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 Lasso(编程语言) 候选基因 接收机工作特性 基因 计算生物学 生物信息学 生物 计算机科学 机器学习 医学 遗传学 病理 内科学 万维网
作者
Xiaoli Zhou,Guangdong Chen,Yang Chen,Zepei Zhang,Jun Miao
出处
期刊:Research Square - Research Square
标识
DOI:10.21203/rs.3.rs-3940690/v1
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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
Abner完成签到,获得积分10
刚刚
高大道之完成签到,获得积分10
1秒前
3秒前
rain123发布了新的文献求助10
4秒前
桐桐应助yuan采纳,获得10
5秒前
六便士在攒完成签到,获得积分10
6秒前
6秒前
青藤发布了新的文献求助10
7秒前
7秒前
称心翠容完成签到,获得积分10
7秒前
8秒前
11秒前
上官若男应助天真的振家采纳,获得30
12秒前
田様应助444采纳,获得10
12秒前
Funeral发布了新的文献求助10
12秒前
12秒前
冰魂应助茵茵采纳,获得10
13秒前
14秒前
Chloe发布了新的文献求助10
14秒前
SciGPT应助今天学习了嘛采纳,获得30
14秒前
15秒前
yuan发布了新的文献求助10
15秒前
音吹关注了科研通微信公众号
16秒前
dingm2完成签到 ,获得积分10
18秒前
18秒前
Funeral完成签到,获得积分10
18秒前
ww应助梅梅超勇敢采纳,获得20
19秒前
汉堡包应助dian采纳,获得10
20秒前
StayGolDay发布了新的文献求助10
20秒前
ibigbird完成签到,获得积分10
20秒前
21秒前
无限若云发布了新的文献求助10
21秒前
OxO完成签到,获得积分10
21秒前
1235774完成签到 ,获得积分10
22秒前
drjj完成签到,获得积分10
22秒前
李沐唅完成签到,获得积分10
23秒前
23秒前
23秒前
24秒前
王博士完成签到,获得积分10
25秒前
高分求助中
The world according to Garb 600
Разработка метода ускоренного контроля качества электрохромных устройств 500
Mass producing individuality 500
Chinesen in Europa – Europäer in China: Journalisten, Spione, Studenten 500
Arthur Ewert: A Life for the Comintern 500
China's Relations With Japan 1945-83: The Role of Liao Chengzhi // Kurt Werner Radtke 500
Two Years in Peking 1965-1966: Book 1: Living and Teaching in Mao's China // Reginald Hunt 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3821784
求助须知:如何正确求助?哪些是违规求助? 3364226
关于积分的说明 10428645
捐赠科研通 3082879
什么是DOI,文献DOI怎么找? 1695869
邀请新用户注册赠送积分活动 815369
科研通“疑难数据库(出版商)”最低求助积分说明 769127