Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data

Moore, Daniel; Simoes, Ricardo de Matos; Dehmer, Matthias; Emmert‐Streib, Frank

doi:10.2174/1389202919666181107122005

Cited by 13 publications

(10 citation statements)

References 53 publications

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“…It is clear that other studies have also used similar associations, but without an explicit description of the mapping between hallmarks and functional annotation terms, they cannot reliably be reproduced for the comparison [ 22 – 26 ].…”

Section: Introductionmentioning

confidence: 99%

Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

2021

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Background The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus. Results Here we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis. Conclusions Reaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.

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Section: Introductionmentioning

confidence: 99%

Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

2021

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“…In men with local or regional disease, it can be successfully treated through surgery or radiation. In the case of metastatic PCa, androgen deprivation therapy (ADT) is used [3]. However, ADT has a low rate of success with aggressive PCa cases.…”

Section: Introductionmentioning

confidence: 99%

A Protein Moonlighting Based Model to Reveal Transcription Factor Druggability for Prostate Carcinoma

Sen¹,

Dey²,

Maulik³

2021

Preprint

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BackgroundProstate cancer is the second leading cause of cancer-related death in men in the United States. Metastasis shows poor survival even though the recovery rate is high. In spite of numerous studies regarding prostate carcinoma, multiple questions are stilled unanswered. In this regards, gene regulatory network can uncover the mechanisms behind cancer progression, and metastasis. In the regulatory network, among the three prime molecular members, transcription factors and microRNAs are known for their regulatory activity where transcription factor can target both microRNAs and genes. Under a feed forward loop, transcription factors can be a good druggable candidate. However, due to the dynamic nature of transcription factors, designing an appropriate drug becomes challenging.ResultWe have proposed a computational model to study the uncertainty of transcription factors and suggest the appropriate cellular conditions for drug targeting. We have selected feed-forward loops depending on the shared list of the functional annotations among transcription factors, genes, and miRNAs. From the potential feed forward loop cores, six transcription factors were identified as druggable targets, which include AR, CEBPB, CREB1, ETS1, NFKB1, and RELA. The selected transcription factors have been investigated based on the evolutionary co-variance study, post-translation modifications, and disordered region identification. The structural unrest of the selected transcription factors can be observed from outcomes. Comparing the results of the aforementioned analyses, probable binding clefts have also been identified. transcription factors are known for their Protein Moonlighting properties, which provide unrelated multi-functionalities within the same or different subcellular localizations. Following that, we have identified such functions that are suitable for drug targeting. Finally, we have tried to identify membraneless organelles for providing more specificity to the proposed time and space theory.ConclusionThe study has provided certain possibilities on TF based therapeutics. The controlled dynamic nature of the TF may have enhanced the chances where TFs can be consider as one of the prime drug targets. The usual presence of TF at nucleus cannot explain its functional multiplicity whereas nucleus speckles can help to explain it. Finally, the combination of membranless phase separation and protein moonlighting has provided possible druggable period within the biological clock.

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“…However, this is often difficult because genes are generally associated with multiple biological functions (Cancer Genome Atlas Research et al, 2017). In cancer transcriptomics, this difficulty is exacerbated by the interdependence between disease-related biomarker genes and biological function terms; furthermore, expression levels of these marker genes can be heterogeneous depending on cancer microenvironments (Moore et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes

Iida

Kondo

Inoue

et al. 2021

Preprint

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Single-cell RNA sequencing (scRNA-seq) analysis has significantly advanced our knowledge of functional states of cells. By analyzing scRNA-seq data, we can deconvolve individual cell states into thousands of gene expression profiles, allowing us to perform cell clustering, and identify significant genes for each cluster. However, interpreting these results remains challenging. Here, we present a novel scRNA-seq analysis pipeline named ASURAT, which simultaneously performs unsupervised cell clustering and biological interpretation in semi-automatic manner, in terms of cell type and various biological functions. We validate the reliable clustering performance of ASURAT by comparing it with existing methods, using six published scRNA-seq datasets from healthy donors and cancer patients. Furthermore, we applied ASURAT to patient-derived scRNA-seq datasets including small cell lung cancers, finding some putative cancer subpopulations showing different resistance mechanisms. ASURAT is expected to open new means of scRNA-seq analysis, focusing more on "biological meaning" than conventional gene-based analyses.

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Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data

Cited by 13 publications

References 53 publications

Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

A Protein Moonlighting Based Model to Reveal Transcription Factor Druggability for Prostate Carcinoma

ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes

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