Exploring the molecular pathogenesis associated with T‑cell prolymphocytic leukemia based on a comprehensive bioinformatics analysis

Shi,Zhangzhen; Yu,Jing; Shao,Hui; Cheng,Kailiang; Zhai,Jingjie; Jiang,Qi; Li,Hongjun

doi:10.3892/ol.2018.8615

Exploring the molecular pathogenesis associated with T‑cell prolymphocytic leukemia based on a comprehensive bioinformatics analysis

Authors:
- Zhangzhen Shi
- Jing Yu
- Hui Shao
- Kailiang Cheng
- Jingjie Zhai
- Qi Jiang
- Hongjun Li
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Affiliations: Department of Hematology and Oncology, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China, Department of Nephrology, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China, Department of Radiology, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China, Department of Implantology, Jilin University Stomatology Hospital, Changchun, Jilin 130021, P.R. China, Medical Examination Center, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
Published online on: May 2, 2018 https://doi.org/10.3892/ol.2018.8615
Pages: 301-307
Copyright: © Shi et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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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 (STAT3; degree, 13) and the core upregulated gene, insulin receptor substrate‑1 (IRS1; degree, 5), that may have important associations with the progression of T‑PLL. Alterations to cell functions, including cell death, and pathways, including apoptosis, may contribute to the process of T‑PLL. Candidate genes identified in the present study, including STAT3 and IRS1, should be targets for additional studies.

