Evidence map›Paper›PMID 40247837›Full record

ArticlePeerJ2025

Prognostic value and immunotherapy analysis of immune cell-related genes in laryngeal cancer.

Sen Zhang, Jianrui Pan, Huina Guo, Xiaoya Guan, Chenxu Yan, Lingling Ji, Xiansha Wu, Hui Huangfu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 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.

Sen ZhangThe First Hospital of Shanxi Medical University, Department of Otolaryngology Head and Neck Surgery, Taiyuan, Shanxi, China.
Jianrui PanThe First Hospital of Shanxi Medical University, Department of Otolaryngology Head and Neck Surgery, Taiyuan, Shanxi, China.
Huina GuoThe First Hospital of Shanxi Medical University, Department of Otolaryngology Head and Neck Surgery, Taiyuan, Shanxi, China.
Xiaoya GuanThe First Hospital of Shanxi Medical University, Department of Otolaryngology Head and Neck Surgery, Taiyuan, Shanxi, China.
Chenxu YanThe First Hospital of Shanxi Medical University, Department of Otolaryngology Head and Neck Surgery, Taiyuan, Shanxi, China.
Lingling JiThe First Hospital of Shanxi Medical University, Department of Otolaryngology Head and Neck Surgery, Taiyuan, Shanxi, China.
Xiansha WuThe First Hospital of Shanxi Medical University, Department of Otolaryngology Head and Neck Surgery, Taiyuan, Shanxi, China.
Hui HuangfuThe First Hospital of Shanxi Medical University, Department of Otolaryngology Head and Neck Surgery, Taiyuan, Shanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Laryngeal cancer (LC) is a prevalent head and neck carcinoma. Extensive research has established a link between immune cells in the tumor microenvironment (TME) and cancer progression, as well as responses to immunotherapy. This study aims to develop a prognostic model based on immune cell-related genes and examine the TME in LC. Methods: RNA-seq data for LC were sourced from The Cancer Genome Atlas (TCGA), and GSE27020 and GSE51985 datasets were retrieved from the Gene Expression Omnibus (GEO) database. Key genes were identified through the intersection of differentially expressed genes (DEGs) between normal and LC samples and module genes derived from weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. The prognostic risk model was constructed using univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Gene Set Variation Analysis (GSVA) was subsequently performed for hallmark and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses in high- and low-risk groups. Immune infiltration analysis between risk groups was conducted Results: A total of 124 key genes were identified through the overlap analysis, predominantly enriched in GO terms such as defense response to viruses and regulation of response to biotic stimuli, as well as KEGG pathways related to phagosome and Epstein-Barr virus infection. Machine learning indicated that the optimal prognostic model was constructed from two biomarkers, Conclusions: This study identified two key prognostic biomarkers,

Indexed as

ImmunotherapyLaryngeal NeoplasmsBiomarkers, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisTumor MicroenvironmentBiomarkers, TumorLaryngeal cancerPrognosisPrognostic biomarkerRisk modelTumor microenvironment

Identifiers

PMID40247837
PMCPMC12005187

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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.