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Identification of Hub Genes and Typing of Tuberculosis Infections Based on Autophagy-Related Genes Cover

Identification of Hub Genes and Typing of Tuberculosis Infections Based on Autophagy-Related Genes

Polish Journal of Microbiology
Volume 72 (2023): Issue 3 (September 2023)
By: Yunfeng Sheng,  Haibo Hua,  Yan Yong and  Lihong Zhou  
Open Access
|Sep 2023

Figures & Tables

Fig. 1.

Differential expression analysis of autophagy-related genes in tuberculosis (TB).A) Acquisition of autophagy-related genes in TB; B) volcano plot of differential expression of autophagy-related genes in TB. C) heat map of differential expression of autophagy-related genes in TB-infected and healthy samples; D) box plots of differentially up-regulated genes associated with autophagy in TB-infected and healthy samples; E) box plots of differentially down-regulated genes associated with autophagy in TB-infected and healthy samples. Green box plots represent normal samples, and red box plots represent TB-infected samples.* p < 0.05, ** p < 0.01, *** p < 0.001.
Differential expression analysis of autophagy-related genes in tuberculosis (TB).A) Acquisition of autophagy-related genes in TB; B) volcano plot of differential expression of autophagy-related genes in TB. C) heat map of differential expression of autophagy-related genes in TB-infected and healthy samples; D) box plots of differentially up-regulated genes associated with autophagy in TB-infected and healthy samples; E) box plots of differentially down-regulated genes associated with autophagy in TB-infected and healthy samples. Green box plots represent normal samples, and red box plots represent TB-infected samples.* p < 0.05, ** p < 0.01, *** p < 0.001.

Fig. 2.

PPI network and correlation analysis of autophagy-related DEGs.A) PPI network of autophagy-related DEGs in tuberculosis; B) degree statistics of top 20 genes in the PPI network. Abscissa represents degree value and the ordinate represents gene. C) correlation analysis of autophagy-related DEGs in tuberculosis.
PPI network and correlation analysis of autophagy-related DEGs.A) PPI network of autophagy-related DEGs in tuberculosis; B) degree statistics of top 20 genes in the PPI network. Abscissa represents degree value and the ordinate represents gene. C) correlation analysis of autophagy-related DEGs in tuberculosis.

Fig. 3.

GO and KEGG enrichment analyses of autophagy-related DEGs.A) Bubble plot of GO enrichment analysis for 47 autophagy-related DEGs; B) bubble plots of KEGG enrichment analysis for 47 autophagy-related DEGs.
GO and KEGG enrichment analyses of autophagy-related DEGs.A) Bubble plot of GO enrichment analysis for 47 autophagy-related DEGs; B) bubble plots of KEGG enrichment analysis for 47 autophagy-related DEGs.

Fig. 4.

Identification of miRNAs and network construction.A) GO enrichment analysis of target genes of six miRNAs; B) intersection of autophagy-related DEGs with miRTarbase target genes; C) regulatory network of miRNA-mRNA.
Identification of miRNAs and network construction.A) GO enrichment analysis of target genes of six miRNAs; B) intersection of autophagy-related DEGs with miRTarbase target genes; C) regulatory network of miRNA-mRNA.

Fig. 5.

Screening of autophagy-related hub genes in tuberculosis.A) Five algorithms were used to screen tuberculosis-related hub genes. B) the GSEA enrichment analysis results of GABARAPL1.
Screening of autophagy-related hub genes in tuberculosis.A) Five algorithms were used to screen tuberculosis-related hub genes. B) the GSEA enrichment analysis results of GABARAPL1.

Fig. 6.

TB subgroups division.A) Consensus clustering map of autophagy-related DEGs; B) consistency CDF graphs; C) relative changes of the area under CDF curve.
TB subgroups division.A) Consensus clustering map of autophagy-related DEGs; B) consistency CDF graphs; C) relative changes of the area under CDF curve.

Fig. 7.

Immune infiltration analyses of different subgroups of tuberculosis.A) Immune microenvironment heat maps of 2 subgroups; B–D) different immune score, ESTIMATE score, and stromal score. E) expression levels of immune checkpoints in different subgroups; F) infiltration of immune cells in different subgroups.* p < 0.05, ** p < 0.01, *** p < 0.001.
Immune infiltration analyses of different subgroups of tuberculosis.A) Immune microenvironment heat maps of 2 subgroups; B–D) different immune score, ESTIMATE score, and stromal score. E) expression levels of immune checkpoints in different subgroups; F) infiltration of immune cells in different subgroups.* p < 0.05, ** p < 0.01, *** p < 0.001.

Top 10 hub genes obtained by five algorithms of the Cytohubba_ cytoHuba

MNCMCCEPCDMNCDegree
SQSTM1GABARAPL1MAPK8ATG16L2SQSTM1
UVRAGUVRAGSQSTM1RAB24MAPK8
MAPK8ULK2UVRAGDRAM1UVRAG
GABARAPL1ATG16L2GABARAPL1RAB5AHSPA5
HSPA5SQSTM1BAXBAK1GABARAPL1
ULK2DRAM1HSPA5ATG2BULK2
BAXRAB24ULK2LAMP2BAX
FOSATG2BFADDBIDVEGFA
VEGFAMAPK8FOSULK2FOS
FADDLAMP2LAMP2GABARAPL1FADD
DOI: https://doi.org/10.33073/pjm-2023-022 | Journal eISSN: 2544-4646 | Journal ISSN: 1733-1331
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Language: English
Page range: 223 - 238
Submitted on: Dec 15, 2022
Accepted on: Apr 19, 2023
Published on: Sep 20, 2023
Published by: Polish Society of Microbiologists
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
tuberculosis,
autophagy,
bioinformatics analysis,
miRNA,
consensus clustering
Related subjects:
Life sciences,
Microbiology and virology

© 2023 Yunfeng Sheng, Haibo Hua, Yan Yong, Lihong Zhou, published by Polish Society of Microbiologists
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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