Investigating combined hypoxia and stemness indices for prognostic transcripts in gastric cancer: Machine learning and network analysis approaches

IF 2.3 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY

Biochemistry and Biophysics Reports Pub Date : 2024-12-19 DOI:10.1016/j.bbrep.2024.101897

Sharareh Mahmoudian-Hamedani , Maryam Lotfi-Shahreza , Parvaneh Nikpour

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引用次数: 0

Abstract

Introduction

Gastric cancer (GC) is among the deadliest malignancies globally, characterized by hypoxia-driven pathways that promote cancer progression, including stemness mechanisms facilitating invasion and metastasis. This study aimed to develop a prognostic decision tree using genes implicated in hypoxia and stemness pathways to predict outcomes in GC patients.

Materials and methods

GC RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to compute hypoxia and stemness scores using Gene Set Variation Analysis (GSVA) and the mRNA expression-based stemness index (mRNAsi). Hierarchical clustering identified clusters with distinct survival outcomes, and differentially expressed genes (DEGs) between clusters were identified. Weighted Gene Co-expression Network Analysis (WGCNA) identified modules and hub genes associated with clinical traits. Overlapping DEGs and hub genes underwent functional enrichment, protein-protein interaction (PPI) network analysis, and survival analysis. A prognostic decision tree was constructed using survival-associated shared genes.

Results

Hierarchical clustering identified six clusters among 375 TCGA GC patients, with significant survival differences between cluster 1 (low hypoxia, high stemness) and cluster 4 (high hypoxia, high stemness). Validation in the GSE62254 dataset corroborated these findings. WGCNA revealed modules linked to clinical traits and survival, with functional enrichment highlighting pathways like cell adhesion and calcium signaling. The decision tree, based on genes such as AKAP6, GLRB, and RUNX1T1, achieved an AUC of 0.81 (training) and 0.67 (test), demonstrating the utility of combined scores in patient stratification.

Conclusion

This study introduces a novel hypoxia-stemness-based prognostic decision tree for GC. The identified genes show promise as prognostic biomarkers, warranting further clinical validation.

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研究胃癌预后转录物的缺氧和干性联合指标：机器学习和网络分析方法。

胃癌（GC）是全球最致命的恶性肿瘤之一，其特点是缺氧驱动的途径促进癌症进展，包括促进侵袭和转移的干细胞机制。本研究旨在利用与缺氧和干性通路相关的基因建立预后决策树来预测胃癌患者的预后。材料和方法：分析来自癌症基因组图谱（TCGA）的GC RNA-seq数据，使用基因集变异分析（GSVA）和基于mRNA表达的干性指数（mRNAsi）计算缺氧和干性评分。分层聚类鉴定了具有不同生存结果的集群，并鉴定了集群之间的差异表达基因（DEGs）。加权基因共表达网络分析（WGCNA）确定了与临床特征相关的模块和中心基因。对重叠的DEGs和hub基因进行功能富集、蛋白相互作用（PPI）网络分析和生存分析。使用生存相关的共享基因构建预后决策树。结果：在375例TCGA GC患者中，分层聚类共鉴定出6个聚类，聚类1（低缺氧、高干度）与聚类4（高缺氧、高干度）患者的生存差异显著。GSE62254数据集的验证证实了这些发现。WGCNA揭示了与临床特征和生存相关的模块，功能富集突出了细胞粘附和钙信号传导等途径。基于AKAP6、GLRB和RUNX1T1等基因的决策树的AUC为0.81（训练）和0.67（测试），证明了综合评分在患者分层中的实用性。结论：本研究引入了一种新的基于缺氧的胃癌预后决策树。已鉴定的基因显示出作为预后生物标志物的希望，需要进一步的临床验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biochemistry and Biophysics Reports Biochemistry, Genetics and Molecular Biology-Biophysics

CiteScore

4.60

自引率

0.00%

发文量

191

审稿时长

59 days

期刊介绍： Open access, online only, peer-reviewed international journal in the Life Sciences, established in 2014 Biochemistry and Biophysics Reports (BB Reports) publishes original research in all aspects of Biochemistry, Biophysics and related areas like Molecular and Cell Biology. BB Reports welcomes solid though more preliminary, descriptive and small scale results if they have the potential to stimulate and/or contribute to future research, leading to new insights or hypothesis. Primary criteria for acceptance is that the work is original, scientifically and technically sound and provides valuable knowledge to life sciences research. We strongly believe all results deserve to be published and documented for the advancement of science. BB Reports specifically appreciates receiving reports on: Negative results, Replication studies, Reanalysis of previous datasets.