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2023 World Conference on Lung Cancer (Posters)
P2.05. A Seven-Lnc RNA Signature for Prognosis Pre ...
P2.05. A Seven-Lnc RNA Signature for Prognosis Prediction of Patients with Lung Squamous Cell Carcinoma through Tumor Immune Escape - PDF(Slides)
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Researchers have developed a prognostic model using seven long non-coding RNAs (LncRNAs) to predict the prognosis of patients with lung squamous cell carcinoma (LUSC). The study involved 471 patients with LUSC, who were randomly divided into a training set and a testing set. Patients were categorized into high and low-risk groups based on their risk-score levels. <br /><br />The high-risk group had significantly worse overall survival compared to the low-risk group. The risk-score also showed good predictive ability for LUSC patients at different time points, as demonstrated by receiver operating characteristic (ROC) curve analysis. The risk group, disease stage, and age were found to be independent predictors of survival in LUSC patients.<br /><br />Further analysis revealed that the seven LncRNAs may be involved in immune escape mechanisms in LUSC. Pathway analysis showed that genes influenced by these LncRNAs were associated with functions related to chemical carcinogenesis, Th17 cell differentiation, NF-κB, and proteoglycans in cancer. Additionally, analysis of immune cell gene expression levels indicated that the immune system in LUSC patients was significantly activated.<br /><br />In conclusion, this study highlights the potential clinical significance of the seven LncRNA signature in predicting survival outcomes in LUSC patients. These findings may contribute to the development of personalized treatment strategies for LUSC based on the assessment of patients' risk profiles.
Asset Subtitle
Zhong Lin
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Speaker
Zhong Lin
Topic
Metastatic NSCLC: Immunotherapy - Biomarker
Keywords
prognostic model
long non-coding RNAs
lung squamous cell carcinoma
LUSC
overall survival
risk-score levels
receiver operating characteristic
immune escape mechanisms
chemical carcinogenesis
personalized treatment strategies
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