Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning

Park, Jee Soo; Seung-Hun, Choi; Kim, Hee Jung; Cho, Nam Hoon; Kim, Sang Wun; Kim, Young Tae; Nam, Eun Ji; Chung, Jin Woo; Kim, Deok Won

doi:10.1097/igc.0000000000000566

Cited by 9 publications

(6 citation statements)

References 35 publications

(31 reference statements)

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“…For example, loss of AOX1 expression by epigenetic regulation could promote bladder cancer via metabolic deregulations; the SNP rs73055188 of AOX1 locus is related to prostate cancer survival time, and AOX1 gene expression level is correlated with recurrence of prostate cancer [44]. However, the roles for AOX1 in ovarian cancer have not been addressed [45]. In this study, we found that knockdown of AOX1 in ovarian cancer cells inhibited cell apoptosis.…”

Section: Discussionmentioning

confidence: 76%

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Zhao¹,

Zhang³

et al. 2020

Bioscience Reports

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Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets. Methods: In the present study, the data of gene expression in ovarian cancer was downloaded from Gene Expression Omnibus and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation. Results: A total of 972 DEGs with P-valueCD24, SOX17, WFDC2, EPCAM, INAVA, and down-regulated AOX1 were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer. Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

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Section: Discussionmentioning

confidence: 76%

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Zhao¹,

Zhang³

et al. 2020

Bioscience Reports

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“…The expression of SH3BP5 is reduced in ovarian cancer samples compared to normal tissue and that silencing of Sab protein expression may lead to chemo-resistance [ 36 ]. The expression of SNTN has high discriminatory power to differentiate between normal tissue, serous borderline ovarian tumors, and serous ovarian carcinoma [ 37 ]. IL22RA2 is highly expressed in various tissues, including those in the female reproductive system [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

Archer

Seffernick

Sun

et al. 2022

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The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P>N, such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.

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“…Expression of SH3BP5 is reduced in ovarian cancer samples compared to normal tissue and that silencing of Sab protein expression may lead to chemo-resistance [35]. Expression of SNTN has high discriminatory power to differentiate between normal tissue, serous borderline ovarian tumors, and serous ovarian carcinoma [36]. IL22RA2 is highly expressed in various tissues including those in the female reproductive system [37].…”

Section: Resultsmentioning

confidence: 99%

Ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

Archer¹,

Seffernick²,

Sun³

et al. 2022

Preprint

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Stage of cancer is a discrete ordinal response that indicates aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with stage of cervical cancer, to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients has been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which is capable of fitting cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P>N, such as for TCGA data. We demonstrate use of this package through application to TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional dataset and effectively performs variable selection.

show abstract

Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning

Cited by 9 publications

References 35 publications

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

Ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

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