Published online Mar 26, 2024. doi: 10.12998/wjcc.v12.i9.1622
Peer-review started: January 7, 2024
First decision: January 16, 2024
Revised: January 23, 2024
Accepted: February 27, 2024
Article in press: February 27, 2024
Published online: March 26, 2024
Processing time: 78 Days and 1 Hours
The pathogenesis of ulcerative colitis (UC) is complex, and recent therapeutic advances remain unable to fully alleviate the condition.
To inform the development of novel UC treatments, bioinformatics was used to explore the autophagy-related pathogenesis associated with the active phase of UC.
The GEO database was searched for UC-related datasets that included healthy controls who met the screening criteria. Differential analysis was conducted to obtain differentially expressed genes (DEGs). Au
A total of 4822 DEGs were obtained, of which 58 were classified as DEARGs. SERPINA1, BAG3, HSPA5, CASP1, and CX3CL1 were identified as core targets. GO enrichment analysis revealed that DEARGs were primarily enriched in processes related to autophagy regulation and macroautophagy. KEGG enrichment analysis showed that DEARGs were predominantly associated with NOD-like receptor signaling and other signaling pathways. Disease enrichment analysis indicated that DEARGs were significantly linked to diseases such as malignant glioma and middle cerebral artery occlusion. Immune infiltration analysis demonstrated a higher presence of immune cells like activated memory CD4 T cells and follicular helper T cells in active UC pa
Autophagy is closely related to the active phase of UC and the potential targets obtained from the analysis in this study may provide new insight into the treatment of active UC patients.
Core Tip: This study used bioinformatics to explore the autophagy-related pathogenesis of ulcerative colitis (UC) during its active phase. A total of 58 differentially expressed autophagy-related genes (DEARGs) were found in gene expression datasets from UC patients and healthy controls. Of these, SERPINA1, BAG3, HSPA5, CASP1, and CX3CL1 were identified as core targets. Enrichment analysis highlighted the involvement of DEARGs in autophagy regulation, and macroautophagy, in addition to NOD-like receptor signaling and other pathways. These DEARGs were also shown to be associated with diseases like malignant glioma and middle cerebral artery occlusion. Immune infiltration analysis revealed an increased presence of immune cells, including activated memory CD4 T cells and follicular helper T cells in active UC pa
- Citation: Gong ZZ, Li T, Yan H, Xu MH, Lian Y, Yang YX, Wei W, Liu T. Exploring the autophagy-related pathogenesis of active ulcerative colitis. World J Clin Cases 2024; 12(9): 1622-1633
- URL: https://www.wjgnet.com/2307-8960/full/v12/i9/1622.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v12.i9.1622
Ulcerative colitis (UC) is a chronic, recurrent inflammatory disease in humans that profoundly affects normal functioning[1]. It has both active and remission phases that are classified according to disease severity. UC is characterized by sym
Autophagy is a finely coordinated process that segregates misfolded proteins, damaged or aged organelles, and mutated proteins into double-membrane vesicles called autophagosomes. The autophagosomes later merge with lyso
Given the importance of autophagy in preserving intestinal balance and the role of autophagy dysfunction in UC development, identifying autophagy-related disease predictors is essential for the design of new UC treatments. The current study uses bioinformatics to define gene expression patterns associated with the autophagy-related pathogenesis of active UC (Figure 1).
Datasets related to active UC which included normal control and active UC samples and had a sample size > 30 were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds/). The selected data set was normalized and the “limma” package was downloaded using “Bioconductor.” R 4.3.1 software was then used to perform differential gene analysis on targets identified in the dataset. A |log FC| ≥ 0.585 and an adj. P < 0.05 were used to obtain differentially expressed genes (DEGs).
To obtain differentially expressed autophagy-related genes (DEARGs), autophagy-related genes were downloaded from the Human Autophagy Database (http://www.autophagy.lu/). Using the “Venn Diagram” package in R, autophagy-related targets were intersected with the DEGs, identifying DEARGs as the central targets for further analysis. DEARG heat maps were generated using the “limma” and “pheatmap” packages.
