Evidence map›Paper›PMID 40255403›Full record

ArticleFrontiers in immunology2025

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

Yonghua Pang, Jiahui Liang, Yakai Deng, Weinan Chen, Yunyan Shen, Jing Li, Xin Wang, Zhiyao Ren

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yonghua Pang *Department of Orthopedics, The 904th Hospital of the Joint Logistics Support Force, People's Liberation Army of China, Wuxi, Jiangsu, China.
Jiahui Liang *Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Yakai Deng *Department of Orthopedics, The 904th Hospital of the Joint Logistics Support Force, People's Liberation Army of China, Wuxi, Jiangsu, China.
Weinan ChenDepartment of Orthopedics, The 904th Hospital of the Joint Logistics Support Force, People's Liberation Army of China, Wuxi, Jiangsu, China.
Yunyan ShenDepartment of Orthopedics, The 904th Hospital of the Joint Logistics Support Force, People's Liberation Army of China, Wuxi, Jiangsu, China.
Jing LiDepartment of Orthopedics, Linyi People's Hospital, Linyi, Shandong, China.
Xin Wang *Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
Zhiyao Ren *Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Biomarkers, TumorBone NeoplasmsHomeodomain ProteinsMachine LearningSarcoma, EwingCell Line, TumorCell MovementCell ProliferationGene Expression ProfilingGene Expression Regulation, NeoplasticHumansReproducibility of ResultsBiomarkers, TumorHomeodomain ProteinsHOXC6 protein, humanbioinformaticsdiagnostic biomarkerEwing sarcomaHOXC6machine learning

Identifiers

PMID40255403
PMCPMC12006176

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.