2023 World Conference on Lung Cancer (Posters)-EP06.05. Twelve Cell Death Patterns-Related Classification of the Immune Microenvironment and Prognosis Prediction of LUSC

EP06.05. Twelve Cell Death Patterns-Related Classification of the Immune Microenvironment and Prognosis Prediction of LUSC - PDF(Abstract)

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This study focused on understanding the immunological characteristics of lung squamous cell carcinoma (LUSC) patients who can benefit from immunotherapy. The researchers used a bioinformatics machine-learning approach to identify important genes in programmed cell death (PCD) patterns that have prognostic influence. They developed a risk model for LUSC, classifying patients into high- and low-risk groups, and evaluated the similarities and differences in their immune microenvironment. They also predicted immunotherapy efficacy and built a prognostic model for LUSC patients.<br /><br />The researchers integrated and screened genes related to 12 PCD types and used statistical methods to reduce the dimensionality of the gene expression profiles of 493 LUSC patients. Through this process, they identified 16 important genes associated with LUSC prognosis and developed a risk function to differentiate high- and low-risk groups. The researchers validated their findings on additional patient datasets.<br /><br />They found that the low-risk group had a significantly longer prognosis compared to the high-risk group. The high-risk group showed higher levels of immune cell infiltration, gene mutation frequency, and drug resistance. Enrichment pathways associated with tumor development and immunology were also identified.<br /><br />Using a machine learning algorithm, the researchers were able to accurately predict the risk type, and they created a prognostic nomogram based on the risk score.<br /><br />Overall, this study enhances the understanding of the immune microenvironment and mutation characteristics of LUSC. It identifies patients who can benefit from immunotherapy and predicts their future survival. This research has implications for personalized treatment strategies for LUSC patients.

Asset Subtitle

Xiaorong Dong

Meta Tag

Speaker Xiaorong Dong

Topic Pathology & Biomarkers

Keywords

lung squamous cell carcinoma

immunotherapy

bioinformatics

machine learning

programmed cell death

prognostic influence

risk model

immune microenvironment

immunotherapy efficacy

prognostic model