Dai Yong, Zhang Huan, Feng Sujuan, Guo Chao, Tian Wenjie, Sun Yimei, Zhang Yi
Department of Neurosurgery, Affiliated Hospital 2 of Nantong University and First People's Hospital of Nantong City, No. 666 Shengli Road, Nantong 226001, China.
State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute & Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Heliyon. 2024 Feb 8;10(4):e25716. doi: 10.1016/j.heliyon.2024.e25716. eCollection 2024 Feb 29.
Glioma is the most frequent type of malignancy that may damage the brain with high morbidity and mortality rates and patients' prognoses are still dismal. Ferroptosis, a newly uncovered mode of programmed cell death, may be triggered to destroy glioma cells. Nevertheless, the significance of ferroptosis-related genes (FRGs) in predicting prognosis in glioma individuals is still a mystery.
The CGGA (The Chinese Glioma Atlas), GEO (Gene Expression Omnibus), and TCGA (The Cancer Genome Atlas) databases were all searched to obtain the glioma expression dataset. First, TCGA was searched to identify differentially expressed genes (DEGs). This was followed by a machine learning algorithm-based screening of the glioma's most relevant genes. Additionally, these genes were subjected to Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses. The chosen biological markers were then submitted to single-cell, immune function, and gene set enrichment analysis (GSEA). In addition, we performed functional enrichment and Mfuzz expression profile clustering on the most promising biological markers to delve deeper into their regulatory mechanisms and assess their clinical diagnostic capacities.
We identified 4444 DEGs via differential analysis and 564 FRGs from the FerrDb database. The two were subjected to intersection analysis, which led to the discovery of 143 overlapping genes. After that, glioma biological markers were identified in fourteen genes by the use of machine learning methods. In terms of its use for clinical diagnosis, SMG9 stands out as the most significant among these biomarkers.
In light of these findings, the identification of SMG9 as a new biological marker has the potential to provide information on the mechanism of action and the effect of the immune milieu in glioma. The promise of SMG9 in glioma prognosis prediction warrants more study.
胶质瘤是最常见的恶性肿瘤类型，可损害大脑，发病率和死亡率高，患者预后仍然不佳。铁死亡是一种新发现的程序性细胞死亡模式，可能被触发以破坏胶质瘤细胞。然而，铁死亡相关基因（FRGs）在预测胶质瘤患者预后中的意义仍是个谜。
检索中国胶质瘤图谱（CGGA）、基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库以获取胶质瘤表达数据集。首先，检索TCGA以鉴定差异表达基因（DEGs）。随后基于机器学习算法筛选胶质瘤最相关基因。此外，对这些基因进行基因本体（GO）和京都基因与基因组百科全书（KEGG）功能富集分析。然后将所选生物标志物进行单细胞、免疫功能和基因集富集分析（GSEA）。此外，我们对最有前景的生物标志物进行功能富集和Mfuzz表达谱聚类，以更深入地探究其调控机制并评估其临床诊断能力。
通过差异分析鉴定出4444个DEGs，从FerrDb数据库中筛选出564个FRGs。对两者进行交集分析，发现143个重叠基因。之后，通过机器学习方法在14个基因中鉴定出胶质瘤生物标志物。就其临床诊断用途而言，SMG9在这些生物标志物中最为显著。
鉴于这些发现，将SMG9鉴定为新的生物标志物有可能提供关于胶质瘤作用机制和免疫环境影响的信息。SMG9在胶质瘤预后预测方面的前景值得进一步研究。
