ArticleFrontiers in oncology2025
A seven-LncRNA signature for prognosis prediction of patients with lung squamous cell carcinoma through tumor immune escape.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Advancing prognostic and therapeutic prediction in lung squamous cell carcinoma through integrated multi-omics analysis and 117 machine learning combinations.Journal of thoracic disease · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lung squamous cell carcinoma (LUSC) is a malignant disease associated with poor therapeutic responses and prognosis. Preliminary studies have shown that the dysregulation of long non-coding RNAs (LncRNAs) is linked to cancer development and prognosis. However, research on the role of LncRNAs in LUSC remains limited. Methods: In this study, we aimed to develop a LncRNA signature for improved prognostic prediction in LUSC and to elucidate the underlying mechanisms. We utilized expression data of LncRNAs and clinical information from 471 LUSC patients in The Cancer Genome Atlas (TCGA), randomly dividing them into a training set (n=236) and a testing set (n=235). Results: A prognostic signature model comprising seven LncRNAs was constructed using multivariate Cox regression analysis based on the training set. Using a risk score cutoff value of -0.12 (log2-transformed), patients were categorized into high-risk (n=101) and low-risk (n=370) groups. The high-risk group demonstrated significantly worse overall survival (OS) compared to the low-risk group (p<0.0001). The risk score showed strong prognostic predictive ability for LUSC patients, as evidenced by the area under the ROC curve (AUC: 0.66, 0.67, and 0.67) and nomogram analysis (C-index, calibration, and decision curve analysis) for 1-, 3-, and 5-year survival predictions. Independent prognostic factors for LUSC were identified, including risk group (HR=0.3, 95% CI: 0.22-0.4), stage (HR=1.78, 95% CI: 1.28-2.48), and age (HR=1.02, 95% CI: 1.00-1.04). KEGG enrichment analysis revealed that mRNAs influenced by the seven targeted LncRNAs, associated with immune evasion, were primarily linked to pathways such as chemical carcinogenesis, Th17 cell differentiation, NF-κB signaling, and proteoglycans in cancer. Expression levels of 14 target genes related to tumor immune tolerance were significantly suppressed, with eight confirmed via real-time PCR and western blot analysis. Additionally, CIBERSORT analysis of immune cell-related gene expression between normal and LUSC tissues indicated activation of the immune system in LUSC patients. Conclusion: In conclusion, our findings highlight the clinical significance of the seven LncRNA signature in predicting survival outcomes for LUSC patients.
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.