子宫体子宫内膜癌与雌激素相关的可靠预后特征

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2024-09-16 DOI:10.1016/j.compbiolchem.2024.108216

Mojuan Li , Shuai Wang , Hao Huang , Li Li

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引用次数: 0

摘要

背景子宫内膜癌（UCEC）是全球最主要的妇科恶性肿瘤。众所周知，过量雌激素暴露是 UCEC 患者的一个重要风险因素。方法UCEC的scRNA-seq数据、大量RNA数据和ERGs分别来自GEO、TCGA和分子特征数据库。通过差异表达分析和交叉分析确定候选基因，并经过单变量 Cox 回归分析、LASSO Cox 回归分析和多变量 Cox 回归分析得出风险评分的最佳基因。GO、KEGG和GSVA富集分析揭示了功能信息。结果对差异表达基因和8734个ERGs进行交叉分析后，在UCEC中发现了86个差异表达的ERGs，它们在一些免疫相关通路和微生物群相关通路中显著富集。其中，GAL、PHGDH、SLC7A2、HNMT、CLU、AREG、MACC1和HMGA1等8个ERG被用于建立预后风险评分。该风险评分能可靠地预测患者的预后，高风险患者的预后较差。结论基于 8 种 ERGs 的预测性风险评分对 UCEC 患者的预后有很好的预测价值，高危患者的生存率较低。不同预后的UCEC患者表现出不同的肿瘤免疫微环境。

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Reliable estrogen-related prognostic signature for uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a predominant gynecological malignancy worldwide. Overdosed estrogen exposure has been widely known as a crucial risk factor for UCEC patients. The purpose of this work is to explore crucial estrogen-related genes (ERGs) in UCEC.

Methods

UCEC scRNA-seq data, bulk RNA data, and ERGs were obtained from GEO, TCGA, and Molecular Signature Database, respectively. Differential expression analysis and cross analysis determined the candidate genes, and optimal genes in risk score were obtained after univariate Cox regression analysis, LASSO Cox regression analysis, and multivariate Cox regression analysis. The functional information was revealed by GO, KEGG, and GSVA enrichment analyses. CCK8 assay was used to detect the drug sensitivity.

Results

After cross analysis of the differentially expressed genes and the 8734 ERGs, 86 differentially expressed ERGs were identified in UCEC, which were significantly enriched in some immune related pathways and microbiota related pathways. Of them, the most optimal 8 ERGs were obtained to build prognostic risk score, including GAL, PHGDH, SLC7A2, HNMT, CLU, AREG, MACC1, and HMGA1. The risk score could reliably predict patient prognosis, and high-risk patients had worse prognosis. Higher HMGA1 gene expression exhibited higher sensitivity to Osimertinib.

Conclusions

Predictive risk score based on 8 ERGs exhibited excellent prognostic value in UCEC patients, and high-risk patients had inferior survival. UCEC patients with distinct prognoses showed different tumor immune microenvironment.

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.