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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

Open Access
|Sep 2023

Figures & Tables

graphic/j_pjm-2023-022_fig_008.jpg
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.

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.

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.

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.

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.

Table I

Top 10 hub genes obtained by five algorithms of the Cytohubba. cytoHuba

MNCMCCEPCDMNCDegree
SQSTM1GABARAPL1MAPK8ATG16L2SQSTM1
UVRAGUVRAGSQSTM1RAB24MAPK8
MAPK8ULK2UVRAGDRAM1UVRAG
GABARAPL1ATG16L2GABARAPL1RAB5AHSPA5
HSPA5SQSTM1BAXBAK1GABARAPL1
ULK2DRAM1HSPA5ATG2BULK2
BAXRAB24ULK2LAMP2BAX
FOSATG2BFADDBIDVEGFA
VEGFAMAPK8FOSULK2FOS
FADDLAMP2LAMP2GABARAPL1FADD
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.

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.

DOI: https://doi.org/10.33073/pjm-2023-022 | Journal eISSN: 2544-4646 | Journal ISSN: 1733-1331
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

© 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.