ArticleInternational journal of molecular medicine2018
A 63 signature genes prediction system is effective for glioblastoma prognosis.
Article in International journal of molecular medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses.BMC cancer · 2024Pooled it
- Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities.EBioMedicine · 2021Trial
- Revealing the heterogeneity of treatment resistance in less-defined subtype diffuse large B cell lymphoma patients by integrating programmed cell death patterns and liquid biopsy.Clinical and translational medicine · 2025Article
- Identification of Novel Molecular and Clinical Biomarkers of Survival in Glioblastoma Multiforme Patients: A Study Based on The Cancer Genome Atlas Data.BioMed research international · 2024Article
- Network pharmacology-based strategy to investigate the effect and mechanism of α-solanine against glioma.BMC complementary medicine and therapies · 2023Article
- AZU1 (HBP/CAP37) and PRKCG (PKC-gamma) may be candidate genes affecting the severity of acute mountain sickness.BMC medical genomics · 2023Article
- Quantitative proteomic landscapes of primary and recurrent glioblastoma reveal a protumorigeneic role for FBXO2-dependent glioma-microenvironment interactions.Neuro-oncology · 2023Article
- Article
- Article
- The dysregulated expression and functional effect of CaMK2 in cancer.Cancer cell international · 2021Review
- Article
- Decreased Expression of PACSIN1 in Brain Glioma Samples Predicts Poor Prognosis.Frontiers in molecular biosciences · 2021Article
- Development of a gene expression-based prognostic signature for IDH wild-type glioblastoma.Neuro-oncology · 2020Article
- Association between SNAP25 and human glioblastoma multiform: a comprehensive bioinformatic analysis.Bioscience reports · 2020Article
- Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis.Experimental and therapeutic medicine · 2019Article
- Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients.Journal of translational medicine · 2018Article
- CaFrontiers in molecular neuroscience · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study aimed to explore possible prognostic marker genes in glioblastoma (GBM). Differentially expressed genes (DEGs) were screened by comparing microarray data of tumor and normal tissue samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset GSE22866. Subsequently, the prognosis‑associated DEGs were screened via Cox regression analysis, followed by construction of gene/protein/pathway interaction networks of these DEGs by calculating the correlation coefficient between the DEGs. Next, a prognostic prediction system was constructed using Bayes discriminant analysis, which was validated by the microarray data of samples from patients with good and bad prognosis from the TCGA and Chinese Glioma Genome Atlas (CGGA), as well as the GEO dataset. Finally, a co‑expression network of the signature genes in the prediction system was constructed in combination with the significant pathways. A total of 288 overlapping DEGs (false discovery rate <0.5 and |log2 of fold change|>1) were screened, 123 of which were identified to be associated with the prognosis of GBM patients. The co‑expression network of these prognosis‑associated DEGs included 1405 interactions and 112 DEGs, and 6 functional modules were identified in the network. The prognostic prediction system was comprised of 63 signature genes with a specificity value of 0.929 and a sensitivity value of 0.948. GBM samples with good and bad prognosis in the TCGA, CGGA and GEO datasets were distinguishable by these signature genes (P=1.33x10‑6, 1.63x10‑4 and 0.00534, respectively). The co‑expression network of signature genes with significant pathways was comprised of 56 genes and 361 interactions. Protein kinase Cγ (PRKCG), protein kinase Cβ (PRKCB) and calcium/calmodulin‑dependent protein kinase IIα (CAMK2A) were important genes in the network, and based on the expression of these genes, it was possible to distinguish between samples with significantly different survival risks. In the present study, an effective prognostic prediction system for GBM patients was constructed and validated. PRKCG, PRKCB and CAMK2A may be potential prognostic factors for GBM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.