Open AccessArticle Identification of Candidate mRNA and miRNA Molecules Associated with Tuberculosis Through Preliminary Analysis and Validation Using Clinical Samples by Yanxi Ma Yanxi Ma Scilit Preprints.org Google Scholar , Yujuan Fu Yujuan Fu Scilit Preprints.org Google Scholar , Jiahui Li Jiahui Li Scilit Preprints.org Google Scholar Guangyu Xu Guangyu Xu Scilit Preprints.org Google Scholar * College of Pharmacy, Beihua University, Jilin 132013, China * Author to whom correspondence should be addressed. Int. J. Mol. Sci. 2026, 27(12), 5177; https://doi.org/10.3390/ijms27125177 (registering DOI) Submission received: 28 April 2026 / Revised: 3 June 2026 / Accepted: 4 June 2026 / Published: 7 June 2026 Abstract Tuberculosis (TB) remains a major global public health burden. This study aimed to identify differentially expressed messenger RNAs (mRNAs) and circulating microRNAs (miRNAs) associated with TB and to validate their potential roles in the disease. We performed RNA sequencing (RNA-Seq) on peripheral blood samples from 10 patients with active pulmonary TB and 10 healthy controls, using peripheral blood mononuclear cells (PBMCs) for mRNA sequencing and plasma for miRNA sequencing. Given the exploratory nature of the plasma miRNA data and the limitations of the U6 normalization method, the results for circulating miRNAs will need to be validated using alternative methods in subsequent experiments. A total of 1323 differentially expressed mRNAs and 49 differentially expressed miRNAs were identified. Functional annotation of differentially expressed genes was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID), followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, which revealed two TB-associated pathways: “MicroRNAs in cancer” and “Small cell lung cancer.” Two key mRNAs—tumor protein p53 ( TP53) and forkhead box protein P1 ( FOXP1)—and one key miRNA ( hsa-miR-29b-3p) were identified as potential core regulatory factors. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) validation confirmed that the expression patterns of these candidate molecules were consistent with the RNA-Seq results. Three potential candidate molecules associated with TB were ultimately identified, although their disease specificity remains to be determined. Keywords: tuberculosis; candidate molecules; microRNAs; transcriptome sequencing; RT-qPCR validation 1. Introduction Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (MTB), seriously endangering human health [ 1]. The outcome of innate host defense against MTB is determined by a multifaceted and sophisticated molecular network [ 2]. However, there has been a lack of systematic research on the post-transcriptional mechanisms that regulate this process [ 3]. This study aims to delineate the molecular regulatory networks involved in tuberculosis, thereby providing new insights into its pathogenesis and facilitating the identification of candidate molecules. We summarized the basic demographic and clinical characteristics of the study subjects ( Table 1). The detailed demographic and clinical information of each participant is provided in Supplementary Table S1. Analysis revealed no statistically significant differences between the tuberculosis group and the healthy control group in terms of age ( p = 0.53) or gender distribution ( p = 0.07), indicating that the two groups were well-matched. All tuberculosis patients had active pulmonary tuberculosis, while none of the control subjects had a history of tuberculosis. MicroRNAs (miRNAs) are a class of small non-coding RNAs that regulate gene expression at the post-transcriptional level [ 4]. By modulating the expression of target genes, they influence a range of critical cellular processes, including differentiation [ 5], proliferation [ 6], immune response [ 7], and apoptosis [ 8]. Through these regulatory functions, miRNAs play an important role in the maintenance and modulation of immune system homeostasis [ 9]. Given their remarkable stability in the blood [ 10] and correlation with disease severity [ 11], miRNAs have been considered as potential non-invasive biomarkers for disease diagnosis and prognosis [ 12]. To investigate the regulatory role of miRNAs in the pathogenesis of tuberculosis, this study employed RNA sequencing (RNA-Seq) technology to analyze differentially expressed messenger RNAs (mRNAs) and miRNAs in peripheral blood samples (peripheral blood mononuclear cells [PBMCs] for mRNA sequencing and plasma for miRNA sequencing) from patients and healthy controls. Candidate targets TP53/ hsa-miR-29b-3p were validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR), whereas LAMA2/ hsa-miR-150-5p did not reach statistical significance. Notably, these regulatory axes were significantly enriched in cancer-related pathways, suggesting molecular interactions between tuberculosis and oncogenic signaling pathways; this finding may offer potential insights for future diagnostic strategies. 