Chencheng Tang

Chencheng Tang

唐晨程
Ph.D. Student
School of Life Sciences
Fudan University

Publications

You can also find my articles on my Google Scholar profile.

Diagnostic accuracy of high-resolution melting curve analysis for discrimination of oncology-associated EGFR mutations: a systematic review and meta-analysis

Published in Journal of International Medical Research, 2025

Objective: To investigate the diagnostic value of high-resolution melting (HRM) analysis for oncology-associated epidermal growth factor receptor (EGFR) gene mutations. Methods: We systematically searched Embase, PubMed, and Web of Science for HRM and EGFR mutation detection studies published through September 2024. True and false positives and negatives were extracted to evaluate the diagnostic accuracy of HRM to detect EGFR mutations. The study was registered at INPLASY (INPLASY202490062). Results: Twenty-six articles were obtained from 416 references. The overall diagnostic sensitivity and specificity were high at 0.95 [95% confidence interval (CI), 0.94-0.96] and 0.99 (95% CI, 0.99-0.99), respectively. Other indicators, including the positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio, were 144.91 (95% CI: 69.07-304.04), 0.08 (95% CI: 0.04-0.13), and 2405.21 (95% CI: 1231.87-4696.13), respectively. The Q value of the summary receiver operating characteristic curve was 0.979, and the area under the curve was 0.997. Conclusion: As a pre-screening method, the high specificity, sensitivity, low cost, rapid turnaround, and simplicity of HRM make it a good alternative for clinical practice, but positive results must still be obtained for diagnostic confirmation. This study provides a transparent overview of relevant studies in design and conduct. Keywords: High-resolution melting curve; diagnostic accuracy; epidermal growth factor receptor mutation; literature review; oncology-associated disease; systematically evaluate.

Recommended citation: Yu, S., Cheng, Y., Tang, C. C., & Liu, Y. P. (2025). Diagnostic accuracy of high-resolution melting curve analysis for discrimination of oncology-associated EGFR mutations: a systematic review and meta-analysis. The Journal of international medical research, 53(2), 3000605241311133.
Download Paper

EigenRF: an improved metabolomics normalization method with scores for reproducibility evaluation on importance rankings of differential metabolites

Published in Analytical Methods, 2024

Screening differential metabolites is of great significance in biomarker discovery in metabolomics research. However, it is susceptible to unwanted variations introduced during experiments. Previous normalization methods have improved the accuracy of inter-group classification by eliminating systematic errors. Nonetheless, the classification ability of differential metabolites obtained through these methods still requires further enhancement, and the reproducibility evaluation on importance rankings of differential metabolites is often disregarded. The EigenRF algorithm was developed as an improvement over the previous metabolomics normalization method referred to as EigenMS, which aims to normalize metabolomics data. Furthermore, scoring metrics, including the local consistency (LC) and overall difference (OD) scores, were introduced to evaluate the reproducibility of importance rankings of differential metabolites from a dual perspective. After conducting validation on three publicly accessible datasets, the EigenRF method has demonstrated enhanced classification ability of differential metabolites as well as improved reproducibility. In summary, EigenRF enhances the reliability of differential metabolites in metabolomics research, benefiting the further exploration of molecular mechanisms underlying biological alterations in complex matrices. The EigenRF algorithm was implemented in an R package: https://www.github.com/YangHuaLab/EigenRF.

Recommended citation: Tang, C., Huang, D., Xing, X., & Yang, H. (2024). EigenRF: an improved metabolomics normalization method with scores for reproducibility evaluation on importance rankings of differential metabolites. Analytical Methods: Advancing Methods and Applications, 17(1), 45–53.
Download Paper