Multi-Algorithm-Integrated Tertiary Lymphoid Structure Gene Signature for Immune Landscape Characterization and...

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• Colorectal cancer (CRC) presents challenges with low survival and immunotherapy response rates.
• Tertiary lymphoid structures (TLS) are increasingly recognized for their role in anti-tumor immunity.
• Predictive biomarkers are crucial for optimizing CRC treatment strategies.

Purpose of the Study:

• To develop a prognostic and predictive model for colorectal cancer using TLS-associated gene signatures.
• To enhance the prediction of patient outcomes and response to immunotherapy.
• To identify novel therapeutic targets within the tumor microenvironment.

Main Methods:

• Utilized TCGA-CRC and GEO datasets to identify TLS-associated genes.
• Employed Cox regression and multiple algorithms for model construction and validation.
• Assessed immune cell infiltration and predicted immunotherapy response using various bioinformatics tools.
• Validated TLS presence and CCL2 expression via immunohistochemistry.

Main Results:

• A three-gene model (CCL2, PDCD1, ICOS) effectively predicted CRC prognosis and immunotherapy response.
• Low-risk patients exhibited significantly higher overall survival and immunotherapy response rates.
• Immunohistochemistry confirmed elevated CCL2 expression in TLS regions.

Conclusions:

• The developed multi-algorithm model robustly predicts CRC patient prognosis and immunotherapy efficacy.
• CCL2 shows potential as a TLS modulator and a therapeutic target in colorectal cancer.
• This approach offers a novel strategy for assessing immunotherapy effectiveness in CRC.