3. Discussion The early diagnosis and treatment of tuberculosis is still a huge challenge to global public health [ 13], and finding new tuberculosis candidate molecules is the key to the early diagnosis and following treatment of tuberculosis. In this study, we systematically screened for potential molecular targets associated with tuberculosis using whole-transcriptome sequencing (RNA-Seq). This integrated analysis provides a novel theoretical and experimental foundation for understanding TB pathogenesis and for developing early diagnostic strategies. In this study, 1323 differentially expressed mRNAs and 49 differentially expressed miRNAs were identified by RNA-Seq screening first. Subsequently, mRNA target genes were predicted for differentially expressed miRNAs and intersected with mRNAs screened by RNA-seq to find 10 key mRNAs. There are three mRNAs ( FOXP1, TP53, and LAMA2), among which LAMA2 expression is not significant. FOXP1 is a member of the FOXP subfamily, and FOXP1 can regulate the activity of transcription factors [ 14], activate T cells [ 15], and enhance autoimmune function [ 16]. The TP53 signaling pathway is involved in regulating the occurrence of many common cancers in humans [ 17]. TP53 can activate the tumor necrosis factor-α (TNF-α) [ 18] to coordinate with the Sonic Hedgehog (SHH) signaling pathway to regulate the homeostasis of cells [ 19]. It has been found that the TP53 signaling pathway is also involved in the immune regulation process of pathogenic bacteria (such as MTB) infection in the body [ 20]. Gene LAMA2 has also been reported to be associated with the immune system [ 21]. The early characteristic of LAMA2 deficiency is the increased expression of pro-inflammatory cytokine TNF-α and interleukin-1 beta (IL-1β) [ 22]. The production of inflammatory factors is believed to be an immune evasion mechanism in the interaction of MTB with the host [ 23]. To sum up, these three mRNAs may be closely related to the host immune system. Furthermore, the downregulation of the FOXP1 gene suggests that its expression may be suppressed in tuberculosis patients, leading to weakened immunity and subsequently triggering latent Mycobacterium tuberculosis infection. MiRNAs can play an important regulatory role in both acquired and innate immunity [ 24], with a fine regulatory effect on the body’s autoimmune system, preventing an excessive immune activation of the body [ 25]. We identified two miRNAs ( hsa-miR-29b-3phsa-miR-150-5p), of which hsa-miR-150-5p was not significantly expressed. Hsa-miR-29b-3p has been confirmed to be differentially expressed in lung cancer, typically downregulated in an advanced lung cancer and upregulated in an early lung cancer, so it can be used as a diagnostic biomarker for lung cancer [ 26]. The abnormal expression of miR-150 can cause some immune system diseases [ 27], and hsa-miR-150-5p is involved in various cellular functions in different types of cells and has also been found to regulate cell cycle progression, proliferation, tumor occurrence, and so on [ 28]. MTB infection and cancers are two diseases that tend to produce resistance to the host immune system. Some studies have shown that tuberculosis and cancers have certain similarities in immune regulation [ 29]. Since hsa-miR-29b-3p is associated with both immunity and cancer, and our validation results are consistent with sequencing data, this miRNA may represent a potential candidate molecule for tuberculosis. The 10 key mRNAs were only enriched in two pathways (microRNAs in cancer and small cell lung cancer pathways). The molecular mechanisms underlying changes in the small cell lung cancer pathway include the expression of protooncogene and the deficiency of tumor suppressor genes, and microRNAs in cancer pathway also shows that many miRNAs are involved in the infection process of tuberculosis, indicating that these two pathways are also involved in the regulation of cancer and tuberculosis infection. A systematic review and meta-analysis indicated that individuals with a history of tuberculosis have a significantly increased risk of lung cancer (adjusted risk ratio 1.51, 95% confidence interval 1.30–1.76), particularly within the first two years following a tuberculosis diagnosis (risk ratio 5.01) [ 30]. The authors attributed this association to chronic inflammation caused by Mycobacterium tuberculosis infection, which can generate