ArticleFrontiers in oncology2024
Exploiting tertiary lymphoid structures gene signature to evaluate tumor microenvironment infiltration and immunotherapy response in colorectal cancer.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Beyond TLS Presence: A Functional Framework Integrating Maturity, Location, and Immune Context.Cancers · 2026Review
- The formation and function of tertiary lymphoid structures.Biomarker research · 2026Review
- Tertiary lymphoid structure-related genes drive tumor microenvironment heterogeneity and prognostic disparities in left-Translational cancer research · 2026Article
- IL21 is predominantly produced by a CXCL13 associated CD4Frontiers in immunology · 2026Article
- Mechanistic roles and clinical applications of tertiary lymphoid structures in colorectal cancer drug resistance.Cancer drug resistance (Alhambra, Calif.) · 2026Review
- Tertiary lymphoid organ-associated transcriptomic features predict rejection and outcome in kidney transplantation: integrative analysis and experimental validation.Clinical and experimental medicine · 2025Article
- Prognostic role of tertiary lymphatic structures and their modulation by adjuvant FOLFOX in stage III colon cancer: a retrospective cohort study.Journal of gastrointestinal oncology · 2025Article
- [Chinese Expert Consensus on Assessment and Clinical Application of Tertiary Lymphoid Structure for Non-small Cell Lung Cancer (2025 Version)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Review
- Involvement of Intestinal Epithelium Aryl Hydrocarbon Receptor Expression and 3, 3'-Diindolylmethane in Colonic Tertiary Lymphoid Tissue Formation.International journal of molecular sciences · 2024Article
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Authors and funding
12 authors.
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Abstract
Background: Tertiary lymphoid structures (TLS) is a particular component of tumor microenvironment (TME). However, its biological mechanisms in colorectal cancer (CRC) have not yet been understood. We desired to reveal the TLS gene signature in CRC and evaluate its role in prognosis and immunotherapy response. Methods: The data was sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Based on TLS-related genes (TRGs), the TLS related subclusters were identified through unsupervised clustering. The TME between subclusters were evaluated by CIBERSORT and xCell. Subsequently, developing a risk model and conducting external validation. Integrating risk score and clinical characteristics to create a comprehensive nomogram. Further analyses were conducted to screen TLS-related hub genes and explore the relationship between hub genes, TME, and biological processes, using random forest analysis, enrichment and variation analysis, and competing endogenous RNA (ceRNA) network analysis. Multiple immunofluorescence (mIF) and immunohistochemistry (IHC) were employed to characterize the existence of TLS and the expression of hub gene. Results: Two subclusters that enriched or depleted in TLS were identified. The two subclusters had distinct prognoses, clinical characteristics, and tumor immune infiltration. We established a TLS-related prognostic risk model including 14 genes and validated its predictive power in two external datasets. The model's AUC values for 1-, 3-, and 5-year overall survival (OS) were 0.704, 0.737, and 0.746. The low-risk group had a superior survival rate, more abundant infiltration of immune cells, lower tumor immune dysfunction and exclusion (TIDE) score, and exhibited better immunotherapy efficacy. In addition, we selected the top important features within the model: Conclusion: We conducted a study to thoroughly describe TLS gene signature in CRC. The TLS-related risk model was applicable for prognostic prediction and assessment of immunotherapy efficacy. The TLS-hub gene
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