ArticleBMC medical genomics2022
Identification of a novel autophagy-related prognostic signature and small molecule drugs for glioblastoma by bioinformatics.
Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Bioinformatics Analysis Screening and Identification of Key Biomarkers and Drug Targets in Human Glioblastoma.Current medicinal chemistry · 2025Article
- ELK4 targets CHMP6 to inhibit ferroptosis and enhance malignant properties of skin cutaneous melanoma cells.Archives of dermatological research · 2024Article
- A systematic computational analysis of the endosomal recycling pathway in glioblastoma.Biochemistry and biophysics reports · 2024Article
- The role of programmed cell death in osteosarcoma: From pathogenesis to therapy.Cancer medicine · 2024Review
- Hirudin inhibits glioma growth through mTOR-regulated autophagy.Journal of cellular and molecular medicine · 2023Article
- Review
- Construction and validation of a novel cuproptosis-mitochondrion prognostic model related with tumor immunity in osteosarcoma.PloS one · 2023Article
- Identification of Candidate Genes Associated With Prognosis in Glioblastoma.Frontiers in molecular neuroscience · 2022Article
- GAPDH in neuroblastoma: Functions in metabolism and survival.Frontiers in oncology · 2022Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo explore the autophagy-related prognostic signature (ARPs) via data mining in gene expression profiles for glioblastoma (GBM).
methodsUsing the Cancer Genome Atlas (TCGA) database, we obtained 156 GBM samples and 5 adjacent normal samples, and denoted them as discovery cohort. Univariate Cox regression analysis was used to screen autophagy genes that related to GBM prognosis. Then, the least absolute shrinkage and selection operator Cox regression model was used to construct an autophagy-based ARPs, which was validated in an external cohort containing 80 GBM samples. The patients in the above-mentioned cohorts were divided into low-risk group and high-risk group according to the median prognostic risk score, and the diagnostic performance of the model was assessed by receiver operating characteristic curve analyses. The gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were performed between the high-risk and low-risk patients. Additionally, the genetic features of ARPs, such as genetic variation profiles, correlations with tumor-infiltrating lymphocytes (TILs), and potential drug sensitivity, were further assessed in the TCGA-GBM data set.
resultsA signature of ARPs including NDUFB9, BAK1, SUPT3H, GAPDH, CDKN1B, CHMP6, and EGFR were detected and validated. We identified a autophagy-related prognosis 7-gene signature correlated survival prognosis, immune infiltration, level of cytokines, and cytokine receptor in tumor microenvironment. Furthermore, the signature was tested in several pathways related to disorders of tumor microenvironment, as well as cancer-related pathways. Additionally, a range of small molecular drugs, shown to have a potential therapeutic effect on GBM.
conclusionsWe constructed an autophagy-based 7-gene signature, which could serve as an independent prognostic indicator for cases of GBM and sheds light on the role of autophagy as a potential therapeutic target in GBM.
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