SMG9 is a novel prognostic-related biomarker in glioma correlating with ferroptosis and immune infiltrates
Yong Dai,
Huan Zhang,
Sujuan Feng,
Chao Guo,
Wenjie Tian,
Yimei Sun,
Yi Zhang
Affiliations
Yong Dai
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
Huan Zhang
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
Sujuan Feng
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
Chao Guo
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
Wenjie Tian
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
Yimei Sun
Department of Neurosurgery, Affiliated Hospital 2 of Nantong University and First People's Hospital of Nantong City, No. 666 Shengli Road, Nantong 226001, China; Corresponding author. Department of Neurosurgery, Affiliated Hospital 2 of Nantong University and First People's Hospital of Nantong City, No. 666 Shengli Road, Nantong 226001, China.
Yi Zhang
Department of Neurosurgery, Affiliated Hospital 2 of Nantong University and First People's Hospital of Nantong City, No. 666 Shengli Road, Nantong 226001, China; Corresponding author. Department of Neurosurgery, Affiliated Hospital 2 of Nantong University and First People's Hospital of Nantong City, No. 666 Shengli Road, Nantong 226001, China.
Background: 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. Methods: 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. Results: 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. Conclusion: 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.