The immune microenvironment is typically composed of immune cells, inflammatory cells, fibroblasts, and mesenchymal stem cells, along with various cytokines and chemokines. Assessing immune cell infiltration is vital for predicting disease progression and treatment response. Several methods exist to analyze immune cell infiltration, including CiberSort, an inverse convolution algorithm developed by BinderG. This method calculates the cellular composition of complex tissues based on normalized gene expression data, allowing specific cell types to be quantified. The CiberSort deconvolution algorithm was used with 100 simulations and subsequent analyses were conducted with a significance threshold of P < 0.05 to determine the proportion of immune cells in different samples. The results were visualized using the “ggpubr” package in R.
Gene ontology (GO) analysis categorizes genes into biological processes (BP), molecular functions (MF), and cellular components (CC), which help to inform their biological functions. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database that integrates genomic, chemical, and systemic information. It is often used for the functional annotation of genes to understand their associated activities and pathways of action. To further understand the target functions of autophagy in patients with active UC and the associated signaling pathways, the “clusterProfiler” package was downloaded from Bioconductor, and GO and KEGG enrichment analysis of the DEARGs was conducted using R. The “clusterProfiler” package was downloaded from “Bioconductor” and the DEARGs were analyzed by GO and KEGG enrichment analysis using R with a threshold value of P < 0.05.
To explore the role of autophagy in UC-related diseases, DEARGs were input into the Metascape platform (https://metascape.org/) using “H. sapiens” as the species setting for both “Input” and “Analysis.” The “Summary of enrichment analysis in DisGeNET” was then exported.
For more precise identification of the core targets, the least absolute shrinkage and selection operator (LASSO) algorithm was used along with the construction of a protein-protein interaction (PPI) network to refine DEARG selection and pre
The LASSO algorithm was used for DEARG validation and feature gene selection using the “glmnet” package in R. The identified genes were then uploaded to the String database (https://cn.string-db.org/) with a “minimum required interaction score” of 0.15 and the results were imported into Cytoscape 3.9.1. To further refine the selection, the “Cyto
GEO database screening identified two UC-related datasets: GSE87466 and GSE53306. GSE53306 includes data on differential gene expression between the active and quiescent stages of UC, providing insight into the disease characteristics. The dataset, which has information on 40 individuals, including 16 active UC cases and 12 normal controls, was pub
A total of 232 autophagy-related genes were obtained from the Human Autophagy Database (http://www.autophagy.lu/). These autophagy-related genes were intersected with the DEGs, resulting in 58 DEARGs (Figure 2). R was then used to analyze the DEARGs and generate heat map and volcano map (Figure 3).
The Cibersort algorithm was used to evaluate immune cell infiltration in two distinct immune states. The following immune cell types were more abundant in active UC cases than in healthy controls: activated memory CD4 T cells, fo
The following immune cell types were significantly higher in the UC group than in the healthy control group: activated memory CD4 T cells (P < 0.001), follicular helper T cells (P < 0.05), gamma delta T cells (P < 0.05), M0 macrophages (P < 0.001), M1 macrophages (P < 0.001), activated dendritic cells (P < 0.001), activated mast cells (P < 0.001), and neutrophils (P <0.001). Meanwhile, CD8 T cells (P < 0.05), resting memory CD4 T cells (P < 0.05), regulatory T cells (Tregs) (P < 0.001), activated NK cells (P < 0.01), monocytes (P < 0.01), M2 macrophages (P < 0.001), resting dendritic cells (P < 0.05), and resting mast cells (P < 0.001) were significantly higher in the healthy control group than in the active UC group. No sig
BP analysis revealed that the DEARGs were primarily associated with the regulation of autophagy, macroautophagy, autophagosome assembly, autophagosome organization, and vacuole organization. CC analysis showed that the DE
The Metascape “Summary of enrichment analysis in DisGeNET” revealed that the DEARGs were mainly enriched in malignant glioma, middle cerebral artery occlusion, infection, glomerulonephritis, and other diseases (Figure 6).
