Identification of adenylate cyclase 2 methylation in bladder cancer with implications for prognosis and immunosuppressive microenvironment

Abstract

BackgroundThe incidence and mortality of bladder cancer (BCa) are increasing, while the existing diagnostic methods have limitations. Therefore, for early detection and response prediction, it is crucial to improve the prognosis and treatment strategies. However, with existing diagnostic methods, detecting BCa in the early stage is challenging. Hence, novel biomarkers are urgently needed to improve early diagnosis and treatment efficiency.MethodsThe gene expression profile and gene methylation profile dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), differentially methylated genes (DMGs), and methylation-regulated differentially expressed genes (MeDEGs) were gradually identified. A cancer genome map was obtained using online gene expression profile interaction analysis, and survival implications were produced using Kaplan-Meier survival analysis. GSEA was employed to predict the marker pathways where DEGs were significantly involved. The study used bisulfite PCR amplification combined with bisulfite amplicon sequencing (BSAS) to screen for methylation analysis of multiple candidate regions of the adenylate cyclase 2 (ADCY2) based on the sequence design of specific gene regions and CpG islands.ResultsIn this study, DEGs and DMGs with significantly up- or down-regulated expression were selected. The intersection method was used to screen the MeDEGs. The interaction network group in STRING was then visualized using Cytoscape, and the PPI network was constructed to identify the key genes. The key genes were then analyzed using functional enrichment. To compare the relationship between key genes and the prognosis of BCa patients, we further investigated ADCY2 and found that ADCY2 can be a potential clinical biomarker in BCa prognosis and immunotherapy response prediction. In human BCa 5637 and MGH1 cells, we developed and verified the effectiveness of ADCY2 primers using BSAS technology. The findings revealed that the expression of ADCY2 is highly regulated by the methylation of the promoter regions.ConclusionThis study revealed that increased expression of ADCY2 was significantly correlated with increased tumor heterogeneity, predicting worse survival and immunotherapy response in BCa patients.

Methods in DNA methylation array dataset analysis: A review

“…Subsequently, hub genes are retrieved by predicting protein-protein interactions (PPI network) among these identified genes. For instance, the identification of the aberrantly methylated differentially expressed genes was done by comparing the raw data grouped as tumor and normal samples of Oesophageal squamous cell carcinoma and bladder cancer, using GEO2R [90] , [91] . Also, Cheng et al, 2022 used the DMRcate algorithm offered by the ChAMP pipeline to process 19 carotid atherosclerotic and 15 control aortic tissue samples for screening differentially methylated genes [92] .…”

“…GO and KEGG pathway analyses aid in identifying signature disease-related genes. GO annotations classify enriched pathways into cellular components, biological processes, and molecular function categories, while KEGG analysis uncovers relevant molecular and metabolic pathways and interacting networks in the context of the disease [87] , [90] , [91] , [92] , [94] , [97] , [164] . To annotate genes closely associated with methylated sites, the study utilized the GRCh38 annotation file from the GENCODE project [86] .…”

Targeted therapies in bladder cancer: signaling pathways, applications, and challenges

“…Recently, many novel gene biomarkers including parathyroid hormone-like hormone, insulin-like growth factor binding protein 7 (IGFBP7), immunogenic cell death, filaggrin, and Forkhead box Q1 (FOXQ1) affecting BC immunotherapy have been identified and validated using bioinformatic analyses of public databases. [74][75][76][77][78] Based on molecular characteristics, patients with MIBC are classified into the following five subtypes: luminal papillary, luminal infiltrated, luminal, basal squamous, and neuronal. 79 Several recent trials [80][81][82] have assessed the relationship between molecular subtypes and responses to ICIs and obtained inconsistent results.…”

“…Gene signatures are another well‐known biomarker for determining drug sensitivity. Recently, many novel gene biomarkers including parathyroid hormone‐like hormone, insulin‐like growth factor binding protein 7 (IGFBP7), immunogenic cell death, filaggrin, and Forkhead box Q1 ( FOXQ1 ) affecting BC immunotherapy have been identified and validated using bioinformatic analyses of public databases 74–78 . Based on molecular characteristics, patients with MIBC are classified into the following five subtypes: luminal papillary, luminal infiltrated, luminal, basal squamous, and neuronal 79 .…”