journal article·2025-06-01Open access

Ultra-rapid analysis of p53 gene for lung cancer diagnosis within 60 s

Yang Yu, Yanguang Ding, Yu Sun, Jing Yang, Liangjie Feng, Chang Liu, Menglong Shu, Fengchao Wang, Jin Chen

AIP Advances· Volume 15· Issue 6

Abstract

Lung cancer is considered as the king of the human cancer globally, which is a major concern affecting public health. In the current work, a lab-built high-speed capillary electrophoresis (HSCE) system coupled with an ultra-short identification microchannel (25 mm) and a facile-to-use space domain internal standard quantification method (SDIS) was employed for the high-efficient analysis of the typical lung-cancer tumor (p53 gene). The application performances including reliability, accuracy, and precision of the provided HSCE system were investigated in detail. Remarkably, codon 249 (118 bp) and codon 248 (200 bp) of the p53 gene could be well identified and quantified in an ultra-short time of 60 s simultaneously. The detected concentration of the two target samples (codon 249 and codon 248 fragments) featured a well linearity determined by the SDIS method, and the theoretical detection limit could achieve 0.016 62 and 0.054 67 ng/μl, respectively. This work may provide a promising detection strategy for lung cancer diagnosis in the clinical field.
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Research context

OpenAlex topics: RNA modifications and cancer · Cancer-related Molecular Pathways · Cancer Research and Treatments

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Sciences, Shanghai Institute of Technology 1 , 100 Haiquan Road, Shanghai 201418,

Department of Thoracic Surgery, Qingpu Branch of Zhongshan Hospital, Fudan University 2 , Shanghai 201700,

Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University 3 , Suzhou, Jiangsu 215006,

Funding

Shanghai Sailing Program, China · Collaborative Innovation Project of Shanghai Institute of Technology · Science and Technology Talent Development Fund for Young and Middle-Aged Teachers of Shanghai Institute of Technology · AI-Enabled Discipline Promotion Plan of Shanghai, China

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