ArticlePloS one2025
A novel prognostic model for lung squamous cell carcinoma based on multi-omics analysis and machine learning.
Article in PloS one, 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.
- The role of the tumor microenvironment in drug resistance acquisition in lung squamous cell carcinoma.Journal of experimental & clinical cancer research : CR · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung squamous-cell carcinoma (LUSC) is a highly aggressive malignancy with a poor prognosis. Tertiary lymphoid structures (TLS) play a crucial role in the immune response and significantly influence the efficacy of immunotherapy. However, the prognostic and immunological implications of TLS-associated molecular subtypes in LUSC remain unclear. In this study, we applied 10 multi-omics integration strategies to perform a multi-omics analysis of the mRNA expression profiles, DNA methylation, and genomic mutation data of 39 TLSs-related genes, along with long non-coding RNA (lncRNA) expression profiles, to generate integrated consensus subtypes of LUSC. Four molecular subtypes were identified: cancer subtype 1 (CS1), CS2, CS3, and CS4. We observed a significant difference in overall survival between cancer subtype 1 (CS1) and CS3. Subsequently, we identified 33 prognosis-related genes based on differential expression between CS1 and CS3, which were further refined to 20 genes using the least absolute shrinkage and selection operator (LASSO) regression algorithm, and constructed a prognostic signature termed the LUSC-Survival Prediction Index (LUSCSPI). The high-LUSCSPI group demonstrated a poor prognosis and was more likely to benefit from treatment with nine chemotherapeutic agents (shikonin, doxorubicin, CMK, S-Trityl-L-cysteine, paclitaxel, DMOG, gemcitabine, erlotinib, and crizotinib). In contrast, the low-LUSCSPI group exhibited a more favorable prognosis, with thapsigargin and cisplatin identified as promising treatment options. In conclusion, our results highlight the potential of LUSCSPI as an independent prognostic factor for LUSC. Further, the multi-omics consensus approach provides a robust foundation for prognostic stratification in LUSC patients, facilitating personalized treatment and disease management.
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.