Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer

被引：15

作者：

Jiang, Shen ^{[1
,2
]}

Yang, Xiping ^{[1
]}

Lin, Yang ^{[1
]}

Liu, Yunfei ^{[3
]}

Tran, Lisa Jia ^{[3
]}

Zhang, Jing ^{[4
]}

Qiu, Chengjun ^{[5
]}

Ye, Fangdie ^{[6
]}

Sun, Zhou ^{[2
,5
]}

机构：

[1] Jilin Canc Hosp, Changchun, Jilin, Peoples R China

[2] Jilin Univ, Dept Urol, China Japan Union Hosp, Changchun, Jilin, Peoples R China

[3] Ludwig Maximilians Univ Munchen, Dept Gen Visceral & Transplant Surg, Munich, Germany

[4] Univ South Dakota, Sanford Sch Med, Div Basic Biomed Sci, Vermillion, SD USA

[5] First Peoples Hosp Jiangxia Dist, Dept Urol, Wuhan 430200, Hubei, Peoples R China

[6] Fudan Univ, Huashan Hosp, Dept Urol, Shanghai 200000, Peoples R China

来源：

JOURNAL OF GENE MEDICINE | 2024年 / 26卷 / 01期

关键词：

AIDPS; Anoikis; bladder cancer; machine learning; Mendelian randomization; PATHWAY; HALLMARK; BMP6;

D O I：

10.1002/jgm.3651

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

BackgroundBladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.MethodsWe obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).ResultsThe performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.ConclusionsIn conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA. Artificial intelligence-derived prognostic signature (AIDPS) was constructed based on Anoikis-related genes with significant prognostic value for bladder cancer, providing new research directions for treating bladder cancer in the future.image

引用

页数：18

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