Evidence map›Paper›PMID 26553226›Full record

ArticleBMC cancer2015

Network analysis of an in vitro model of androgen-resistance in prostate cancer.

Sujitra Detchokul, Aparna Elangovan, Edmund J Crampin, Melissa J Davis, Albert G Frauman

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Sujitra DetchokulClinical Pharmacology and Therapeutics, Department of Medicine, The University of Melbourne, Austin Health, Heidelberg, VIC, Australia. sdet@unimelb.edu.au.
Aparna ElangovanSystems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, Australia. aparnae@student.unimelb.edu.au.
Edmund J CrampinSystems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, Australia. edmund.crampin@unimelb.edu.au.
Melissa J DavisSystems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, Australia. melissa.davis@unimelb.edu.au.
Albert G FraumanClinical Pharmacology and Therapeutics, Department of Medicine, The University of Melbourne, Austin Health, Heidelberg, VIC, Australia. albertf@unimelb.edu.au.
University of Melbourne · AUAustin Health · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe development of androgen resistance is a major limitation to androgen deprivation treatment in prostate cancer. We have developed an in vitro model of androgen-resistance to characterise molecular changes occurring as androgen resistance evolves over time. Our aim is to understand biological network profiles of transcriptomic changes occurring during the transition to androgen-resistance and to validate these changes between our in vitro model and clinical datasets (paired samples before and after androgen-deprivation therapy of patients with advanced prostate cancer).

methodsWe established an androgen-independent subline from LNCaP cells by prolonged exposure to androgen-deprivation. We examined phenotypic profiles and performed RNA-sequencing. The reads generated were compared to human clinical samples and were analysed using differential expression, pathway analysis and protein-protein interaction networks.

resultsAfter 24 weeks of androgen-deprivation, LNCaP cells had increased proliferative and invasive behaviour compared to parental LNCaP, and its growth was no longer responsive to androgen. We identified key genes and pathways that overlap between our cell line and clinical RNA sequencing datasets and analysed the overlapping protein-protein interaction network that shared the same pattern of behaviour in both datasets. Mechanisms bypassing androgen receptor signalling pathways are significantly enriched. Several steroid hormone receptors are differentially expressed in both datasets. In particular, the progesterone receptor is significantly differentially expressed and is part of the interaction network disrupted in both datasets. Other signalling pathways commonly altered in prostate cancer, MAPK and PI3K-Akt pathways, are significantly enriched in both datasets.

conclusionsThe overlap between the human and cell-line differential expression profiles and protein networks was statistically significant showing that the cell-line model reproduces molecular patterns observed in clinical castrate resistant prostate cancer samples, making this cell line a useful tool in understanding castrate resistant prostate cancer. Pathway analysis revealed similar patterns of enriched pathways from differentially expressed genes of both human clinical and cell line datasets. Our analysis revealed several potential mechanisms and network interactions, including cooperative behaviours of other nuclear receptors, in particular the subfamily of steroid hormone receptors such as PGR and alteration to gene expression in both the MAPK and PI3K-Akt signalling pathways.

Indexed as

AndrogensCell Line, TumorCell MovementCell ProliferationCell SurvivalGene Expression Regulation, NeoplasticHigh-Throughput Nucleotide SequencingHumansMaleMitogen-Activated Protein Kinase KinasesNeoplasm ProteinsPhosphatidylinositol 3-KinasesProstatic Neoplasms, Castration-ResistantProtein Interaction MapsReceptors, AndrogenReceptors, ProgesteroneAndrogensMitogen-Activated Protein Kinase KinasesNeoplasm ProteinsPhosphatidylinositol 3-KinasesReceptors, AndrogenReceptors, Progesterone

Identifiers

PMID26553226
PMCPMC4640359
OpenAlexW2095704110

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.