Development of an autophagy-related signature in pancreatic adenocarcinoma

Yue, Peipei; Zhu, Chen; Gao, Yaxian; Li, Yan; Wang, Qi; Zhang, Kexin; Gao, Shuting; Shi, Yu Xiu; Wu, Yanju; Wang, Biao; Xie, Jianwei; Meng, Xin

doi:10.1016/j.biopha.2020.110080

“…Wang et.al developed a three ARGs risk score in glioblastoma which integrated the expression of NRG1, ITGA3 and MAP1LC3A [20]. In pancreatic adenocarcinoma (PCa), a predictive ARGs model including KRAS, CDKN2A, TP53, and SMAD4 were constructed which precisely forecasted the outcomes of PCa patients [21]. Based on TCGA non-small cell lung cancer (NSCLC) data, Liu et.al also develop a 22-gene prognostic prediction model for NSCLC patients based on the expression pro les of autophagy-associated genes.…”

Section: Discussionmentioning

confidence: 99%

Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

Jiang

¹

,

Sun

²

,

Shen

³

et al. 2020

Preprint

0

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Background Autophagy, as a lysosomal degradation pathway, has been reported to be involved in various pathologies, including cancer. However, the expression profiles of autophagy-related genes (ARGs) in endometrial cancer (EC) remain poorly understood. Methods In this study, we analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated to EC patients’ prognosis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DE-MRGs were investigated. LASSO algorithm and Cox regression analysis were performed to select MRGs closely related to EC patients’ outcomes. A prognostic signature was developed and the efficacy were validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability. Results Ninety-four ARGs significantly dysregulated in EC samples compared with the normal control samples. Functional enrichment analysis showed these differentially expressed ARGs (DE-ARGs) were highly enriched in apoptosis, P53 signaling pathway, and various cancer development. Among the 94 DE-ARGs, we subsequently screen out four-ARGs closely related to EC patients outcomes, which are ERBB2, PTEN, TP73 and ARSA. Based on the expression and coefficiency of 4 DE-ARGs, we developed a prognostic signature and further validated its efficacy in part of and the entire TCGA EC cohort. The four ARGs signature was independent of other clinical features, and was proved to effectively distinguish high- or low-risk EC patients and predicted patients' OS accurately. Moreover, the nomogram showed the excellent consistency between the prediction and actual observation in terms of patients' 3- and 5-year survival rates. Conclusions It was suggested that the ARG prognostic model and the comprehensive nomogram may guide the precise outcome prediction and rational therapy in clinical practice.

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“…Wang et.al developed a three ARGs risk score in glioblastoma which integrated the expression of NRG1, ITGA3 and MAP1LC3A [20]. In pancreatic adenocarcinoma (PCa), a predictive ARGs model including KRAS, CDKN2A, TP53, and SMAD4 were constructed which precisely forecasted the outcomes of PCa patients [21]. Based on TCGA nonsmall cell lung cancer (NSCLC) data, Liu et.al also develop a 22-gene prognostic prediction model for NSCLC patients based on the expression profiles of autophagy-associated genes.…”

Section: Discussionmentioning

confidence: 99%

Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

Jiang

¹

,

Sun

²

,

Shen

³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

Background Autophagy, as a lysosomal degradation pathway, has been reported to be involved in various pathologies, including cancer. However, the expression profiles of autophagy-related genes (ARGs) in endometrial cancer (EC) remain poorly understood. Methods In this study, we analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated to EC patients’ prognosis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DE-MRGs were investigated. LASSO algorithm and Cox regression analysis were performed to select MRGs closely related to EC patients’ outcomes. A prognostic signature was developed and the efficacy were validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability. Results Ninety-four ARGs significantly dysregulated in EC samples compared with the normal control samples. Functional enrichment analysis showed these differentially expressed ARGs (DE-ARGs) were highly enriched in apoptosis, P53 signaling pathway, and various cancer development. Among the 94 DE-ARGs, we subsequently screen out four-ARGs closely related to EC patients outcomes, which are ERBB2, PTEN, TP73 and ARSA. Based on the expression and coefficiency of 4 DE-ARGs, we developed a prognostic signature and further validated its efficacy in part of and the entire TCGA EC cohort. The four ARGs signature was independent of other clinical features, and was proved to effectively distinguish high- or low-risk EC patients and predicted patients' OS accurately. Moreover, the nomogram showed the excellent consistency between the prediction and actual observation in terms of patients' 3- and 5-year survival rates. Conclusions It was suggested that the ARG prognostic model and the comprehensive nomogram may guide the precise outcome prediction and rational therapy in clinical practice.

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“…Previous studies indicated that AGRs-based signature can serve as effective biomarkers for predicting patient's prognosis [22][23][24][25], but the role in sarcoma patients remains unclear. In the present study, two ARGs-based signatures were generated to predict the OS and DFS for sarcoma patients, respectively.…”

Section: Construction Of Arg Signatures For Os and Dfs Of Sarcoma Patmentioning

confidence: 99%

Systematic profiling of diagnostic and prognostic value of autophagy-related genes for sarcoma patients

Wang

¹

,

Li

²

,

Shao

³

et al. 2021

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Background Autophagy-related genes (ARGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of ARGs and their clinical significance in sarcoma patients is lacking. Methods Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed ARGs (DEARGs) were determined by matching the DEG and HADb gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEARGs was conducted, and associations with tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for disease-free survival (DFS), were established and validated in an independent set. Results In total, 84 DEARGs and two clusters were identified. TME scores, five immune checkpoints, and several types of immune cells were found to be significantly different between two clusters. Two prognostic signatures incorporating DEARGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.818 and 0.747 for the OS and DFS nomograms, respectively. Conclusion This comprehensive analyses of the ARG landscape in sarcoma showed novel ARGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

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Development of an autophagy-related signature in pancreatic adenocarcinoma

Cited by 32 publications

References 23 publications

Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

Systematic profiling of diagnostic and prognostic value of autophagy-related genes for sarcoma patients

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