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Updated: Jul 4, 2025
MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
Published on: October 26, 2017
Data mining-based identification of epigenetic signatures with discrimination potential of lung adenocarcinoma and
Wen-Guang Pang1,2, Min Ye2, Jia-Rong Chen3
1Department of Thoracic Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
Background:
Mounting evidence suggests that lung adenocarcinoma (LAC) and lung squamous cell carcinoma (LSC) have different biological behaviors and therapeutic regimens in clinical practice. However, limited improvements in molecular differential diagnosis of the two entities have been achieved in recent decades. We aimed to find novel markers that could define non-small cell lung cancer (NSCLC) subtypes.
Methods:
We first explored publically available databases to search for DNA methylation signatures that enable a precise discrimination of LAC and LSC. Next-generation sequencing (NGS) was then used to analyze the methylation status and sites of candidate genes in LAC/LSC tissue samples, and a quantitative methylation-sensitive PCR (qMS-PCR) assay was conducted to test the performance of the selected maker in tissue samples and bronchoalveolar lavage fluid (BALF) specimens.
Results:
We screened 19 top-ranked methylation loci that are differentially methylated between LAC and LSC. Among these hits, 6 methylation sites are enriched within the PREX1 gene promoter, thus becoming our focus. NGS analysis confirmed markedly higher PREX1 methylation levels in LAC than in LSC and revealed the right sites for detection of PREX1 methylation. Furthermore, PREX1 methylation analysis in lung cancer tissue samples defined 9 of 11 pathologically proven LACs, as well as 12 of 14 LSCs. In addition, ~ 80% LAC BALF samples showed methylated PREX1 compared to substantially lower test positivity (0-9%) of it in LSC and other lung conditions (P < 0.01).
Conclusion:
Our pilot study identified a unique epigenetic signature that could effectively distinguish LAC from LSC in various lung samples. It may enhance our in-depth understanding of the biology of lung cancer and pave the way for better accurate diagnosis and treatment stratification in the future.
