ICAM-1 and VCAM-1: Gatekeepers in various inflammatory and cardiovascular disorders

Singh, Varinder; Kaur, Rupinder; Kumari, Pratima; Pasricha, Chirag; Singh, Ravinder J.

doi:10.1016/j.cca.2023.117487

Cited by 60 publications

(25 citation statements)

References 90 publications

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“…LMNA (lamin A/C) [676], VCAM1 [986], PHGDH (phosphoglycerate dehydrogenase) [1100], S100A9 [1045], FKBP5 [1046], FHL2 [1003], ATF3 [85] and MPO (myeloperoxidase) [1084] plays an important role in the development of obesity. LMNA (lamin A/C) [1127], VCAM1 [1110], S100A9 [239], TP63 [1125], JUND (JunD proto-oncogene, AP-1 transcription factor subunit) [1118], FHL2 [1132], ATF3 [93], BPI (bactericidal permeability increasing protein) [1178], DYSF (dysferlin) [1180], MPO (myeloperoxidase) [1195] and ALPL (alkaline phosphatase, biomineralization associated) [1194] were reported to be associated with the prognosis of heart problems. LMNA (lamin A/C) [1313], VCAM1 [1299], PHGDH (phosphoglycerate dehydrogenase) [1382], S100A9 [81], TP63 [1312], FHL2 [1318], ATF3 [80] and MPO (myeloperoxidase) [1375] have been identified as a key genes in idiopathic pulmonary fibrosis.…”

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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“…PPP1CC [549], VCAM1 [626], ALB (albumin) [631], SNCA (synuclein alpha) [574] and DPP4 [607] are mainly associated with diabetes mellitus. BMI1 [95], VCAM1 [220], ALB (albumin) [227] and DPP4 [192] plays a role in diagnosis of cardiovascular disorders. BMI1 [245] and SNCA (synuclein alpha) [251] have been known to be involved in kidney fibrosis progression.…”

Section: Discussionmentioning

confidence: 99%

“…PPP1CC [549], E2F1 [543], EEF1A2 [538], VCAM1 [626], CDH1 [622], AGTR1 [79], SNCA (synuclein alpha) [574], hsa-mir-221 [780], YY1 [781], STAT3 [782] and PDX1 [783] are closely involved with diabetes mellitus. E2F1 [110], BMI1 [95], EEF1A2 [104], ACTA1 [128], VCAM1 [220], AGTR1 [58], hsa-mir-221 [784], MEF2A [785], FOXC1 [786], YY1 [787], STAT3 [788] and BRCA1 [789] were significantly associated with cardiovascular disorders. E2F1 [265], BMI1 [260], VCAM1 [321], AGTR1 [64], hsa-mir-638 [790], hsa-mir-221 [791], FOXC1 [792], YY1 [793], STAT3 [794] and BRCA1 [795] provided a clear picture of the prognosis of patients with hypertension.…”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Acute kidney injury (AKI) is a type of renal disease occurs frequently in hospitalized patients, which may cause abnormal renal function and structure with increase in serum creatinine level with or without reduced urine output. With the incidence of AKI is increasing. However, the molecular mechanisms of AKI have not been elucidated. It is significant to further explore the molecular mechanisms of AKI. We downloaded the GSE139061 next generation sequencing (NGS) dataset from the Gene Expression Omnibus (GEO) database. Limma R bioconductor package was used to screen the differentially expressed genes (DEGs). Then, the enrichment analysis of DEGs in Gene Ontology (GO) function and REACTOME pathways was analyzed by g:Profiler. Next, the protein-protein interaction (PPI) network and modules was constructed and analyzed, and the hub genes were identified. Next, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were built. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. Overall, 956 DEGs were identified, including 478 up regulated and 478 down regulated genes. The enrichment functions and pathways of DEGs involve primary metabolic process, small molecule metabolic process, striated muscle contraction and metabolism. Topological analysis of the PPI network and module revealed that hub genes, including PPP1CC, RPS2, MDFI, BMI1, RPL23A, VCAM1, ALB, SNCA, DPP4 and RPL26L1, might be involved in the development of AKI. miRNA-hub gene and TF-hub gene regulatory networks revealed that miRNAs and TFs including hsa-mir-510-3p, hsa-mir-6086 and mir-146a-5p, MAX and PAX2, might be involved in the development of AKI. Various known and newtherapeutic targets were obtained via network analysis. The results of the current investigation might be beneficial for the diagnosis and treatment of AKI.

