Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning

肌萎缩 骨质疏松症 候选基因 Lasso(编程语言) 接收机工作特性 基因 医学 生物信息学 生物 计算机科学 遗传学 病理 内科学 万维网
作者
Xiaoli Zhou,Lina Zhao,Zepei Zhang,Yang Chen,Guangdong Chen,Jun Miao,Xiaohui Li
出处
期刊:BMC Musculoskeletal Disorders [Springer Nature]
卷期号:25 (1): 435-435 被引量:2
标识
DOI:10.1186/s12891-024-07555-2
摘要

Abstract Background Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia. Methods Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein–protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated. Results The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93). Conclusion We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
wxy发布了新的文献求助10
刚刚
阳先森发布了新的文献求助10
刚刚
77发布了新的文献求助10
1秒前
xuxin完成签到 ,获得积分10
1秒前
2秒前
2秒前
小佳发布了新的文献求助10
4秒前
ivyjianjie完成签到,获得积分10
4秒前
西瓜皮完成签到 ,获得积分10
4秒前
Lee完成签到,获得积分10
5秒前
5秒前
7秒前
8秒前
9秒前
wangzx完成签到,获得积分10
9秒前
酷酷的听筠完成签到,获得积分10
9秒前
9秒前
12day完成签到,获得积分10
9秒前
9秒前
weixiao发布了新的文献求助10
10秒前
10秒前
11秒前
爱吃冰糖葫芦关注了科研通微信公众号
11秒前
11秒前
勤恳雅莉应助科研通管家采纳,获得10
12秒前
研友_VZG7GZ应助科研通管家采纳,获得10
12秒前
12秒前
斯文败类应助科研通管家采纳,获得10
12秒前
wanci应助科研通管家采纳,获得10
12秒前
打打应助科研通管家采纳,获得10
12秒前
12秒前
思源应助科研通管家采纳,获得10
12秒前
SciGPT应助科研通管家采纳,获得10
12秒前
小蘑菇应助科研通管家采纳,获得10
12秒前
GR应助科研通管家采纳,获得30
12秒前
12秒前
yyup应助科研通管家采纳,获得20
12秒前
丘比特应助老实的百招采纳,获得10
12秒前
12秒前
14秒前
高分求助中
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 临床微生物学程序手册,多卷,第5版 2000
List of 1,091 Public Pension Profiles by Region 1621
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] | NHBS Field Guides & Natural History 1500
The Victim–Offender Overlap During the Global Pandemic: A Comparative Study Across Western and Non-Western Countries 1000
Lloyd's Register of Shipping's Approach to the Control of Incidents of Brittle Fracture in Ship Structures 1000
Brittle fracture in welded ships 1000
King Tyrant 680
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5583326
求助须知:如何正确求助?哪些是违规求助? 4667155
关于积分的说明 14765758
捐赠科研通 4609337
什么是DOI,文献DOI怎么找? 2529123
邀请新用户注册赠送积分活动 1498393
关于科研通互助平台的介绍 1467043