Evidence map›Paper›PMID 42729829›Full record

ArticleOncology letters2026

Construction of a new predictive model in head and neck squamous cell carcinoma based on the investigation of extracellular matrix-associated genes.

Liangqian Tu, Yiyin Qiu, Handan Zheng, Bixin Fang, Ming Chen

Abstract read
In one paragraph

Article in Oncology letters, 2026. 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
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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

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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

5 authors.

Liangqian TuDepartment of Otolaryngology, The Liangzhu Street Community Health Service Center, Hangzhou, Zhejiang 311113, P.R. China.
Yiyin QiuDepartment of Otolaryngology, Ninghai First Hospital, Ninghai, Ningbo, Zhejiang 315600, P.R. China.
Handan ZhengDepartment of Otolaryngology, The Second Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310005, P.R. China.
Bixin FangDepartment of Otolaryngology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310009, P.R. China.
Ming ChenDepartment of Otolaryngology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310009, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A key aspect influencing immune cell infiltration is the composition of the extracellular matrix (ECM). Therefore, investigating the association between ECM-associated proteins and immune cell infiltration is key for the identification of new biomarkers to distinguish 'immune-hot' solid tumors and predict patient prognosis. A total of 513 head and neck squamous cell carcinoma (HNSCC) cases as training samples from The Cancer Genome Atlas and an additional 270 as testing samples from the Gene Expression Omnibus were obtained for use in the present study. Using a single-sample Gene Set Enrichment Analysis method, the 513 training samples were divided into Cluster 1 and Cluster 2. Subsequently, the present analysis uncovered 1,573 differentially expressed genes distinguishing the two clusters. After performing an intersection analysis with 751 ECM-associated genes, 103 differentially expressed ECM-associated genes were identified. Least absolute shrinkage and selection operator-Cox and multivariate Cox regression analyses were employed to identify candidate ECM risk genes (P<0.05) and to construct a predictive model. Finally, a nomogram and a three gene (cerebellin 2, galectin-10 and cathepsin G) predictive model were developed. Therefore, the present prognostic risk score model can evaluate the immune infiltration, predict the prognosis of HNSCC, and potentially guide more personalized immunotherapy interventions.

Indexed as

extracellular matrixhead and neck squamous cell carcinomaimmunotherapypredictive modeltumor microenvironment

Identifiers

PMID42729829
PMCPMC13563026

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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.