Construction and Validation of a Prognostic Model Based on Pyroptosisrelated Genes in Bladder Cancer

Chong Shen; Chenyang Han; Zhi Li; Yan Yan; Chenyun Li; Houyuan Chen; Zhenqian Fan; Hailong Hu

doi:10.2174/0113862073256363230929200157

Construction and Validation of a Prognostic Model Based on Pyroptosisrelated Genes in Bladder Cancer

Comb Chem High Throughput Screen. 2023 Oct 17. doi: 10.2174/0113862073256363230929200157. Online ahead of print.

Authors

Chong Shen¹, Chenyang Han², Zhi Li^{3

1}, Yan Yan⁴, Chenyun Li^{3

1}, Houyuan Chen^{3

1}, Zhenqian Fan⁵, Hailong Hu^{3

1}

Affiliations

¹ Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, P.R. China.
² Department of Pain Therapeutic Centre, The Second Hospital of Tianjin Medical University, Tianjin, P.R. China.
³ Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, P.R. China.
⁴ Department of Vascular Surgery, University Hospital Aachen, Pauwelsstr 30, Aachen 52074, Germany.
⁵ Department of Endocrinology, The Second Hospital of Tianjin Medical University, Tianjin, China.

PMID: 37849225
DOI: 10.2174/0113862073256363230929200157

Abstract

Background: Bladder cancer (BCa) is a highly prevalent disease with a poor prognosis. There is no better forecasting method for it yet. Current studies demonstrate that pyroptosis is involved in the development and progression of various cancers.

Methods: This study employed bioinformatics techniques to analyze the data of BCa patients obtained from the TCGA and GEO databases in order to construct a prognostic risk model. The TCGA dataset was used for the training set, and the multiple external datasets (including GSE13507, GSE31684, GSE48075, IMvigor210, and GSE32894) were applied as the validation sets. Prognostic-associated pyroptosis genes screened by univariate Cox regression analysis were utilized to construct the lasso Cox regression model. GO and KEGG analysis results identified the selected genes that are primarily involved in the inflammation and cell death processes. The related patients were grouped into low- and high-risk groups. Kaplan-Meier survival analysis was performed to compare survival differences between the risk groups. The accuracy of this risk prediction model was assessed by ROC. We also applied the Human Protein Atlas (HPA) to detect the protein expression of these genes. Subsequently, qRT-PCR was performed to verify the expression of these model genes.

Results: There are 29 pyroptosis-related genes with significant expression differences between BCa and corresponding adjacent tissues, and 11 genes (SH2D2A, CHMP4C, MRFAP1L1, GBP2, EHBP1, RAD9A, ANXA1, TMEM109, HEYL, APOL2, ORMDL1) were picked by univariate and LASSO Cox regression analysis. Immunological cell infiltration and ssGSEA results further indicated that the low and high-risk groups were substantially correlated with the immune status of BCa patients. According to TCGA and multiple external datasets, Kaplan-Meier survival curves showed the overall survival rate of the high-risk group to be decreased. ROC curves showed the model established to be accurate and reliable. Moreover, the HPA database also demonstrated the verification of the modeled genes' expression in BCa and normal bladder tissue using the HPA database. qRT-PCR results also suggested the up-regulated EHBP1 and down-regulated RAD9A mRNA expression levels to be confirmed in 15 pairs of BCa and corresponding adjacent tissues.

Conclusion: This study presents the development and validation of a novel gene signature associated with pyroptosis, which holds the potential for predicting patient outcomes in BCa and providing insights into the immune microenvironment of BCa.

Keywords: Immune infiltration; bladder cancer; genetic characteristics; prognosis; pyroptosis; risk score.