ArticleAnnals of translational medicine2022
Construction of survival-related co-expression modules and identification of potential prognostic biomarkers of osteosarcoma using WGCNA.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed, 6 citations in OpenAlex.
- CCDC78 is required for prostate cancer cell growth and serves as a prognostic biomarker for high-risk disease.Discover oncology · 2026Article
- Article
- Gene-network based analysis of human placental trophoblast subtypes identifies critical genes as potential targets of therapeutic drugs.Journal of integrative bioinformatics · 2023Article
- Identification of biomarkers associated with the invasion of nonfunctional pituitary neuroendocrine tumors based on the immune microenvironment.Frontiers in endocrinology · 2023Article
- Identification of mRNA expression biomarkers associated with epilepsy and response to valproate with co-expression analysis.Frontiers in neurology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Osteosarcoma (OS) is a primary malignant bone tumor. Patients with different immune characteristics respond differently to chemotherapy and have a lower chance of survival. The potential pathogenesis and therapeutic targets of OS must be investigated further. Methods: OS expression profile data and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and the Gene Expression Omnibus (GEO) databases. The immune-related gene set was obtained from the ImmPort database, and the immune-related gene expression profiles were used for non-negative matrix factorization (NMF) cluster analysis of patients in the 2 databases to find the best clustering number. In the TARGET database, OS patients were classified into low-risk and high-risk groups based on the differences in their survival rates. Weighted correlation network analysis (WGCNA) was applied to the low-risk and high-risk groups to determine the module with the lowest conservatism in order to differentiate the prognosis of the 2 groups. Results: A total of 500 key genes associated with poor prognosis were identified. Gene Ontology (GO) enrichment analysis revealed that the biological processes of these genes were primarily focused on the regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, collagen-containing extracellular matrix, and Rho GTPase binding. A random survival forest identified EPHB3, TEAD1, and KRR1P1 as key genes. Their expression level was linked to overall survival. We discovered that the core genes were associated to immune cell infiltration. Simultaneously, paired survival analysis of two genes revealed differences in survival. We also reverse-predicted the main genes and developed their competitive endogenous RNA (ceRNA) network. Finally, utilizing the CellMiner database, we observed that the genes TEAD1 and EPHB3 were connected to drug sensitivity. Conclusions: In this study, we identified the modules and key genes related to the poor prognosis of OS patients by using WGCNA, and verified their impact on the prognosis of OS patients and their role in the immune microenvironment of OS. In addition, targeted gene related antitumor drugs were screened out. The discoveries may lead to novel molecular targets and treatment methods for OS patients. Keywords: Osteosarcoma (OS); weighted gene co-expression network analysis (WGCNA); gene.
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.