Cisplatin's potential for type 2 diabetes repositioning by inhibiting CDKN1A, FAS, and SESN1

Muhammad, Syed Aun; Naqvi, Syeda Tahira Qousain; Nguyen, Thanh; Wu, Xiaogang; Munir, Fahad; Jamshed, Muhammad; Zhang, Qiyu

doi:10.1016/j.compbiomed.2021.104640

Cited by 8 publications

(4 citation statements)

References 77 publications

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“…[181], FLT1 [182], ABCA3 [183], DUOX1 [184], KL (klotho) [185], ALOX5AP [186], FPR2 [187], IL27 [188], FZD4 [189], MPO (myeloperoxidase) [190], CTSD (cathepsin D) ABCA3 [636], KL (klotho) [637], PLK1 [638], GAPDH (glyceraldehyde-3phosphate dehydrogenase) [639], MPO (myeloperoxidase) [640], LPL (lipoprotein lipase) [641], TLR8 [642], MUC1 [643], FOXF1 [644], VCAN (versican) [645] and FDPS (farnesyl diphosphate synthase) [646] have been proposed as the most promising biomarkers for pulmonary hypertension. PTGS2 [647], NR4A1 [648], IL6 [649], CXCL8 [650], CD69 [651], CXCL1 [652], SOCS2 [653], SOCS3 [654], CCL3 [655], CCL5 [656], VCAM1 [657], CCL2 [658], CXCL5 [659], DUSP5 [660], CXCL12 [661], KLF10 [662], KLK5 [663], CD3E [664], CD3D [665], IL1B [666], AREG (amphiregulin) [667], SERPINE1 [668], OSM ( [682], BMP2 [683], LDLR (low density lipoprotein receptor) [684], CDKN1A [685], RGS16 [686], TNFAIP3 [687], BMP4 [688], ICAM1…”

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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“…Altered expression of BCL3 [112], TRAF2 [113], NEU1 [114], SNAP29 [115], AGPAT2 [116], LPCAT3 [117], ADORA2B [118], CTSD (cathepsin D) [119], ACADS (acyl-CoA dehydrogenase short chain) [120], ACAD9 [121], E4F1 [122], IRF7 [123], TAF1 [124], S1PR1 [125], RASSF1 [126], ELAC2 [127], RNF146 [128], COX15 [129], SMYD2 [130], IDH1 [131], MTO1 [132], IL15 [133], PIK3R1 [134], ASB1 [135], OSM (oncostatin M) [136], ZNF791 [137], GBA (glucosylceramidase beta) [138], SOCS3 [139], SLC39A7 [140], AKIP1 [141], AMIGO2 [142], GLUL (glutamate-ammonia ligase) [143], SEMA4D [144], KDM2A [145], TP53 [146], JARID2 [147], CTBP1 [148], ATP6 [149], RPL7 [150], HSP90AA1 [151], BRD4 [152], PSMB4 [153], COX2 [154], JUND (JunD proto-oncogene, AP-1 transcription factor subunit) [155], RPS5 [156], RACK1 [157], ND1 [158], CCND2 [159], COX1 [160], TLK1 [161] and TMPO (thymopoietin) [162] are associated with cardiovascular complications. A previous study found that LMNA (lamin A/C) [85], SLC11A2 [163], CRTC2 [164], TBK1 [165], GRN (granulin precursor) [166], CTSD (cathepsin D) [167], STARD10 [168], PGRMC1 [169], TFE3 [170], POR (cytochrome p450 oxidoreductase) [171], SESN1 [172], IL15 [173], PIK3R1 [134], OSM (oncostatin M) [98], SOCS3 [174], USP21 [100], GLUL (glutamate-ammonia ligase) [175], IL1R1 [176], TP53 [177], PPM1A [178], CTBP1 [179], DNAJC3 […”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Vastrad¹,

Vastrad²

2022

Preprint

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Type 1 diabetes mellitus (T1DM) comprise the most common forms of autoimmune disease. The aim of this investigation was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of T1DM. The next generation sequencing (NGS) dataset GSE182870 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. Protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network, and TF-hub gene regulatory network were conducted via comprehensive target prediction and network analyses. Finally, hub genes were validated by using receiver operating characteristic curve analysis. A total of 860 DEGs were screened out from NGS dataset, among which 477 genes were up regulated and 383 genes were down regulated. GO enrichment analysis indicated that up regulated genes were mainly involved in cellular metabolic process, intracellular anatomical structure and catalytic activity, and the down regulated genes were significantly enriched in cellular nitrogen compound biosynthetic process, protein containing complex and heterocyclic compound binding. REACTOME pathway enrichment analysis showed that the up regulated genes were mainly enriched in metabolism of carbohydrates and the down regulated genes were significantly enriched in metabolism of RNA. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and hub genes (MAPK14, RHOC, MAD2L1, TAF1, TRAF2, HSP90AA1, TP53, HSP90AB1, UBA52 and RACK1) were identified. This study provides further insights into the underlying pathogenesis of T1DM.

