RNA sequencing and bioinformatics analysis of differentially expressed genes in the peripheral serum of ankylosing spondylitis patients
© 2023. The Author(s)..
BACKGROUND: Ankylosing spondylitis (AS) is a chronic progressive autoimmune disease characterized by spinal and sacroiliac arthritis, but its pathogenesis and genetic basis are largely unclear.
METHODS: We randomly selected three serum samples each from an AS and a normal control (NC) group for high-throughput sequencing followed by using edgeR to find differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes, Reactome pathway analyses, and Gene Set Enrichment Analysis were used to comprehensively analyze the possible functions and pathways involved with these DEGs. Protein-protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of these DEGs were identified using MCODE and CytoHubba plugins. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate the expression levels of candidate genes in serum samples from AS patients and healthy controls.
RESULTS: We successfully identified 100 significant DEGs in serum. When we compared them with the NC group, 49 of these genes were upregulated in AS patients and 51 were downregulated. GO function and pathway enrichment analysis indicated that these DEGs were mainly enriched in several signaling pathways associated with endoplasmic reticulum stress, including protein processing in the endoplasmic reticulum, unfolded protein response, and ubiquitin-mediated proteolysis. We also constructed a PPI network and identified the highly connected top 10 hub genes. The expression levels of the candidate hub genes PPARG, MDM2, DNA2, STUB1, UBTF, and SLC25A37 were then validated by RT-qPCR analysis. Finally, receiver operating characteristic curve analysis suggested that PPARG and MDM2 may be the potential biomarkers of AS.
CONCLUSIONS: These findings may help to further elucidate the pathogenesis of AS and provide valuable potential gene biomarkers or targets for the diagnosis and treatment of AS.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
|---|---|
| Enthalten in: |
Journal of orthopaedic surgery and research - 18(2023), 1 vom: 30. Mai, Seite 394 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Bie, Yongchen [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Ankylosing spondylitis |
|---|
| Anmerkungen: |
Date Completed 01.06.2023 Date Revised 01.06.2023 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1186/s13018-023-03871-w |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM357563239 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM357563239 | ||
| 003 | DE-627 | ||
| 005 | 20231226072847.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1186/s13018-023-03871-w |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1191.xml |
| 035 | |a (DE-627)NLM357563239 | ||
| 035 | |a (NLM)37254181 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Bie, Yongchen |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a RNA sequencing and bioinformatics analysis of differentially expressed genes in the peripheral serum of ankylosing spondylitis patients |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 01.06.2023 | ||
| 500 | |a Date Revised 01.06.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a BACKGROUND: Ankylosing spondylitis (AS) is a chronic progressive autoimmune disease characterized by spinal and sacroiliac arthritis, but its pathogenesis and genetic basis are largely unclear | ||
| 520 | |a METHODS: We randomly selected three serum samples each from an AS and a normal control (NC) group for high-throughput sequencing followed by using edgeR to find differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes, Reactome pathway analyses, and Gene Set Enrichment Analysis were used to comprehensively analyze the possible functions and pathways involved with these DEGs. Protein-protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of these DEGs were identified using MCODE and CytoHubba plugins. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate the expression levels of candidate genes in serum samples from AS patients and healthy controls | ||
| 520 | |a RESULTS: We successfully identified 100 significant DEGs in serum. When we compared them with the NC group, 49 of these genes were upregulated in AS patients and 51 were downregulated. GO function and pathway enrichment analysis indicated that these DEGs were mainly enriched in several signaling pathways associated with endoplasmic reticulum stress, including protein processing in the endoplasmic reticulum, unfolded protein response, and ubiquitin-mediated proteolysis. We also constructed a PPI network and identified the highly connected top 10 hub genes. The expression levels of the candidate hub genes PPARG, MDM2, DNA2, STUB1, UBTF, and SLC25A37 were then validated by RT-qPCR analysis. Finally, receiver operating characteristic curve analysis suggested that PPARG and MDM2 may be the potential biomarkers of AS | ||
| 520 | |a CONCLUSIONS: These findings may help to further elucidate the pathogenesis of AS and provide valuable potential gene biomarkers or targets for the diagnosis and treatment of AS | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Ankylosing spondylitis | |
| 650 | 4 | |a Biomarker | |
| 650 | 4 | |a Differentially expressed genes | |
| 650 | 4 | |a RNA sequencing | |
| 650 | 4 | |a Serum | |
| 650 | 7 | |a PPAR gamma |2 NLM | |
| 650 | 7 | |a Biomarkers |2 NLM | |
| 650 | 7 | |a STUB1 protein, human |2 NLM | |
| 650 | 7 | |a EC 2.3.2.27 |2 NLM | |
| 650 | 7 | |a Ubiquitin-Protein Ligases |2 NLM | |
| 650 | 7 | |a EC 2.3.2.27 |2 NLM | |
| 700 | 1 | |a Zheng, Xiujun |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xiaojiong |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xiangyun |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Liqin |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Yuanliang |e verfasserin |4 aut | |
| 700 | 1 | |a Kou, Jianqiang |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of orthopaedic surgery and research |d 2006 |g 18(2023), 1 vom: 30. Mai, Seite 394 |w (DE-627)NLM167010948 |x 1749-799X |7 nnns |
| 773 | 1 | 8 | |g volume:18 |g year:2023 |g number:1 |g day:30 |g month:05 |g pages:394 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1186/s13018-023-03871-w |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 18 |j 2023 |e 1 |b 30 |c 05 |h 394 | ||