Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments

队列 生物标志物 机器学习 支持向量机 医学 Lasso(编程语言) 人工智能 算法 交叉验证 特征选择 肿瘤科 金标准(测试) 肉瘤 内科学 计算机科学 生物 病理 遗传学 万维网
作者
Yonghua Pang,Jiahui Liang,Yi Deng,Weinan Chen,Yunyan Shen,Jing Li,Xin Wang,Zhiyao Ren
出处
期刊:Frontiers in Immunology [Frontiers Media]
卷期号:16
标识
DOI:10.3389/fimmu.2025.1449355
摘要

Introduction Early diagnosis of Ewing sarcoma (ES) is critical for improving patient prognosis. However, the accurate diagnosis of ES remains challenging, underscoring the need for novel diagnostic biomarkers to enhance diagnostic precision and reliability. This study aimed to identify potential gene expression-based biomarkers for the diagnosis of ES. Methods We selected the GSE17679, GSE45544, and GSE68776 datasets from the Gene Expression Omnibus (GEO) database. After correcting for batch effects, we combined ES and normal tissue samples from the GSE17679 and GSE45544 datasets to create a combined cohort. Two-thirds of both the tumor and normal samples from the combined cohort were randomly selected for the training cohort, while the remaining one-third served as the internal validation cohort. Additionally, the GSE68776 dataset was used for external validation. To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF). Results HOXC6 was identified as a key diagnostic biomarker for ES. It demonstrated strong diagnostic performance across all cohorts, with area under the curve (AUC) values of 0.956 (95% CI: 0.909−0.990) in the training cohort, 0.995 (95% CI: 0.977−1.000) in the internal validation cohort, and 0.966 (95% CI: 0.910−0.999) in the external validation cohort. Functional validation through HOXC6 knockdown in the RD-ES cell line revealed that its suppression significantly inhibited cell proliferation and migration. Furthermore, transcriptome sequencing suggested potential oncogenic mechanisms underlying HOXC6 function. Discussion These findings highlight HOXC6 as a promising diagnostic biomarker for ES, demonstrating robust performance across multiple datasets. Additionally, its functional role suggests potential as a therapeutic target.

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
刚刚
刚刚
1秒前
1秒前
李振聪发布了新的文献求助10
1秒前
1秒前
1秒前
2秒前
SciGPT应助蓝天采纳,获得10
2秒前
3秒前
3秒前
3秒前
李振聪发布了新的文献求助10
3秒前
李振聪发布了新的文献求助10
3秒前
3秒前
李振聪发布了新的文献求助10
3秒前
李振聪发布了新的文献求助10
3秒前
3秒前
李振聪发布了新的文献求助10
3秒前
李振聪发布了新的文献求助10
4秒前
甜芋发布了新的文献求助10
4秒前
4秒前
李振聪发布了新的文献求助10
4秒前
4秒前
李振聪发布了新的文献求助10
4秒前
CHBW发布了新的文献求助10
4秒前
4秒前
李振聪发布了新的文献求助10
5秒前
李振聪发布了新的文献求助10
5秒前
5秒前
李振聪发布了新的文献求助10
5秒前
李振聪发布了新的文献求助10
5秒前
李振聪发布了新的文献求助10
5秒前
5秒前
李振聪发布了新的文献求助10
5秒前
李振聪发布了新的文献求助10
5秒前
李振聪发布了新的文献求助10
5秒前
李振聪发布了新的文献求助10
5秒前
李振聪发布了新的文献求助10
5秒前
5秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443673
求助须知:如何正确求助?哪些是违规求助? 8257473
关于积分的说明 17587196
捐赠科研通 5502394
什么是DOI,文献DOI怎么找? 2900959
邀请新用户注册赠送积分活动 1877987
关于科研通互助平台的介绍 1717534