ArticleOncology letters2018
Exploring the molecular pathogenesis associated with T-cell prolymphocytic leukemia based on a comprehensive bioinformatics analysis.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Prolymphocytic Leukaemia: an Update on Biology and Treatment.Current oncology reports · 2024Review
- Identification of microRNA editing sites in three subtypes of leukemia.Frontiers in molecular biosciences · 2022Article
- First report of clinical response to venetoclax combination with pentostatin in T-cell-prolymphocytic leukemia (T-PLL).Leukemia & lymphoma · 2020Article
- The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer-aided detection.International journal of cancer · 2020Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a rare hematological malignancy, T-cell prolymphocytic leukemia (T-PLL) has a high mortality rate. However, the comprehensive mechanisms of the underlying pathogenesis of T-PLL are unknown. The purpose of the present study was to investigate the pathogenesis of T-PLL based on a comprehensive bioinformatics analysis. The differentially expressed genes (DEGs) between T-PLL blood cell samples and normal peripheral blood cell samples were investigated using the GSE5788 Affymetrix microarray data from the Gene Expression Omnibus database. To investigate the functional changes associated with tumor progression, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used on the identified DEGs, followed by protein-protein interaction (PPI) and sub-PPI analysis. Transcription factors and tumor-associated genes (TAGs) were investigated further. The results identified 84 upregulated genes and 354 downregulated genes in T-PLL samples when compared with healthy samples. These DEGs featured in various functions including cell death and various pathways including apoptosis. The functional analysis of DEGs revealed 17 dysregulated transcription factors and 37 dysregulated TAGs. Furthermore, the PPI network analysis based on node degree (a network topology attribute) identified 61 genes, including the core downregulated gene of the sub-PPI network, signal transducer and activator of transcription 3 (
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