Identification of a five-gene signature of the RGS gene family with prognostic value in ovarian cancer

Hu, Yuexin; Zheng, Mingjun; Wang, Shuang; Gao, Lingling; Gou, Rui; Liu, Ouxuan; Dong, Hui; Li, Xiao; Lin, Bei

doi:10.1016/j.ygeno.2021.04.012

Cited by 47 publications

(39 citation statements)

References 36 publications

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“…Other genes, while not yet described in cervical cancer, have been found to be prognostic in ovarian cancer (RGS11 [30], CHAD and CBLN2 [31], NETO1 [32], HSPE1 [33], and BIRC6 which Lnc-TTC27-9 is intronic to [34]). 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].…”

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.

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Section: Resultsmentioning

confidence: 99%

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

Archer¹,

Seffernick²,

Sun³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The reduced expression of miR-142-3p and ARL6IP5 in plasma exosomes from the cancer group may indicate their role as tumor suppressors during cervical tumorigenesis. The expression of CXCL5 and KIF2A was upregulated in various cancer tissues, including cervical cancer [ 24 , 25 , 27 , 38 ]; additionally, the expression of RGS18 in the tissues of patients with ovarian cancer was also upregulated [ 30 ]. However, each of the three mRNAs can contribute to tumorigenesis through a different biological mechanism.…”

Section: Discussionmentioning

confidence: 99%

Screening Plasma Exosomal RNAs as Diagnostic Markers for Cervical Cancer: An Analysis of Patients Who Underwent Primary Chemoradiotherapy

2021

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This preliminary study aimed to screen non-coding RNAs (ncRNAs) from plasma exosomes as a new method for cervical cancer diagnosis. Differentially expressed RNAs were initially selected from among a group of 12 healthy individuals (normal group) and a pretreatment group of 30 patients with cervical cancer (cancer group). Then, we analyzed the association between an ncRNA-mRNA network and cancer using ingenuity pathway analysis after secondary selection according to the number and correlation of mRNAs (or ncRNAs) relative to changes in the expression of primarily selected ncRNAs (or mRNAs) before and after chemoradiotherapy. The number of RNAs selected from the initial RNAs was one from 13 miRNAs, four from 42 piRNAs, four from 28 lncRNAs, nine from 18 snoRNAs, 10 from 76 snRNAs, nine from 474 tRNAs, nine from 64 yRNAs, and five from 67 mRNAs. The combination of miRNA (miR-142-3p), mRNAs (CXCL5, KIF2A, RGS18, APL6IP5, and DAPP1), and snoRNAs (SNORD17, SCARNA12, SNORA6, SNORA12, SCRNA1, SNORD97, SNORD62, and SNORD38A) clearly distinguished the normal samples from the cancer group samples. We present a method for efficiently screening eight classes of RNAs isolated from exosomes for cervical cancer diagnosis using mRNAs (or ncRNAs) altered by chemoradiotherapy.

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“…Using a random forests model for feature selection, researchers identified a six-gene signature for predicting survival status in patients with head and neck squamous cell carcinoma (HNSCC) from the TCGA-HNSCC dataset 14 . Another five-gene signature (including RGS11, RGS10, RGS13, RGS4, and RGS3) has been identified as independent prognostic factors for ovarian cancer patients by using Lasso cox analysis 15 . In a study of melanoma, the feature selection approach was applied to discover and validate metastasis-related biomarkers based on single cell gene expression datasets 16 .…”

Section: Introductionmentioning

confidence: 99%

Identification of gene signatures for COAD using feature selection and Bayesian network approaches

Wang

Gao²,

Ru³

et al. 2022

Sci Rep

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The combination of TCGA and GTEx databases will provide more comprehensive information for characterizing the human genome in health and disease, especially for underlying the cancer genetic alterations. Here we analyzed the gene expression profile of COAD in both tumor samples from TCGA and normal colon tissues from GTEx. Using the SNR-PPFS feature selection algorithms, we discovered a 38 gene signatures that performed well in distinguishing COAD tumors from normal samples. Bayesian network of the 38 genes revealed that DEGs with similar expression patterns or functions interacted more closely. We identified 14 up-DEGs that were significantly correlated with tumor stages. Cox regression analysis demonstrated that tumor stage, STMN4 and FAM135B dysregulation were independent prognostic factors for COAD survival outcomes. Overall, this study indicates that using feature selection approaches to select key gene signatures from high-dimensional datasets can be an effective way for studying cancer genomic characteristics.

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Identification of a five-gene signature of the RGS gene family with prognostic value in ovarian cancer

Cited by 47 publications

References 36 publications

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

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

Screening Plasma Exosomal RNAs as Diagnostic Markers for Cervical Cancer: An Analysis of Patients Who Underwent Primary Chemoradiotherapy

Identification of gene signatures for COAD using feature selection and Bayesian network approaches

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