Targeting RB1 Loss in Cancers

Linn, Paing; Kohno, Susumu; Sheng, Jindan; Kulathunga, Nilakshi; Zhang, Zhiheng; Voon, Dominic Chih-Cheng; Watanabe, Yoshihiro; Takahashi, Chiaki

doi:10.3390/cancers13153737

Cited by 35 publications

(21 citation statements)

References 103 publications

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“…As a result, the cell cycle was arrested and the cell proliferation were restrained ( 51 ). Furthermore, overexpression of RB1 interfered with the regulation of cell cycle, playing a critical role in the occurrence and development of cancer ( 52 , 53 ).…”

Section: Discussionmentioning

confidence: 99%

RB1 Is an Immune-Related Prognostic Biomarker for Ovarian Cancer

Xie¹,

Tan²,

Ren³

et al. 2022

Front. Oncol.

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BackgroundOvarian cancer (OC) is one of the most lethal gynecologic malignancies and a leading cause of death in the world. Thus, this necessitates identification of prognostic biomarkers which will be helpful in its treatment.MethodsThe gene expression profiles from The Cancer Genome Atlas (TCGA) and GSE31245 were selected as the training cohort and validation cohort, respectively. The Kaplan–Meier (KM) survival analysis was used to analyze the difference in overall survival (OS) between high and low RB transcriptional corepressor 1 (RB1) expression groups. To confirm whether RB1 was an independent risk factor for OC, we constructed a multivariate Cox regression model. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses were conducted to identify the functions of differentially expressed genes (DEGs). The associations of RB1 with immune infiltration and immune checkpoints were studied by the Tumor Immune Estimation Resource (TIMER 2.0) and the Gene Expression Profiling Interactive Analysis (GEPIA). The immunohistochemistry (IHC) was performed to compare the expression level of RB1 in normal tissues and tumor samples, and to predict the prognosis of OC.ResultsThe KM survival curve of the TCGA indicated that the OS in the high-risk group was lower than that in the low-risk group (HR = 1.61, 95% CI: 1.28-2.02, P = 3×10-5), which was validated in GSE31245 (HR = 4.08, 95% CI: 1.21–13.74, P = 0.01) and IHC. Multivariate Cox regression analysis revealed that RB1 was an independent prognostic biomarker (HR = 1.66, 95% CI: 1.31-2.10, P = 2.02×10-5). Enrichment analysis suggested that the DEGs were mainly involved in cell cycle, DNA replication, and mitochondrial transition. The infiltration levels of fibroblast, neutrophil, monocyte and macrophage were positively correlated with RB1. Furthermore, RB1 was associated with immune checkpoint molecules (CTLA4, LAG3, and CD274). The IHC staining revealed higher expression of RB1 in tumor tissues as compared to that in normal tissues (P = 0.019). Overexpression of RB1 was associated with poor prognosis of OC (P = 0.01).ConclusionThese findings suggest that RB1 was a novel and immune-related prognostic biomarker for OC, which may be a promising target for OC treatment.

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Section: Discussionmentioning

confidence: 99%

RB1 Is an Immune-Related Prognostic Biomarker for Ovarian Cancer

Xie¹,

Tan²,

Ren³

et al. 2022

Front. Oncol.

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“…Of note, the aberrant regulation of mitotic cell cycle due to RB1 defects pathway was identified in LUAD, KIRC and LGG. According to prior study, irreversible defects in RB1 tumor suppressor functions often predict poor outcomes in many human cancers, such as lung adenocarcinoma [39].…”

Section: Discussionmentioning

confidence: 99%

Risk stratification and pathway analysis based on graph neural network and interpretable algorithm

Liang

Gong

et al. 2022

BMC Bioinformatics

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Background Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result. Results To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival. Conclusion PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power.

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“…All these three pairs have very strong confirmation in cell line tests and are confirmed either by SoF or SurvLRT patient data test. For the cell cycle genes, we rediscovered known SL interactions between RB1 and E2F3 (second in the focus ranking [62][63][64]) and RB1 and SKP2 (third in the focus ranking [63,65,66]). Those two pairs have full confirmation in 4 cell line data tests and are also confirmed by SoF patient data test.…”

Section: Previously Known Sl Pairs For Focus Genesmentioning

confidence: 99%

SLIDE-VIP: a comprehensive, cell line- and patient-based framework for synthetic lethality prediction in DNA damage repair, chromatin remodeling and cell cycle

Markowska

Budzinska

Coenen-Stass

et al. 2022

Preprint

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Discovering synthetic lethal (SL) gene partners of cancer genes is an important step in developing cancer therapies. However, identification of SL interactions is challenging, due to a large number of possible gene pairs, inherent noise and confounding factors in the observed signal. To discover robust SL interactions, we devised SLIDE-VIP, a novel framework combining eight statistical tests, including a new patient data-based test iSurvLRT. SLIDE-VIP leverages multi-omics data from four different sources: gene inactivation cell line screens, cancer patient data, drug screens and gene pathways. We applied SLIDE-VIP to discover SL interactions between genes involved in DNA damage repair, chromatin remodeling and cell cycle, and their potentially druggable partners. The top 883 ranking SL candidates had strong evidence in cell line and patient data, 250-fold reducing the initial space of 200K pairs. Drug screen and pathway tests provided additional corroboration and insights into these interactions. We rediscovered well-known SL pairs such as RB1 and E2F3 or PRKDC and ATM, and in addition, proposed strong novel SL candidates such as PTEN and PIK3CB. In summary, SLIDE-VIP opens the door to the discovery of SL interactions with clinical potential. All analysis and visualizations are available via the online SLIDE-VIP WebApp.

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Targeting RB1 Loss in Cancers

Cited by 35 publications

References 103 publications

RB1 Is an Immune-Related Prognostic Biomarker for Ovarian Cancer

RB1 Is an Immune-Related Prognostic Biomarker for Ovarian Cancer

Risk stratification and pathway analysis based on graph neural network and interpretable algorithm

SLIDE-VIP: a comprehensive, cell line- and patient-based framework for synthetic lethality prediction in DNA damage repair, chromatin remodeling and cell cycle

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