Hepatocellular carcinoma (HCC) is one of the most general malignant tumors. Ferroptosis, a type of necrotic cell death that is oxidative and iron-dependent, has a strong correlation with the development of tumors and the progression of cancer. The present study was designed to identify potential diagnostic Ferroptosis-related genes (FRGs) using machine learning. From GEO datasets, two publicly available gene expression profiles (GSE65372 and GSE84402) from HCC and nontumor tissues were retrieved. The GSE65372 database was used to screen for FRGs with differential expression between HCC cases and nontumor specimens. Following this, a pathway enrichment analysis of FRGs was carried out. In order to locate potential biomarkers, an analysis using the support vector machine recursive feature elimination (SVM-RFE) model and the LASSO regression model were carried out. The levels of the novel biomarkers were validated further using data from the GSE84402 dataset and the TCGA datasets. In this study, 40 of 237 FRGs exhibited a dysregulated level between HCC specimens and nontumor specimens from GSE65372, including 27 increased and 13 decreased genes. The results of KEGG assays indicated that the 40 differential expressed FRGs were mainly enriched in the longevity regulating pathway, AMPK signaling pathway, the mTOR signaling pathway, and hepatocellular carcinoma. Subsequently, HSPB1, CDKN2A, LPIN1, MTDH, DCAF7, TRIM26, PIR, BCAT2, EZH2, and ADAMTS13 were identified as potential diagnostic biomarkers. ROC assays confirmed the diagnostic value of the new model. The expression of some FRGs among 11 FRGs was further confirmed by the GSE84402 dataset and TCGA datasets. Overall, our findings provided a novel diagnostic model using FRGs. Prior to its application in a clinical context, there is a need for additional research to evaluate the diagnostic value for HCC.

中文翻译：

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使用机器和深度学习方法构建基于铁死亡相关基因的肝细胞癌新型诊断模型

肝细胞癌（HCC）是最常见的恶性肿瘤之一。铁死亡是一种氧化和铁依赖性坏死性细胞死亡，与肿瘤的发展和癌症的进展有很强的相关性。本研究旨在使用机器学习识别潜在的诊断性铁死亡相关基因 (FRG)。从 GEO 数据集中，检索了来自 HCC 和非肿瘤组织的两个公开可用的基因表达谱（GSE65372 和 GSE84402）。GSE65372 数据库用于筛选在 HCC 病例和非肿瘤标本之间具有差异表达的 FRG。在此之后，对 FRG 进行了通路富集分析。为了找到潜在的生物标志物，使用支持向量机递归特征消除 (SVM-RFE) 模型和 LASSO 回归模型进行了分析。使用来自 GSE84402 数据集和 TCGA 数据集的数据进一步验证了新型生物标志物的水平。在这项研究中，237 个 FRG 中的 40 个在来自 GSE65372 的 HCC 标本和非肿瘤标本之间表现出失调水平，包括 27 个增加的基因和 13 个减少的基因。KEGG检测结果表明，40个差异表达的FRGs主要富集在长寿调节通路、AMPK信号通路、mTOR信号通路和肝细胞癌中。随后，HSPB1、CDKN2A、LPIN1、MTDH、DCAF7、TRIM26、PIR、BCAT2、EZH2 和 ADAMTS13 被确定为潜在的诊断生物标志物。ROC 分析证实了新模型的诊断价值。GSE84402 数据集和 TCGA 数据集进一步证实了 11 个 FRG 中部分 FRG 的表达。总体而言，我们的研究结果提供了一种使用 FRG 的新型诊断模型。在将其应用于临床之前，需要进行额外的研究来评估 HCC 的诊断价值。