Evidence map›Paper›PMID 41596281›Full record

ArticleInternational journal of molecular sciences2026

Transcriptomic Analysis Identifies Acrolein Exposure-Related Pathways and Constructs a Prognostic Model in Oral Squamous Cell Carcinoma.

Yiting Feng, Lijuan Lou, Liangliang Ren

Abstract read
In one paragraph

Article in International journal of molecular sciences, 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
–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

3 authors.

Yiting FengState Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Lijuan LouState Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Liangliang RenState Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.

Funding

Beijing Natural Science Foundation 7252012State Key Laboratory of Medical Proteomics SKLP-O202401State Key Laboratory of Medical Proteomics SKLP-X202402
6 · The paper itself

Abstract

Acrolein, a highly reactive environmental toxicant widely present in urban air and tobacco smoke, has been implicated in the development of multiple malignancies. In oral tissues, chronic acrolein exposure induces oxidative stress, inflammation, and genetic mutations, all of which are closely linked to the development of oral squamous cell carcinoma (OSCC). Although accumulating evidence indicates a strong association between acrolein exposure and OSCC, its prognostic significance remains poorly understood. In this study, we analyzed transcriptome data to identify differentially expressed genes (DEGs) between tumor and adjacent normal tissues, and screened acrolein-related candidates by intersecting DEGs with previously identified acrolein-associated gene sets. Functional alterations of these genes were assessed using Gene Set Variation Analysis (GSVA), and a protein-protein interaction (PPI) network was constructed to identify key regulatory genes. A prognostic model was developed using Support Vector Machine-Recursive Feature Elimination (SVM-RFE) combined with LASSO-Cox regression and validated in an independent external cohort. Among the acrolein-related DEGs, four key genes (PLK1, AURKA, CTLA4, and PPARG) were ultimately selected for model construction. Kaplan-Meier analysis showed significantly worse overall survival in the high-risk group (

Indexed as

AcroleinCarcinoma, Squamous CellGene Expression ProfilingMouth NeoplasmsTranscriptomeGene Expression Regulation, NeoplasticHumansPrognosisProtein Interaction MapsAcroleinacroleinbioinformaticsenvironmental pollutantsmachine learningOSCCprognostic model

Identifiers

PMID41596281
PMCPMC12841475

What OpenQuestion holds

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Registered trials

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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.