: S-EPMC11344935 | biostudies-literature | 2024 Aug
REPOSITORIES: biostudies-literature
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Characterization of the stem cell landscape and identification of a stemness-associated prognostic signature in bladder cancer.
Lin Gaoteng G Lin Jiamei J Wang Hao H Wang Liucheng L Zhan Fangfang F Wu Liqian L Xue Liang L Dong Yang Y Wei Wanqing W Liu Lin L
Cancer cell international 20240824 1
It is accepted that cancer stem cells (CSCs) are key to the occurrence, progression, drug resistance, and recurrence of bladder cancer (BLCA). Here, we aimed to characterize the landscapes of CSCs and investigate the biological and clinical signatures based on a prognostic model constructed by genes associated with CSCs. The malignant epithelial cells were discovered and sorted into six clusters through single cell analysis. C2 was identified as the CSCs. The signaling involved in the interactio ...[more]
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Project description:BackgroundBladder urothelial carcinoma (BLCA) is one of the most common urinary system malignancies with a high metastasis rate. Cancer stem cells (CSCs) play an important role in the occurrence and progression of BLCA, however, its roles in bone metastasis and the prognostic stemness biomarkers have not been identified in BLCA.MethodIn order to identify the roles of CSC in the tumorigenesis, bone metastasis and prognosis of BLCA, the RNA sequencing data of patients with BLCA were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) and the differential expressed genes (DEGs) were evaluated and identified. The associations between mRNAsi and the tumorigenesis, bone metastasis, clinical stage and overall survival (OS) were also established. 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 the receiver operator characteristic (ROC) curve and the risk score. Additionally, in order to explore the key regulatory network, the relationship among differentially expressing TFs, PSRGs, and absolute quantification of 50 hallmarks of cancer were also identified by Pearson correlation analysis. To verify the identified key TFs and PSRGs, their expression levels were identified by our clinical samples via immunohistochemistry (IHC).ResultsA total of 8,647 DEGs were identified between 411 primary BLCAs and 19 normal solid tissue samples. According to the clinical stage, mRNAsi and bone metastasis of BLCA, 2,383 stage-related DEGs, 3,680 stemness-related DEGs and 716 bone metastasis-associated DEGs were uncovered, respectively. Additionally, compared with normal tissue, mRNAsi was significantly upregulated in the primary BLCA and also associated with the prognosis (P = 0.016), bone metastasis (P < 0.001) and AJCC clinical stage (P < 0.001) of BLCA patients. A total of 20 PSRGs were further screened by Lasso regression, and based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.699). Moreover, we found two key TFs (EPO, ARID3A), four key PRSGs (CACNA1E, LINC01356, CGA and SSX3) and five key hallmarks of cancer gene sets (DNA repair, myc targets, E2F targets, mTORC1 signaling and unfolded protein response) in the regulatory network. The tissue microarray of BLCA and BLCA bone metastasis also revealed high expression of the key TFs (EPO, ARID3A) and PRSGs (SSX3) in BLCA.ConclusionOur study identifies mRNAsi as a reliable index in predicting the tumorigenesis, bone metastasis and prognosis of patients with BLCA and provides a well-applied model for predicting the OS for patients with BLCA based on 20 PSRGs. Besides, we also identified the regulatory network between key PSRGs and cancer gene sets in mediating the BLCA bone metastasis.
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