ArticlePeerJ2021
Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma.
Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- PLEKHG7 Expression: A Biomarker for Prognosis and Targeted Therapy in Diffuse Large B-cell Lymphoma.Protein and peptide letters · 2025Article
- Treatment with β-Adrenoceptor Agonist Isoproterenol Reduces Non-parenchymal Cell Responses in LPS/D-GalN-Induced Liver Injury.Inflammation · 2024Article
- Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma.Current computer-aided drug design · 2024Article
- Exploring the Molecular Mechanism of Niuxi-Mugua Formula in Treating Coronavirus Disease 2019Current computer-aided drug design · 2024Article
- Identification and Validation of a DNA Damage Repair-Related Signature for Diffuse Large B-Cell Lymphoma.BioMed research international · 2022Article
- Identifying the Effect of COVID-19 Infection in Multiple Myeloma and Diffuse Large B-Cell Lymphoma Patients Using Bioinformatics and System Biology.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy with varied outcomes. However, the fundamental mechanisms remain to be fully defined.
aimWe aimed to identify core differentially co-expressed hub genes and perturbed pathways relevant to the pathogenesis and prognosis of DLBCL.
methodsWe retrieved the raw gene expression profile and clinical information of GSE12453 from the Gene Expression Omnibus (GEO) database. We used integrated bioinformatics analysis to identify differentially co-expressed genes. The CIBERSORT analysis was also applied to predict tumor-infiltrating immune cells (TIICs) in the GSE12453 dataset. We performed survival and ssGSEA (single-sample Gene Set Enrichment Analysis) (for TIICs) analyses and validated the hub genes using GEPIA2 and an independent GSE31312 dataset.
resultsWe identified 46 differentially co-expressed hub genes in the GSE12453 dataset. Gene expression levels and survival analysis found 15 differentially co-expressed core hub genes. The core genes prognostic values and expression levels were further validated in the GEPIA2 database and GSE31312 dataset to be reliable (
conclusionOur study found differentially co-expressed core hub genes and relevant pathways involved in ribosome and COVID-19 disease that may be potential targets for prognosis and novel therapeutic intervention in DLBCL.
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