ArticleVaccines2022
Comprehensive Characterization of Immune Landscape Based on Tumor Microenvironment for Oral Squamous Cell Carcinoma Prognosis.
Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- A Narrative Review of Prognostic Gene Signatures in Oral Squamous Cell Carcinoma Using LASSO Cox Regression.Biomedicines · 2025Review
- Prussian Blue Nanoparticle-Induced Alteration of the Polarization State of Tumor-Associated Macrophages as a Substantial Antitumor Mechanism Against Oral Squamous Cell Carcinoma (OSCC).International journal of nanomedicine · 2025Article
- Unveiling the Potential of Serum MiR-483-5p: A Promising Diagnostic and Prognostic Biomarker in OLP and OSCC Patients byCurrent pharmaceutical design · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThis study aims to identify an immune-related signature to predict clinical outcomes of oral squamous cell carcinoma (OSCC) patients.
methodsGene transcriptome data of both tumor and normal tissues from OSCC and the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA). Tumor Immune Estimation Resource algorithm (ESTIMATE) was used to calculate the immune/stromal-related scores. The immune/stromal scores and associated clinical characteristics of OSCC patients were evaluated. Univariate Cox proportional hazards regression analyses, least absolute shrinkage, and selection operator (LASSO) and receiver operating characteristic (ROC) curve analyses were performed to assess the prognostic prediction capacity. Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) function annotation were used to analysis the functions of TME-related genes.
resultsEleven predictor genes were identified in the immune-related signature and overall survival (OS) in the high-risk group was significantly shorter than in the low-risk group. An ROC analysis showed the TME-related signature could predict the total OS of OSCC patients. Moreover, GSEA and GO function annotation proved that immunity and immune-related pathways were mainly enriched in the high-risk group.
conclusionsWe identified an immune-related signature that was closely correlated with the prognosis and immune response of OSCC patients. This signature may have important implications for improving the clinical survival rate of OSCC patients and provide a potential strategy for cancer immunotherapy.
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Registered trials
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