Evidence map›Paper›PMID 34311724›Full record

SynthesisBMC cancer2021

Transcriptional landscape of PTEN loss in primary prostate cancer.

Eddie Luidy Imada, Diego Fernando Sanchez, Wikum Dinalankara, Thiago Vidotto, Ericka M Ebot, Svitlana Tyekucheva, Gloria Regina Franco, Lorelei Ann Mucci, Massimo Loda, Edward Matthew Schaeffer and 2 more

Open access · goldAbstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 14% 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

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  12. Exploring therapeutic applications ofFrontiers in oncology · 2025
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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

12 authors at 5 institutions in 2 countries.

Eddie Luidy ImadaDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA. eimada@med.cornell.edu.ORCID http://orcid.org/0000-0001-9527-3703
Diego Fernando SanchezDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Wikum DinalankaraDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
Thiago VidottoDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ericka M EbotDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Svitlana TyekuchevaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Gloria Regina FrancoDepartamento de Bioquímica e Imunologia, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
Lorelei Ann MucciDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Massimo LodaDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
Edward Matthew SchaefferDepartment of Urology, Northwestern University, Evanston, IL, USA.
Tamara LotanDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Luigi MarchionniDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA. marchion@med.cornell.edu.
Johns Hopkins University · USHarvard University · USUniversidade Federal de Minas Gerais · BRCornell University · USNorthwestern University · US

Funding

Multidiciplinary Integrative Genomic Approach to Distinguish Lethal from Indolent Prostate Cancer in Men of Europena and African AncestryU01CA196390 · NCI · JOHNS HOPKINS UNIVERSITY · PI DE MARZO, ANGELO MICHAEL, PIENTA, KENNETH J. · 2015 to 2020
$5.8M
Hardwiring Mechanism into Predicting Cancer Phenotypes by Computational LearningR01CA200859 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI MARCHIONNI, LUIGI · 2016 to 2020
$1.9M
NCI NIH HHS R01 CA200859NCI NIH HHS R01CA200859NCI NIH HHS U01 CA196390NCI NIH HHS U01CA196390
6 · The paper itself

Abstract

backgroundPTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa.

methodsUsing a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas.

resultsThe signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression.

conclusionWe created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.

Indexed as

Gene Expression Regulation, NeoplasticTranscriptomeAdaptive ImmunityBiomarkers, TumorGene Expression ProfilingHumansImmunity, InnateImmunohistochemistryMaleProstatic NeoplasmsPTEN PhosphohydrolaseTranscriptional Regulator ERGBiomarkers, TumorPTEN PhosphohydrolasePTEN protein, humanTranscriptional Regulator ERGlncRNAMeta-analysisNon-coding RNAProstate CancerPTEN deletionPTEN loss

Identifiers

PMID34311724
PMCPMC8314517
OpenAlexW3185572424

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.