https://orcid.org/0000-0002-9155-6615
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ABSTRACT
In recent years, genes associated with N6-methyladenosine (m6A) modification were found to participate in modulation of multiple tumor biological processes. Concomitantly, the significantly complicated dual effects of tumor microenvironment have been observed on cancer progression. The present study aims to investigate m6A-related immune genes (m6AIGs) for their signatures and prognostic values in bladder cancer (BC). Out of 2856 differentially expressed genes (DEGs) of BC, a total of 85 genes were obtained following intersection of DEGs, immune genes and m6A-related genes. The results of multivariate Cox regression analysis illustrated four genes (BGN, GRK5, IL32, and SREBF1) were significantly associated with the prognosis of BC patients. The BC samples were divided into two types based on the consensus clustering, and the principal component analysis demonstrated a separation between them. It was found that high expression of BGN and GRK5 were linked with advanced T and N stage, and the expression of SREBF1 in early T stage was higher than that in advanced T stage. Subsequently, the nomogram to predict 3- and 5-year survival probability of BC patients was developed and calibrated. GSEA analysis for risk subgroups showed WNT and TGF-beta signaling pathways were involved in regulation of BC progression in high risk level group. In the low risk level group, cytosolic DNA-Sensing cGAS-STING and RIG-I-like receptors signaling pathways were found to be correlated with BC development. These findings provide a novel insight on studies for BC progression.
Graphical abstract
Acknowledgements
We greatly appreciate the TCGA program, GEO database, the RMVar database and the InnateDB database for providing the open-source data and the Tumor IMmune Estimation Resource for online analysis.
Abbreviation
BC: bladder cancer; m6A: N6-methyladenosine; m6AIGs: m6A-related immune genes; TCGA: The Cancer Genome Atlas; DEGs: differentially expressed genes; PCA: principal component analysis; TME: tumor microenvironment; TIICs: tumor infiltrating immune cells; LASSO: Least Absolute Shrinkage and Selection Operator; KM: Kaplan–Meier; C-index: the concordance index; ROC: receiver operating characteristic; GSEA: Gene Set Enrichment Analysis.
Disclosure of potential conflicts of interest
The authors have declared that no competing interest exists.
Author Contributions
Conception and design: GL and GTL. Data collection: GTL, JWZ and YQW. Data analysis and interpretation: GTL, JWZ and YQW. Manuscript writing: GTL and JWZ. Funding: SMZ. Final approval of manuscript: All authors.
Data availability statement
The data used and analyzed during the present study are available from TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31684), the RMVar (http://rmvar.renlab.org/index.html) and the InnateDB (https://www.innatedb.com/), the Tumor IMmune Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer/).
Ethics approval and consent to participate
The study protocols were approved by the Ethical Committee Review Board of the Second Hospital of Tianjin Medical University (Tianjin, China). All participants provided written informed consent.
Supplementary material
Supplemental data for this article can be accessed here.