Comprehensive analysis of peripheral blood non-coding RNAs identifies a diagnostic panel for fungal infection after transplantation. Issue 2 (1st February 2022)

• Record Type: Journal Article
• Authors: Yang, Anli; Chen, Huadi; Lin, Jianwei; Han, Ming; Yuan, Xiaopeng; Zhang, Tao; Nian, Qingwei; Peng, Mengran; Li, Dian; Wu, Chenglin; He, Xiaoshun
• Is Part Of: Bioengineered. Volume 13:Issue 2(2022)
• Page Start: 4039
• Page End: 4050
• Publication Date: 2022-02-01
• Subjects: Fungal infection -- diagnosis -- transplantation -- non-coding RNAs; Biomedical engineering -- Periodicals; Biotechnology -- Periodicals; Microbiology -- Periodicals; 660.6
• DOI: 10.1080/21655979.2022.2032963
• Languages: English
• ISSNs: 2165-5987

Abstract

Abstract: The occurrence of fungal infection seriously affects the survival and life quality of transplanted patients. The accurate diagnosis is of particular importance in the early stage of infection. To develop a novel diagnostic method for this kind of patient, we established a post-transplant immunosuppressed mice model with fungus inoculation and collected their peripheral blood at specific time points after infection. After screening by microarray, differentially expressed miRNAs and lncRNAs were selected and homologously analyzed with those of human beings from the gene database. These miRNAs and lncRNAs candidates were validated by qRT-PCR in peripheral blood samples from transplanted patients. We found that, compared with normal transplanted patients, the levels of miR-215 and miR-let-7 c were up-regulated in the plasma of patients with fungal infection ( P < 0.01), while levels of miR-154, miR-193a, NR_027669.1, and NR_036506.1 were down-regulated in their peripheral blood mononuclear cells ( P < 0.01). Principal component analysis shows that the expression pattern of the above RNAs was different between the two groups. A 6-noncoding-RNA detection panel was established by the support vector machine analysis, whose area under the ROC curve was 0.927. The accuracy, precision, sensitivity, and specificity of this model were 0.928, 0.919, 0.944, and 0.910, respectively. Though our detection panel has excellent diagnostic efficacy, its clinical application value still needs to be further confirmed by multi-center prospective clinical trials. Graphical Abstract: uf0001