Evidence map›Paper›PMID 41353468›Full record

ArticleScientific reports2025

Unveiling CCR9 and CCL13 as immune biomarkers and therapeutic targets in thymoma.

Peng Lu, Lixun Chai

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Peng LuDepartment of Thoracic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China.
Lixun ChaiDepartment of Thoracic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China. chailixun@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study employed bioinformatics to identify immune-related diagnostic and therapeutic target genes in thymoma, offering a theoretical foundation for clinical diagnosis and treatment of the disease. In this study, machine learning techniques were employed to identify and validate potential biomarkers, leading to the construction of a diagnostic nomogram. Immune infiltration analysis was subsequently conducted to explore the relationships between these biomarkers and immune cell populations. Finally, the findings were validated through RT-qPCR and Western blotting experiments, ensuring the robustness of the results. Three critical biomarkers were selected for thymoma, with CCR9 and CCL13 confirmed as the final biomarkers through ROC analysis, both demonstrating AUC values exceeding 0.7. Immune infiltration analysis revealed significant differences in 14 types of immune cells between the thymoma and control groups, highlighting a strong association between macrophages and the biomarkers. A nomogram for thymoma was constructed using these 2 biomarkers, exhibiting robust performance. Ultimately, validation experiments using RT-qPCR and Western blotting confirmed the consistency of CCR9 and CCLI3 expression with the bioinformatics results. CCR9 and CCL13 were identified in this study as immune-related biomarkers associated with thymoma, which might provide a theoretical foundation for the development of targeted gene therapies.

Indexed as

Biomarkers, TumorReceptors, CCRThymomaThymus NeoplasmsComputational BiologyFemaleGene Expression Regulation, NeoplasticHumansMachine LearningMaleBiomarkers, TumorCC chemokine receptor 9Receptors, CCRBiomarkersDiagnostic nomogramImmune-related genesThymoma

Identifiers

PMID41353468
PMCPMC12789674

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.