reactive oxygen/nitrogen species, lead to deoxyribonucleic acid (DNA) damage, and activate oncogenic signaling pathways. Therefore, although our KEGG enrichment analysis identified only cancer-related pathways (microRNAs in cancer and small cell lung cancer), this observation is consistent with the concept that tuberculosis-associated chronic inflammation may trigger nonspecific carcinogenic signals. We also observed that the expression levels of hsa-miR-29b-3pLAMA2 were negatively correlated, and that the expression levels of hsa-miR-150-5pTP53 were also negatively correlated. Furthermore, TP53LAMA2 are both present in the small cell lung cancer pathway. Meanwhile, both TP53FOXP1 existed in the microRNAs in the cancer pathway. Therefore, it is speculated that the three genes may be involved in the regulation of microRNAs in cancer and small cell lung cancer pathways, ultimately leading to the occurrence of tuberculosis. However, the expression results for TP53, LAMA2, FOXP1, hsa-miR-29b-3p, and hsa-miR-150-5p are not fully consistent with the RNA-Seq findings; specifically, the expression levels of LAMA2hsa-miR-150-5p were not significant, which may be attributed to the small sample size. In addition, TP53, LAMA2FOXP1 are all associated with mitochondrial dysfunction. Hsa-miR-150-5p can increase the expression of TP53, and mutated TP53 can regulate the metabolism and function of mitochondria [ 31]. Hsa-miR-29b-3p can inhibit the expression of LAMA2 and is expressed by the extracellular matrix (ECM) in the small cell lung cancer pathway, while mitochondrial dysfunction will affect the composition of the ECM [ 32], and the related ECM–receptor interaction pathway has been proved to be related to tuberculosis [ 33]. Moreover, the lack of FOXP1 has also been proven to be associated with mitochondrial dysfunction [ 34]. To sum up, we speculate that the occurrence of tuberculosis may be related to both the immune system and energy metabolism. In this study, U6 small nuclear RNA ( U6) was used as an internal control for miRNA quantification. However, U6 is a nuclear transcript that is unstable and present at low concentrations in cell-free samples such as plasma and serum. Therefore, it is not a suitable internal control for studies of circulating miRNAs. This may affect the accuracy of miRNA expression quantification. Consequently, the relevant miRNA results in this study should be considered preliminary observations. Future studies should use established internal controls (such as miR-16miR-93) or incorporate exogenous controls (such as cel-miR-39) to ensure data reliability. 4. Materials and Methods 4.1. Human Sample Acquisition In this study, 20 blood samples were collected, including 10 patients with pulmonary tuberculosis and 10 healthy controls. These 20 volunteers were all over 20 years old, and were excluded from the past history of malignant tumors. This study was approved by the Medical Ethics Committee of Jilin Provincial Tuberculosis Hospital. All 20 participants provided written informed consent to participate in the study. We have uploaded the RNA transcriptome sequencing data to the National Center for Biotechnology Information (NCBI) database. RNA-Seq data can be obtained at ID 876021-BioProject-NCBI (nih.gov). Available clinical characteristics of these patients are summarized ( Table S7). 4.2. mRNA and miRNA Sequencing and Data Analysis First, peripheral blood mononuclear cells (PBMCs) were isolated from whole blood samples by Ficoll-Paque (GE Healthcare, Chicago, IL, USA) density gradient centrifugation. Total RNA was then extracted and purified from the isolated PBMCs using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). To remove residual genomic DNA (gDNA) that could interfere with downstream reverse transcription and quantitative PCR (qPCR) quantification (especially for the U6 internal control, which contains multiple pseudogenes), we treated the RNA samples with DNase I. The procedure is as follows: Mix 1 μg of total RNA with 1 μL of gDNA Eraser (from the PrimeScript TM RT Kit, RR047A; Takara Bio Inc., Kusatsu, Shiga, Japan) to a total volume of 10 μL, incubate at 42 °C for 2 min, and then immediately place on ice. This step effectively degrades gDNA. After DNase treatment, RNA quality was assessed using an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Only RNA samples with an RNA integrity number (RIN) ≥ 7.0 were selected to ensure the construction of high-quality total RNA sequencing libraries for downstream analysis. The sequencing criteria were as follows: RNA concentration ≥ 100 ng/µL, total RNA amount > 2 μg, OD260/280 ratio between 1.8 and 2.2, and OD260/230 ≥ 2.0. Finally, the