Evidence map›Paper›PMID 34095132›Full record

ArticleFrontiers in cell and developmental biology2021

Accurate Prediction of Prognosis by Integrating Clinical and Molecular Characteristics in Colon Cancer.

Liru Wang, Mu Su, Mengyan Zhang, Hongyan Zhao, Hongli Wang, Jie Xing, Chenyu Guo, Dianshuang Zhou, Wenhui Xue, Haibo Lu and 1 more

Abstract read
In one paragraph

Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Prognosis of subarachnoid hemorrhage determined by intracranial pressure thresholds.Journal of cerebrovascular and endovascular neurosurgery · 2025
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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

11 authors.

Liru WangSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.
Mu SuSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.
Mengyan ZhangSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.
Hongyan ZhaoDepartment of Gastroenterology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Hongli WangSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.
Jie XingSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.
Chenyu GuoSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.
Dianshuang ZhouSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.
Wenhui XueSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.
Haibo LuDepartment of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
Yan ZhangSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Various factors affect the prognosis of patients with colon cancer. Complicated factors are found to be conducive to accurate assessment of prognosis. In this study, we developed a series of prognostic prediction models for survival time of colon cancer patients after surgery. Analysis of nine clinical characteristics showed that the most important factor was the positive lymph node ratio (LNR). High LNR was the most important clinical factor affecting 1- and 3-year survival; M0&age < 70 was the most important feature for 5 years. The performance of the model was improved through the integration of clinical characteristics and four types of molecule features (mRNA, lncRNA, miRNA, DNA methylation). The model provides guidance for clinical practice. According to the high-risk molecular features combined with age ≥ 70&T3, poorly differentiated or undifferentiated, M0&well differentiated, M0&T2, LNR high, T4&poorly differentiated, or undifferentiated, the survival time may be less than 1 year; for patients with high risk of molecular features combined with M0&T2, M0&T4, LNR 0& M0, LNR median &T3, and LNR high, the survival is predicted less than 3 years; and the survival of patients with M1&T3, M0 and high risk molecular features is less than 5 years. Using multidimensional and complex patient information, this study establishes potential criteria for clinicians to evaluate the survival of patients for colon cancer.

Indexed as

clinical featurecombinationconlon cancermolecular characteristicprognostic model

Identifiers

PMID34095132
PMCPMC8176021

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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.