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1	Herling M, Khoury JD, Washington LT, Duvic M, Keating MJ and Jones D: A systematic approach to diagnosis of mature T-cell leukemias reveals heterogeneity among WHO categories. Blood. 104:328–335. 2004. View Article : Google Scholar : PubMed/NCBI
2	Matutes E, Brito-Babapulle V, Swansbury J, Ellis J, Morilla R, Dearden C, Sempere A and Catovsky D: Clinical and laboratory features of 78 cases of T-prolymphocytic leukemia. Blood. 78:3269–3274. 1991.PubMed/NCBI
3	Soulier J, Pierron G, Vecchione D, Garand R, Brizard F, Sigaux F, Stern MH and Aurias A: A complex pattern of recurrent chromosomal losses and gains in T-cell prolymphocytic leukemia. Genes Chromosomes Cancer. 31:248–254. 2001. View Article : Google Scholar : PubMed/NCBI
4	Vivekanandarajah A, Atallah JP and Gupta S: T-cell prolymphocytic leukaemia (T-PLL): A rare disease with a grave prognosis. BMJ Case Rep. 2013:pii: bcr2013009808. 2013. View Article : Google Scholar : PubMed/NCBI
5	K A and D C: T-cell prolymphocytic leukemia. Clin Lymph Myelom. 9 Suppl 3:S239–S243. 2009. View Article : Google Scholar
6	Brito-Babapulle V, Pomfret M, Matutes E and Catovsky D: Cytogenetic studies on prolymphocytic leukemia. II. T cell prolymphocytic leukemia. Blood. 70:926–931. 1987.PubMed/NCBI
7	Kiel MJ, Velusamy T, Rolland D, Sahasrabuddhe AA, Chung F, Bailey NG, Schrader A, Li B, Li JZ, Ozel AB, et al: Integrated genomic sequencing reveals mutational landscape of T-cell prolymphocytic leukemia. Blood. 124:1460–1472. 2014. View Article : Google Scholar : PubMed/NCBI
8	Bergmann AK, Schneppenheim S, Seifert M, Betts MJ, Haake A, Lopez C, Maria Murga Penas E, Vater I, Jayne S, Dyer MJ, et al: Recurrent mutation of JAK3 in T-cell prolymphocytic leukemia. Genes Chromosomes Cancer. 53:309–316. 2014. View Article : Google Scholar : PubMed/NCBI
9	Yokohama A, Saitoh A, Nakahashi H, Mitsui T, Koiso H, Kim Y, Uchiumi H, Saitoh T, Handa H, Jimbo T, et al: TCL1A gene involvement in T-cell prolymphocytic leukemia in Japanese patients. Int J Hematol. 95:77–85. 2012. View Article : Google Scholar : PubMed/NCBI
10	Bug S, Dürig J, Oyen F, Klein-Hitpass L, Martin-Subero JI, Harder L, Baudis M, Arnold N, Kordes U, Dührsen U, et al: Recurrent loss, but lack of mutations, of the SMARCB1 tumor suppressor gene in T-cell prolymphocytic leukemia with TCL1A-TCRAD juxtaposition. Cancer Genet Cytogenet. 192:44–47. 2009. View Article : Google Scholar : PubMed/NCBI
11	Ozpuyan F, Meyer P, Ni H, Al-Masri H and Alkan S: Bortezomib induces apoptosis in T-cell prolymphocytic leukemia (T-PLL). Leuk Lymphoma. 48:2247–2250. 2007. View Article : Google Scholar : PubMed/NCBI
12	Wang J, Hasui K, Utsunomiya A, Jia X, Matsuyama T and Murata F: Association of high proliferation in adult T-cell leukemia cells with apoptosis, and expression of p53 protein in acute type ATL. J Clin Exp Hematop. 48:1–10. 2008. View Article : Google Scholar : PubMed/NCBI
13	Dürig J, Bug S, Klein-Hitpass L, Boes T, Jöns T, Martin-Subero JI, Harder L, Baudis M, Dührsen U and Siebert R: Combined single nucleotide polymorphism-based genomic mapping and global gene expression profiling identifies novel chromosomal imbalances, mechanisms and candidate genes important in the pathogenesis of T-cell prolymphocytic leukemia with inv(14)(q11q32). Leukemia. 21:2153–2163. 2007. View Article : Google Scholar : PubMed/NCBI
14	Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP: Exploration, normalization and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI
15	Gautier L, Cope L, Bolstad BM and Irizarry RA: Affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI
16	Wang P, Ding F, Chiang H, Thompson RC, Watson SJ and Meng F: ProbeMatchDB-a web database for finding equivalent probes across microarray platforms and species. Bioinformatics. 18:488–489. 2002. View Article : Google Scholar : PubMed/NCBI
17	Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 3:Article32004. View Article : Google Scholar : PubMed/NCBI
18	Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
19	Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
20	da Huang W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
21	Zhao M, Sun J and Zhao Z: TSGene: A web resource for tumor suppressor genes. Nucleic Acids Res. 41:(Database Issue). D970–D976. 2013. View Article : Google Scholar : PubMed/NCBI
22	Chen JS, Hung WS, Chan HH, Tsai SJ and Sun HS: In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma. Bioinformatics. 29:420–427. 2013. View Article : Google Scholar : PubMed/NCBI
23	Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C and Jensen LJ: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41:(Database Issue). D808–D815. 2013. View Article : Google Scholar : PubMed/NCBI
24	Beisser D, Klau GW, Dandekar T, Müller T and Dittrich MT: BioNet: An R-Package for the functional analysis of biological networks. Bioinformatics. 26:1129–1130. 2010. View Article : Google Scholar : PubMed/NCBI
25	Kibbe WA, Arze C, Felix V, Mitraka E, Bolton E, Fu G, Mungall CJ, Binder JX, Malone J, Vasant D, et al: Disease Ontology 2015 update: An expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res. 43:(Database Issue). D1071–D1078. 2015. View Article : Google Scholar : PubMed/NCBI
26	Liu YJ, Zhu Y, Yuan HX, Zhang JP, Guo JM and Lin ZM: Overexpression of HOXC11 homeobox gene in clear cell renal cell carcinoma induces cellular proliferation and is associated with poor prognosis. Tumour Biol. 36:2821–2829. 2015. View Article : Google Scholar : PubMed/NCBI
27	Li W, Li K, Zhao L and Zou H: Bioinformatics analysis reveals disturbance mechanism of MAPK signaling pathway and cell cycle in Glioblastoma multiforme. Gene. 547:346–350. 2014. View Article : Google Scholar : PubMed/NCBI
28	Verstrepen L and Beyaert R: Receptor proximal kinases in NF-κB signaling as potential therapeutic targets in cancer and inflammation. Biochem Pharmacol. 92:519–529. 2014. View Article : Google Scholar : PubMed/NCBI
29	Liu J, Li J, Li H, Li A, Liu B and Han L: A comprehensive analysis of candidate genes and pathways in pancreatic cancer. Tumour Biol. 36:1849–1857. 2015. View Article : Google Scholar : PubMed/NCBI
30	Kim HS, Li A, Ahn S, Song H and Zhang W: Inositol Polyphosphate-5-Phosphatase F (INPP5F) inhibits STAT3 activity and suppresses gliomas tumorigenicity. Sci Rep. 4:73302014. View Article : Google Scholar : PubMed/NCBI
31	Tell RW and Horvath CM: Bioinformatic analysis reveals a pattern of STAT3-associated gene expression specific to basal-like breast cancers in human tumors. Proc Natl Acad Sci USA. 111:12787–12792. 2014. View Article : Google Scholar : PubMed/NCBI
32	Santillán-Benítez JG, Mendieta-Zerón H, Gómez-Oliván LM, Quiroz Ordóñez A, Torres-Juárez JJ and González-Bañales JM: JAK2, STAT3 and SOCS3 gene expression in women with and without breast cancer. Gene. 547:70–76. 2014. View Article : Google Scholar : PubMed/NCBI
33	Gao SM, Chen CQ, Wang LY, Hong LL, Wu JB, Dong PH and Yu FJ: Histone deacetylases inhibitor sodium butyrate inhibits JAK2/STAT signaling through upregulation of SOCS1 and SOCS3 mediated by HDAC8 inhibition in myeloproliferative neoplasms. Exp Hematol. 41:261–270.e4. 2013. View Article : Google Scholar : PubMed/NCBI
34	Grozovsky R, Begonja AJ, Liu K, Visner G, Hartwig JH, Falet H and Hoffmeister KM: The Ashwell-Morell receptor regulates hepatic thrombopoietin production via JAK2-STAT3 signaling. Nat Med. 21:47–54. 2015. View Article : Google Scholar : PubMed/NCBI
35	Jorvig JE and Chakraborty A: Zerumbone inhibits growth of hormone refractory prostate cancer cells by inhibiting JAK2/STAT3 pathway and increases paclitaxel sensitivity. Anticancer Drugs. 26:160–166. 2015. View Article : Google Scholar : PubMed/NCBI
36	Ozoe A, Sone M, Fukushima T, Kataoka N, Arai T, Chida K, Asano T, Hakuno F and Takahashi S: Insulin receptor substrate-1 (IRS-1) forms a ribonucleoprotein complex associated with polysomes. FEBS Lett. 587:2319–2324. 2013. View Article : Google Scholar : PubMed/NCBI
37	Hu Y, He K, Wang D, Yuan X, Liu Y, Ji H and Song J: TMEPAI regulates EMT in lung cancer cells by modulating the ROS and IRS-1 signaling pathways. Carcinogenesis. 34:1764–1772. 2013. View Article : Google Scholar : PubMed/NCBI
38	Li P, Wang L, Liu L, Jiang H, Ma C and Hao T: Association between IRS-1 Gly972Arg polymorphism and colorectal cancer risk. Tumour Biol. 35:6581–6585. 2014. View Article : Google Scholar : PubMed/NCBI