ArticlePeerJ2026
A risk scoring model for lung squamous cell carcinoma based on epithelial-mesenchymal transition-related genes: an integrative analysis of prognosis and immune infiltration characteristics.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Despite expanding therapeutic options, the prognosis of lung squamous cell carcinoma (LUSC) remains poor. Immune checkpoint inhibitors benefit only a subset of patients, and epithelial-mesenchymal transition (EMT) has been implicated in invasion, metastasis, treatment resistance, and immune heterogeneity. Therefore, EMT-related biomarkers may offer improved risk stratification. Aim: To identify differentially expressed EMT-related genes (DEEMTGs) in LUSC, construct an EMT-based prognostic signature, and evaluate its associations with the tumor microenvironment (TME), tumor mutational burden (TMB), and tissue-level expression patterns. Methods: The Cancer Genome Atlas (TCGA) RNA-seq and clinical data were analyzed to obtain DEEMTGs. A prognostic model was built using LASSO and multivariable Cox regression. Survival performance was assessed Results: A total of 1,651 DEEMTGs were identified, and a six-gene signature (GAB2, ALDOA, PCDHA3, TMEM92, ERH, IRS4) was established. The risk score independently predicted overall survival and corresponded to distinct TME patterns: low-risk tumors showed higher CD8 Conclusion: We developed a biologically interpretable EMT-based prognostic model that stratifies survival and reflects immune-microenvironment heterogeneity in LUSC. Larger, stage-balanced and immunotherapy-treated cohorts are needed to further validate its clinical utility.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.