The LASSO algorithm narrowed the range of DEARGs and identified 13 targets: proliferation and apoptosis adaptor protein 15 (PEA15), heat shock 70-kDa protein 5 (HSPA5), caspase 1 (CASP1), serine protease inhibitor A1 (SERPINA1), C-X3-C chemokine ligand 1 (CX3CL1), Bcl2-associated athanogene 3 (BAG3), tumor protein p53 inducible nuclear protein 2 (TP53INP2), and peroxisomal biogenesis factor 14 (PEX14) (Figure 7). Their relationships were further established using the String database. The Cytoscape 3.9.1 software “CytoNCA” plug-in was used to sort the 13 targets according to their degree values, and the top five were selected as the core targets. The Fold Change (logFC) of these targets was obtained from the difference analysis results. All five were up-regulated and had the following parameters: SERPINA1 (logFC = 1.051), BAG3 (logFC = 0.661), HSPA5 (logFC = 0.790), CASP1 (logFC = 1.231), and CX3CL1 (logFC = 0.837) (Figure 8).
The current study identified HSPA5, CASP1, SERPINA1, CX3CL1, and BAG3 as core autophagy-related targets in active UC, all of which were upregulated during the disease. Key signaling pathways linked to these targets included auto
The results, including those predicted using core targeting and immune infiltration analysis, are supported by existing literature. R-HSPA5 is a specific form of HSPA5 that is localized in the endoplasmic reticulum (ER) and shown to play a critical role in autophagy-mediated lysosomal protein hydrolysis. Significant overexpression of HSPA5 mRNA and pro
The disease enrichment analysis results discussed here are confirmed by prior studies. Different stages of IBD are linked to the development of thrombosis, with IBD episodes or activity serving as a primary risk factor[33]. The predicted core genes are critical to the pathogenesis+ADs- hSPA5, for example, offering neuroprotection in ischemic strokes[34]. BAG3 overexpression is shown to improve neurological outcomes associated with middle cerebral artery embolism in mice, reducing infarct volume and enhancing cell survival by activating autophagy and inhibiting apoptosis[35]. Ische
In summary, HSPA5, CASP1, SERPINA1, CX3CL1, and BAG3 were identified as core autophagy-related targets that are upregulated in active UC patients. These targets are associated with key signaling pathways, including autophagy in ani
The etiology of ulcerative colitis (UC), a chronic inflammatory bowel disease (IBD), remains poorly understood. The pathogenesis of UC is complex and is influenced by genetic, environmental, and immune-related factors. While some recent progress has been made in the development of effective UC treatments, few patients experience complete relief of their symptoms. Thus, finding new therapeutic avenues to improve UC patient quality of life remains an urgent need. Autophagy is a cellular self-degradation and repair process that can help remove harmful proteins and organelles from cells and maintain intracellular homeostasis. Recent studies suggest that autophagy may play a key role in the patho
The motivation of this study was to provide an in-depth investigation of the autophagy-related pathogenesis of active phase UC. Bioinformatics analysis was used to better understand whether autophagy plays a key role in active UC and which autophagy-related genes may contribute to the disease process.
This study sought to provide new ideas and potential therapeutic targets for the treatment of active UC to better understand the pathogenesis of the disease and improve clinical symptoms.
A bioinformatics approach was used to compare gene expression data between patients with active UC and healthy controls to identify core genes associated with autophagy and to obtain more information about the role of autophagy in this disease.
HSPA5, CASP1, SERPINA1, CX3CL1, and BAG3 were identified as core targets associated with autophagy-related pathogenesis in active UC, all of which were upregulated. Key signaling pathways linked to these targets include autophagy in animals, other autophagy pathways, and lipids and atherosclerosis pathways. DisGeNET enrichment ana
HSPA5, CASP1, SERPINA1, CX3CL1, and BAG3 were identified as core autophagy-related targets in active UC patients, all of which were upregulated. These targets are associated with key signaling pathways, including autophagy in animals, other autophagy pathways, and lipid and atherosclerosis pathways. DisGeNET enrichment analysis revealed a significant connection between middle cerebral artery occlusion, glomerulonephritis, and the autophagy-related patho
Future research in this field should focus on better understanding the molecular mechanisms by which HSPA5, CASP1, SERPINA1, CX3CL1, and BAG3 contribute to autophagy in patients with active UC. Investigating the specific roles of these core targets in UC pathogenesis and their interactions with the identified key signaling molecules should be a priority. Interventions that target the core autophagy-related genes and pathways could offer promising treatment op
We thank the GEO database for providing their platforms and contributors for their valuable datasets.
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Specialty type: Gastroenterology and hepatology
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P-Reviewer: Lin SR, Taiwan S-Editor: Gong ZM L-Editor: A P-Editor: Xu ZH
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