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“…HSD17B2 [109], EFNA5 [957], MMP12 [958], PROK1 [959], PRL (prolactin) [960], NLRP2 [961], NDNF (neuron derived neurotrophic factor) [962], MEI4 [963], CD24 [964], LHCGR (luteinizing hormone/choriogonadotropin receptor) [965], CD4 [966], FOXL2 [967], KDR (kinase insert domain receptor) [968], ADRB2 [969], CYP1A1 [970], NTN1 [971], MSTN (myostatin) [972], BMP2 [973], APOE (apolipoprotein E) [974], ACE (angiotensin I converting enzyme) [975], PGR (progesterone receptor) [976], GREM2 [977], ALPP (alkaline phosphatase, placental) [978], MMP3 [979], GDNF (glial cell derived neurotrophic factor) [980], FGFR2 [981], IL33 [982], NGF (nerve growth factor) [983], COMP (cartilage oligomeric matrix protein) [984], CECR2 [985], FST (follistatin) [986], GATA6 [987], PTGS2 [153], BDNF (brain derived neurotrophic factor) [988], SGK1 [989], ANGPTL4 [990], THBS1 [991], ID3 [992], NOX4 [993], IGF2BP1 [994], SALL4 [995], VDR (vitamin D receptor) [996], SULF1 [997], TLR2 [998], IL6 [999], GPC3 [1000], CCNO (cyclin O) [1001], IGFBP3 [1002], CHL1 [1003], NTRK2 [1004], SLIT2 [1005], APLN (apelin) [1006], NOTCH2 [1007], PGF (placental growth factor) [1008], LIF (LIF interleukin 6 family cytokine) [1009], CD200 [1010], TFPI2 [1011], KISS1 [752] and TRH (thyrotropin releasing hormone) [1012] were associated with a favorable prognosis for infertility. A altered expression level of VCAM1 [761], STRA6 [1013], COCH (cochlin) [1014], GRP (gastrin releasing peptide) [1015], AQP8 [1016], FABP4 [1017], ANO1 [1018], SOX6 [1019], TFAP2C [...…”

Section: Discussionmentioning

confidence: 99%

“…VCAM1 [615], ADRB2 [653] and DAPK1 [751] are associated to the risk of hypertension. VCAM1 [761], PRKCB (protein kinase C beta) [780], ADRB2 [808], PRKD1 [920], DAPK1 [947], ACTC1 [924] and NFIX (nuclear factor I X) [1660] are found to be associated with cardiovascular diseases. Studies show that VCAM1 [761], SNCA (synuclein alpha) [1026], ADRB2 [1073], FOXQ1 [1044], PRKD1 [1188], DAPK1 [947], ACTC1 [1192], CST1 [1030], NFIB (nuclear factor I B) [1198] and ERG (ETS transcription factor ERG) [1661] are involved in the process of inflammation.…”

Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein-protein interaction (PPI) network was constructed and modules were analysed using the Human Integrated Protein-Protein Interaction rEference (HIPIE) database and Cytoscape software, and hub genes were identified. Subsequantely, a network between miRNAs and hub genes, and network between TFss and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve (ROC) analysis was used to validate the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 and ACTC1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including TCF3 and CLOCK, were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment, and monitoring of endometriosis.

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ICAM-1 and VCAM-1: Gatekeepers in various inflammatory and cardiovascular disorders

Cited by 60 publications

References 90 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 novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

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