scholarly journals Identification of a Five Autophagy Subtype-Related Gene Expression Pattern for Improving the Prognosis of Lung Adenocarcinoma

Author(s):  
Meng-Yu Zhang ◽  
Chen Huo ◽  
Jian-Yu Liu ◽  
Zhuang-E. Shi ◽  
Wen-Di Zhang ◽  
...  

Background: Autophagy plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the autophagy-related gene (ARG) expression pattern and to identify promising autophagy-related biomarkers to improve the prognosis of LUAD.Methods: The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. The Human Autophagy Database (HADb) was used to extract ARGs. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the differentially expressed ARGs (DEARGs). Then, consensus clustering revealed autophagy-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, the univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic ARGs. After overlapping DEGs and the independent prognostic ARGs, the predictive risk model was established and validated. Correlation analyses between ARGs and clinicopathological variables were also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels and the risk score as well as clinicopathological variables in the predictive risk model.Results: A total of 222 genes from the HADb were identified as ARGs, and 28 of the 222 genes were pooled as DEARGs. The most significant GO term was autophagy (p = 3.05E-07), and KEGG analysis results indicated that 28 DEARGs were significantly enriched in the ErbB signaling pathway (p < 0.001). Then, consensus clustering analysis divided the LUAD into two clusters, and a total of 168 DEGs were identified according to cluster subtypes. Then univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators. After overlapping 168 DEGs and 12 genes, 10 genes (ATG4A, BAK1, CAPNS1, CCR2, CTSD, EIF2AK3, ITGB1, MBTPS2, SPHK1, ST13) were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model identified the prognosis (p = 4.379E-10). Combined with the correlation analysis results between ARGs and clinicopathological variables, five ARGs were screened as prognostic genes. Among them, SPHK1 expression levels were positively correlated with CD4+ T cells and dendritic cell infiltration levels.Conclusions: In this study, we constructed a predictive risk model and identified a five autophagy subtype-related gene expression pattern to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful to accurately characterize the LUAD and develop personalized treatment.

2003 ◽  
Vol 5 (2) ◽  
pp. 145-156 ◽  
Author(s):  
Fabien Avaron ◽  
Christelle Thaeron-Antono ◽  
Caroline W. Beck ◽  
Veronique Borday-Birraux ◽  
Jacqueline Geraudie ◽  
...  

2016 ◽  
Vol 43 (2) ◽  
pp. 493-516 ◽  
Author(s):  
Mónica B. Betancor ◽  
Aurelio Ortega ◽  
Fernando de la Gándara ◽  
Douglas R. Tocher ◽  
Gabriel Mourente

2021 ◽  
Author(s):  
Tenggang Ma ◽  
Hongyu Zhang ◽  
Ziwen Feng ◽  
Renzhong Wang

Abstract Ulcerative colitis (UC) is a chronic inflammatory disease that is prone to recurrent attacks. It has complex pathogenesis, which is closely related to genetics, constitution, dietary habits, and environmental factors. From the comprehensive Gene Expression Omnibus database (GEO), we retrieved gene expression profiles and classified 197 cases of UC into three subgroups for the purpose of predicting the basic molecular characteristics for different types of ulcerative colitis. As expected, each group showed its own clinical peculiarity and way of presentation. In this article, consensus clustering was used to divide the sample into three. The WGCNA analysis was applied to evaluate specific modules and reveal transcriptional differences among the subgroups. Subsequently, pathway and function enrichment analysis was conducted based on WGCNA. In subgroup Ⅰ, fructose and mannose metabolism pathway and cell cycle 11/36 pathway are up-regulated, which could be an indicator of exacerbation. Furthermore, the hematopoietic cell lineage pathway, which was significantly up-regulated in subgroup Ⅱ, might be indicating a disease correlation. In subgroup Ⅲ, the gene expression pattern of the peroxisome pathway is similar to the normal group, which may indicate an early stage of UC. Although no significant prognostic difference existing among the groups, there were significant differences in their underlying biological characteristics. This suggests that transcriptome classifications also represent risk factors for different disease states and ages. In summary, the bioinformatics techniques used in this study contribute to identifying molecular subtypes for diagnosing human ulcerative colitis. The transcriptome classification of UC cases suggests that each subgroup may have its own gene expression pattern and pathway, providing further personalized treatment guidance for patients with ulcerative colitis.


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