Exosomal miRNAs as biomarkers of recurrent lung cancer
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Prognosis of lung cancer still remains grim largely due to recurrence and aggressive metastasis of the disease. In this study, we examined the potential of exosomal miRNAs as biomarkers of recurrent lung cancer. Initially, in vitro miRNA profiles of normal lung (Beas-2b) and lung cancer (H1299) cells and of exosomes isolated from conditioned media were determined. In vivo study involved establishing subcutaneous primary and recurrent lung cancer xenografts in nude mouse model and examining tumor and serum exosomal miRNA alteration in secondary/recurrent lung tumors. A total of 77 miRNAs were observed to be significantly modulated in the H1299 cells (47 miRNA upregulated and 30 downregulated) compared to the Beas-2b cells. The exosomes isolated from conditioned media indicated several miRNAs which were in agreement with cells of origin. A similarity was also observed between miRNAs from serum exosomes and tumors, indicating their origin from the lung tumors. Two miRNAs, miR-21 and miR-155, were found to be significantly upregulated in recurrent tumors compared to primary tumors. These miRNAs were also upregulated in serum exosomes of recurrent tumor-bearing animals versus non-tumor- or primary tumor-bearing animals. Increased expression of the recurrent disease markers were also observed in recurrent tumors compared with primary tumors. Serum exosomes from recurrent tumor mice mirrored its tumor profile in expressing higher levels of these proteins compared with exosomes from primary tumor mice. Our data suggest that exosomal miRNA signatures may be a true representation of a pathological profile of lung cancer; thus, miRNAs could serve as promising biomarkers for non-invasive diagnosis of the disease.
KeywordsExosomes miRNA Lung cancer Recurrent lung tumors Biomarker
This work was supported from the Agnes Brown Duggan Endowment and Helmsley Trust Funds. Ramesh C. Gupta holds the Agnes Brown Duggan Chair in Oncological Research. The Microarray core is gratefully acknowledged for assisting with sample analysis and Xiaohong Li, Biostatistician, University of Louisville Bioinformatics Core, for her assistance in analysis of the data. We thank Manicka V. Vadhanam for useful discussions during the study.
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