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pmid:36036283

Bioinformatics analysis to obtain critical genes regulated in subcutaneous adipose tissue after bariatric surgery.

Shuai Chen · Yicheng Jiang · Xiaoyang Qi · Peng Song · Liming Tang · Hanyang Liu

Adipocyte · 2022 doi:10.1080/21623945.2022.2115212

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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pmid:36036283
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science-record
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2026-11-01T18:15:13Z
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2026-11-01T22:49:55Z
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paper object · 10 fields
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title
Bioinformatics analysis to obtain critical genes regulated in subcutaneous adipose tissue after bariatric surgery.
doi
10.1080/21623945.2022.2115212
pmid
36036283
authors array · 6 items
[0]
Shuai Chen
[1]
Yicheng Jiang
[2]
Xiaoyang Qi
[3]
Peng Song
[4]
Liming Tang
[5]
Hanyang Liu
journal
Adipocyte
year
2022
abstract
Bariatric surgery (BS) is a dependable method for managing obesity and metabolic diseases, however, the regulatory processes of lipid metabolism are still not well elucidated. Differentially expressed genes (DEGs) were analysed through three transcriptomic datasets of GSE29409, GSE59034 and GSE72158 from the GEO database regarding subcutaneous adipose tissue (SAT) after BS, and 37 DEGs were identified. The weighted gene co-expression network analysis (WGCNA), last absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms further screened four key genes involved in the regulation of STMN2, SFRP4, APOE and MXRA5. The GSE53376 dataset was used to further confirm the differential expression of SFRP4, APOE and MXRA5 in the postoperative period. GSEA analysis reveals activation of immune-related regulatory pathways after surgery. Finally, the silencing of MXRA5 was found by experimental methods to affect the expression of PPARγ and CEBPα during the differentiation of preadipocytes, as well as to affect the formation of lipid droplets. In conclusion, SAT immunoregulation was mobilized after BS, while MXRA5 was involved in the regulation of lipid metabolism.
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A co-expression network was formed through the WGCNA R package [27] in order to find the most relevant modules to the phenotype.
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A co-expression network was formed through the WGCNA R package [27] in order to find the most relevant modules to the phenotype.
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The weighted gene co-expression network analysis (WGCNA), last absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms further screened four key genes involved in the regulation of STMN2, SFRP4, APOE and MXRA5.
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The weighted gene co-expression network analysis (WGCNA), last absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms further screened four key genes involved in the regulation of STMN2, SFRP4, APOE and MXRA5.
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The weighted gene co-expression network analysis (WGCNA), last absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms further screened four key genes involved in the regulation of STMN2, SFRP4, APOE and MXRA5.
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Adipose transcriptome and microRNA profiles after surgery-induced weight loss [mRNA]
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A Memory for Obesity in Adipose Tissue
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Adipose tissue remodeling during drastic weight loss
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sed genes (DEGs) were analysed through three transcriptomic datasets of GSE29409, GSE59034 and GSE72158
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A co-expression network was formed through the WGCNA R package [27] in order to find the most relevant modules to the phenotype.
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Complete file
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Differentially expressed genes (DEGs) were analysed through three transcriptomic datasets of GSE29409, GSE59034 and GSE72158 from the GEO database regarding subcutaneous adipose tissue (SAT) after BS, and 37 DEGs were identified. The weighted gene co-expression network analysis (WGCNA), last absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms further screened four key genes involved in the regulation of STMN2, SFRP4, APOE and MXRA5. The GSE53376 dataset was used to further confirm the differential expression of SFRP4, APOE and MXRA5 in the postoperative period. GSEA analysis reveals activation of immune-related regulatory pathways after surgery. Finally, the silencing of MXRA5 was found by experimental methods to affect the expression of PPARγ and CEBPα during the differentiation of preadipocytes, as well as to affect the formation of lipid droplets. 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