ArticleJournal of cancer research and clinical oncology2023
The evaluation of six genes combined value in glioma diagnosis and prognosis.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A novel golgi related genes based correlation prognostic index can better predict the prognosis of glioma and responses to immunotherapy.Discover oncology · 2025Article
- Identifying core genes in keloid and investigating immune infiltration and pan-cancer associations using eQTL and machine learning.Bioinformatics advances · 2025Article
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4 authors.
Funding
Abstract
purposeGlioma is the most common and fatal type of brain tumour. Owing to its aggressiveness and lethality, early diagnosis and prediction of patient survival are very important. This study aimed to identify key genes and biomarkers for glioma that can guide clinicians in making rapid diagnosis and prognostication.
methodsData mining of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data, and Genotype-Tissue Expression Project brain expression data revealed significantly differentially expressed genes (DEGs), and the risk scores of individual patients were calculated. WGCNA was utilized to screen for genes most related to clinical diagnosis. Prognostic genes associated with glioma were selected via combining the LASSO regression with univariate and multivariate Cox regression and protein-protein interaction network analyses. Then, a nomogram was constructed. And CGGA dataset was utilized to validated. The protein expression levels of the signature were detected using the human protein atlas. Drug response prediction was carried out using the package "pRRophetic".
resultsA six-gene signature (KLF6, CHI3L1, SERPINE1, ANGPT2, TGFBR1, and PTX3) was identified and used to stratify patients into low- and high-risk groups. Survival, ROC curve, and Cox analyses clarified that the six hub genes were a favourable independent prognostic factor for patients with glioma. A nomogram was set up by integrating clinical parameters with risk signatures, showing high precision for predicting 2-, 3-, 4-, 5-years survival. In addition, the expression of most genes was consistent with protein expression. Furthermore, the sensitivity to the top ten drugs in the GDSC database of the high-risk group was significantly higher than the low-risk group.
conclusionBased on genetic profiles and clinicopathological features, including age, grade, isocitrate dehydrogenase mutation status, we constructed a comprehensive prognostic model for patients with glioma. These signatures can be regarded as biomarkers to predict the prognosis of gliomas, possibly providing more therapeutic strategies for future clinical research.
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