libraries were sequenced using a 2 × 150 bp paired-end sequencing strategy. For miRNA sequencing, we prepared small RNA libraries using the TruSeq small RNA Sample Preparation Kit (Illumina, San Diego, CA, USA). The specific procedure was as follows. Total RNA (≥200 ng/µL, RIN ≥ 7) was sequentially ligated with T4 RNA ligase 2 (truncated) and T4 RNA ligase (both from New England Biolabs, Ipswich, MA, USA) to attach 3′ and 5′ adapters, respectively. Reverse transcription was performed using SuperScript TM IV Reverse Transcriptase (Thermo Fisher Scientific, Waltham, MA, USA), followed by polymerase chain reaction (PCR) amplification (12–15 cycles) to enrich complementary DNA (cDNA) fragments containing the adapter sequences. The small RNA libraries were size-selected (140–160 bp) by 6% polyacrylamide gel electrophoresis (PAGE), purified, and quantified using an Agilent 2100 Bioanalyzer. Finally, the libraries were sequenced on an Illumina HiSeq 2500 platform (Illumina, San Diego, CA, USA) with 2 × 150 bp paired-end reads. To confirm the specificity of the U6 primer (which served as an internal control for miRNA quantification), we performed an NCBI Primer-Basic Local Alignment Search Tool (BLAST) analysis ( https://blast.ncbi.nlm.nih.gov/Blast.cgi accessed on 23 October 2022). The primer pair matched multiple internal U6 pseudogenes, including the KIF1B gene on chromosome 1 and the TMEM222 gene on chromosome 2. Therefore, rigorous DNase I treatment was performed prior to reverse transcription. In addition, each qPCR experiment included a no-reverse-transcription (NRT) control (i.e., using pure water in place of reverse transcriptase). No cycle threshold (Ct) values were detected in any of the NRT controls. The melting curve for U6 exhibited a single sharp peak ( Figure S1), confirming that the amplification signal originated entirely from the U6 transcript and not from genomic DNA. FastQC (v0.11.8) and R (v3.5.1) was used to evaluate the quality of raw sequencing data. Adapter sequences and low-quality bases (Q 1 is considered upregulated, and 0.05. Figure 5. RT-qPCR analysis of mRNA and miRNA data. *, p ≤ 0.05; **, p ≤ 0.01; ns, p > 0.05. Table 1. Baseline characteristics of tuberculosis (TB) patients and healthy controls. Table 1. Baseline characteristics of tuberculosis (TB) patients and healthy controls. Characteristic TB Patients ( n = 10) Healthy Controls ( n = 10) p Value Age (years, mean ± standard deviation [SD]) ୪୯.୨ ବ୍ଦ ୧୬.୦ ୪୫.୬ ବ୍ଦ ୭.୩ 0.5283757 Sex (male/female) 8/2 3/7 0.06977852 Pulmonary tuberculosis (active) 10 0 N/A Table 2. Ten key mRNAs. Table 2. Ten key mRNAs. No. Gene Symbol log2 Fold Change p Value 1 EIF5 1.550223837 0.001095924 2 TP53 2.787677009 0.000192395 3 TSPAN3 −1.108075776 0.006530098 4 CSGALNACT1 1.914176985 0.019506938 5 AAK1 2.494081353 0.026885414 6 DNMT3A 1.043820128 0.043709217 7 LAMA2 −1.330648879 ୧.୩୯୦୨୪ ୍ଠ ୧୦ −68 MXD1 1.325623476 0.000217589 9 TNFAIP8 −1.085659727 0.003859843 10 FOXP1 −2.482678899 ୨.୧୩୧୧୧ ୍ଠ ୧୦ −6 Table 3. Primer sequences. Table 3. Primer sequences. Name Forward Primer Reverse Primer FOXP1 AACCTCTTGCTCAAGGCATGATT GCTGTGATTGTTGCCTGTGG TP53 GCGCTTCGAGATGTTCCGAG ATGGCGGGAGGTAGACTGAC LAMA2 ACCCGAAGAATTGGTCCAGTGA GGTTGTTCCAGATCGGCAGG hsa-miR-150 GCGGTCTCCCAACCCTTGTA GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCACTGG hsa-miR-29b-3p GGCGCTAGCACCATTTGAAATC GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAACACT GAPDH GGAGTCCACTGGCGTCTTCA GCAGAGGGGGCAGAGATGAT U6 GTGCTCGCTTCGGCAGCACATA GCGCAGGGGCCATGCTAATCTTC Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Ma, Y.; Fu, Y.; Li, J.; Xu, G. Identification of Candidate mRNA and miRNA Molecules Associated with Tuberculosis Through Preliminary Analysis and Validation Using Clinical Samples. Int. J. Mol. Sci. 2026, 27, 5177. https://doi.org/10.3390/ijms27125177 Ma Y, Fu Y, Li J, Xu G. Identification of Candidate mRNA and miRNA Molecules Associated with Tuberculosis Through Preliminary Analysis and Validation Using Clinical Samples. International Journal of Molecular Sciences. 2026; 27(12):5177. https://doi.org/10.3390/ijms27125177 Ma, Yanxi, Yujuan Fu, Jiahui Li, and Guangyu Xu. 2026. "Identification of Candidate mRNA and miRNA Molecules Associated with Tuberculosis Through Preliminary Analysis and Validation Using Clinical Samples" International Journal of Molecular Sciences 27, no. 12: 5177. https://doi.org/10.3390/ijms27125177 Ma, Y., Fu, Y., Li, J., & Xu, G. (2026). Identification of Candidate mRNA and miRNA Molecules Associated with Tuberculosis Through Preliminary Analysis and Validation Using Clinical Samples. International Journal of Molecular Sciences, 27(12), 5177. https://doi.org/10.3390/ijms27125177
