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基于溶酶体相关基因的卵巢癌预后模型的开发：对肿瘤微环境、突变模式和个性化治疗策略的见解

Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies.

作者信息

Sun Ran, Li Siyi, Ye Wanlu, Lu Yanming

机构信息

Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110022, China.

出版信息

Cancer Cell Int. 2024 Dec 19;24(1):419. doi: 10.1186/s12935-024-03586-w.

DOI:10.1186/s12935-024-03586-w
PMID:39702158
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11661007/
Abstract

BACKGROUND

Ovarian cancer (OC) is often associated with an unfavorable prognosis. Given the crucial involvement of lysosomes in tumor advancement, lysosome-related genes (LRGs) hold promise as potential therapeutic targets.

METHODS

To identify differentially expressed lysosome-related genes (DE-LRGs), we performed a matching analysis between differentially expressed genes (DEGs) in OC and the pool of LRGs. Genes with prognostic significance were analyzed using multiple regression analyses to construct a prognostic risk signature. The model's efficacy was validated through survival analysis in various cohorts. We further explored the model's correlation with clinical attributes, tumor microenvironment (TME), mutational patterns, and drug sensitivity. The quantitative real-time polymerase chain reaction (qRT-PCR) validated gene expression in OC cells.

RESULTS

A 10-gene prognostic risk signature was established. Survival analysis confirmed its predictive accuracy across cohorts. The signature served as an independent prognostic element for OC. The high-risk and low-risk groups demonstrated notable disparities in terms of immune infiltration patterns, mutational characteristics, and sensitivity to therapeutic agents. The qRT-PCR results corroborated and validated the findings obtained from the bioinformatic analyses.

CONCLUSIONS

We devised a 10-LRG prognostic model linked to TME, offering insights for tailored OC treatments.

摘要

背景

卵巢癌（OC）通常预后不良。鉴于溶酶体在肿瘤进展中的关键作用，溶酶体相关基因（LRGs）有望成为潜在的治疗靶点。

方法

为了鉴定差异表达的溶酶体相关基因（DE-LRGs），我们对OC中的差异表达基因（DEGs）与LRGs库进行了匹配分析。使用多元回归分析对具有预后意义的基因进行分析，以构建预后风险特征。通过在不同队列中的生存分析验证了该模型的有效性。我们进一步探讨了该模型与临床特征、肿瘤微环境（TME）、突变模式和药物敏感性的相关性。通过定量实时聚合酶链反应（qRT-PCR）验证了OC细胞中的基因表达。

结果

建立了一个包含10个基因的预后风险特征。生存分析证实了其在各队列中的预测准确性。该特征可作为OC的独立预后因素。高风险和低风险组在免疫浸润模式、突变特征和对治疗药物的敏感性方面表现出显著差异。qRT-PCR结果证实并验证了从生物信息学分析中获得的结果。

结论

我们设计了一个与TME相关的10-LRG预后模型，为OC的个性化治疗提供了见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/6f0e3677d615/12935_2024_3586_Fig12_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/6f0e3677d615/12935_2024_3586_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/b424f432cac0/12935_2024_3586_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/e81ff7314da0/12935_2024_3586_Fig3_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/df1f872ab70d/12935_2024_3586_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/66ce2217d91c/12935_2024_3586_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/537308fb130c/12935_2024_3586_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/9d6136e18327/12935_2024_3586_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/69f5c46dc7df/12935_2024_3586_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/598d27ef65e5/12935_2024_3586_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/044e40012455/12935_2024_3586_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5443/11661007/6f0e3677d615/12935_2024_3586_Fig12_HTML.jpg

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