Department of Breast Surgery, Thyroid Surgery, Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University, Huangshi, Hubei, China.
Department of Pathology, Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University, Huangshi, Hubei, China.
PLoS One. 2021 Jul 12;16(7):e0254368. doi: 10.1371/journal.pone.0254368. eCollection 2021.
Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC).
Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established.
An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients.
The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.
铁死亡是一种新的细胞死亡形式，在肿瘤发生中起着关键作用。本研究旨在建立一个与铁死亡相关的基因（FRG）特征，并评估其在胃癌（GC）中的临床预后。
使用来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的基因表达谱，鉴定差异表达的 FRG。采用单因素和最小绝对值收缩和选择算子（LASSO）Cox 回归分析构建预后模型。使用独立的 GEO 数据集验证模型，并建立整合风险评分和临床病理特征的基因组-临床病理列线图。
构建了一个 8-FRG signature，以计算风险评分，并根据风险评分的中位数将 GC 患者分为两个风险组（高风险和低风险）。该signature 在分层分析中显示出强大的预测能力。高风险评分与晚期临床病理特征和不良预后相关。该 signature 的预测准确性在独立的 GSE84437 数据集上得到了验证。两组患者的免疫细胞和免疫相关通路存在不同的富集。最后，我们建立了一个基于风险评分、年龄和肿瘤分期的基因组-临床病理列线图（nomogram），以预测 GC 患者的总生存期（OS）。
该新的 FRG signature 可能是一种可靠的工具，有助于临床医生预测 GC 患者的 OS，并可能促进个性化治疗。
