Evidence map›Paper›PMID 30720004›Full record

Trial reportSaudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association

COX-2/C-MET/KRAS status-based prognostic nomogram for colorectal cancer: A multicenter cohort study.

Jianhua Liu, Chengzhi Huang, Junjiang Wang, Ling Huang, Shaojie Chen

Abstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

5 authors.

Jianhua LiuDepartment of Oncology, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Chengzhi HuangDepartment of Gastrointestinal Surgery, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Junjiang WangDepartment of Gastrointestinal Surgery, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Ling HuangDepartment of Oncology, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Shaojie ChenDepartment of Gastroenterology, Sun Yat-Sen Memorial Hospital, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimTo construct quantitative prognostic models for colorectal cancer (CRC) based on COX-2/C-MET/KRAS expression status in clinical practice. PATIENTS AND

methodsClinical factors and COX-2/C-MET/KRAS expression status of 578 eligible patients from two Chinese hospitals were included. The patients were randomly allocated into training and validation datasets. We created several models using Cox proportional hazard models: Signature

resultsFor PFS, among the three signatures, Signature

conclusionsCombining clinical factors and COX-2/C-MET/KRAS expression status, our models provided accurate prognostic information in CRC. They can be used to aid treatment decisions in clinical practice.

Indexed as

Gene Expression Regulation, NeoplasticBiomarkers, TumorColorectal NeoplasmsCyclooxygenase 2DNA, NeoplasmFemaleFollow-Up StudiesHumansMagnetic Resonance ImagingMaleMiddle AgedNeoplasm StagingNomogramsPrognosisProto-Oncogene Proteins c-metProto-Oncogene Proteins p21(ras)Biomarkers, TumorCyclooxygenase 2DNA, NeoplasmKRAS protein, humanProto-Oncogene Proteins c-metProto-Oncogene Proteins p21(ras)C-METcolorectal cancerCOX-2KRASprognosis

Identifiers

PMID30720004
PMCPMC6784436

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Registered trials

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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.