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已发表论文
全基因组药物靶点孟德尔随机化分析确定了治疗腰痛、椎间盘退变和坐骨神经痛的不同靶点
Authors Sun A, Li Z, Du Y, Liu H, Zhan Q, Liu Z
Received 25 May 2025
Accepted for publication 22 October 2025
Published 4 November 2025 Volume 2025:18 Pages 5769—5780
DOI https://doi.org/10.2147/JPR.S542713
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Alaa Abd-Elsayed
Aochuan Sun,1,* Zhuangzhuang Li,1,2,* Yike Du,1,3 Hao Liu,1 Qiuzhong Zhan,1,4 Zhengtang Liu1
1The Department of Geriatrics, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 2The Department of Geriatrics, Suzhou Hospital of Traditional Chinese Medicine, Suzhou, Jiangsu, People’s Republic of China; 3Graduate School, Beijing University of Chinese Medicine, Beijing, People’s Republic of China; 4Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Zhengtang Liu, The Department of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China, Email doctorzht@126.com
Objective: There are no effective drugs for the treatment of low back pain (LBP), intervertebral disk degeneration (IVDD), or sciatica. We aimed to identify potential therapeutic targets through druggable genome-wide Mendelian randomization (MR) analysis.
Methods: This study utilized large-scale expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs), integrating existing druggable genome data. Conducted two-sample MR analysis to estimate the causal relationships between druggable genes with LBP, IVDD, and sciatica. Furthermore, we employed Bayesian colocalization, summary data-based Mendelian randomization (SMR) analysis, and the Steiger filtering test to validate our results and identify therapeutic targets. Additionally, we used a phenome-wide MR approach to assess the side effects or other indications of the identified therapeutic targets.
Results: MR analysis identified 10 candidate druggable genes associated with LBP, 18 candidate druggable genes with IVDD, and 8 candidate druggable genes with sciatica. By applying Bayesian colocalization (posterior probability for H4> 80%), SMR analysis (P< 0.05), and the Steiger filtering test (TRUE), we identified one therapeutic target for LBP (P2RY13), four for IVDD (CAPN10, AKR1C2, BTN1A1, EIF2AK3), and four for sciatica (NT5C, GPX1, SUMO2, DAG1). Phenome-wide MR analysis revealed potential adverse cardiac metabolic effects associated with NT5C.
Conclusion: Our study integrated eQTL and pQTL data to identify nine phenotype-specific therapeutic targets for LBP, IVDD, and sciatica. These findings highlight potential candidates for future drug repurposing, experimental validation, and the development of mechanism-based therapies tailored to each condition.
Keywords: Mendelian randomization, colocalization, druggable genes, low back pain, intervertebral disk degeneration, sciatica
1The Department of Geriatrics, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 2The Department of Geriatrics, Suzhou Hospital of Traditional Chinese Medicine, Suzhou, Jiangsu, People’s Republic of China; 3Graduate School, Beijing University of Chinese Medicine, Beijing, People’s Republic of China; 4Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Zhengtang Liu, The Department of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China, Email doctorzht@126.com
Objective: There are no effective drugs for the treatment of low back pain (LBP), intervertebral disk degeneration (IVDD), or sciatica. We aimed to identify potential therapeutic targets through druggable genome-wide Mendelian randomization (MR) analysis.
Methods: This study utilized large-scale expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs), integrating existing druggable genome data. Conducted two-sample MR analysis to estimate the causal relationships between druggable genes with LBP, IVDD, and sciatica. Furthermore, we employed Bayesian colocalization, summary data-based Mendelian randomization (SMR) analysis, and the Steiger filtering test to validate our results and identify therapeutic targets. Additionally, we used a phenome-wide MR approach to assess the side effects or other indications of the identified therapeutic targets.
Results: MR analysis identified 10 candidate druggable genes associated with LBP, 18 candidate druggable genes with IVDD, and 8 candidate druggable genes with sciatica. By applying Bayesian colocalization (posterior probability for H4> 80%), SMR analysis (P< 0.05), and the Steiger filtering test (TRUE), we identified one therapeutic target for LBP (P2RY13), four for IVDD (CAPN10, AKR1C2, BTN1A1, EIF2AK3), and four for sciatica (NT5C, GPX1, SUMO2, DAG1). Phenome-wide MR analysis revealed potential adverse cardiac metabolic effects associated with NT5C.
Conclusion: Our study integrated eQTL and pQTL data to identify nine phenotype-specific therapeutic targets for LBP, IVDD, and sciatica. These findings highlight potential candidates for future drug repurposing, experimental validation, and the development of mechanism-based therapies tailored to each condition.
Keywords: Mendelian randomization, colocalization, druggable genes, low back pain, intervertebral disk degeneration, sciatica