Screening and identification of iron death-related gene MT1G in tuberculosis based on bioinformatics

doi:10.19982/j.issn.1000-6621.20250387

Abstract

Abstract:

Objective: To identify potential diagnostic biomarkers for tuberculosis by screening ferroptosis-related differentially expressed genes in tuberculosis through bioinformatics methods. Methods: Firstly, the expression of the ferroptosis-related marker FACL4 in tuberculosis was validated by immunohistochemistry using lung tissue sections from 6 pulmonary tuberculosis patients collected at Weifang Maternal and Child Health Hospital. Then, tuberculosis-associated differentially expressed genes were screened from the Gene Expression Omnibus (GEO) database, followed by bioinformatics analysis including kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) enrichment analysis and immune microenvironment validation. Next, the tuberculosis-related differentially expressed genes were intersected with the ferroptosis-related gene set to obtain a set of candidate genes associated with ferroptosis in tuberculosis, followed by enrichment analysis. To identify significantly and consistently high-expressed key genes, tuberculosis samples were further stratified into C1 and C2 molecular subtypes, and subtype-specific differentially expressed genes were re-screened using clustering and differential analysis. Finally, key gene expression was validated using online datasets, immunohistochemistry, immunofluorescence, and RT-PCR. Results: Immunohistochemical results showed that FACL4 was highly expressed in pulmonary tuberculosis tissues. A total of 1142 tuberculosis-associated differentially expressed genes were identified from the GEO database, all enriched in immune-related pathways and closely linked to immune microenvironment. A set of 438 ferroptosis-related genes was screened, and their intersection with the 624 upregulated tuberculosis-related differential genes resulted in 52 common ferroptosis-related differential genes. Further the 52 genes in tuberculosis samples were further screened into C1 and C2 subtypes for differential gene identification, ultimately identified 4 key candidate genes: MT1G, CDH1, IL1β, and PTGS2. RT-PCR, immunohistochemistry, and immunofluorescence confirmed that MT1G was significantly upregulated supporting its potential as a diagnostic biomarker. Conclusion: This study preliminarily validated the potential of the ferroptosis-related gene MT1G as a biomarker for tuberculosis, providing new strategies for exploring the immuno-pathological mechanisms of tuberculosis and the treatment of drug-resistant tuberculosis.

Key words: Tuberculosis, Ferritins, Cell death, Molecular biology, Gene expression

CLC Number:

R373.1

Gao Yan, Peng Yingjie, Ge Xiao, Xu Jian, Liu Xiaohua, Liu Jinlong, Wang Yuanyuan. Screening and identification of iron death-related gene MT1G in tuberculosis based on bioinformatics[J]. Chinese Journal of Antituberculosis, 2026, 48(3): 366-376. doi: 10.19982/j.issn.1000-6621.20250387

Figures/Tables 14

References 24

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基因	正向引物(5'-3')	反向引物(5'-3')
MT1G	GCTGCTGCTCCTGCTGCC	TTCAAGTCAAAATTGTTTTATTGTCAC
GAPDH	CAGGTGGTCTCCTCTGACTTCAAC	TCTCTTCCTCTTGTGCTCTTGCTG

基因	HC组	TB组	t值	P值
IL1β	7.384±0.238	9.780±0.049	-17.051	<0.001
CDH1	3.134±0.129	3.117±0.173	0.137	0.898
PTGS2	5.286±0.120	5.516±0.107	-2.480	0.069
MT1G	4.668±0.139	5.576±0.024	-11.142	<0.001