A novel algorithm to differentiate between primary lung tumors and distant liver metastasis in lung cancers using an exosome based multi gene biomarker panel

The lack of non-invasive methods for detection of early metastasis is a crucial reason for the poor prognosis of lung cancer (LC) liver metastasis (LM) patients. In this study, the goal was to identify circulating biomarkers based on a biomarker model for the early diagnosis and monitoring of patien...

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Bibliographic Details
Published in:Scientific reports 2024-06, Vol.14 (1), p.13769-14
Main Authors: Shah, Kanisha A., Rawal, Rakesh M.
Format: Article
Language:English
Subjects:
631/67
631/67/322
Aged
Algorithms
Biomarkers
Biomarkers, Tumor - genetics
Diagnosis
Diagnosis, Differential
Exosomes
Exosomes - genetics
Exosomes - metabolism
Female
Humanities and Social Sciences
Humans
Liquid biopsy
Liver cancer
Liver metastasis
Liver Neoplasms - diagnosis
Liver Neoplasms - genetics
Liver Neoplasms - secondary
Lung cancer
Lung Neoplasms - genetics
Lung Neoplasms - pathology
Male
Medical prognosis
Metastases
Metastasis
Middle Aged
multidisciplinary
Multivariate analysis
Non-invasive
Prediction models
Predictive biomarkers
Prognosis
Science
Science (multidisciplinary)
Survival
Survival analysis
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Summary:The lack of non-invasive methods for detection of early metastasis is a crucial reason for the poor prognosis of lung cancer (LC) liver metastasis (LM) patients. In this study, the goal was to identify circulating biomarkers based on a biomarker model for the early diagnosis and monitoring of patients with LCLM. An 8-gene panel identified in our previous study was validated in CTC, cfRNA and exosomes isolated from primary lung cancer with & without metastasis. Further multivariate analysis including PCA & ROC was performed to determine the sensitivity and specificity of the biomarker panel. Model validation cohort ( n  = 79) was used to verify the stability of the constructed predictive model. Further, clinic-pathological factors, survival analysis and immune infiltration correlations were also performed. In comparison to our previous tissue data, exosomes demonstrated a good discriminative value with an AUC of 0.7247, specificity (72.48%) and sensitivity (96.87%) for the 8-gene panel. Further individual gene patterns led us to a 5- gene panel that showed an AUC of 0.9488 ( p  =  
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-63252-z