The association of growth differentiation factor-15 levels and osteoporosis in patients with thalassemia

Teawtrakul, Nattiya; Chansai, Siriyakorn; Yamsri, Supawadee; Chansung, Kanchana; Wanitpongpun, Chinadol; Lanamtieng, Theerin; Phiphitaporn, Pisa; Fucharoen, Supan; Pongchaiyakul, Chatlert

doi:10.1016/j.amjms.2023.05.002

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Signaling pathways include immune system [109], signal transduction [110], extracellular matrix organization [111], adaptive immune system [112], neutrophil degranulation [113], innate immune system [114], metabolism [115], diseases of metabolism [116] and Hemostasis [117] were responsible for advancement of COPD. MPO (myeloperoxidase) [190], CTSD (cathepsin D) [1389], KLF10 [1390], FRZB (frizzled related protein) [1391], DUSP1 [1392], IL1B [1393], CCL11 [1394], LMNA (lamin A/C) [1395], WIF1 [1396], IL1RN [1397], IFNG (interferon gamma) [1398], CX3CL1 [1399], BMP2 [1400], CDKN1A [1401], BMP4 [1402], ICAM1 [1403], CD34 [1404], IL33 [1405], FASLG (Fas ligand) [1406], KDM6B [1407], TNF (tumor necrosis factor) [1408], GDF15 [1409], IL2 [1410], INPP4B [1411], FABP4 [1412], CYP2C19 [1413], CXCR4 [1414], DKK2 [1415], PLCB4 [1416], ANXA1 [1417], DUSP6 [1418], CORIN (corin, serine peptidase) [1419], F8 [1420], DDIT4 [1421], IL1R2 [1422], S100A4…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Identification of accurate biomarkers is still particularly urgent for improving the poor survival of chronic obstructive pulmonary disease (COPD) patients. In this investigation, we aimed to identity the potential biomarkers in COPD via bioinformatics and next generation sequencing (NGS) data analysis. In this investigation, the differentially expressed genes (DEGs) in COPD were identified using NGS dataset (GSE239897) from Gene Expression Omnibus (GEO) database. Subsequently, gene ontology (GO) and pathway enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of COPD. Protein-protein interaction (PPI), modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to determine the hub genes, miRNAs and TFs. The receiver operating characteristic (ROC) analysis was performed to determine the diagnostic value of hub genes. A total of 956 overlapping DEGs (478 up regulated and 478 down regulated genes) were identified in the NGS dataset. DEGs were mainly associated with GO functional terms and pathways in cellular response to stimulus. response to stimulus, immune system and neutrophil degranulation. There were 10 hub genes (MYC, LMNA, VCAM1, MAPK6, DDX3X, SHMT2, PHGDH, S100A9, FKBP5 and RPS6KA2) identified by PPI, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis. In conclusion, the DEGs, relative GO terms, pathways and hub genes identified in the present investigation might aid in understanding of the molecular mechanisms underlying COPD progression and provide potential molecular targets and biomarkers for COPD.

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Section: Discussionmentioning

confidence: 99%

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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“…Recent reports have revealed that IL2 [451] acted as biomarker in coagulation and fibrinolysis. Research has shown that IL2 [452], IGFBP3 [70], ENPP1 [453], FASLG (Fas ligand) [454], FAP (fibroblast activation protein alpha) [455], IFNG (interferon gamma) [456], AKT3 [457], BMPR1A [458], SPRY1 [459], ANXA1 [460], PTCH1 [461], IGF2 [462], MMP9 [463], HP (haptoglobin) [464], GDF15 [465], KCNMA1 [466], ACE2 [467], TRPV4 [468], ADM (adrenomedullin) [469], CD274 [470], IGFBP2 [471], SOCS3 [472], TREM2 [473], CAV1 [474], PROK2 [475], MAPK14 [476] and CDKN1A [477] plays an important role in the pathogenesis of osteoporosis. Studies have revealed that IL2 [478], ENPP1 [479], FASLG (Fas ligand) [480], FAP (fibroblast activation protein alpha) [481], BIRC3 [482], IFNG (interferon gamma) [483], NCR1 [484], RAG1 [485], SEMA3A [486], TIGIT (T cell immunoreceptor with Ig and ITIM domains) [487], CD19 [488], TGFBR3 [489], IL5 [490], MMP9 [491], HP (haptoglobin) [492], GDF15 [493], ACE2 [494], DYSF (d...…”

