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

Abstract

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.

The prognostic value and immune landscape of a cuproptosis-related lncRNA signature in head and neck squamous cell carcinoma

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Characterization of Necroptosis-Related Molecular Subtypes and Therapeutic Response in Lung Adenocarcinoma

“…In this study, we also explored the genetic characteristics of NRGs in LUAD patients and it revealed that a high somatic mutation frequency (52.76% samples) was detected and most NRGs possessed copy number amplification, suggesting that necroptosis might be closely associated with a genetic mutation in LUAD patients. The classification of LUAD patients based on various biological signatures has been considered a promising method and applied to various studies including immune cell infiltration (ICI) ( Li et al, 2021 ), autophagy ( Zhang M.-Y. et al, 2021 ), pyroptosis ( Dong et al, 2021 ), and m6A RNA methylation ( Zhou et al, 2021 ).…”

“…The subtype stratification of LUAD patients based on transcriptome sequencing profiles has been recognized as a novel methodology that can quickly obtain biological characteristics of subtypes and help us further identify the optimal treatment strategies for patients ( Jang et al, 2020 ). In addition, multiple biological signatures have also been applied to explore novel molecular subtypes for the prognosis of LUAD, such as immune cell infiltration (ICI) ( Li et al, 2021 ), autophagy ( Zhang M.-Y. et al, 2021 ), pyroptosis ( Dong et al, 2021 ), m6A RNA methylation ( Zhou et al, 2021 ), and so on.…”