Computational screening of biomarkers and potential drugs for arthrofibrosis based on combination of sequencing and large nature language model
© 2023 The Authors..
Background: Arthrofibrosis (AF) is a fibrotic joint disease resulting from excessive collagen production and fibrous scar formation after total knee arthroplasty (TKA). This devastating complication may cause consistent pain and dramatically reduction of functionality. Unfortunately, the conservative treatments to prevent the AF in the early stage are largely unknown due to the lack of specific biomarkers and reliable therapeutic targets.
Methods: In this study, we extracted1782 fibrosis related genes (FRGs) from 373,461published literature based on the large natural language processing models (ChatGPT) and intersected with the 2750 differential expressed genes (DEGs) from mRNA microarray (GSE135854). A total of 311 potential AF biomarker genes (PABGs) were obtained and functional analysis were performed including gene ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Subsequently, we accomplished validation in AF animal models with immobilization of the unilateral knee joints of 16 rabbits for 1-week, 2-weeks, 3-weeks and 4-weeks. Finally, we tested the biomarkers in a retrospective cohort enrolled 35 AF patients and 35 control group patients.
Results: We identified G-protein-coupled receptor 17 (GPR17) as a reliable therapeutic biomarker for AF diagnosis with higher AUC (0.819) in the ROC curve. A total of 21 potential drugs targeted to GPR17 were screened. Among them, pranlukast and montelukast have achieved therapeutic effect in animal models. In addition, we established an online AF database for data integration (https://chenxi2023.shinyapps.io/afdbv1).
Conclusions: These results unveiling therapeutic biomarkers for AF diagnosis, and provide potential drugs for clinical treatment.
The translational potential of this article: Our study demonstrated that GPR17 holds significant promise as a potential biomarker and therapeutic target for arthrofibrosis. Moreover, pranlukast and montelukast targeted to GPR17 that could be instrumental in the treatment of AF.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:44 |
|---|---|
| Enthalten in: |
Journal of orthopaedic translation - 44(2024) vom: 31. Jan., Seite 102-113 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chen, Xi [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Revised 03.02.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1016/j.jot.2023.11.002 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM367939800 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM367939800 | ||
| 003 | DE-627 | ||
| 005 | 20240203232031.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240202s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jot.2023.11.002 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1279.xml |
| 035 | |a (DE-627)NLM367939800 | ||
| 035 | |a (NLM)38304615 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chen, Xi |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Computational screening of biomarkers and potential drugs for arthrofibrosis based on combination of sequencing and large nature language model |
| 264 | 1 | |c 2024 | |
| 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 Revised 03.02.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023 The Authors. | ||
| 520 | |a Background: Arthrofibrosis (AF) is a fibrotic joint disease resulting from excessive collagen production and fibrous scar formation after total knee arthroplasty (TKA). This devastating complication may cause consistent pain and dramatically reduction of functionality. Unfortunately, the conservative treatments to prevent the AF in the early stage are largely unknown due to the lack of specific biomarkers and reliable therapeutic targets | ||
| 520 | |a Methods: In this study, we extracted1782 fibrosis related genes (FRGs) from 373,461published literature based on the large natural language processing models (ChatGPT) and intersected with the 2750 differential expressed genes (DEGs) from mRNA microarray (GSE135854). A total of 311 potential AF biomarker genes (PABGs) were obtained and functional analysis were performed including gene ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Subsequently, we accomplished validation in AF animal models with immobilization of the unilateral knee joints of 16 rabbits for 1-week, 2-weeks, 3-weeks and 4-weeks. Finally, we tested the biomarkers in a retrospective cohort enrolled 35 AF patients and 35 control group patients | ||
| 520 | |a Results: We identified G-protein-coupled receptor 17 (GPR17) as a reliable therapeutic biomarker for AF diagnosis with higher AUC (0.819) in the ROC curve. A total of 21 potential drugs targeted to GPR17 were screened. Among them, pranlukast and montelukast have achieved therapeutic effect in animal models. In addition, we established an online AF database for data integration (https://chenxi2023.shinyapps.io/afdbv1) | ||
| 520 | |a Conclusions: These results unveiling therapeutic biomarkers for AF diagnosis, and provide potential drugs for clinical treatment | ||
| 520 | |a The translational potential of this article: Our study demonstrated that GPR17 holds significant promise as a potential biomarker and therapeutic target for arthrofibrosis. Moreover, pranlukast and montelukast targeted to GPR17 that could be instrumental in the treatment of AF | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a AF | |
| 650 | 4 | |a GPR17 | |
| 650 | 4 | |a Stiff knee | |
| 650 | 4 | |a biomarker | |
| 650 | 4 | |a macrophage | |
| 700 | 1 | |a Li, Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Ziyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Yixin |e verfasserin |4 aut | |
| 700 | 1 | |a Chu, Ming |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of orthopaedic translation |d 2014 |g 44(2024) vom: 31. Jan., Seite 102-113 |w (DE-627)NLM254880614 |x 2214-031X |7 nnns |
| 773 | 1 | 8 | |g volume:44 |g year:2024 |g day:31 |g month:01 |g pages:102-113 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.jot.2023.11.002 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 44 |j 2024 |b 31 |c 01 |h 102-113 | ||