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(Abstract)

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Researchers conducted a study to identify the tumor microenvironment and predict prognosis in early-stage lung adenocarcinoma. They collected protein mass spectrometry data from the Taiwan Lung Cancer Moonshot project and analyzed it to explore the tumor microenvironment. They used multiple cohorts to identify and verify biomarkers associated with recurrence-free survival and overall survival in post-operative lung adenocarcinoma. They employed the MaxQuant software and Wilcoxon signed-rank test to analyze the protein abundance differences between tumor and normal adjacent tissues. Additionally, they used Qiagen's Ingenuity Pathway Analysis system to analyze the protein expression data and identify pathways related to disease and biological functions.<br /><br />Through their analysis, the researchers identified five genes that were of prognostic significance in lung adenocarcinoma. They constructed a risk score model based on these genes and validated it in seven independent lung adenocarcinoma cohorts. The risk score model was found to be an independent predictor of recurrence-free survival and overall survival in both early and advanced-stage lung adenocarcinoma.<br /><br />The findings of this study have implications for clinicians in identifying patients at risk of recurrence and guiding the development of new treatment strategies. Understanding the tumor microenvironment and utilizing proteomic analysis can help improve prognosis prediction and personalized treatment approaches for lung adenocarcinoma patients.

Asset Subtitle

Hsuan-Yu Chen

Meta Tag

Speaker Hsuan-Yu Chen

Topic Early-Stage NSCLC: Progress in Pathology

Keywords

tumor microenvironment

prognosis

lung adenocarcinoma

biomarkers

protein mass spectrometry

recurrence-free survival

overall survival

MaxQuant software

proteomic analysis

personalized treatment