pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks

Ülgen, Ege; Özışık, Özan; Sezerman, Uğur

doi:10.1101/272450

Cited by 22 publications

(20 citation statements)

References 58 publications

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“…Gene ontology enrichment analysis performed on pathfindR (v. 1.2.1) [69] showed that GPR161 (upregulated) was associated with the negative regulation of the hedgehog signaling pathway. However, previous studies revealed that abnormal activation of this pathway is related to cell proliferation in endometrial cancer [70,71].…”

Section: Resultsmentioning

confidence: 99%

“…KEGG analysis also performed on pathfindR (v. 1.2.1) [69] indicated that the upregulated genes HDAC7, GTF2A1L, and MAPKAPK2 were involved in the viral carcinogenesis pathway. Previous studies have demonstrated that inhibition of histone deacetylases, such as HDAC7, induces apoptosis, cell cycle arrest, and growth inhibition in endometrial cancer cells [76][77][78][79].…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

Mello

Freitas

Coutinho

et al. 2020

BioMed Research International

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Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancers, including UCEC. Thus, we hypothesized that long noncoding RNAs (lncRNAs) could serve as efficient factors to discriminate solid primary (TP) and normal adjacent (NT) tissues in UCEC with high accuracy. We performed an in silico differential expression analysis comparing TP and NT from a set of samples downloaded from the Cancer Genome Atlas (TCGA) database, targeting highly differentially expressed lncRNAs that could potentially serve as gene expression markers. All analyses were performed in R software. The receiver operator characteristics (ROC) analyses and both supervised and unsupervised machine learning indicated a set of 14 lncRNAs that may serve as biomarkers for UCEC. Functions and putative pathways were assessed through a coexpression network and target enrichment analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

Mello

Freitas

Coutinho

et al. 2020

BioMed Research International

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“…We obtained the frequently mutated cancer driver genes from the COSMIC [40] database. Using these cancer driver DE genes, we identified enriched biological pathways listed in REACTOME via a pathway enrichment tool Pathfinder [41]. Similarly, we identified cancer driver DE genes for each cell line and computed cell line-specific enriched REACTOME based pathways.…”

Section: Computing Sample and Cell Line-specific Biological Pathwaysmentioning

confidence: 99%

CTDPathSim: Cell line-tumor deconvoluted pathway-based similarity in the context of precision medicine in cancer^*

Bose

Bozdag

2020

Preprint

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In cancer research and drug development, human tumor-derived cell lines are used as popular model for cancer patients to evaluate the biological functions of genes, drug efficacy, side-effects, and drug metabolism. Using these cell lines, the functional relationship between genes and drug response and prediction of drug response based on genomic and chemical features have been studied. Knowing the drug response on the real patients, however, is a more important and challenging task. To tackle this challenge, some studies integrate data from primary tumors and cancer cell lines to find associations between cell lines and tumors. These studies, however, do not integrate multi-omics datasets to their full extent. Also, several studies rely on a genome-wide correlation-based approach between cell lines and bulk tumor samples without considering the heterogeneous cell population in bulk tumors. To address these gaps, we developed a computational pipeline, CTDPathSim, a pathway activity-based approach to compute similarity between primary tumor samples and cell lines at genetic, genomic, and epigenetic levels integrating multi-omics datasets. We utilized a deconvolution method to get cell type-specific DNA methylation and gene expression profiles and computed deconvoluted methylation and expression profiles of tumor samples. We assessed CTDPathSim by applying on breast and ovarian cancer data in The Cancer Genome Atlas (TCGA) and cancer cell lines data in the Cancer Cell Line Encyclopedia (CCLE) databases. Our results showed that highly similar sample-cell line pairs have similar drug response compared to lowly similar pairs in several FDA-approved cancer drugs, such as Paclitaxel, Vinorelbine and Mitomycin-c. CTDPathSim outperformed state-of-the-art methods in recapitulating the known drug responses between samples and cell lines. Also, CTDPathSim selected higher number of significant cell lines belonging to the same cancer types than other methods. Furthermore, our aligned cell lines to samples were found to be clinical biomarkers for patients' survival whereas unaligned cell lines were not. Our method could guide the selection of appropriate cell lines to be more intently serve as proxy of patient tumors and could direct the pre-clinical translation of drug testing into clinical platform towards the personalized therapies.

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“…Countless tools for pathway analysis exist. Some of the widely used pathway analysis tools are GSEA [114], DAVID [115], IPA [116], SPIA [117], pathfindR [118], enrichr [119], reactomePA [120], MetaCore [121], and PathVisio [122]. Additionally, many different pathway resources exist, the most popular of which are Kyoto Encyclopedia of Genes and Genomes [123], Reactome [124], WikiPathways [125], MSigDb [126], STRINGDB [127], Pathway Commons [128], Ingenuity Knowledge Base [129], and Pathway Studio [130].…”

Section: Clinical Interpretation Of Genomic Variationsmentioning

confidence: 99%

Bioinformatics Workflows for Genomic Variant Discovery, Interpretation and Prioritization

Sezerman¹,

Ülgen²,

Seymen³

et al. 2019

Bioinformatics Tools for Detection and Clinical Interpretation of Genomic Variations

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Next-generation sequencing (NGS) techniques allow high-throughput detection of a vast amount of variations in a cost-efficient manner. However, there still are inconsistencies and debates about how to process and analyse this 'big data'.To accurately extract clinically relevant information from genomics data, choosing appropriate tools, knowing how to best utilize them and interpreting the results correctly is crucial. This chapter reviews state-of-the-art bioinformatics approaches in clinically relevant genomic variant detection. Best practices of reads-to-variant discovery workflows for germline and somatic short genomic variants are presented along with the most commonly utilized tools for each step. Additionally, methods for detecting structural variations are overviewed. Finally, approaches and current guidelines for clinical interpretation of genomic variants are discussed. As emphasized in this chapter, data processing and variant discovery steps are relatively wellunderstood. The differences in prioritization algorithms on the other hand can be perplexing, thus creating a bottleneck during interpretation. This review aims to shed light on the pros and cons of these differences to help experts give more informed decisions.

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pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks

Cited by 22 publications

References 58 publications

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

CTDPathSim: Cell line-tumor deconvoluted pathway-based similarity in the context of precision medicine in cancer^*

Bioinformatics Workflows for Genomic Variant Discovery, Interpretation and Prioritization

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pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks

Cited by 22 publications

References 58 publications

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

CTDPathSim: Cell line-tumor deconvoluted pathway-based similarity in the context of precision medicine in cancer*

Bioinformatics Workflows for Genomic Variant Discovery, Interpretation and Prioritization

Contact Info

Product

Resources

About

CTDPathSim: Cell line-tumor deconvoluted pathway-based similarity in the context of precision medicine in cancer^*