Evidence map›Paper›PMID 27123272›Full record

ArticleMolecular and clinical oncology2016

An 80-gene set to predict response to preoperative chemoradiotherapy for rectal cancer by principle component analysis.

Shinichiro Empuku, Kentaro Nakajima, Tomonori Akagi, Kunihiko Kaneko, Naoki Hijiya, Tsuyoshi Etoh, Norio Shiraishi, Masatsugu Moriyama, Masafumi Inomata

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Article in Molecular and clinical oncology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Pre-TreatmentCancers · 2025
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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Shinichiro EmpukuDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Yufu, Oita 879-5593, Japan.
Kentaro NakajimaDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Yufu, Oita 879-5593, Japan.
Tomonori AkagiDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Yufu, Oita 879-5593, Japan.
Kunihiko KanekoDepartment of Computer Science, Fukuyama University Faculty of Engineering, Fukuyama, Hiroshima 720-0292, Japan.
Naoki HijiyaDepartment of Molecular Pathology, Oita University Faculty of Medicine, Yufu, Oita 879-5593, Japan.
Tsuyoshi EtohDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Yufu, Oita 879-5593, Japan.
Norio ShiraishiCenter for Community Medicine, Oita University Faculty of Medicine, Yufu, Oita 879-5593, Japan.
Masatsugu MoriyamaDepartment of Molecular Pathology, Oita University Faculty of Medicine, Yufu, Oita 879-5593, Japan.
Masafumi InomataDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Yufu, Oita 879-5593, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preoperative chemoradiotherapy (CRT) for locally advanced rectal cancer not only improves the postoperative local control rate, but also induces downstaging. However, it has not been established how to individually select patients who receive effective preoperative CRT. The aim of this study was to identify a predictor of response to preoperative CRT for locally advanced rectal cancer. This study is additional to our multicenter phase II study evaluating the safety and efficacy of preoperative CRT using oral fluorouracil (UMIN ID: 03396). From April, 2009 to August, 2011, 26 biopsy specimens obtained prior to CRT were analyzed by cyclopedic microarray analysis. Response to CRT was evaluated according to a histological grading system using surgically resected specimens. To decide on the number of genes for dividing into responder and non-responder groups, we statistically analyzed the data using a dimension reduction method, a principle component analysis. Of the 26 cases, 11 were responders and 15 non-responders. No significant difference was found in clinical background data between the two groups. We determined that the optimal number of genes for the prediction of response was 80 of 40,000 and the functions of these genes were analyzed. When comparing non-responders with responders, genes expressed at a high level functioned in alternative splicing, whereas those expressed at a low level functioned in the septin complex. Thus, an 80-gene expression set that predicts response to preoperative CRT for locally advanced rectal cancer was identified using a novel statistical method.

Indexed as

histological responsemicroarray analysispreoperative chemoradiotherapyprinciple component analysisrectal cancer

Identifiers

PMID27123272
PMCPMC4840568

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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.