Section: Identification Of Degsmentioning

confidence: 99%

Identification of differentially expressed genes and enriched pathways in inflammatory bowel disease using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Inflammatory bowel disease (IBD) is the most common chronic digestive disorders and inflammation in the gastrointestinal tract globally that is characterized by episodes of abdominal pain, diarrhea, bloody stools and weight loss. However, the pathophysiologic mechanisms of IBD have not been thoroughly investigated. To explore potential targets for treatment of IBD, we reorganized and analyzed next generation sequencing (NGS) dataset (GSE186507). The R package DESeq2 tool was used to screen for differentially expressed genes (DEGs) between IBD and normal control samples. We used the g:Profiler database to perform Gene Ontology (GO) enrichment analysis and the REACTOME for pathway enrichment analysis. Protein-protein interaction (PPI) network construction and module analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes. Then miRNA-hub gene regulatory network and TF-hub gene regulatory network of these hub genes were visualized by Cytoscape. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 957 DEGs (478 up regulated genes and 479 down regulated genes) were detected in NGS dataset. And they were mainly enriched in the terms of multicellular organismal process, response to stimulus, GPCR ligand binding and immune system. Based on the data of PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network the top hub genes were ranked, including IL7R, ERBB2, SMAD1, RPS26, TLE1, HNF4A, CDKN1A, SRPK1, H3C12 and SFN. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of IDB, and certain novel genes might be used as candidate target molecules to diagnose, monitor and treat IDB.

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“…GDF-15 expression is often induced under stress conditions to maintain cell and tissue homeostasis. Increased GDF-15 levels are associated with pathologic conditions such as inflammation, infection, tissue injury, and liver, kidney, cardiovascular diseases, and cancers ( Wischhusen et al, 2020 ; Delrue et al, 2023 ; Losch et al, 2023 ; Merchant et al, 2023 ; Morita-Tanaka et al, 2023 ; Teawtrakul et al, 2023 ). GDF-15 has thus been widely explored as a biomarker for disease prognosis.…”

Section: Introductionmentioning

confidence: 99%

Circulating GDF-15: a biomarker for metabolic dysregulation and aging in people living with HIV

Wang,

Zhao,

Schank

et al. 2024

Front. Aging

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Despite effective control of HIV replication by antiretroviral therapy (ART), a significant number of people living with HIV (PLWH) fail to achieve complete immune reconstitution and thus are deemed immune non-responders (INRs). Compared with immune responders (IRs) who have restored their CD4 T cell numbers and functions, CD4 T cells from these INRs exhibit prominent mitochondrial dysfunction and premature aging, which play a major role in increasing the incidence of non-AIDS, non-communicable diseases (NCDs). To date, there are no reliable biomarkers that can be used to typify and manage PLWH, especially INRs with non-AIDS NCDs. Growth differential factor-15 (GDF-15) is a transforming growth factor-β (TGF-β) family member known to regulate several biological processes involved in cell aging and stress responses. Since PLWH exhibit premature aging and metabolic dysregulation, here we measured the plasma levels of GDF-15 by ELISA and metabolic proteins by proteomic array and correlated the results with clinical parameters in ART-controlled PLWH (including INRs and IRs) and healthy subjects (HS). We found that GDF-15 levels were significantly elevated in PLWH compared to HS. GDF-15 levels were positively correlated with age and negatively associated with body mass and LDL cholesterol levels in the study subjects. Also, elevated GDF-15 levels were correlated with differential dysregulation of multiple metabolic proteins in PLWH. These results suggest that GDF-15 protein may serve as a biomarker of metabolic dysregulation and aging, and this biomarker will be useful in clinical trials targeting aging and metabolic disorders in ART-treated PLWH.

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The association of growth differentiation factor-15 levels and osteoporosis in patients with thalassemia

Cited by 6 publications

References 24 publications

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Identification of differentially expressed genes and enriched pathways in inflammatory bowel disease using bioinformatics and next generation sequencing data analysis

Circulating GDF-15: a biomarker for metabolic dysregulation and aging in people living with HIV

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