Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung...

Area of Science:

• Oncology
• Immunology
• Bioinformatics
• Genomics

Background:

• The role of Antibody-Dependent Cellular Cytotoxicity (ADCC) in lung adenocarcinoma (LUAD) has been understudied.
• No systematic compilation of ADCC-associated genes for prognostic signatures in LUAD exists.

Purpose of the Study:

• To construct and validate a prognostic signature based on ADCC-associated genes for LUAD.
• To evaluate the signature's predictive ability for patient survival and treatment response.
• To explore the association of the signature with the tumor microenvironment and pan-cancer characteristics.

Main Methods:

• Utilized machine learning algorithms to construct a 16-gene ADCC score (ADCCRS) from LUAD, NSCLC, and diverse cancer cohorts.
• Validated prognostic power across 28 independent datasets and compared it with existing signatures.
• Integrated bulk and single-cell transcriptome data, analyzed copy number variations (CNV), single nucleotide variations (SNV), and predicted drug sensitivity and immunotherapy response.

Main Results:

• Identified two distinct LUAD subtypes with differential clinical characteristics.
• The ADCCRS demonstrated superior predictive ability for LUAD patient survival compared to 102 previous signatures.
• High ADCCRS correlated with increased gene mutation, specific tumor microenvironment (TME) modulators, and predicted sensitivity to chemotherapy/targeted therapy but resistance to immunotherapy.
• ADCCRS showed significant prognostic value and acted as a risk factor across various cancer types.

Conclusions:

• The ADCCRS provides crucial prognostic insights for LUAD patients.
• It aids in understanding the tumor microenvironment and predicting treatment responsiveness.
• The signature holds potential for guiding clinical decisions in LUAD and other cancers.