Farhangniya Mansoureh, Samadikuchaksaraei Ali, Mohamadi Farsani Farzaneh
Cellular and Molecular Research Center, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Health Metrics Research Center, Iranian Institute for Health Sciences Research, ACECR, Tehran, Iran.
Med J Islam Repub Iran. 2024 Jul 17;38:82. doi: 10.47176/mjiri.38.82. eCollection 2024.
The skin is the biggest organ in the body and has several important functions in protection and regulation. However, wound development can disrupt the natural healing process, leading to challenges such as chronic wounds, persistent infections, and impaired angiogenesis. These issues not only affect individuals' well-being but also pose significant economic burdens on healthcare systems. Despite advancements in wound care research, managing chronic wounds remains a pressing concern, with obstacles such as persistent infection and impaired angiogenesis hindering the healing process. Understanding the complex genetic pathways involved in wound healing is crucial for developing effective therapeutic strategies and reducing the socio-economic impact of chronic wounds. Weighted Gene Co-Expression Network Analysis (WGCNA) offers a promising approach to uncovering key genes and modules associated with different stages of wound healing, providing valuable insights for targeted interventions to enhance tissue repair and promote efficient wound healing.
Data collection involved retrieving microarray gene expression datasets from the Gene Expression Omnibus website, with 65 series selected according to inclusion and exclusion criteria. Preprocessing of raw data was performed using the Robust MultiArray Averaging approach for background correction, normalization, and gene expression calculation. Weighted Gene Co-Expression Network Analysis was employed to identify co-expression patterns among genes associated with wound healing processes. This involved steps such as network construction, topological analysis, module identification, and association with clinical traits. Functional analysis included enrichment analysis and identification of hub genes through gene-gene functional interaction network analysis using the GeneMANIA database.
The analysis using WGCNA indicated significant correlations between wound healing and the black, brown, and light green modules. These modules were further examined for their relevance to wound healing traits and subjected to functional enrichment analysis. A total of 16 genes were singled out as potential hub genes critical for wound healing. These hub genes were then scrutinized, revealing a gene-gene functional interaction network within the module network based on the KEGG enrichment database. Noteworthy pathways such as MAPK, EGFR, and ErbB signaling pathways, as well as essential cellular processes including autophagy and mitophagy, emerged as the most notable significant pathways.
We identified consensus modules relating to wound healing across nine microarray datasets. Among these, 16 hub genes were uncovered within the brown and black modules. KEGG enrichment analysis identified co-expression genes within these modules and highlighted pathways most closely associated with the development of wound healing traits, including autophagy and mitophagy. The hub genes identified in this study represent potential candidates for future research endeavors. These findings serve as a stepping stone toward further exploration of the implications of these co-expressed modules on wound healing traits.
皮肤是人体最大的器官，在保护和调节方面具有多种重要功能。然而，伤口的形成会扰乱自然愈合过程，导致诸如慢性伤口、持续性感染和血管生成受损等问题。这些问题不仅影响个人的健康，还对医疗保健系统造成重大经济负担。尽管伤口护理研究取得了进展，但慢性伤口的管理仍然是一个紧迫的问题，持续性感染和血管生成受损等障碍阻碍了愈合过程。了解伤口愈合中涉及的复杂基因途径对于制定有效的治疗策略和减少慢性伤口的社会经济影响至关重要。加权基因共表达网络分析（WGCNA）为揭示与伤口愈合不同阶段相关的关键基因和模块提供了一种有前景的方法，为有针对性的干预措施提供了有价值的见解，以增强组织修复并促进高效的伤口愈合。
数据收集包括从基因表达综合数据库网站检索微阵列基因表达数据集，根据纳入和排除标准选择了65个系列。使用稳健多阵列平均方法对原始数据进行预处理，以进行背景校正、归一化和基因表达计算。采用加权基因共表达网络分析来识别与伤口愈合过程相关的基因之间的共表达模式。这包括网络构建、拓扑分析、模块识别以及与临床特征的关联等步骤。功能分析包括富集分析以及通过使用GeneMANIA数据库的基因-基因功能相互作用网络分析来识别枢纽基因。
使用WGCNA进行的分析表明伤口愈合与黑色、棕色和浅绿色模块之间存在显著相关性。对这些模块进一步检查其与伤口愈合特征的相关性，并进行功能富集分析。总共筛选出16个基因作为对伤口愈合至关重要的潜在枢纽基因。然后对这些枢纽基因进行仔细研究，基于KEGG富集数据库揭示了模块网络内的基因-基因功能相互作用网络。诸如MAPK、EGFR和ErbB信号通路等值得注意的途径，以及包括自噬和线粒体自噬在内的基本细胞过程，成为最显著的重要途径。
我们在九个微阵列数据集中确定了与伤口愈合相关的共识模块。其中，在棕色和黑色模块中发现了16个枢纽基因。KEGG富集分析确定了这些模块内的共表达基因，并突出了与伤口愈合特征发展最密切相关的途径，包括自噬和线粒体自噬。本研究中确定的枢纽基因代表了未来研究努力的潜在候选者。这些发现为进一步探索这些共表达模块对伤口愈合特征的影响奠定了基础。
