Evidence map›Paper›PMID 32704448›Full record

ArticlePeerJ2020

Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer.

Xinxin Zhang, Jinyuan Xu, Yujia Lan, Fenghua Guo, Yun Xiao, Yixue Li, Xia Li

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 62% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

7 authors at 1 institution in 1 country.

Xinxin Zhang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Jinyuan Xu *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Yujia LanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Fenghua GuoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Yun XiaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Yixue LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although much progress has been made to improve treatment, colon cancer remains a leading cause of cancer death worldwide. Metabolic reprogramming is a significant ability of cancer cells to ensure the necessary energy supply in uncontrolled proliferation. Since reprogramming energy metabolism has emerged as a new hallmark of cancer cells, accumulating evidences have suggested that metabolism-related genes may serve as key regulators of tumorigenesis and potential biomarkers. In this study, we analyzed a set of reprogramming energy metabolism-related genes by transcriptome analysis in colon cancer and revealed a five-gene signature that could significantly predict the overall survival. The reprogramming energy metabolism-related signature could distinguish patients into high-risk and low-risk groups with significantly different survival times (

Indexed as

Colon cancerMetabolismOverall survivalReprogramming energy metabolismSignature

Identifiers

PMID32704448
PMCPMC7350917
OpenAlexW3040647184

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.