2023 World Conference on Lung Cancer (Posters)-EP07.04. Identification of Tumor Microenvironment and Prognosis Risk Prediction through Proteomics Analysis in Stage I Lung Adenocarcinoma

EP07.04. Identification of Tumor Microenvironment and Prognosis Risk Prediction through Proteomics Analysis in Stage I Lung Adenocarcinoma - PDF(Slides)

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Researchers in Taiwan have developed a multi-omics computational approach to identify the tumor microenvironment (TME) and predict prognosis risk in stage I lung adenocarcinoma. The study aims to address the high rate of recurrence and second primary cancer development in stage I non-small-cell lung cancer despite CT screening. The researchers used protein mass spectrometry data from the Taiwan Lung Cancer Moonshot project to explore the TME and identify biomarkers associated with recurrence-free survival and overall survival. They utilized various cohorts for training and validation. The protein expression data was analyzed using MaxQuant and the differential abundance of proteins between tumor and normal adjacent tissues (NAT) was determined. Protein expression data was also analyzed using Qiagen's Ingenuity Pathway Analysis system to identify associated diseases and biological functions. The researchers then quantile normalized and log2 transformed gene expression levels in patients with lung adenocarcinoma and conducted Cox model analyses to discover prognostic biomarkers. Patients were categorized into high and low risk groups based on their risk scores. Kaplan-Meier analysis and Cox proportional hazards regression analysis were used to estimate recurrence-free survival and overall survival. The researchers found that their multi-omics computational approach successfully identified prognostic markers associated with lung adenocarcinoma recurrence and survival. They conclude that their model can help clinicians identify patients at risk of recurrence and guide the development of new treatment strategies.

Asset Subtitle

Hsuan-Yu Chen

Meta Tag

Speaker Hsuan-Yu Chen

Topic Early-Stage NSCLC: Progress in Pathology

Keywords

Taiwan

multi-omics

computational approach

tumor microenvironment

prognosis risk

stage I lung adenocarcinoma

recurrence

second primary cancer

protein mass spectrometry

biomarkers