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“…CXCL5 has been shown to be activated abortion [56]. [85], NOTCH2 [86], LRP1 [87], CLU (clusterin) [88], FCN1 [89], CDKN1A [90], SMAD3 [91], HLA-E [92], PTPRC (protein tyrosine phosphatase receptor type C) [93], MYH9 [94], JAK3 [95], IL6R [96], TIMP1 [97], DOCK8 [98], TNFRSF1B [99], ITGAL (integrin subunit alpha L) [100], CD47 [101], RARA (retinoic acid receptor alpha) [102], DGKD (diacylglycerol kinase delta) [103], PLEK (pleckstrin) [104], PREX1 [105], BSCL2 [106], PANX1 [107], IRF7 [108] [116] were revealed to be expressed in diabetes mellitus, but these genes might be novel targets for GDM. SIX1 [117], GREM1 [118], GHRHR (growth hormone releasing hormone receptor) [119], GPR37L1 [120], CYP2J2 [121], AQP4 [122], ROS1 [123], LBP (lipopolysaccharide binding protein) [124], SGCD (sarcoglycan delta) [133], PTHLH [134], FAP (fibroblast activation protein alpha) [135], THSD7A [136], SHROOM3 [137], ETV1 [138], CYP24A1 [139], SLIT2 [140], GJC1 [141], PPARGC1A [142], TRPM3 [143], IGF1 [144], TRPV6 [145], TLR3 [146], BMP7 [147], DSG2 [148], POSTN (periostin) [149], ENOX1…”

Section: Discussionmentioning

confidence: 99%

“…Pathways include hemostasis [57], neutrophil degranulation [58], immune system [59] and cytokine signaling in immune system [60] are responsible for progression of GDM. LGR5 [61], GREM1 [62], GLRA3 [63], NEUROD4 [64], CYP2J2 [65], KCNH6 [66], LBP (lipopolysaccharide binding protein) [67], CXCL14 [68], RGN (regucalcin) [69], NPY2R [70], SERPINB13 [71], WNT5A [72], EDA (ectodysplasin A) [73], HSD11B2 [74], ACVR1C [75], NEUROD1 [76], SLIT2 [77], PPARGC1A [78], IGF1 [79], OSR1 [80], CYP46A1 [81], TLR3 [82], BMP7 [83], SELP (selectin P) [84], HLA-A [85], NOTCH2 [86], LRP1 [87], CLU (clusterin) [88], FCN1 [89], CDKN1A [90], SMAD3 [91], HLA-E [92], PTPRC (protein tyrosine phosphatase receptor type C) [93], MYH9 [94], JAK3 [95], IL6R [96], TIMP1 [97], DOCK8 [98], TNFRSF1B [99], ITGAL (integrin subunit alpha L) [100], CD47 [101], RARA (retinoic acid receptor alpha) [102], DGKD (diacylglycerol kinase delta) [103], PLEK (pleckstrin) [104], PREX1 [105], BSCL2 [106], PANX1 [107], IRF7 [108], NOTCH1 [109], STIM1 [110], TRIM13 [111], LRBA (LPS responsive beige-like anchor protein) [112], CXCR4 [113], MDM4 [114], MYO9B [115] and PDE5A [116] were revealed to be expressed in diabetes mellitus, but these genes might be novel targets for GDM. SIX1 [117], GREM1 [118], GHRHR (growth hormone releasing hormone receptor) [119], GPR37L1 [120], CYP2J2 [121], AQP4 [122], ROS1 [123], LBP (lipopolysaccharide binding protein) [124], SGCD (sarcoglycan delta) [125], CXCL14 [126], RGN...…”

Section: Discussionmentioning

confidence: 99%

Identification of differentially expressed genes and signaling pathways in gestational diabetes mellitus by integrated bioinformatics analysis

Vastrad¹,

Vastrad²

2021

Preprint

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Gestational diabetes mellitus (GDM) is a metabolic disorder during pregnancy. Numerous biomarkers have been identified that are linked with the occurrence and development of GDM. The aim of this investigation was to identify differentially expressed genes (DEGs) in GDM using a bioinformatics approach to elucidate their molecular pathogenesis. GDM associated expression profiling by high throughput sequencing dataset (GSE154377) was obtained from Gene Expression Omnibus (GEO) database including 28 normal pregnancy samples and 33 GDM samples. DEGs were identified using DESeq2. The gene ontology (GO) and REACTOME pathway enrichments of DEGs were performed by g:Profiler. Protein-protein interaction (PPI) networks were assembled with Cytoscape software and separated into modules using the PEWCC1 algorithm. MiRNA-hub gene regulatory network and TF-hub gene regulatory network were performed with the miRNet database and NetworkAnalyst database. Receiver Operating Characteristic (ROC) analyses was conducted to validate the hub genes. A total of 953 DEGs were identified, of which 478 DEGs were up regulated and 475 DEGs were down regulated. Furthermore, GO and REACTOME pathway enrichment analysis demonstrated that these DEGs were mainly enriched in multicellular organismal process, cell activation, formation of the cornified envelope and hemostasis. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB were identified as key genes in the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB in GDM were validated using ROC analysis. This investigation provides further insights into the molecular pathogenesis of GDM, which might facilitate the diagnosis and treatment of GDM.

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Cisplatin's potential for type 2 diabetes repositioning by inhibiting CDKN1A, FAS, and SESN1

Cited by 8 publications

References 77 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

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Identification of differentially expressed genes and signaling pathways in gestational diabetes mellitus by integrated bioinformatics analysis

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