Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer

Chen, Xingyu; Lan, Hua; He, Dong; Wang, Zhanwang; Xu, Runshi; Yuan, Jing; Xiao, Mengqing; Zhang, Yao; Gong, Lian; Xiao, Songshu; Cao, Ke

doi:10.3389/fonc.2021.616133

Cited by 18 publications

(11 citation statements)

References 55 publications

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“…However, due to inadequate sample size and the lack of available multi-omics data, only a few studies have applied genomic analysis to study OC from an immunological perspective. In our previous study, based on multi-omics data from the Cancer Genome Atlas (TCGA) cohort, we found that autophagy can affect the OC immune microenvironment ( 18 ). In the present study, to further explore the immune microenvironment of OC, we studied the impact of hypoxia on the immune microenvironment of OC.…”

Section: Introductionmentioning

confidence: 99%

Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis

Chen

Lan

et al. 2021

Front. Immunol.

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BackgroundOvarian cancer (OC) has the highest mortality rate among gynecologic malignancy. Hypoxia is a driver of the malignant progression in OC, which results in poor prognosis. We herein aimed to develop a validated model that was based on the hypoxia genes to systematically evaluate its prognosis in tumor immune microenvironment (TIM).ResultsWe identified 395 hypoxia-immune genes using weighted gene co-expression network analysis (WGCNA). We then established a nine hypoxia-related genes risk model using least absolute shrinkage and selection operator (LASSO) Cox regression, which efficiently distinguished high-risk patients from low-risk ones. We found that high-risk patients were significantly related to poor prognosis. The high-risk group showed unique immunosuppressive microenvironment, lower antigen presentation, and higher levels of inhibitory cytokines. There were also significant differences in somatic copy number alterations (SCNAs) and mutations between the high- and low-risk groups, indicating immune escape in the high-risk group. Tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms showed that low-risk patients are significantly responsive to programmed cell death protein-1 (PD-1) inhibitors.ConclusionsIn this study, we highlighted the clinical significance of hypoxia in OC and established a hypoxia-related model for predicting prognosis and providing potential immunotherapy strategies.

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Section: Introductionmentioning

confidence: 99%

Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis

Chen

Lan

et al. 2021

Front. Immunol.

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“…As clearly illustrated by Bru-seq trace diagrams (Figure ), QN523 increases the transcription of select autophagy-related genes WIPI1 (12.8-fold, Figure A), FAM129A (16.8-fold, Figure B), homocysteine-inducible ER protein with ubiquitin like domain 1 (HERPUD1) (9.8-fold, Figure C), GABARAPL1 (7.8-fold, Figure D), HMOX1 (13-fold, Figure E), and MAP1LC3B (6.8-fold, Figure F). WIPI1, GABARAPL1, and MAP1LC3B are well-established autophagy-related genes and relevant cancer markers at both gene and protein levels. − HERPUD1 is a part of the ER-associated degradation machinery involved in ubiquitin-dependent degradation of misfolded proteins in the ER . FAM129A an ATF4 target genes plays a direct role in autophagy. , Heme oxygenase (HMOX1) acts as a cytoprotective gene against several forms of cell death including autophagy .…”

Section: Results and Discussionmentioning

confidence: 99%

Induction of Genes Implicated in Stress Response and Autophagy by a Novel Quinolin-8-yl-nicotinamide QN523 in Pancreatic Cancer

Kuang

Kyani

et al. 2022

J. Med. Chem.

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Using a cytotoxicity-based phenotypic screen of a highly diverse library of 20,000 small-molecule compounds, we identified a quinolin-8-yl-nicotinamide, QN519, as a promising lead. QN519 represents a novel scaffold with drug-like properties, showing potent in vitro cytotoxicity in a panel of 12 cancer cell lines. Subsequently, lead optimization campaign generated compounds with IC50 values < 1 μM. An optimized compound, QN523, shows significant in vivo efficacy in a pancreatic cancer xenograft model. QN523 treatment significantly increased the expression of HSPA5, DDIT3, TRIB3, and ATF3 genes, suggesting activation of the stress response pathway. We also observed a significant increase in the expression of WIPI1, HERPUD1, GABARAPL1, and MAP1LC3B, implicating autophagy as a major mechanism of action. Due to the lack of effective treatments for pancreatic cancer, discovery of novel agents such as the QN series of compounds with unique mechanism of action has the potential to fulfill a clear unmet medical need.

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“…There were some other studies concentrating on different features of biological process and the comprehensive analyses of functional related genes for OC [ 40 , 41 ], especially for lncRNAs. For example, one study identified and validated risk model based on five immune-related lncRNAs is an independent prognostic factor for OC patients.…”

Section: Discussionmentioning

confidence: 99%

Development and verification of a 7-lncRNA prognostic model based on tumor immunity for patients with ovarian cancer

Feng

Yu²,

Yin³

et al. 2023

J Ovarian Res

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Background Both immune-reaction and lncRNAs play significant roles in the proliferation, invasion, and metastasis of ovarian cancer (OC). In this study, we aimed to construct an immune-related lncRNA risk model for patients with OC. Method Single sample GSEA (ssGSEA) algorithm was used to analyze the proportion of immune cells in The Cancer Genome Atlas (TCGA) and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells for OC patients. The stromal and immune scores were computed utilizing the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analyses were utilized to detect immune cluster-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression was conducted for lncRNA selection. The selected lncRNAs were used to construct a prognosis-related risk model, which was then validated in Gene Expression Omnibus (GEO) database and in vitro validation. Results We identify two subtypes based on the ssGSEA analysis, high immunity cluster (immunity_H) and low immunity cluster (immunity_L). The proportion of patients in immunity_H cluster was significantly higher than that in immunity_L cluster. The ESTIMATE related scores are relative high in immunity_H group. Through WGCNA and LASSO analyses, we identified 141 immune cluster-related lncRNAs and found that these genes were mainly enriched in autophagy. A signature consisting of 7 lncRNAs, including AL391832.3, LINC00892, LINC02207, LINC02416, PSMB8.AS1, AC078788.1 and AC104971.3, were selected as the basis for classifying patients into high- and low-risk groups. Survival analysis and area under the ROC curve (AUC) of the signature pointed out that this risk model had high accuracy in predicting the prognosis of patients with OC. We also conducted the drug sensitive prediction and found that rapamycin outperformed in patient with high risk score. In vitro experiments also confirmed our prediction. Conclusions We identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients.

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Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer

Cited by 18 publications

References 55 publications

Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis

Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis

Induction of Genes Implicated in Stress Response and Autophagy by a Novel Quinolin-8-yl-nicotinamide QN523 in Pancreatic Cancer

Development and verification of a 7-lncRNA prognostic model based on tumor immunity for patients with ovarian cancer

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