Development of an autophagy-related gene expression signature for prognosis prediction in prostate cancer patients

Hu, Daixing; Jiang, Li; Luo, Shengjun; Zhao, Xin; Hu, Hao; Zhao, Guozhi; Tang, Wei

doi:10.1186/s12967-020-02323-x

Cited by 84 publications

(79 citation statements)

References 29 publications

(29 reference statements)

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“…Jia et al demonstrated that overexpression of MAFG-AS1 lncRNA primarily downregulated miR-339-5p levels in non-small-cell carcinoma cells, and MMP15 is a miR-339-5p target [40] . In addition, Hu et al illustrated that MAP2K7 might be a promising prognostic molecular biomarker in PCa [41] . Nevertheless, the role of hsa-miR-6825-5p in PCa development has not been explored.…”

Section: Discussionmentioning

confidence: 99%

Identification of Androgen Receptor Variant 7-related RNAs Affecting Abiraterone Efficacy in Castration-resistant Prostate Cancer Treatment by RNA-sequencing

Wang¹,

Cao²,

Tang³

et al. 2020

Preprint

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Background: This study aimed to identify androgen receptor variant 7 (AR-V7)-related RNAs affecting Abiraterone treatment of castration-resistant prostate cancer (CRPC) using RNA-sequencing. Methods: To identify AR-V7-related RNAs affecting Abiraterone treatment of CRPC, a series of in vitro experiments were employed, including cell proliferation assay, cell apoptosis assay, Western blot analysis, and Real-time quantitative reverse transcription PCR (qRT-PCR). After RNA-sequencing, the differentially expressed (DE) mRNAs and DE long non-coding RNAs (lncRNAs) were screened and the lncRNA-mRNA pairs were identified. In addition, enrichment and Protein-protein interaction (PPI) network analyses were performed. Finally, the mRNA-miRNA-lncRNA competing endogenous RNAs (ceRNA) network was built, along with survival analysis. Results: A total of 1,387 AR-V7-related RNAs affecting Abiraterone treatment of CRPC were identified. The enrichment analysis showed that the target genes of DEmRNAs and DElncRNAs were primarily involved in cancer-related pathways, including the ErbB signaling pathway, pathways in cancer, and basal cell carcinoma. In addition, notch receptor 1 (NOTCH1), ionotropic NMDA glutamate receptor type subunit 1 (GRIN1), U-box containing protein 1 (STUB1), and mitogen-activated protein kinase 7 (MAP2K7) with high degrees in the PPI network. Moreover, MAP2K7 was regulated by hsa-miR-6825-5p, which in turn was regulated by MAFG-AS1 lncRNA in the ceRNA network. The survival analysis revealed that a total of four lncRNAs, including MAFG-AS1, and 17 mRNAs, including high muscle blind-like splicing regulator 2 (MBNL2), were associated with disease-free survival (RFS). Among them, only MBNL2 overexpression correlated with good survival outcome. The NOTCH1, GRIN1, STUB1, MBNL2, and ErbB signaling pathways are likely related to the efficacy of Abiraterone in CRPC treatment. Moreover, the MAFG-AS1-hsa-miR-6825-5p-MAP2K7 axis could be a therapeutic target for Abiraterone in CRPC treatment.Conclusions: NOTCH1, GRIN1, STUB1, MBNL2, and the ErbB signaling pathway may relate to the progression of CRPC. MAFG-AS1-hsa-miR-6825-5p-MAP2K7 axis might be a potential therapeutic target for Abiraterone in CRPC.

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Section: Discussionmentioning

confidence: 99%

Identification of Androgen Receptor Variant 7-related RNAs Affecting Abiraterone Efficacy in Castration-resistant Prostate Cancer Treatment by RNA-sequencing

Wang¹,

Cao²,

Tang³

et al. 2020

Preprint

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“…The Wilcoxon signed-rank test in package "limma" in R software (version 3.6.3) was applied to screen the differentially expressed ARGs between HCC and normal tissues, with the criteria of |log 2 fold change (FC)|>1.5 and adjusted P-value < 0.05 [16,18]. Then, we combined the expression data of the differentially expressed ARGs with the corresponding clinical data.…”

Section: Differentially Expressed Args In Hccmentioning

confidence: 99%

“…Then, we combined the expression data of the differentially expressed ARGs with the corresponding clinical data. Univariate Cox regression and multivariate Cox regression analyses were applied to pick out the ARGs that were closely related to OS in HCC [18]. We also performed the Lasso regression to remove ARGs that might be closely correlated with others [14,16].…”

