CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization

Xu, Taosheng; Le, Thuc Duy; Liu, Lin; Su, Ning; Wang, Rujing; Sun, Bingyu; Colaprico, Antonio; Bontempi, Gianluca; Li, Jiuyong

doi:10.1093/bioinformatics/btx378

Cited by 200 publications

(174 citation statements)

References 8 publications

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“…Therefore, we attempted to construct a novel molecular subtype based on both the 52 stroma‐related lncRNAs identified in this study and 100 TME genes. We chose three as the optimal clustering number (see Figure ) and samples were then divided into three distinct subtypes based on SNF‐CC approach (combination of ‘similarity network fusion’ method and ‘consensus clustering’ method) using ‘CancerSubtypes’ R package (Figure A). The survival analyses revealed a significant survival difference between the three subtypes: The relapse and death risks were highest in subtype 3, whereas subtype 2 showed the best prognosis (Figure B).…”

Section: Resultsmentioning

confidence: 99%

A stroma‐related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early‐stage colon cancer

Zhou

Sun

Zheng

et al. 2020

J Cellular Molecular Medi

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The heterogeneity in prognoses and chemotherapeutic responses of colon cancer patients with similar clinical features emphasized the necessity for new biomarkers that help to improve the survival prediction and tailor therapies more rationally and precisely. In the present study, we established a stroma‐related lncRNA signature (SLS) based on 52 lncRNAs to comprehensively predict clinical outcome. The SLS model could not only distinguish patients with different recurrence and mortality risks through univariate analysis, but also served as an independent factor for relapse‐free and overall survival. Compared with the conventionally used TNM stage system, the SLS model clearly possessed higher predictive accuracy. Moreover, the SLS model also effectively screened chemotherapy‐responsive patients, as only patients in the low‐SLS group could benefit from adjuvant chemotherapy. The following cell infiltration and competing endogenous RNA (ceRNA) network functional analyses further confirmed the association between the SLS model and stromal activation‐related biological processes. Additionally, this study also identified three phenotypically distinct colon cancer subtypes that varied in clinical outcome and chemotherapy benefits. In conclusion, our SLS model may be a significant determinant of survival and chemotherapeutic decision‐making in colon cancer and may have a strong clinical transformation value.

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Section: Resultsmentioning

confidence: 99%

A stroma‐related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early‐stage colon cancer

Zhou

Sun

Zheng

et al. 2020

J Cellular Molecular Medi

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“…We obtain the BRCA gene expression data from Zhang et al (2019), which includes clinical data, for survival analysis. We use the first predicted coding driver groups in Table 1, including FOS, MBD3, JUN, E2F6, MYB, and SPI1, and the Similarity Network Fusion (SNF) method (Xu et al, 2017;Wang et al, 2014) to cluster cancer patients (see Section 4 of the Supplement for the results with the second and the third driver groups). SNF takes expression of these genes (i.e.…”

Section: Identifying Coding and Mirna Cancer Driver Groupsmentioning

confidence: 99%

DriverGroup: A novel method for identifying driver gene groups

Pham

Liu

Bracken

et al. 2020

Preprint

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Motivation:Identifying cancer driver genes is a key task in cancer informatics. Most exisiting methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesise that there are driver gene groups that work in concert to regulate cancer and we develop a novel computational method to detect those driver gene groups. Results: We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (1) Constructing the gene network, (2) Discovering critical nodes of the constructed network, and (3) Identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify coding and non-coding driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup.

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“…In addition, we use the miRBaseConverter R package [35] to convert miRNA names to the latest version of miRBase. Finally, we use the FSbyCox function (a feature selection based on Cox regression model) from the CancerSubtypes R package [36] to identify significant miRNAs and mRNAs. After the feature selection, we identify expression data of 79 miRNAs and 1314 mRNAs in 753 breast cancer samples at a significant level (p-value < 0.05) in total.…”

Section: Data Sourcementioning

confidence: 99%

Identifying miRNA synergism using multiple-intervention causal inference

Zhang

Pham

Liu

et al. 2019

Preprint

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ConclusionsTaken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer. Keywords miRNA -miRNA synergistic network -miRNA synergistic module -Multiple intervention causal inference -Breast cancer Background MicroRNAs (miRNAs) are a class of short non-coding RNAs with ~23 nucleotides (nts) in length. They play an important regulatory role at the post-transcriptional level by targeting messenger RNAs (mRNAs) for degradation or translation repression [1].Previous studies have demonstrated that miRNAs play a crucial role in regulating gene expression involved in diverse biological processes, including cell proliferation, cell death, cell apoptosis and human cancers [2][3][4]. Generally, the relationships between miRNAs and their target genes are not one-to-one but many-to-many, indicating cooperative regulation of miRNAs. The co-regulation of miRNAs has been accepted and confirmed by cross-linking and immunoprecipitation technologies, and may be related to human complex diseases [5]. Therefore, studying miRNA synergism can greatly help to understand the synergistic regulation mechanism of miRNAs in human diseases.Until now, a number of computational methods have been proposed to identify miRNA synergism. These methods can be divided into three different categories: (1) sequence-based [6-8], (2) correlation-based [9][10][11][12][13][14], and (3) causality-based [15]. In the first category, the sequence data mainly includes putative miRNA-target interactions and protein-protein interactions (PPIs). For each candidate miRNA-

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CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 200 publications

References 8 publications

A stroma‐related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early‐stage colon cancer

A stroma‐related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early‐stage colon cancer

DriverGroup: A novel method for identifying driver gene groups

Identifying miRNA synergism using multiple-intervention causal inference

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