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基于测序与大型自然语言模型相结合的关节纤维化生物标志物及潜在药物的计算筛选

Computational screening of biomarkers and potential drugs for arthrofibrosis based on combination of sequencing and large nature language model.

作者信息

Chen Xi, Li Cheng, Wang Ziyuan, Zhou Yixin, Chu Ming

机构信息

Department of Adult Joint Reconstructive Surgery, Beijing Jishuitan Hospital, Capital Medical University, 31 East Xinjiekou Street, Beijing, 100035, China.

Department of Immunology, School of Basic Medical Sciences, Peking University, Beijing, 100191, China.

出版信息

J Orthop Translat. 2024 Jan 20;44:102-113. doi: 10.1016/j.jot.2023.11.002. eCollection 2024 Jan.

Abstract

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.

摘要

背景

关节纤维化(AF)是一种纤维化关节疾病,由全膝关节置换术(TKA)后胶原蛋白过度产生和纤维瘢痕形成所致。这种严重的并发症可能导致持续疼痛并显著降低关节功能。不幸的是,由于缺乏特异性生物标志物和可靠的治疗靶点,早期预防AF的保守治疗方法很大程度上尚不明确。

方法

在本研究中,我们基于大型自然语言处理模型(ChatGPT)从373461篇已发表文献中提取了1782个纤维化相关基因(FRG),并与来自mRNA微阵列(GSE135854)的2750个差异表达基因(DEG)进行交叉分析。共获得311个潜在的AF生物标志物基因(PABG),并进行了功能分析,包括基因本体(GO)注释和京都基因与基因组百科全书(KEGG)通路富集分析。随后,我们在16只兔子的单侧膝关节固定1周、2周、3周和4周的AF动物模型中完成了验证。最后,我们在一个纳入35例AF患者和35例对照组患者的回顾性队列中对生物标志物进行了检测。

结果

我们确定G蛋白偶联受体17(GPR17)是用于AF诊断的可靠治疗生物标志物,在ROC曲线中的AUC较高(0.819)。共筛选出21种靶向GPR17的潜在药物。其中,普仑司特和孟鲁司特在动物模型中已取得治疗效果。此外,我们建立了一个用于数据整合的在线AF数据库(https://chenxi2023.shinyapps.io/afdbv1)。

结论

这些结果揭示了用于AF诊断的治疗生物标志物,并为临床治疗提供了潜在药物。

本文的转化潜力

我们的研究表明,GPR17作为关节纤维化的潜在生物标志物和治疗靶点具有巨大潜力。此外,靶向GPR17的普仑司特和孟鲁司特可能有助于AF的治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f9c/10831815/ec7151ce3506/ga1.jpg

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