Novel Biomarker Panel of Let-7d-5p and MiR-140-5p Can Distinguish Latent Tuberculosis Infection from Active Tuberculosis Patients - Liu J; Li Y; Liu T; Shi Y; Wang Y; Wu J; Qi Y (Infection and Drug Resistance, 2023)
Jiaxing Liu,1 Ye Li,2 Ting Liu,2 Yuru Shi,2 Yun Wang,2 Jing Wu,2 Yingjie Qi2 1Department of Clinical Laboratory, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu Province, 210008, People’s Republic of China; 2Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230000, People’s Republic of ChinaCorrespondence: Yingjie Qi, Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230000, People’s Republic of China, Email 7yingjie@sina.comBackground: Mycobacterium tuberculosis (Mtb) survives inside a human host for a long time in the form of latent tuberculosis infection (LTBI). Latent infection of tuberculosis has the opportunity of developing into active tuberculosis (ATB), which has greatly endangered human health. The existing diagnostic methods cannot effectively distinguish LTBI from ATB. Therefore, more effective diagnostic biomarkers and methods are urgently needed.Methods: Here, we screened the GEO data set, conducted joint differential analysis and target gene enrichment analysis, after filtering the disease-related database, we screened the differential miRNA related to TB. The qPCR was used to verify the miRNAs in 84 serum samples. Different combinations of biomarkers were evaluated by logistic regression to obtain a biomarker panel with good performance for diagnosing LTBI.Results: A panel with two miRNAs (hsa-let-7d-5p, hsa-miR-140-5p) was established to differentiate LTBI from ATB. Receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) are 0.930 (sensitivity = 100%, specificity = 88.5%) and 0.923 (sensitivity = 100%, specificity = 92.3%) with the biomarker panel for the training set and test set respectively.Conclusion: The findings indicated that the logistic regression model built by let-7d-5p and miR-140-5p has the ability to distinguish LTBI from active TB patients.Keywords: tuberculosis, active tuberculosis, latent tuberculosis infection, miRNA