Pulmonary tuberculosis (TB) is a chronic pulmonary infectious disease caused by Mycobacterium tuberculosis (Mtb) and is the second most predominant infectious disease across the world (Bando-Campos et al., 2019). Current diagnostic tools comprise smear microscopy, microbiological culture, and molecular detection by Xpert MTB/RIF (Xpert) or Xpert MTB/RIF Ultra (Ultra). However, there are additional shortcomings of each approach, such as the insufficient sensitivity of microscopy, the time delay for culture, the high cost of molecular tests, and false-positive Ultra results (Turner et al., 2020).

Mitochondria are critical organelles for maintaining cell energy metabolism and play an important role in the development and progression of lung cancer (Roberts and Thomas, 2013). The human mitochondrial DNA (mtDNA) encodes 37 genes including 2 rRNAs, 22 tRNAs, and 13 protein-coding genes (Larriba et al., 2018). Moreover, approximately 12% of the unique small RNAs identified were encoded in the mitochondrial genome (Riggs and Podrabsky, 2017; Hirose et al., 2019). Recent studies reveal different types of sncRNAs that are associated with the mitochondrial genome, and these sncRNAs generated from the mitochondrial DNA were proposed to regulate and communicate with various pathways that interact with the nuclear genome (Larriba et al., 2018). Therefore, mitochondrial-derived RNAs (mtRNAs) could play an important role in pathophysiological processes and infectious diseases. Since various sncRNAs, such as miRNA, snoRNA, and piRNA, are widely studied in the diagnosis of TB, no clear research has been given for mitochondria-derived small RNAs (mtRNAs) (Wang et al., 2011; de Araujo et al., 2019). Moreover, most studies of sncRNA normalization methods are based on synthetic external spiked-in controls or published endogenous miRNA controls. However, those references are too labile to use directly in sncRNA studies. The ratio-based method provides a solution for the difficult normalization problem for sncRNA data to identify reliable biomarkers to reach the real clinical application (Deng et al., 2019). Here, we aim to develop a new reliable model to predict TB patients based on peripheral blood mtRNAs and the ratio-based method.

Globally, M. tuberculosis drastically affects not only TB patients but also asymptomatic undiagnosed subjects in the community. Fast and precise diagnosis is critical for the control of TB spread and sufficient antimicrobial therapy. Although there are multiple methods in the clinical diagnosis of pulmonary TB, such as sputum smear which provides rapid results and is widely used in clinical laboratories, this traditional method shows a low positive rate of 20% to 30%. Moreover, the gold standard of pulmonary TB diagnosis requires a long incubation time (4–8 weeks) (McNerney et al., 2012). In the early stage of TB infection, one unmet challenge in TB diagnosis is to accurately differentiate other lung diseases from TB with similar clinical symptoms and radiological features.

Circulating small non-coding RNAs have been broadly explored as novel and non-invasive diagnostic and prognostic biomarkers. Many studies have shown that circulating miRNAs serve as potential biomarkers for the detection of TB. However, the performance of miRNA-based TB diagnostic signatures is limited (Zhang et al., 2013; Latorre et al., 2015). Interestingly, our mtRNA signature, derived from the PBMC non-canonical sncRNAs, shows superiority over the miRNA-based signature in diagnosing pulmonary tuberculosis (Pedersen et al., 2019). Compared with miRNA, non-canonical small RNAs such as tsRNAs exhibit a surprising complexity and variability in their sequence (Shi et al., 2019). Moreover, their extraordinary performance in cancer diagnosis and prognosis may be due to the additional complex coating of non-canonical small non-coding RNAs (Gu et al., 2020; Zhu et al., 2021; Zuo et al., 2021).

An affordable, reproducible, and non-invasive method for predicting the severity of TB is required to support longitudinal management and clinical decision-making. In this study, we aimed to develop blood-based screening to improve the sensitivity and specificity of classifications between normal and TB patients. To our knowledge, this is the first time that machine learning algorithms have been used to diagnose TB by mtRNA on the clinical system. Furthermore, this algorithm has been implemented into a user-friendly Shiny app, an R package that makes it easy to build interactive web apps straight from R, to support further independent investigations of its clinical practice (Sievert, 2020). Previous miRNA-based TB diagnostic tools were either inaccurate or difficult to use (Zhou et al., 2016; Sampath et al., 2021). This shiny app only needs to input the expression ratio of mtRNA, and the result can be obtained quickly after clicking submit, which greatly reduces the user’s time.

However, the model was established for the diagnosis of TB and the questions that require further investigation still remain. First, the mtRNA-based signature functions are unknown. Second, large-scale, multicenter case–control studies are warranted to validate our results and identify the signature. Third, since we obtained the sequences of mtRNA signature, all candidate genes need to be validated by quantitative PCR.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .

The Ethics Committee of Bengbu Medical College approved this study, with written informed consent obtained from all subjects, which conformed to the standard indicated by the Declaration of Helsinki. The patients/participants provided their written informed consent to participate in this study.

ZL and SH had the design and launched the study. ZL and SH processed the statistical data analyses, and all authors revised the manuscript and approved the version for publication. All authors contributed to the article and approved the submitted version.

This work was supported in part by funding from the Key Research and Development Program of Guangxi Zhuang Autonomous Region (No. AB16380152), in part from the Key Research and Development Program of Liuzhou (2018BJ10509) and in part from the ‘139’ Incubation Program for high-level medical talents in Guangxi.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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