Phenylalanylphenylalanine as a Diagnostic Biomarker for Lung Cancer and Tuberculosis.

Abstract Background: Worldwide, lung cancer has the highest mortality rate, and pulmonary tuberculosis has a high incidence in China, and both may be misdiagnosed frequently because of similar clinical presentation and atypical imaging findings. Diagnostic biomarkers to distinguish between lung cancer and other pulmonary diseases can be detected by metabolomics to avoid non-essential treatment.Methods: This cohort study employed non-targeted and targeted metabolomic analysis in participants enrolled from three independent centers. Multivariate statistics, variable importance in the projection parameter, receiver operating characteristics (ROC) curve were used to build potential key diagnostic biomarkers model of lung cancer and these were subsequently analyzed using targeted metabolomics in test set. Quantitative analysis of differences in biomarker levels was conducted, and a support vector machine (SVM) classifier was used to identify the prediction rate of diagnostic biomarker model. Results: Phenylalanylphenylalanine showed opposite trends in lung cancer and tuberculosis. The area under the curve 0.8887 (95% CI 0.8064–0.9710, p<0.001, sensitivity 85.45%, specificity 84%), 0.8149 (95% CI 0.7419–0.8878, p<0.001, the sensitivity was 73.26%, the specificity was 78.43%) and SVM results (prediction rate 77.94%) showed the feasibility of using phenylalanylphenylalanine as a diagnostic marker for the differential diagnosis of lung cancer and tuberculosis.Conclusions: Changes in the levels of phenylalanylphenylalanine facilitate differential diagnosis between lung cancer and tuberculosis, thereby potentially reducing the damage caused by misdiagnosis in the clinical setting, and enabling early treatment of lung cancer patients.Trial registration: This study is registered in the China Clinical Trial Registration Center (registration number ChiCTR2000040666, Registered 07 December 2020, http://www.chictr.org.cn/index.aspx).

Medienart:

Preprint

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

ResearchSquare.com - (2021) vom: 05. Aug. Zur Gesamtaufnahme - year:2021

Sprache:

Englisch

Beteiligte Personen:

Chen, Siyu [VerfasserIn]
Li, Chunyan [VerfasserIn]
Qin, Zhonghua [VerfasserIn]
Song, Lili [VerfasserIn]
Zhang, Shiyuan [VerfasserIn]
Sun, Chongxiang [VerfasserIn]
Zhuang, Pengwei [VerfasserIn]
Wang, Yuming [VerfasserIn]
Yang, Bin [VerfasserIn]
Zhang, Yanjun [VerfasserIn]
Li, Yubo [VerfasserIn]

Links:

Volltext [kostenfrei]

doi:

10.21203/rs.3.rs-775519/v1

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XRA034467564