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

Yunfeng Sheng
Yunfeng Sheng
, 
Haibo Hua
Haibo Hua
, 
Yan Yong
Yan Yong
 and 
Lihong Zhou
Lihong Zhou
   | Sep 20, 2023
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Article Category: Original Paper
Published Online: Sep 20, 2023
Page range: 223 - 238
Received: Dec 15, 2022
Accepted: Apr 19, 2023
DOI: https://doi.org/10.33073/pjm-2023-022
Keywords
© 2023 Yunfeng Sheng et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

MNC MCC EPC DMNC Degree
SQSTM1 GABARAPL1 MAPK8 ATG16L2 SQSTM1
UVRAG UVRAG SQSTM1 RAB24 MAPK8
MAPK8 ULK2 UVRAG DRAM1 UVRAG
GABARAPL1 ATG16L2 GABARAPL1 RAB5A HSPA5
HSPA5 SQSTM1 BAX BAK1 GABARAPL1
ULK2 DRAM1 HSPA5 ATG2B ULK2
BAX RAB24 ULK2 LAMP2 BAX
FOS ATG2B FADD BID VEGFA
VEGFA MAPK8 FOS ULK2 FOS
FADD LAMP2 LAMP2 GABARAPL1 FADD
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