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pmid:34421979

Bioinformatics Analysis Identifies Molecular Markers Regulating Development and Progression of Endometriosis and Potential Therapeutic Drugs.

Ying Peng · Cheng Peng · Zheng Fang · Gang Chen

Frontiers in genetics · 2021 doi:10.3389/fgene.2021.622683

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:34421979
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science-record
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2026-11-01T15:41:48Z
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2026-11-01T22:04:31Z
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paper object · 10 fields
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title
Bioinformatics Analysis Identifies Molecular Markers Regulating Development and Progression of Endometriosis and Potential Therapeutic Drugs.
doi
10.3389/fgene.2021.622683
pmid
34421979
authors array · 4 items
[0]
Ying Peng
[1]
Cheng Peng
[2]
Zheng Fang
[3]
Gang Chen
journal
Frontiers in genetics
year
2021
abstract
Endometriosis, a common disease that presents as polymorphism, invasiveness, and extensiveness, with clinical manifestations including dysmenorrhea, infertility, and menstrual abnormalities, seriously affects quality of life in women. To date, its underlying etiological mechanism of action and the associated regulatory genes remain unclear. This study aimed to identify molecular markers and elucidate mechanisms underlying the development and progression of endometriosis. Specifically, we downloaded five microarray expression datasets, namely, GSE11691, GSE23339, GSE25628, GSE7305, and GSE105764, from the Gene Expression Omnibus (GEO) database. These datasets, obtained from endometriosis tissues, alongside normal controls, were subjected to in-depth bioinformatics analysis for identification of differentially expressed genes (DEGs), followed by analysis of their function and pathways via gene ontology (GO) and KEGG pathway enrichment analyses. Moreover, we constructed a protein-protein interaction (PPI) network to explore the hub genes and modules, and then applied machine learning algorithms support vector machine-recursive feature elimination and least absolute shrinkage and selection operator (LASSO) analysis to identify key genes. Furthermore, we adopted the CIBERSORTx algorithm to estimate levels of immune cell infiltration while the connective map (CMAP) database was used to identify potential therapeutic drugs in endometriosis. As a result, a total of 423 DEGs, namely, 233 and 190 upregulated and downregulated, were identified. On the other hand, a total of 1,733 PPIs were obtained from the PPI network. The DEGs were mainly enriched in immune-related mechanisms. Furthermore, machine learning and LASSO algorithms identified three key genes, namely, apelin receptor (APLNR), C-C motif chemokine ligand 21 (CCL21), and Fc fragment of IgG receptor IIa (FCGR2A). Furthermore, 16 small molecular compounds associated with endometriosis treatment were identified, and their mechanism of action was also revealed. Taken together, the findings of this study provide new insights into the molecular factors regulating occurrence and progression of endometriosis and its underlying mechanism of action. The identified therapeutic drugs and molecular markers may have clinical significance in early diagnosis of endometriosis.
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ROC analysis was performed to demonstrate the diagnostic power of the three genes in the two external validation datasets.
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sections[24] "Supplementary Material" paragraph 3
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ROC analysis was performed to demonstrate the diagnostic power of the three genes in the two external validation datasets.
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
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sections[19] "Data Availability Statement" paragraph 2
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
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lncRNA expression profilings in ectopic and eutopic endometria of ovarian endometriosis.
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Euctopic and ectopic human endometrium (endometriosis)
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
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Gene expression profiles of endometriosis
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2026-11-01T15:39:54Z
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571
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Endometriosis transcription profiling
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2026-11-01T15:39:54Z
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
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570
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Homo sapiens
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Human endometriosis vs normal endometrium study - transcriptional profiling
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2026-11-01T15:39:55Z
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[14]
review.machine_critique
assays array · 1 item
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f2132e96ae6c7a4e
required_operations array · 3 items
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roc_auc
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source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
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Complete file
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To date, its underlying etiological mechanism of action and the associated regulatory genes remain unclear. This study aimed to identify molecular markers and elucidate mechanisms underlying the development and progression of endometriosis. Specifically, we downloaded five microarray expression datasets, namely, GSE11691, GSE23339, GSE25628, GSE7305, and GSE105764, from the Gene Expression Omnibus (GEO) database. These datasets, obtained from endometriosis tissues, alongside normal controls, were subjected to in-depth bioinformatics analysis for identification of differentially expressed genes (DEGs), followed by analysis of their function and pathways via gene ontology (GO) and KEGG pathway enrichment analyses. Moreover, we constructed a protein-protein interaction (PPI) network to explore the hub genes and modules, and then applied machine learning algorithms support vector machine-recursive feature elimination and least absolute shrinkage and selection operator (LASSO) analysis to identify key genes. Furthermore, we adopted the CIBERSORTx algorithm to estimate levels of immune cell infiltration while the connective map (CMAP) database was used to identify potential therapeutic drugs in endometriosis. As a result, a total of 423 DEGs, namely, 233 and 190 upregulated and downregulated, were identified. On the other hand, a total of 1,733 PPIs were obtained from the PPI network. The DEGs were mainly enriched in immune-related mechanisms. Furthermore, machine learning and LASSO algorithms identified three key genes, namely, apelin receptor (APLNR), C-C motif chemokine ligand 21 (CCL21), and Fc fragment of IgG receptor IIa (FCGR2A). Furthermore, 16 small molecular compounds associated with endometriosis treatment were identified, and their mechanism of action was also revealed. Taken together, the findings of this study provide new insights into the molecular factors regulating occurrence and progression of endometriosis and its underlying mechanism of action. 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