{"title":"程序性细胞死亡和增生性疤痕之间的因果关系:多组学孟德尔随机化的综合分析和初步实验验证","authors":"Yushen Zhang, Chenyuyao Zhao, Ran Zhao","doi":"10.1016/j.burns.2025.107667","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>This study aims to explore the causal relationship between programmed cell death (PCD) genes and the formation of hypertrophic scars (HS) using integrative multi-omics analysis (including DNA methylation, gene expression, and protein abundance) alongside preliminary experimental validation.</div></div><div><h3>Methods</h3><div>We leveraged publicly available databases (eQTL Gen, UKB-PPP, and FinnGen) to obtain quantitative trait loci (QTLs) data of DNA methylation, gene expression and protein abundance. We employed Mendelian randomization (MR) approaches to uncover causal relationships and validate robustness. The methods used included inverse variance weighted (IVW) analysis, false discovery rate (FDR), Cochran's Q test, I² statistic, MR-Egger regression, MR-PRESSO, leave-one-out method, co-localization analysis, and Steiger filtering test. Then, the multi-omic MR results were integrated and three tiers of genes were identified. Further, the tier 1 genes were chosen to perform drug prediction in DSigDB and molecular docking analyses with Autodock Vina. Lastly, the effects of the selected genes and drugs in HS were validated at both the tissue and cellular levels.</div></div><div><h3>Results</h3><div>Through integrating multi-omics data, we identified one tier 1 gene (GLB1), twelve tier 2 genes (including DAPK2, AP4E1, ARSA, CTSF, MSH6, NEDD4, PDK1, PELI3, RB1, UNC13D, CTSC, and GZMB), and two tier 3 genes (NOS3 and ITGA6), all of which show varying associations with HS. Particularly, the GLB1(cg05120113) was causal associated with HS risk in DNA methylation (OR=1.0972, 95 % CI: 1.0532–1.1430, FDR=0.0163), gene expression (OR=1.2923, 95 % CI: 1.1816–1.4135, FDR<0.001) and protein abundance (OR=1.5430, 95 % CI: 1.3296–1.7905, FDR<0.001). The candidate drugs for GLB1 included Fulvestrant (adjusted <em>P</em> = 0.046, Affinity=-8.8 kcal/mol) and Cyperquat (adjusted <em>P</em> = 0.036, Affinity=-6.2 kcal/mol). Further, the GLB1 expression and inhibitory effect of Fulvestrant were validated in HS tissues and HSFs. Additionally, significant changes in the mRNA and protein expression levels of fibrosis-related markers, including TGF-β1 and α-SMA, were observed in HSFs.</div></div><div><h3>Findings</h3><div>This study provides robust evidence for the causal involvement of PCD genes in HS formation and identified GLB1 along with 14 other potential genes. Fulvestrant demonstrated therapeutic potential for HS by modulating fibrosis-related pathways in fibroblasts.</div></div>","PeriodicalId":50717,"journal":{"name":"Burns","volume":"51 8","pages":"Article 107667"},"PeriodicalIF":2.9000,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Causal links between programmed cell death and hypertrophic scars: Integrative analysis of multi-omics Mendelian randomization and preliminary experimental validation\",\"authors\":\"Yushen Zhang, Chenyuyao Zhao, Ran Zhao\",\"doi\":\"10.1016/j.burns.2025.107667\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>This study aims to explore the causal relationship between programmed cell death (PCD) genes and the formation of hypertrophic scars (HS) using integrative multi-omics analysis (including DNA methylation, gene expression, and protein abundance) alongside preliminary experimental validation.</div></div><div><h3>Methods</h3><div>We leveraged publicly available databases (eQTL Gen, UKB-PPP, and FinnGen) to obtain quantitative trait loci (QTLs) data of DNA methylation, gene expression and protein abundance. We employed Mendelian randomization (MR) approaches to uncover causal relationships and validate robustness. The methods used included inverse variance weighted (IVW) analysis, false discovery rate (FDR), Cochran's Q test, I² statistic, MR-Egger regression, MR-PRESSO, leave-one-out method, co-localization analysis, and Steiger filtering test. Then, the multi-omic MR results were integrated and three tiers of genes were identified. Further, the tier 1 genes were chosen to perform drug prediction in DSigDB and molecular docking analyses with Autodock Vina. Lastly, the effects of the selected genes and drugs in HS were validated at both the tissue and cellular levels.</div></div><div><h3>Results</h3><div>Through integrating multi-omics data, we identified one tier 1 gene (GLB1), twelve tier 2 genes (including DAPK2, AP4E1, ARSA, CTSF, MSH6, NEDD4, PDK1, PELI3, RB1, UNC13D, CTSC, and GZMB), and two tier 3 genes (NOS3 and ITGA6), all of which show varying associations with HS. Particularly, the GLB1(cg05120113) was causal associated with HS risk in DNA methylation (OR=1.0972, 95 % CI: 1.0532–1.1430, FDR=0.0163), gene expression (OR=1.2923, 95 % CI: 1.1816–1.4135, FDR<0.001) and protein abundance (OR=1.5430, 95 % CI: 1.3296–1.7905, FDR<0.001). The candidate drugs for GLB1 included Fulvestrant (adjusted <em>P</em> = 0.046, Affinity=-8.8 kcal/mol) and Cyperquat (adjusted <em>P</em> = 0.036, Affinity=-6.2 kcal/mol). Further, the GLB1 expression and inhibitory effect of Fulvestrant were validated in HS tissues and HSFs. Additionally, significant changes in the mRNA and protein expression levels of fibrosis-related markers, including TGF-β1 and α-SMA, were observed in HSFs.