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Updated: Jul 16, 2026

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
Published on: October 26, 2017
Inflammation markers in lung cancer: prognostic and predictive value
Paschalina Tsopa1, Nikolaos Syrigos1, Christos Kosmas2
1Oncology Department, 3rd Internal Medicine Department "Sotiria" Hospital, National and Kapodestrian University of Athens.
Abstract:
Lung cancer is a complex and heterogeneous disease with significant morbidity and mortality worldwide. Over the years, several inflammation markers have been studied, such as molecules, cells, genes, etc., that are implicated in the extremely complex interactions taking place in the inflammatory process implicated in cancer development. This narrative review aims to present the most commonly studied inflammation markers in lung cancer, including C-reactive protein, tumor necrosis factor family members, and prostaglandin E synthase enzyme 3, as well as the significant number of scores and indexes that have been developed to improve the prognostic and predictive potential for non-small cell lung cancer and small cell lung cancer patients of different stages and treatment approaches. Scores and indexes originating from a combination of variables used in everyday clinical practice are emphasized due to their simplicity and cost-effectiveness. Studies addressing the prognostic and predictive value of the most important and recently studied markers, indexes, and scores in lung cancer are summarized, revealing their potential as indicators of overall survival, therapeutic response, and tumor immune characteristics. Limitations in utilizing inflammation markers as predictive biomarkers are discussed, including assay standardization, the complexity of the inflammatory response, confounding factors, and the dynamic nature of marker assessment. The progress of biotechnology, along with the combination of routine clinical practice insights, could result in the development of inflammation markers with improved prognostic and predictive value guiding treatment decisions for lung cancer patients in the context of precision medicine.
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