Identification of NFASC and CHL1 as Two Novel Hub Genes in Endometriosis Using Integrated Bioinformatic Analysis and Experimental Verification

Chen, Pei; Yao, Mengyun; Fang, Tao; Ye, Chaoshuang; Du, Yongjiang; Yang, Jin; Wu, Renyi

doi:10.2147/pgpm.s354957

Cited by 5 publications

(2 citation statements)

References 52 publications

(58 reference statements)

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“…Signaling pathways include extracellular matrix organization [101], nervous system development [102], signal transduction [103], hemostasis [104], muscle contraction [105], signaling by retinoic acid [106] and diseases of glycosylation [107] were responsible for advancement of endometriosis. Altered expression of L1CAM [108], HSD17B2 [109], VCAM1 [110], SOX6 [111], FGF10 [112], MMP12 [113], CCR1 [114], PROK1 [115], PRL (prolactin) [116], TIMP3 [117], ADAMTS9 [118], NDNF (neuron derived neurotrophic factor) [119], LHCGR (luteinizing hormone/choriogonadotropin receptor) [120], PDGFB (platelet derived growth factor subunit B) [121], LDLR (low density lipoprotein receptor) [122], CD4 [123], FOXL2 [124], TRPA1 [125], ADRB2 [126], PLAU (plasminogen activator, urokinase) [127], EPCAM (epithelial cell adhesion molecule) [128], UCN2 [129], CYP1A1 [130], NTN1 [131], IL15 [132], BMP2 [133], APOE (apolipoprotein E) [134], CASP1 [135], ABCG2 [136], ACE (angiotensin I converting enzyme) [137], PGR (progesterone receptor) [138], ALPP (alkaline phosphatase, placental) [139], LPAR4 [140], ATRNL1 [141], HLA-C [142], MMP3 [143], PDLIM3 [144], NFASC (neurofascin) [145], IL33 [146], NGF (nerve growth factor) [147], COMP (cartilage oligomeric matrix protein) [148], FST (follistatin) [149], EFEMP1 [150], GATA6 [151], TCF21 [152], PTGS2 [153], HOXC8 [154], AKR1C3 [155], BDNF (brain derived neurotrophic factor) [119], EPHA3 [156], INHBA (inhibin subunit beta A) [157], RAP1GAP [158], TLR3 [159], NOX4 [160], TGFBI (transforming growth factor beta induced) [161], IGF2BP1 [162], DLX5 [163], VDR (vitamin D receptor) [164], F...…”

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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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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“…The polyclonal anti-CLCA4 was purchased from Novus (NBP1-86688). IHC was performed, and the slides were scored as described previously (13).…”

Section: Immunohistochemistry (Ihc) Analysismentioning

confidence: 99%

A novel enterocyte-related 4-gene signature for predicting prognosis in colon adenocarcinoma

Cheng

Wei²,

et al. 2022

Front. Immunol.

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BackgroundColon adenocarcinoma (COAD) is a fatal disease, and its cases are constantly increasing worldwide. Further, the therapeutic and management strategies for patients with COAD are still unsatisfactory due to the lack of accurate patient classification and prognostic models. Therefore, our study aims to identify prognostic markers in patients with COAD and construct a cell subtype-specific prognostic model with high accuracy and robustness.MethodsSingle-cell transcriptomic data of six samples were retrieved from the Gene expression omnibus (GEO) database. The cluster annotation and cell-cell communication analysis identified enterocytes as a key player mediating signal communication networks. A four-gene signature prognostic model was constructed based on the enterocyte-related differentially expressed genes (ERDEGs) in patients with COAD of the Cancer Genome Atlas cohort. The prognostic model was validated on three external validation cohorts from the GEO database. The correlation between immune cell infiltration, immunotherapy response, drug sensitivity, and the four-gene signature prognostic model was investigated. Finally, immunohistochemistry (IHC) was performed to determine the expression of the four genes.ResultsWe found that the proportion of epithelial cells was obviously large in COAD samples. Further analysis of epithelial cells showed that the activity of the enterocytes was highest in the cell-cell communication network. Based on enterocyte data, 30 ERDEGs were identified and a 4-gene prognostic model including CPM, CLCA4, ELOVL6, and ATP2A3 was developed and validated. The risk score derived from this model was considered as an independent variable factor to predict overall survival. The patients were divided into high- and low-risk groups based on the median riskscore value. The correlation between immune cell infiltration, immunotherapy response, immune status, clinical characteristics, drug sensitivity, and risk score was analyzed. IHC confirmed the expression of signature genes in tissues from normal individuals, patients with polyps, and COAD.ConclusionIn this study, we constructed and validated a novel four-gene signature prognostic model, which could effectively predict the response to immunotherapy and overall survival in patients with COAD. More importantly, this model provides useful insight into the management of colon cancer patients and aids in designing personalized therapy.

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Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis

Hosseini

Hammami

Kazemi

2023

J Assist Reprod Genet

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Identification of NFASC and CHL1 as Two Novel Hub Genes in Endometriosis Using Integrated Bioinformatic Analysis and Experimental Verification

Cited by 5 publications

References 52 publications

Screening and identification of key biomarkers associated with endometriosis 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

A novel enterocyte-related 4-gene signature for predicting prognosis in colon adenocarcinoma

Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis

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