Clinical value of anoikis-related genes and molecular subtypes identification in bladder urothelial carcinoma and in vitro validation

Ying Dong; Chaojie Xu; Ganglin Su; Yanfeng Li; Bing Yan; Yuhan Liu; Tao Yin; Shuanzhu Mou; Hongbing Mei

doi:10.3389/fimmu.2023.1122570

Clinical value of anoikis-related genes and molecular subtypes identification in bladder urothelial carcinoma and in vitro validation

Front Immunol. 2023 May 18:14:1122570. doi: 10.3389/fimmu.2023.1122570. eCollection 2023.

Authors

Ying Dong^{1

2

3}, Chaojie Xu⁴, Ganglin Su⁵, Yanfeng Li^{1

2

3}, Bing Yan^{1

2

3}, Yuhan Liu^{1

2

3}, Tao Yin^{1

2

3}, Shuanzhu Mou^{1

2

3}, Hongbing Mei^{1

2

3}

Affiliations

¹ Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen University, Shenzhen, China.
² Key Laboratory of Medical Reprogramming Technology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.
³ Guangdong Key Laboratory of Systems Biology and Synthetic Biology for Urogenital Tumors, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.
⁴ Department of Urology, Peking University First Hospital, Institution of Urology, Peking University, Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, National Urological Cancer Center, Beijing, China.
⁵ Department of Urology, Peking University First Hospital, Beijing, China.

Abstract

Background: Anoikis is a programmed cell death process that was proven to be associated with cancer. Uroepithelial carcinoma of the bladder (BLCA) is a malignant disease of the urinary tract and has a strong metastatic potential. To determine whether anoikis-associated genes can predict the prognosis of BLCA accurately, we evaluated the prognostic value of anoikis-associated genes in BLCA and constructed the best model to predict prognosis.

Method: The BLCA transcriptome data were downloaded from TCGA and GEO databases, and genes with differential expression were selected and then clustered using non-negative matrix factorization (NMF). The genes with the most correlation with anoikis were screened and identified using univariate Cox regression, lasso regression, and multivariate Cox regression. The GEO dataset was used for external validation. Nomograms were created based on risk characteristics in combination with clinical variants and the performance of the model was validated with receiver operating characteristic (ROC) curves. The immunotherapeutic significance of this risk score was assessed using the immune phenomenon score (IPS). IC50 values of predictive chemotherapeutic agents were calculated. Finally, we used RT-qPCR to determine the mRNA expression of four genes, CALR, FASN, CASP6, and RAD9A.

Result: We screened 406 tumor samples and 19 normal tissue samples from the TCGA database. Based on anoikis-associated genes, we classified patients into two subtypes (C1 and C2) using NMF method. Subsequently, nine core genes were screened by multiple methods after analysis, which were used to construct risk profiles. The design of nomograms based on risk profiles and clinical variables, ROC, and calibration curves confirmed that the model could well have the ability to predict the survival of BLCA patients at 1, 3, and 5 years. By predicting the IC50 values of chemotherapeutic drugs, it was learned that the high-risk group (HRG) was more susceptible to paclitaxel, gemcitabine, and cisplatin, and the low-risk group (LRG) was more susceptible to veriparib and afatinib.

Conclusion: In summary, the risk score of anoikis-associated genes can be applied as a predictor to predict the prognosis of BLCA in clinical practice.

Keywords: anoikis; bladder urothelial carcinoma; immunotherapy; risk score; tumor microenvironment.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Anoikis / genetics
Carcinoma, Transitional Cell* / genetics
Genes, cdc
Humans
Urinary Bladder
Urinary Bladder Neoplasms* / genetics

Grants and funding

This work was supported by The National Science Foundation Projects of Guangdong Province, China (No. 2022A1515011353), the Shenzhen "Medical and Health Sanming Project" (No. SZSM20211100), the Shenzhen Medical Key Discipline Construction Funds (No. SZXK020), and the Shenzhen High-Level Hospital Construction Fund.