Evidence map›Paper›PMID 27903980›Full record

ArticleOncotarget2017

Identification of biomarker microRNAs for predicting the response of colorectal cancer to neoadjuvant chemoradiotherapy based on microRNA regulatory network.

Yaqun Zhu, Qiliang Peng, Yuxin Lin, Li Zou, Peipei Shen, Feifei Chen, Ming Min, Li Shen, Jiajia Chen, Bairong Shen

Abstract readValidation Study
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. UEG Week 2019 Poster Presentations.United European gastroenterology journal · 2019
    Article
  14. Article
  15. Article
  16. Article
  17. A Role of Tumor-Released Exosomes in Paracrine Dissemination and Metastasis.International journal of molecular sciences · 2018
    Review
  18. Review
  19. The Glucose-RegulatedCancers · 2018
    Review
  20. Review
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

10 authors.

Yaqun ZhuDepartment of Radiotherapy and Oncology, Second Affiliated Hospital of Soochow University, Suzhou, China.
Qiliang PengDepartment of Radiotherapy and Oncology, Second Affiliated Hospital of Soochow University, Suzhou, China.
Yuxin LinCenter for Systems Biology, Soochow University, Suzhou, China.
Li ZouDepartment of Radiotherapy and Oncology, Second Affiliated Hospital of Soochow University, Suzhou, China.
Peipei ShenDepartment of Radiotherapy and Oncology, Second Affiliated Hospital of Soochow University, Suzhou, China.
Feifei ChenCenter for Systems Biology, Soochow University, Suzhou, China.
Ming MinCenter for Systems Biology, Soochow University, Suzhou, China.
Li ShenCenter for Systems Biology, Soochow University, Suzhou, China.
Jiajia ChenSchool of Chemistry, Biology and Material Engineering, Suzhou University of Science and Technology, Suzhou, China.
Bairong ShenCenter for Systems Biology, Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preoperative radiotherapy or chemoradiotherapy has become a standard procedure for treatment of patients with locally advanced colorectal cancer (CRC). However, patients' responses to treatment are different and personalized. MicroRNAs (miRNAs) are promising biomarkers for predicting personalized responses. In this study, we collected 30 publicly reported miRNAs associated with chemoradiotherapy of CRC. We extracted 46 differentially expressed miRNAs from samples of responders and non-responders to preoperative radiotherapy from the Gene Expression Omnibus dataset (Student's t test, p-value < 0.05 and |fold-change| > 2). We performed a systematic and integrative bioinformatics analysis to identify biomarker miRNAs for prediction of CRC responses to chemoradiotherapy. Using the bioinformatics model, miR-198, miR-765, miR-671-5p, miR-630, miR-371-5p, miR-575, miR-202, miR-483-5p and miR-513a-5p were screened as putative biomarkers for treatment response. Literature validation and functional enrichment analysis were exploited to confirm the reliability of the predicted miRNAs. Quantitative polymerase chain reaction showed that seven of the candidates were significantly differentially expressed between radiosensitive and insensitive CRC cell lines. The unique target genes of miR-198 and miR-765 were altered significantly upon transfection of specific miRNA mimics in the radiosensitive cell line. These results demonstrated the predictive power of our model and suggested that miR-198, miR-765, miR-630, miR-371-5p, miR-575, miR-202 and miR-513a-5p could be used for predicting the response of CRC to preoperative chemoradiotherapy.

Indexed as

Gene Regulatory NetworksBiomarkers, PharmacologicalBiomarkers, TumorChemoradiotherapyColorectal NeoplasmsGene Expression ProfilingGene Expression Regulation, NeoplasticGenetic Association StudiesHCT116 CellsHT29 CellsHumansMicroRNAsMonitoring, PhysiologicNeoadjuvant TherapyBiomarkers, PharmacologicalBiomarkers, TumorMicroRNAsbioinformatics modelbiomarker microRNAcolorectal cancermicroRNA regulatory networkneoadjuvant chemoradiotherapy

Identifiers

PMID27903980
PMCPMC5356795

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.