Construction and validation of a prognostic signature using WGCNA-identified key genes in osteosarcoma for treatment evaluation.

IF 1.5 4区医学 Q4 ONCOLOGY

Translational cancer research Pub Date : 2025-01-31 Epub Date: 2025-01-23 DOI:10.21037/tcr-24-1398

Zhuo Chen, Renhua Ni, Yuanyu Hu, Yiyuan Yang, Jiawen Chen, Yun Tian

{"title":"Construction and validation of a prognostic signature using WGCNA-identified key genes in osteosarcoma for treatment evaluation.","authors":"Zhuo Chen, Renhua Ni, Yuanyu Hu, Yiyuan Yang, Jiawen Chen, Yun Tian","doi":"10.21037/tcr-24-1398","DOIUrl":null,"url":null,"abstract":"Background: Osteosarcoma (OS) is an aggressive and fast-growing malignant tumor associated with high mortality. Early diagnosis and prompt treatment can markedly enhance prognosis and increase survival rates. Constructing prognostic models can effectively predict OS progression, assist in patient diagnosis, and provide personalized treatment plans. In this study, we identified OS-related prognostic genes using the weighted gene co-expression network analysis (WGCNA) method to construct and validate a robust prognostic model, providing guidance for patient risk assessment and clinical treatment.Methods: Clinical data for OS samples were collected from the Gene Expression Omnibus (GEO) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases. Statistical analyses, including enrichment analysis, cluster analysis, and model construction, were performed using the R programme.Results: The WGCNA method was used to identify genes which were important to OS development and progression, screening for those relevant to prognosis to build a reliable and widely applicable model. To enhance the model's applicability to diverse OS patient populations, we initially conducted a clustering analysis based on the identified prognostic-related key genes. We then identified differentially expressed genes (DEGs) between clusters and used these genes to subtype OS patients, assessing their ability to distinguish among different patient populations. Subsequently, we selected prognostic-related DEGs to establish the prognostic model, resulting in a risk scoring method utilizing the expression of creatine kinase, mitochondrial 2 (CKMT2) and cell growth regulator with EF-hand domain 1 (CGREF1). We validated the predictive capability of the constructed prognostic model, confirming its robust predictive performance. Finally, based on our prognostic model, we analyzed the immune infiltration and drug sensitivity of OS patients, aiding in evaluating responses to immunotherapy and optimizing treatment plans.Conclusions: A predictive model based on OS-related prognostic genes was constructed to accurately evaluate risk and guide treatment in OS patients, and CKMT2 and CGREF1 were identified as potential therapeutic targets.","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 1","pages":"254-271"},"PeriodicalIF":1.5000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833431/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tcr-24-1398","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/23 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Osteosarcoma (OS) is an aggressive and fast-growing malignant tumor associated with high mortality. Early diagnosis and prompt treatment can markedly enhance prognosis and increase survival rates. Constructing prognostic models can effectively predict OS progression, assist in patient diagnosis, and provide personalized treatment plans. In this study, we identified OS-related prognostic genes using the weighted gene co-expression network analysis (WGCNA) method to construct and validate a robust prognostic model, providing guidance for patient risk assessment and clinical treatment.

Methods: Clinical data for OS samples were collected from the Gene Expression Omnibus (GEO) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases. Statistical analyses, including enrichment analysis, cluster analysis, and model construction, were performed using the R programme.

Results: The WGCNA method was used to identify genes which were important to OS development and progression, screening for those relevant to prognosis to build a reliable and widely applicable model. To enhance the model's applicability to diverse OS patient populations, we initially conducted a clustering analysis based on the identified prognostic-related key genes. We then identified differentially expressed genes (DEGs) between clusters and used these genes to subtype OS patients, assessing their ability to distinguish among different patient populations. Subsequently, we selected prognostic-related DEGs to establish the prognostic model, resulting in a risk scoring method utilizing the expression of creatine kinase, mitochondrial 2 (CKMT2) and cell growth regulator with EF-hand domain 1 (CGREF1). We validated the predictive capability of the constructed prognostic model, confirming its robust predictive performance. Finally, based on our prognostic model, we analyzed the immune infiltration and drug sensitivity of OS patients, aiding in evaluating responses to immunotherapy and optimizing treatment plans.

Conclusions: A predictive model based on OS-related prognostic genes was constructed to accurately evaluate risk and guide treatment in OS patients, and CKMT2 and CGREF1 were identified as potential therapeutic targets.

查看原文本刊更多论文

使用wgna鉴定的骨肉瘤关键基因进行治疗评估的预后标记的构建和验证。

背景：骨肉瘤（Osteosarcoma， OS）是一种侵袭性、快速生长的恶性肿瘤，死亡率高。早期诊断和及时治疗可显著改善预后，提高生存率。构建预后模型可以有效预测OS的进展，协助患者诊断，提供个性化的治疗方案。在本研究中，我们使用加权基因共表达网络分析（WGCNA）方法识别os相关预后基因，构建并验证稳健的预后模型，为患者风险评估和临床治疗提供指导。方法：从Gene Expression Omnibus （GEO）和therapeutic applied Research to Generate Effective therapies （TARGET）数据库中收集OS样本的临床数据。使用R程序进行统计分析，包括富集分析、聚类分析和模型构建。结果：采用WGCNA方法鉴定与OS发生进展相关的基因，筛选与预后相关的基因，建立可靠且广泛适用的模型。为了增强模型对不同OS患者群体的适用性，我们首先基于已确定的预后相关关键基因进行了聚类分析。然后，我们鉴定了簇之间的差异表达基因（DEGs），并使用这些基因对OS患者进行亚型划分，评估其区分不同患者群体的能力。随后，我们选择预后相关的deg来建立预后模型，从而建立了一种利用肌酸激酶、线粒体2 （CKMT2）和EF-hand结构域1细胞生长调节剂（CGREF1）表达的风险评分方法。我们验证了所构建的预测模型的预测能力，证实了其稳健的预测性能。最后，基于我们的预后模型，我们分析了OS患者的免疫浸润和药物敏感性，以帮助评估免疫治疗的反应和优化治疗方案。结论：构建基于OS相关预后基因的预测模型，可准确评估OS患者的风险，指导治疗，CKMT2和CGREF1可作为潜在的治疗靶点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Translational cancer research ONCOLOGY-

CiteScore

2.10

自引率

0.00%

发文量

252

期刊介绍： Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.