Identification of Potential Biomarkers for Progression and Prognosis of Bladder Cancer by Comprehensive Bioinformatics Analysis

Abstract

Background. Bladder cancer (BLCA) is a highly malignant tumor that develops in the urinary system. Identification of biomarkers in progression and prognosis is crucial for the treatment of BLCA. BLCA-related differentially expressed genes (DEGs) were authenticated by screening the DEGs and weighted gene coexpression network analysis (WGCNA). LASSO and SVM-RFE algorithms were utilized to screen the feature genes in BLCA. Survival analysis was performed using the Kaplan–Meier curve provided by the ‘survival’ R package. The BLCA samples were clustered by hclust based on the immune score matrix calculated by the single-sample GSEA (ssGSEA) algorithm. The immune, stromal, and ESTIMATE scores of each BLCA patient were calculated by applying the ESTIMATE algorithm. ssGSEA was conducted to explore the function of characteristic genes in BLCA. The expression of characteristic genes in clinical cancer tissue, and the pericancerous tissue of BLCA patients was verified using qRT-PCR assays. A total of 189 BLCA-related DEGs were identified. Fourteen feature genes were defined by LASSO and SVM-RFE algorithms. Five characteristic genes, including SMYD2, GAPDHP1, ATP1A2, CILP, and THSD4, were related to the OS of BLCA. The correlation analysis of five characteristic genes and clinicopathological factors showed that five genes played a role in the progression of BLCA. Additionally, the expression of five characteristic genes in clinical cancer tissues and pericarcinomatous tissues from BLCA patients was verified by qRT-PCR, which was consistent with the result from the public database. Finally, we discovered five prognostic genes linked to BLCA progression, which might serve as a theoretical basis for prognosis and treatment targets for BLCA patients.

Identification of Novel Prognostic Biomarkers That are Associated with Immune Microenvironment Based on GABA-Related Molecular Subtypes in Gastric Cancer

“…We also found that a higher level of ATP1A2 was associated with shorter overall survival time in GC patients, which was consistent with the effect of ATP1A2 on prognosis in ovarian cancer and bladder cancer. 45 , 47 In addition, this result was validated in other independent datasets. These results indicated that ATP1A2 has potential as a tumor promoter and its high expression was significantly associated with poor prognosis in patients with GC.…”

“…This abnormal expression is thought to play a role in the development and onset of cancer. 44 , 45 In addition, ATP1A2 suppressed invasion, migration, apoptosis, and proliferation in prostate cancer by inactivating the Smad/TGF-β pathway. 46 In our study, the results of multiple databases showed that ATP1A2 was down-regulated in GC patients.…”

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

“…Crosstalk between cancer cells and the TME has been proven to play an important role in tumor progression and metastasis ( 43 ). And TIICs are an important component of the TME, and their composition and distribution are closely related to tumorigenesis and development ( 44 ). First, we explored the correlation between risk score and infiltrating immune cell abundance according to XCELL, TIMER, QUANTISEQ, MCPCOUNTER, CIBERSORT, CIBERSORT-ABS and EPIC algorithms, where the CD8+ T cell, NK cell infiltration were all positively correlated with the risk score ( Figure 7A ).…”

Unraveling the role of the circadian clock genes in cervical squamous cell carcinoma and endocervical adenocarcinoma: A prognostic indicator for prognostic, immunotherapy response, and chemotherapy sensitivity

“…It has been established that the interaction between cancer cells and the TME is critical for the development and spread of tumors 47 . An essential part of the TME is TIICs, whose distribution and composition are intimately linked to tumor growth 46 , 48 . Using the XCELL, TIMER, QUANTISEQ, MCPCOUNTER, CIBERSORT, CIBERSORT-ABS, and EPIC algorithms, we first investigated the relationship between risk ratings and the quantity of invading immune cells.…”