Bi-clustering based biological and clinical characterization of colorectal cancer in complementary to CMS classification

Cao, Sha; Chang, Wennan; Wan, Chunli; Zhang, Yong; Zhao, Jing; Li, Bo; Ma, Qin; Zhang, Chi

doi:10.1101/508275

Cited by 2 publications

(2 citation statements)

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“…Bioinformatics, 36(4), 1143-1149. https://doi.org/10.1093/bioinformatics/btz692 scRNA-Seq data can further facilitate the discovery of gene signatures of specific cell types, and most importantly, the identified FGMs can be used to study the complex interactions among individual cells in response to certain stimuli, that is, cell type prediction. Our recent identification of FGMs specific to a subset of patients revealed biological characteristics of different disease subtypes and alternative drug resistance mechanisms in colorectal cancer (Cao, et al, 2018). In addition, we have demonstrated the correspondence between the cell sample-specific FMGs and gene coregulation modules in scRNA-Seq data (Wan, et al, 2018).…”

Section: Introductionmentioning

confidence: 73%

QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data

Xie

Zhang

et al. 2019

Bioinformatics

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Motivation The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed. Results We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq. Availability and implementation The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2. Contact czhang87@iu.edu or qin.ma@osumc.edu Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 73%

QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data

Xie

Zhang

et al. 2019

Bioinformatics

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“…On the contrary, a transcriptomic study on human NCI-60 tumors showed that OGT expression is negatively correlated with FdUMP sensitivity, no correlation with 5-FU sensitivity was established ( 78 ). OGT was also identified in a cluster of co-expressed genes associated with 5-FU resistance in colorectal cancer (CRC) ( 79 ). Moreover, the 5-FU pathway actors SLC29A1 ( 80 ), TP ( 10 ), TK ( 81 , 82 ), and TS ( 34 ) are direct targets for O -GlcNAcylation ( Figure 2 ).…”

Section: Impact Of O -Glcnacylation On Response To...mentioning

confidence: 99%

Targeting O-GlcNAcylation to overcome resistance to anti-cancer therapies

Very

Yazidi‐Belkoura

2022

Front. Oncol.

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In cancer cells, metabolic reprogramming is associated with an alteration of the O-GlcNAcylation homeostasis. This post-translational modification (PTM) that attaches O-GlcNAc moiety to intracellular proteins is dynamically and finely regulated by the O-GlcNAc Transferase (OGT) and the O-GlcNAcase (OGA). It is now established that O-GlcNAcylation participates in many features of cancer cells including a high rate of cell growth, invasion, and metastasis but little is known about its impact on the response to therapies. The purpose of this review is to highlight the role of O-GlcNAc protein modification in cancer resistance to therapies. We summarize the current knowledge about the crosstalk between O-GlcNAcylation and molecular mechanisms underlying tumor sensitivity/resistance to targeted therapies, chemotherapies, immunotherapy, and radiotherapy. We also discuss potential benefits and strategies of targeting O-GlcNAcylation to overcome cancer resistance.

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Bi-clustering based biological and clinical characterization of colorectal cancer in complementary to CMS classification

Abstract: 19

Cited by 2 publications

References 43 publications

QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data

QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data

Targeting O-GlcNAcylation to overcome resistance to anti-cancer therapies

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