Evidence map›Paper›PMID 34168974›Full record

ArticleFrontiers in oncology2021

A Correlation Study of Prognostic Risk Prediction for Colorectal Cancer Based on Autophagy Signature Genes.

Haibi Zhao, Chengzhi Huang, Yuwen Luo, Xiaoya Yao, Yong Hu, Muqing Wang, Xin Chen, Jun Zeng, Weixian Hu, Junjiang Wang and 2 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 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. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. MCM3AP-AS1: An Indispensable Cancer-Related LncRNA.Frontiers in cell and developmental biology · 2021
    Review
  9. 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

12 authors.

Haibi ZhaoSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.
Chengzhi HuangDepartment of Gastrointestinal Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Yuwen LuoDepartment of Gastrointestinal Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Xiaoya YaoSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.
Yong HuSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.
Muqing WangDepartment of Gastrointestinal Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Xin ChenDepartment of Gastrointestinal Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Jun ZengDepartment of General Surgery, Baoan Central Hospital, The Fifth Affiliated Hospital of Shen Zhen University, Shen Zhen, China.
Weixian HuDepartment of Gastrointestinal Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Junjiang WangDepartment of Gastrointestinal Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Rongjiang LiDepartment of General Surgery, Baoan Central Hospital, The Fifth Affiliated Hospital of Shen Zhen University, Shen Zhen, China.
Xueqing YaoSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autophagy plays a complex role in tumors, sometimes promoting cancer cell survival and sometimes inducing apoptosis, and its role in the colorectal tumor microenvironment is controversial. The purpose of this study was to investigate the prognostic value of autophagy-related genes (ARGs) in colorectal cancer. We identified 37 differentially expressed autophagy-related genes by collecting TCGA colorectal tumor transcriptome data. A single-factor COX regression equation was used to identify 11 key prognostic genes, and a prognostic risk prediction model was constructed based on multifactor COX analysis. We classified patients into high and low risk groups according to prognostic risk parameters (p <0.001) and determined the prognostic value they possessed by survival analysis and the receiver operating characteristic (ROC) curve in the training and test sets of internal tests. In a multifactorial independent prognostic analysis, this risk value could be used as an independent prognostic indicator (HR=1.167, 95% CI=1.078-1.264, P<0.001) and was a robust predictor without any staging interference. To make it more applicable to clinical procedures, we constructed nomogram based on risk parameters and parameters of key clinical characteristics. The area under ROC curve for 3-year and 5-year survival rates were 0.735 and 0.718, respectively. These will better enable us to monitor patient prognosis, thus improve patient outcomes.

Indexed as

ARGsCRCnomogramprognostic indicatorrisk model

Identifiers

PMID34168974
PMCPMC8218632

What Socratic holds

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LicenceCC BY
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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 Socratic graph.