Evidence map›Paper›PMID 24055427›Full record

ArticleUrologic oncology2014

Differentiating progressive from nonprogressive T1 bladder cancer by gene expression profiling: applying RNA-sequencing analysis on archived specimens.

Xuanhui Sharron Lin, Lan Hu, Kirley Sandy, Mick Correll, John Quackenbush, Chin-Lee Wu, William Scott McDougal

Open access · greenAbstract read
In one paragraph

Article in Urologic oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

10 citing papers in PubMed, 27 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

7 authors at 2 institutions in 1 country.

Xuanhui Sharron LinDepartment of Urology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.
Lan HuDepartment of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA.
Kirley SandyDepartment of Urology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.
Mick CorrellDepartment of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA.
John QuackenbushDepartment of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA.
Chin-Lee WuDepartment of Urology, Massachusetts General Hospital and Harvard Medical School, Boston, MA; Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA. Electronic address: cwu2@partners.org.
William Scott McDougalDepartment of Urology, Massachusetts General Hospital and Harvard Medical School, Boston, MA. Electronic address: wmcdougal@partners.org.
Harvard University · USMassachusetts General Hospital · US

Funding

Epidemiologic StudiesU19CA148065 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI AHSAN, HABIBUL, BRUGGE, JOAN SIEFERT · 2010 to 2014
$10.6M
Integrated approaches to deriving predictive networks from public data sourcesR01LM010129 · NLM · DANA-FARBER CANCER INST · PI QUACKENBUSH, JOHN · 2009 to 2010
$1.4M
NLM NIH HHS R01 LM010129
6 · The paper itself

Abstract

objectivesTo identify gene signatures in transitional cell carcinoma that can differentiate high-grade T1 nonprogressive (T1NP) bladder cancer (BCa) from those T1 progressive (T1P) tumors that progress to muscularis propria-invasive T2 tumors. MATERIALS AND

methodsWe performed a high-throughput RNA sequencing (RNA-Seq) on formalin-fixed and paraffin-embedded BCa specimens with clinical pathologic characteristics best representing the general clinical development of the disease. For the T1NP group, only patients with long-term follow-up (6-17y) and periodic examinations (average of 4 resections and 9 cytology tests) were selected. For the T1P group, only patients in whom a complete resection was performed after a minimum of 8 months after the initial T1 diagnosis were selected, therefore eliminating the possibility of underdiagnosis. Only samples in which muscularis propria was present and uninvolved were included, further assuring a correct diagnosis. The RNA-Seq reads were mapped to the human genome build NCBI 36 (hg18) using TopHat with no mismatch. After alignment to the transcriptome and expression quantification, a linear statistical model was built using Limma between T1NP and T1P samples to identify differentially expressed genes.

resultsOverall, 5,561 genes were mapped to all samples and used for RNA-Seq analysis to identify a gene signature that was significantly and differentially expressed between patients with T1NP BCa and patients with T1P BCa. Signature-based stratification indicated the gene signature correlated notably with the time of T1 development to T2 tumor, suggesting that the molecular signature might be used as an independent predictor for the pace of high-grade T1 BCa progression.

conclusionsThis is the first demonstration that RNA-Seq can be applied as a powerful tool to study BCa using formalin-fixed and paraffin-embedded specimens. We identified a gene signature that can distinguish patients diagnosed with high-grade T1 BCas that remain as non-muscle invasive tumors from those patients with cancers progressing to muscle-invasive tumors. Our findings will make future large-scale clinical cohort studies and clinical trial-based studies possible and help the development of prognostic tools for accurate prediction of T1 BCa progression that may considerably influence the clinical decision-making process, treatment regimen, and patient survival.

Indexed as

AgedCarcinoma, Transitional CellDisease ProgressionFemaleGene Expression ProfilingHigh-Throughput Screening AssaysHumansMaleMiddle AgedTranscriptomeUrinary Bladder NeoplasmsMolecular signatureRNA-SeqT1 bladder cancerTumor progression

Identifiers

PMID24055427
PMCPMC4446054
OpenAlexW1985367259

What OpenQuestion holds

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LicenceTDM
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.