Identification of angiogenesis-related subtypes and risk models for predicting the prognosis of gastric cancer patients

IF 2.6 4区 生物学 Q2 BIOLOGY
Jie Luo , Mengyun Liang , Tengfei Ma , Bizhen Dong , Liping Jia , Meifang Su
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Abstract

Gastric cancer (GC) is a leading cause of cancer-related mortality and is characterized by significant heterogeneity, highlighting the need for further studies aimed at personalized treatment strategies. Tumor angiogenesis is critical for tumor development and metastasis, yet its role in molecular subtyping and prognosis prediction remains underexplored. This study aims to identify angiogenesis-related subtypes and develop a prognostic model for GC patients. Using data from The Cancer Genome Atlas (TCGA), we performed consensus cluster analysis on differentially expressed angiogenesis-related genes (ARGs), identifying two patient subtypes with distinct survival outcomes. Differentially expressed genes between the subtypes were analyzed via Cox and LASSO regression, leading to the establishment of a subtype-based prognostic model using a machine learning algorithm. Patients were classified into high- and low-risk groups based on the risk score. Validation was performed using independent datasets (ICGC and GSE15459). We utilized a deconvolution algorithm to investigate the tumor immune microenvironment in different risk groups and conducted analyses on genetic profiling, sensitivity and combination of anti-tumor drug. Our study identified ten prognostic signature genes, enabling the calculation of a risk score to predict prognosis and overall survival. This provides critical data for stratified diagnosis and treatment upon patient admission, monitoring disease progression throughout the entire course, evaluating immunotherapy efficacy, and selecting personalized medications for GC patients.

确定血管生成相关亚型和风险模型以预测胃癌患者的预后
胃癌(GC)是导致癌症相关死亡的主要原因之一,其特点是异质性明显,这就突出了进一步研究个性化治疗策略的必要性。肿瘤血管生成对肿瘤的发展和转移至关重要,但其在分子亚型和预后预测中的作用仍未得到充分探索。本研究旨在确定血管生成相关亚型,并为GC患者建立预后模型。利用癌症基因组图谱(TCGA)的数据,我们对差异表达的血管生成相关基因(ARGs)进行了共识聚类分析,确定了两种具有不同生存结果的患者亚型。我们通过 Cox 和 LASSO 回归分析了亚型之间的差异表达基因,并利用机器学习算法建立了基于亚型的预后模型。根据风险评分将患者分为高风险组和低风险组。利用独立数据集(ICGC 和 GSE15459)进行了验证。我们利用去卷积算法研究了不同风险组的肿瘤免疫微环境,并对基因图谱、抗肿瘤药物的敏感性和联合用药进行了分析。我们的研究确定了十个预后特征基因,从而计算出预测预后和总生存期的风险评分。这为患者入院后的分层诊断和治疗、全程监测疾病进展、评估免疫疗法疗效以及为 GC 患者选择个性化药物提供了重要数据。
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来源期刊
Computational Biology and Chemistry
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.
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