Research ArticleOriginal Article
Open Access

Combining bioinformatics and biological detection to identify novel biomarkers for diagnosis and prognosis of pulmonary tuberculosis

Guanren Zhao, Xiaobo Luo, Xue Han and Zhen Liu
Saudi Medical Journal April 2020, 41 (4) 351-360; DOI: https://doi.org/10.15537/smj.2020.4.24989

Article Figures & Data

Figures

  • Figure 1
    Figure 1

    Heatmap analysis displaying the overlapped targets which were dis-regulated under tuberculosis infections among the Gene Expression Omnibus public datasets GSE20050 (clinic), GSE57275 (in vivo), and GSE52819 (cultured cells based).

  • Figure 2
    Figure 2

    Venn diagram showing the distribution of the common A) up-regulated or B) down-regulated targets under Mycobacterium tuberculosis infections among the Gene Expression Omnibus public datasets.

  • Figure 3
    Figure 3

    Kyoto Encyclopedia of Genes and Genomes bioinformatics analysis determined the pathways related to the differentially expressed genes identified in Figure 1.

  • Figure 4
    Figure 4

    Gene Ontology bioinformatics analysis enriched the biological functions related to the differentially expressed genes in Figure 1. FDR: false discovery rate

  • Figure 5
    Figure 5

    Validated the enhancement of the targets that were involved in negative regulation of immune system process in A) whole and B) peripheral blood monocular cell between active pulmonary tuberculosis (PTB) patients and healthy controls using reverse transcription polymerase chain reaction. COPD: chronic obstructive pulmonary disease

  • Figure 6
    Figure 6

    The protein abundance of A) SLAMF8, B) LILRB4, and C) IL-10Ra in different subgroup of pulmonary tuberculosis patients (PTB) with COPD and control subjects by using ELISA assay. Determination of the relative of D) SLAMF8, E) LILRB4, and F) IL-10Ra expressions in smear negative and smear positive PTB patients by using enzyme-linked immunosorbent assays assay. HC: healthy control, SP: smear positive, SN: smear negative

  • Figure 7
    Figure 7

    The receiving operating curve curve for the expressions of SLAMF8, LILRB4, and IL-10Ra in relation to the pulmonary tuberculosis patients (PTB).

  • Figure 8
    Figure 8

    Kaplan-Meier curves of patients with tuberculosis produced according to the protein abundance of A) SLAMF8, B) LILRB4, and C) IL-10Ra expression. The p-values were determined by the log rank test.

  • Figure 9
    Figure 9

    Weekly bacteriologic sterilization according to the protein abundance of A) SLAMF8, B) LILRB4, and C) IL-10Ra expression, were evaluated as the colony forming units per milliliter sputum, in the first 8 weeks treatment of pulmonary tuberculosis patients.

  • Figure 10
    Figure 10

    Weekly bacteriologic sterilization according to the A) SLAMF8, B) LILRB4, and C) IL-10Ra expression, were evaluated as the smear conversation rate, in the first 8 weeks treatment of pulmonary tuberculosis patients.

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