Comprehensive pathway-related genes signature for prognosis and recurrence of ovarian cancer

Zhao, Xinnan; He, Miao

doi:10.7717/peerj.10437

Cited by 4 publications

(6 citation statements)

References 43 publications

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“…Our findings that the proliferation pathways G2M CHECKPOINT (p=1e-04) and E2F TARGETS (p=8e-07) were associated with poorer survival, are in line with the study 8 where the authors found these pathways significantly enriched in ovarian cancer suggesting that they might play a critical role in the development of ovarian cancer. The prior findings in the literature reviewed by 8 suggest that the E2F family is crucial for cancer initiation, progression, and resistance to therapy.…”

Section: Discussionsupporting

confidence: 90%

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Learning to train and to explain a deep survival model with large scale ovarian cancer transcriptomic data

Menand

Vries-Brilland

Tessier

et al. 2022

Preprint

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Ovarian cancer is a complex disease with poor outcome affecting women worldwide. The lack of successful therapeutic options for ovarian cancer patients results in the strong need to identify new biomarkers for patient selection. The development of outcome predictors based on gene expression is important not only for patient stratification but also to recognize categories of patients that are more likely to respond to particular therapies. In this paper, we proposed a new deep learning survival model trained on the high-dimensional transcriptomic data for the task of ovarian cancer prognostication. We validated our deep learning survival model on an independent clinical and molecular datatset. Finally, we illustrated the way our model can be interpreted, by calculating the contributions of the input features to the network outputs. We demonstrated how these contributions can be related to the molecular pathways to uncover biological processes associated with ovarian cancer patients survival.

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Section: Discussionsupporting

confidence: 90%

“…Multiple studies have attempted to establish molecular signatures based on gene expression to predict survival of ovarian cancer patients 6,7 . However, only a small number of prognostic signatures have been developed, and none have been directly applied in clinical practice 8 .…”

Section: Introductionmentioning

confidence: 99%

Learning to train and to explain a deep survival model with large scale ovarian cancer transcriptomic data

Menand

Vries-Brilland

Tessier

et al. 2022

Preprint

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“…Moreover, the classical TNM staging system, relying mainly on anatomical information without any molecular biological features, had difficulties in accurately stratifying indolent and aggressive cancers to a certain extent [ 29 , 30 ]. Therefore, it was urgent to identify novel and effective biomarkers for OC prognosis and to further explore its related mechanisms [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

CACNA1C is a prognostic predictor for patients with ovarian cancer

Chang

Dong

2021

J Ovarian Res

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Background CACNA1C, as a type of voltage-dependent calcium ion transmembrane channel, played regulatory roles in the development and progress of multiple tumors. This study was aimed to analyze the roles of CACNA1C in ovarian cancer (OC) of overall survival (OS) and to explore its relationships with immunity. Methods Single gene mRNA sequencing data and corresponding clinical information were obtained from The Cancer Genome Atlas Database (TCGA) and the International Cancer Genome Consortium (ICGC) datasets. Gene set enrichment analysis (GSEA) was used to identify CACNA1C-related signal pathways. Univariate and multivariate Cox regression analyses were applied to evaluate independent prognostic factors. Besides, associations between CACNA1C and immunity were also explored. Results CACNA1C had a lower expression in OC tumor tissues than in normal tissues (P < 0.001), with significant OS (P = 0.013) and a low diagnostic efficiency. We further validated the expression levels of CACNA1C in OC by means of the ICGC dataset (P = 0.01), qRT-PCR results (P < 0.001) and the HPA database. Univariate and multivariate Cox hazard regression analyses indicated that CACNA1C could be an independent risk factor of OS for OC patients (both P < 0.001). Five significant CACNA1C-related signaling pathways were identified by means of GSEA. As for genetic alteration analysis, altered CACNA1C groups were significantly associated with OS (P = 0.0169), progression-free survival (P = 0.0404), disease-free survival (P = 0.0417) and disease-specific survival (P = 9.280e-3), compared with unaltered groups in OC. Besides, CACNA1C was dramatically associated with microsatellite instability (MSI) and immunity. Conclusions Our results shed light on that CACNA1C could be a prognostic predictor of OS in OC and it was closely related to immunity.

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“…have established a nine-gene-based signature for OS using LASSO Cox regression analysis of TCGA dataset [25]. Time-dependent ROC analysis shows that the AUC values for OS are 0.640, 0.663, 0.758, and 0.891 at 1, 3, 5, and 10 years, respectively.…”

Section: Discussionmentioning

confidence: 99%

Role of survival-associated alternative splicing events in the prognosis of ovarian cancer

Yue¹,

Zhao²,

Zhang³

et al. 2021

Preprint

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Objective Alternative splicing (AS) events play a crucial role in the tumorigenesis and progression of various cancers. In the present study, we aimed to identify specific AS events, which might be prognostic markers and therapeutic targets for ovarian cancer (OV). Methods Transcriptome data, clinical information, and Percent Spliced In (PSI) values were downloaded from TCGA database and TCGA SpliceSeq to explore the role of AS events in the prognosis of OV patients. Univariate and multivariate Cox regression analyses were performed to identify survival-associated AS events and develop multi-AS-based prognostic models. The K-M curves and ROC curves were conducted based on prognostic AS event models. Moreover, a splicing regulatory network was established according to the correlation between AS events and splicing factors (SFs). Finally, we performed functional enrichment analysis by GO terms and KEGG pathways. Results We identified 1,472 AS events that were associated with the survival of OV patients, and exon skipping (ES) was the most important type. We also found that prognostic models based on AS events were good predictors of OV prognosis, which could discriminate the high-risk group from the low-risk group (P < 0.05). Notably, the AUC value of AD, AP, AT, ES, ME, and the whole cohort was more than 0.70, indicating that these six models had valuable prediction strength. The risk score of prognostic models was identified as an independent prognostic factor. Furthermore, the AS-SF correlation network revealed several hub SF genes, including DDX39B, PNN, LUC7L3, ZC3H4 and SRSF11, and so on. Conclusions In the present study, we constructed powerful prognostic predictors for OV patients and uncovered interesting splicing networks. Collectively, our findings provided valuable insights into the underlying mechanisms of OV.

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Comprehensive pathway-related genes signature for prognosis and recurrence of ovarian cancer

Cited by 4 publications

References 43 publications

Learning to train and to explain a deep survival model with large scale ovarian cancer transcriptomic data

Learning to train and to explain a deep survival model with large scale ovarian cancer transcriptomic data

CACNA1C is a prognostic predictor for patients with ovarian cancer

Role of survival-associated alternative splicing events in the prognosis of ovarian cancer

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