Evidence map›Paper›PMID 31475771›Full record

ArticleEMBO molecular medicine2019

A circRNA signature predicts postoperative recurrence in stage II/III colon cancer.

Huai-Qiang Ju, Qi Zhao, Feng Wang, Ping Lan, Zixian Wang, Zhi-Xiang Zuo, Qi-Nian Wu, Xin-Juan Fan, Hai-Yu Mo, Li Chen and 7 more

Erratum issuedAbstract read
In one paragraph

Article in EMBO molecular medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 84 papers.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed
–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

84 citing papers in PubMed.

  1. Review
  2. Review
  3. Tumor-suppressive circRNAs in colorectal cancer spheroids and patients.Journal of the Chinese Medical Association : JCMA · 2026
    Article
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  11. Review
  12. Review
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  14. Leptin increases chemosensitivity by inhibitingJournal of gastrointestinal oncology · 2024
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

24 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Huai-Qiang JuState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.ORCID 0000-0003-1713-5465
Qi ZhaoState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.ORCID 0000-0002-8683-6145
Feng WangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Ping LanThe Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Zixian WangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Zhi-Xiang ZuoState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Qi-Nian WuState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Xin-Juan FanThe Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Hai-Yu MoState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Li ChenState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Ting LiState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Chao RenState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Xiang-Bo WanThe Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Gong ChenState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Yu-Hong LiState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Wei-Hua JiaState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Rui-Hua XuState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.ORCID 0000-0001-9771-8534

Funding

Health Medical Collaborative Innovation Program of Guangzhou 201704020228Health Medical Collaborative Innovation Program of Guangzhou 201803040019Natural Science Foundation of Guangdong Province (Guangdong Natural Science Foundation) 2014A030312015Natural Science Foundation of Guangdong Province (Guangdong Natural Science Foundation) 2018B030306049Pearl River S&T Nova Program of Guangzhou 201806010002Science and Technology Planning Project of Guangdong Province 2015B020232008Science and Technology Planning Project of Guangdong Province A2019398The National Natural Science Foundation of China 81930065
6 · The paper itself

Abstract

Accurate risk stratification for patients with stage II/III colon cancer is pivotal for postoperative treatment decisions. Here, we aimed to identify and validate a circRNA-based signature that could improve postoperative prognostic stratification for these patients. In current retrospective analysis, we included 667 patients with R0 resected stage II/III colon cancer. Using RNA-seq analysis of 20 paired frozen tissues collected postoperation, we profiled differential circRNA expression between patients with and without recurrence, followed by quantitative validation. With clinical information, we generated a four-circRNA-based cirScore to classify patients into high-risk and low-risk groups in the training cohort. The patients with high cirScores in the training cohort had a shorter disease-free survival (DFS) and overall survival (OS) than patients with low cirScores. The prognostic capacity of the classifier was validated in the internal and external cohorts. Loss-of-function assays indicated that the selected circRNAs played functional roles in colon cancer progression. Overall, our four-circRNA-based classifier is a reliable prognostic tool for postoperative disease recurrence in patients with stage II/III colon cancer.

Indexed as

AgedBiopsyClinical Decision RulesColonColonic NeoplasmsFemaleFollow-Up StudiesHumansMaleMiddle AgedNeoplasm Recurrence, LocalPrognosisRetrospective StudiesRNA, CircularSurvival AnalysisRNA, Circularbiomarkercircular RNArecurrencestage II/III colon cancer

Identifiers

PMID31475771
PMCPMC6783650

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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.