ArticleGenes2026
Identification of a Tertiary Lymphoid Structure Signature for Predicting Tumor Outcomes Through Transcriptomics Analysis.
Article in Genes, 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
4 authors.
Funding
Abstract
backgroundTertiary lymphoid structures (TLSs) play a crucial role in regulating tumor invasion and metastasis and serve as a promising prognostic biomarker in immunotherapy, influencing survival and immune response in multiple cancers. However, existing studies rely on limited gene signatures to assess TLSs, and there remains a lack of comprehensive TLS-related features for pan-cancer prognosis or immunotherapy response prediction.
methodsBased on published TLS gene signatures, mutation data, and expression profiles from 33 tumor types in TCGA, along with data from 15 immune checkpoint blockade (ICB) cohorts, we first systematically evaluated six TLS gene signatures in relation to immune-related indicators and assessed their predictive and prognostic performance across tumors and immunotherapy. Subsequently, using meta-analysis, we constructed a de novo TLS-related gene feature set, termed predictTLS, designed to predict ICB efficacy and prognosis. The rationality and effectiveness of predictTLS were validated using internal validation sets, single-cell transcriptomic, and spatial transcriptomic data.
resultsThe evaluation revealed associations between TLS gene signatures and key immune-related indicators. The newly constructed predictTLS feature set demonstrated effectiveness in predicting both ICB therapy outcomes and patient prognosis across the analyzed cohorts. Validation across internal datasets, single-cell profiles, and spatial transcriptomics supported the robustness and biological relevance of predictTLS.
conclusionsThis study provides a systematically validated, de novo TLS-related gene signature that can serve as a clinical biomarker for predicting immunotherapy response and prognosis in pan-cancer settings. These findings offer new tools for risk stratification and potential therapeutic targeting in tumor immunotherapy.
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.