AUTHOR=Lin Xue , Bo Zi-Hao , Lv Wenqi , Zhou Zhanping , Huang Qin , Du Wenli , Shan Xiaohui , Fu Rongxin , Jin Xiangyu , Yang Han , Su Ya , Jiang Kai , Guo Yuchen , Wang Hongwu , Xu Feng , Huang Guoliang 

TITLE=Miniaturized microfluidic-based nucleic acid analyzer to identify new biomarkers of biopsy lung cancer samples for subtyping

JOURNAL=Frontiers in Chemistry

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2022.946157

DOI=10.3389/fchem.2022.946157

ISSN=2296-2646

ABSTRACT=Identifying new biomarkers is necessary and important to diagnose and treat malignant lung cancer. However, existing protein marker detection methods usually require complex operation steps, leading to a lag time for diagnosis. Herein, we developed a rapid, minimally invasive, and convenient nucleic acid biomarker recognizing method, which enabled the combined specific detection of 11 lung cancer typing markers in a microliter reaction system after only one sampling. The primers for combined specific detection of 11 lung cancer typing markers were designed and screened. The microfluidic chip for parallel detection of multiple markers in a microliter reaction system after only one sampling was designed and developed. And a portable microfluidic-based analyzer was constructed too. By developing a microfluidic chip and a portable nucleic acid analyzer, we enabled the detection of the mRNA expression levels of multiple biomarkers in rice-sized tissue samples. This microfluidic chip-based portable nucleic acid analyzer could detect ≥10 copies of nucleic acids. The cell volume of the typing reaction on the microfluidic chip was only 0.94 μL, less than 1/25 of that of the conventional 25 μL Eppendorf (EP) tube PCR method, which significantly reduced the testing cost and greatly simplified the analysis of multiple biomarker detection in parallel. With a simple injection operation, reverse transcription-loop-mediated isothermal amplification (RT-LAMP) and real-time detection of 11 lung cancer nucleic acid biomarkers were carried out within 45 min. Given these compelling features, 86 clinical samples were tested using the portable nucleic acid analyzer and classified according to the cutoff values of the 11 biomarkers. Furthermore, multi-biomarker analysis was conducted by a machine learning model to classify different subtypes of lung cancer, with an average area under the curve (AUC) of 0.934. This method shows great potential for the identification of new nucleic acid biomarkers and the accurate diagnosis of lung cancer.