Section: Differentially Expressed Args In Hccmentioning

confidence: 99%

Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma

Yang

Niu

Zhao³

et al. 2020

Preprint

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Background: Hepatocellular carcinoma (HCC) is one of the devastating tumors with increasing incidence. Autophagy-associated genes (ARGs) are widely participated in the cellular processes of HCC. This study proposed to identify the novel prognostic gene signature based on ARGs in HCC. Methods: We downloaded the RNA sequencing data and clinical information of HCC and normal tissues from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Functional enrichment analyses were conducted to explore the biological implications and mechanisms of ARGs in HCC. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs which related to overall survival (OS). The OS-related ARGs were then used to establish a prognostic prediction model. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were both applied to evaluate the accuracy of the model. GSE14520 dataset was downloaded as the testing cohort to validate the prognostic risk model in TCGA. A nomogram based on the clinical features and risk signature was established to predict the 3-year and 5-year survival rate of HCC patients. Results: Totally 27 differentially expressed ARGs were screened in this study. Then, 3 OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these 3 ARGs, a prognostic prediction model was constructed. HCC patients in high-risk group presented poorer prognosis than those with low risk score in TCGA cohort (3-year OS, 53.7% vs 70.2%; 5-year OS, 42.0 % vs 55.2%; P=4.478e-04) and in the testing group (3-year OS, 57.7% vs 73.5%; 5-year OS, 43.2% vs 63.0%; P=1.274e-03). The risk score curve showed a well feasibility in predicting the patients’ survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict the 3-year and 5-year survival rate in HCC patients. Conclusions: The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.

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“…Kaplan-Meier method was applied to evaluate the differences in high-risk and low-risk groups in term of the predictive signature. Besides, the accuracy of prognostic prediction model was determined by the ROC curve in package "survivalROC" in R software [21].…”

Section: Validation the Prognostic Modelmentioning

confidence: 99%

“…To investigate the molecular mechanisms of autophagy-associated genes in GC, GO and KEGG enrichment analyses were carried out and visualized by R software with "ggplot2", "GO plot", "DOSE", and "Cluster Pro ler" packages, etc [21]. The p-value < 0.05 and q-value < 0.05 were set as the criteria.…”

Section: Functional Enrichment Analysismentioning

confidence: 99%

Development of a Survival Model Based on Autophagy-Associated Genes for Predicting Prognosis of Gastric Cancer

Yang¹,

Duan²,

Zhao³

et al. 2020

Preprint

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Background: Gastric cancer (GC) is one of lethal diseases worldwide. Autophagy-associated genes play a crucial role in the cellular processes of GC. Our study aimed to investigate and identify the prognostic potential of autophagy-associated genes signature in GC. Methods: RNA-seq and clinical information of GC and normal controls were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the Wilcoxon signed-rank test was used to pick out the differentially expressed autophagy-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the potential roles and mechanisms of autophagy-associated genes in GC. Cox proportional hazard regression analysis and Lasso regression analysis were carried out to identify the overall survival (OS) related autophagy-associated genes, which were then collected to construct a predictive model. Kaplan-Meier method and receiver operating characteristic (ROC) curve were utilized to validate the accuracy of this model. Finally, a clinical nomogram was established by combining the clinical factors and autophagy-associated genes signature. Results: A total of 28 differentially expressed autophagy-associated genes were identified. GO and KEGG analyses revealed that several important cellular processes and signaling pathways were correlated with these genes. Through Cox regression and Lasso regression analyses, we identified 4 OS-related autophagy-associated genes (GRID2, ATG4D, GABARAPL2, and CXCR4) and constructed a prognosis prediction model. GC Patients with high-risk had a worse OS than those in low-risk group (5-year OS, 27.7% vs 38.3%; P=9.524e-07). The area under the ROC curve (AUC) of the prediction model was 0.67. The nomogram was demonstrated to perform better for predicting 3-year and 5-year survival possibility for GC patients with a concordance index (C-index) of 0.70 (95% CI: 0.65-0.72). The calibration curves also presented good concordance between nomogram-predicted survival and actual survival. Conclusions: We constructed and evaluated a survival model based on the autophagy-associated genes for GC patients, which may improve the prognosis prediction in GC.

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Development of an autophagy-related gene expression signature for prognosis prediction in prostate cancer patients

Cited by 84 publications

References 29 publications

Identification of Androgen Receptor Variant 7-related RNAs Affecting Abiraterone Efficacy in Castration-resistant Prostate Cancer Treatment by RNA-sequencing

Identification of Androgen Receptor Variant 7-related RNAs Affecting Abiraterone Efficacy in Castration-resistant Prostate Cancer Treatment by RNA-sequencing

Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma

Development of a Survival Model Based on Autophagy-Associated Genes for Predicting Prognosis of Gastric Cancer

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