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

IF 3.2 4区医学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Journal of Gene Medicine Pub Date : 2024-01-11 DOI:10.1002/jgm.3651

Shen Jiang, Xiping Yang, Yang Lin, Yunfei Liu, Lisa Jia Tran, Jing Zhang, Chengjun Qiu, Fangdie Ye, Zhou Sun

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Abstract

Background

Bladder 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.

Methods

We 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).

Results

The 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.

Conclusions

In 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.

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揭示 Anoikis 相关基因：膀胱癌预后的突破性进展

背景膀胱癌（BLCA）是一种全球流行的恶性肿瘤。Anoikis仍然是一种新的细胞死亡形式。有必要研究与 Anoikis 相关的基因对 BLCA 预后的影响。方法我们从癌症基因组图谱（The Cancer Genome Atlas，TCGA）和基因表达总库（Gene Expression Omnibus）数据库中获取 RNA 表达谱进行降维分析，并分离上皮细胞、T 细胞和成纤维细胞进行拷贝数变异分析、伪时间分析和基于 R 软件包的转录因子分析。我们整合了机器学习算法，开发了人工智能衍生预后特征（AIDPS）。结果 AIDPS与临床指标相结合，在预测BLCA方面表现稳定、稳健，与其他模型相比，AIDPS在每个验证数据集中都表现出更好的性能。进行了孟德尔随机分析。rs3100578（HK2）和rs66467677（HSP90B1）的单核苷酸多态性（SNP）位点与膀胱问题（非癌症）和膀胱癌有显著相关性，而rs3100578（HK2）和rs947939（BAD）的SNP位点与膀胱结石和膀胱癌有相关性。TCGA-BLCA队列的免疫浸润分析是通过ESTIMATE（即使用表达数据估算恶性肿瘤中的基质和免疫细胞）算法计算得出的，该算法包含基质、免疫和估算分数。我们还发现，硼替佐米_1191、多西他赛_1007、Staurosporine_1034 和雷帕霉素_1084 的 IC50 值在高风险组和低风险组之间存在明显差异。结论总之，这些研究结果表明了 BLCA 中与 Anoikis 相关的预后基因，并构建了一个创新的 AIDPS 机器学习模型，该模型对 BLCA 具有较高的预后价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Gene Medicine 医学-生物工程与应用微生物

CiteScore

6.40

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： The aims and scope of The Journal of Gene Medicine include cutting-edge science of gene transfer and its applications in gene and cell therapy, genome editing with precision nucleases, epigenetic modifications of host genome by small molecules, siRNA, microRNA and other noncoding RNAs as therapeutic gene-modulating agents or targets, biomarkers for precision medicine, and gene-based prognostic/diagnostic studies. Key areas of interest are the design of novel synthetic and viral vectors, novel therapeutic nucleic acids such as mRNA, modified microRNAs and siRNAs, antagomirs, aptamers, antisense and exon-skipping agents, refined genome editing tools using nucleic acid /protein combinations, physically or biologically targeted delivery and gene modulation, ex vivo or in vivo pharmacological studies including animal models, and human clinical trials. Papers presenting research into the mechanisms underlying transfer and action of gene medicines, the application of the new technologies for stem cell modification or nucleic acid based vaccines, the identification of new genetic or epigenetic variations as biomarkers to direct precision medicine, and the preclinical/clinical development of gene/expression signatures indicative of diagnosis or predictive of prognosis are also encouraged.