</div></div><div><h3>Findings</h3><div>This study provides robust evidence for the causal involvement of PCD genes in HS formation and identified GLB1 along with 14 other potential genes. Fulvestrant demonstrated therapeutic potential for HS by modulating fibrosis-related pathways in fibroblasts.</div></div>\",\"PeriodicalId\":50717,\"journal\":{\"name\":\"Burns\",\"volume\":\"51 8\",\"pages\":\"Article 107667\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Burns\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0305417925002967\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CRITICAL CARE MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Burns","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305417925002967","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
Causal links between programmed cell death and hypertrophic scars: Integrative analysis of multi-omics Mendelian randomization and preliminary experimental validation
Objective
This study aims to explore the causal relationship between programmed cell death (PCD) genes and the formation of hypertrophic scars (HS) using integrative multi-omics analysis (including DNA methylation, gene expression, and protein abundance) alongside preliminary experimental validation.
Methods
We leveraged publicly available databases (eQTL Gen, UKB-PPP, and FinnGen) to obtain quantitative trait loci (QTLs) data of DNA methylation, gene expression and protein abundance. We employed Mendelian randomization (MR) approaches to uncover causal relationships and validate robustness. The methods used included inverse variance weighted (IVW) analysis, false discovery rate (FDR), Cochran's Q test, I² statistic, MR-Egger regression, MR-PRESSO, leave-one-out method, co-localization analysis, and Steiger filtering test. Then, the multi-omic MR results were integrated and three tiers of genes were identified. Further, the tier 1 genes were chosen to perform drug prediction in DSigDB and molecular docking analyses with Autodock Vina. Lastly, the effects of the selected genes and drugs in HS were validated at both the tissue and cellular levels.
Results
Through integrating multi-omics data, we identified one tier 1 gene (GLB1), twelve tier 2 genes (including DAPK2, AP4E1, ARSA, CTSF, MSH6, NEDD4, PDK1, PELI3, RB1, UNC13D, CTSC, and GZMB), and two tier 3 genes (NOS3 and ITGA6), all of which show varying associations with HS. Particularly, the GLB1(cg05120113) was causal associated with HS risk in DNA methylation (OR=1.0972, 95 % CI: 1.0532–1.1430, FDR=0.0163), gene expression (OR=1.2923, 95 % CI: 1.1816–1.4135, FDR<0.001) and protein abundance (OR=1.5430, 95 % CI: 1.3296–1.7905, FDR<0.001). The candidate drugs for GLB1 included Fulvestrant (adjusted P = 0.046, Affinity=-8.8 kcal/mol) and Cyperquat (adjusted P = 0.036, Affinity=-6.2 kcal/mol). Further, the GLB1 expression and inhibitory effect of Fulvestrant were validated in HS tissues and HSFs. Additionally, significant changes in the mRNA and protein expression levels of fibrosis-related markers, including TGF-β1 and α-SMA, were observed in HSFs.
Findings
This study provides robust evidence for the causal involvement of PCD genes in HS formation and identified GLB1 along with 14 other potential genes. Fulvestrant demonstrated therapeutic potential for HS by modulating fibrosis-related pathways in fibroblasts.
期刊介绍:
Burns aims to foster the exchange of information among all engaged in preventing and treating the effects of burns. The journal focuses on clinical, scientific and social aspects of these injuries and covers the prevention of the injury, the epidemiology of such injuries and all aspects of treatment including development of new techniques and technologies and verification of existing ones. Regular features include clinical and scientific papers, state of the art reviews and descriptions of burn-care in practice.
Topics covered by Burns include: the effects of smoke on man and animals, their tissues and cells; the responses to and treatment of patients and animals with chemical injuries to the skin; the biological and clinical effects of cold injuries; surgical techniques which are, or may be relevant to the treatment of burned patients during the acute or reconstructive phase following injury; well controlled laboratory studies of the effectiveness of anti-microbial agents on infection and new materials on scarring and healing; inflammatory responses to injury, effectiveness of related agents and other compounds used to modify the physiological and cellular responses to the injury; experimental studies of burns and the outcome of burn wound healing; regenerative medicine concerning the skin.