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基于膀胱癌质膜张力特征的预后及治疗结果模型的构建与验证

Construction and validation of prognosis and treatment outcome models based on plasma membrane tension characteristics in bladder cancer.

作者信息

Wang Zhipeng, Li Sheng, Zheng Fuchun, Xiong Situ, Zhang Lei, Wan Liangwei, Wang Chen, Liu Xiaoqiang, Deng Jun

机构信息

Department of Urology, First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.

Jiangxi Institute of Urology, Nanchang, Jiangxi, China.

出版信息

PeerJ. 2025 Jan 6;13:e18816. doi: 10.7717/peerj.18816. eCollection 2025.

DOI:10.7717/peerj.18816
PMID:39790460
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11716045/
Abstract

BACKGROUND

Plasma membrane tension-related genes (MTRGs) are known to play a crucial role in tumor progression by influencing cell migration and adhesion. However, their specific mechanisms in bladder cancer (BLCA) remain unclear.

METHODS

Transcriptomic, clinical and mutation data from BLCA patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Clusters associated with MTRGs were identified by consensus unsupervised cluster analysis. The genes of different clusters were analyzed by GO and KEGG gene enrichment analysis. Differentially expressed genes (DEGs) were screened from different clusters. Consensus cluster analysis of prognostic DEGs was performed to identify gene subtypes. Patients were then randomly divided into training and validation groups, and MTRG scores were constructed by logistic minimum absolute contraction and selection operator (LASSO) and Cox regression analysis. We assessed changes in clinical outcomes and immune-related factors between different patient groups. The single-cell RNA sequencing (scRNA-seq) dataset for BLCA was collected and analyzed from the Tumor Immune Single-cell Hub (TISCH) database. Biological functions were investigated using a series of experiments including quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), wound healing, transwell, .

RESULTS

Our MTRG score is based on eight genes (HTRA1, GOLT1A, DCBLD2, UGT1A1, FOSL1, DSC2, IGFBP3 and TAC3). Higher scores were characterized by lower cancer stem cell (CSC) indices, as well as higher tumor microenvironment (TME) stromal and immune scores, suggesting that high scores were associated with poorer prognosis. In addition, some drugs such as cisplatin, paclitaxel, doxorubicin, and docetaxel exhibited lower IC50 values in the high MTRG score group. Functional experiments have demonstrated that downregulation of DCBLD2 affects tumor cell migration, but not proliferation.

CONCLUSIONS

Our study sheds light on the prognostic significance of MTRGs within the TME and their correlation with immune infiltration patterns, ultimately impacting patient survival in BLCA. Notably, our findings highlight DCBLD2 as a promising candidate for targeted therapeutic interventions in the clinical management of BLCA.

摘要

背景

已知质膜张力相关基因(MTRGs)通过影响细胞迁移和黏附在肿瘤进展中起关键作用。然而,它们在膀胱癌(BLCA)中的具体机制仍不清楚。

方法

从癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)收集BLCA患者的转录组、临床和突变数据。通过一致性无监督聚类分析确定与MTRGs相关的簇。通过GO和KEGG基因富集分析对不同簇的基因进行分析。从不同簇中筛选差异表达基因(DEGs)。对预后DEGs进行一致性聚类分析以鉴定基因亚型。然后将患者随机分为训练组和验证组,并通过逻辑最小绝对收缩和选择算子(LASSO)和Cox回归分析构建MTRG评分。我们评估了不同患者组之间临床结局和免疫相关因素的变化。从肿瘤免疫单细胞中心(TISCH)数据库收集并分析了BLCA的单细胞RNA测序(scRNA-seq)数据集。使用包括定量逆转录聚合酶链反应(qRT-PCR)、伤口愈合、Transwell等一系列实验研究生物学功能。

结果

我们的MTRG评分基于八个基因(HTRA1、GOLT1A、DCBLD2、UGT1A1、FOSL1、DSC2、IGFBP3和TAC3)。较高的评分特征是较低的癌症干细胞(CSC)指数,以及较高的肿瘤微环境(TME)基质和免疫评分,这表明高分与较差的预后相关。此外,一些药物如顺铂、紫杉醇、阿霉素和多西他赛在高MTRG评分组中表现出较低的IC50值。功能实验表明,DCBLD2的下调影响肿瘤细胞迁移,但不影响增殖。

结论

我们的研究揭示了TME内MTRGs的预后意义及其与免疫浸润模式的相关性,最终影响BLCA患者的生存。值得注意的是,我们的发现突出了DCBLD2作为BLCA临床管理中靶向治疗干预的有前景的候选者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/19e00bec73aa/peerj-13-18816-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/de7669026753/peerj-13-18816-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/2534cde3a303/peerj-13-18816-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/cbe0442ac35b/peerj-13-18816-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/19e00bec73aa/peerj-13-18816-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/de7669026753/peerj-13-18816-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/3575d22847ea/peerj-13-18816-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/aaf0d5f76f50/peerj-13-18816-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/20231352b411/peerj-13-18816-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/cbe0442ac35b/peerj-13-18816-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daaf/11716045/19e00bec73aa/peerj-13-18816-g007.jpg

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