ArticleDiscover oncology2025
Identification and external validation of a prognostic signature based on N6-methyladenosine- and tertiary lymphoid structures-related genes to evaluate survival prognosis and treatment efficacy in lung adenocarcinoma.
Article in Discover oncology, 2025. 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
4 authors.
Funding
Abstract
backgroundThe N6-Methyladenosine (m6A) RNA modification critically regulates cancer biology, and tertiary lymphoid structures (TLSs) shape antitumor immunity. However, their combined prognostic roles in lung adenocarcinoma (LUAD) remain unclear. This study applies an integrative multi-omics approach to construct an m6A- and TLS-related prognostic model and uncover underlying molecular mechanisms.
methodsWe identified m6A and TLS-related genes (MTGs) associated with LUAD and constructed a prognostic model using machine learning, which was validated with nomograms. Subsequent analyses included immune microenvironment profiling, tumor mutational burden (TMB), enrichment assays, drug sensitivity testing, and single-cell RNA sequencing (scRNA-seq). The expression of MTGs was detected using quantitative reverse transcription polymerase chain reaction (RT-qPCR).
resultsThe risk model we developed demonstrated strong prognostic value, with areas under the curve (AUCs) exceeding 0.8 at 1, 3, and 5 years. The prognosis of the high-risk cohort (HRC) was significantly worse (P < 0.001). A nomogram incorporating this risk model (AUC = 0.825) outperformed one without it. TMB analysis revealed a higher TMB in the HRC, which is likely associated with a poorer prognosis. Drugs targeting the microtubule dynamics and apoptosis pathways showed increased efficacy in the HRC. Enrichment analysis indicated that the MTGs are primarily involved in cell adhesion, immune response, hematopoietic cell lineage, and cell cycle regulation. The scRNA-seq analysis further revealed that these 8 MTGs are predominantly expressed in fibroblasts and T/NK cell clusters, indicating their possible involvement in regulating local immune responses. RT-qPCR analysis confirmed the differential expression of MTGs.
conclusionsThis study demonstrates that the integrative multi-omics model reveals not only potential therapeutic targets but also new perspectives on LUAD immunogenetics.
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.