Evidence map›Paper›PMID 38264748›Full record

ArticleFrontiers in oncology2023

Predictors based on cuproptosis closely related to angiogenesis predict colorectal cancer recurrence.

Haoran Li, Yingru Zhang, Yuanyuan Feng, Xueqing Hu, Ling Bi, Huirong Zhu, Yan Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Haoran Li *Oncology Institute, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yingru Zhang *Oncology Institute, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yuanyuan FengDepartment of Medical Oncology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Xueqing HuOncology Institute, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Ling BiOncology Institute, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Huirong ZhuOncology Institute, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yan WangOncology Institute, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Up to one-third of colorectal cancer (CRC) patients experience recurrence after radical surgery, and it is still very difficult to assess and predict the risk of recurrence. Angiogenesis is the key factor of recurrence as metastasis of CRC is closely related to copper metabolism. Expression profiling by microarray from two datasets in Gene Expression Omnibus (GEO) was selected for quality control, genome annotation, normalization, etc. The identified angiogenesis-derived and cuproptosis-related Long non-coding RNAs (lncRNAs) and clinical data were screened and used as predictors to construct a Cox regression model. The stability of the model was evaluated, and a nomogram was drawn. The samples were divided into high-risk and low-risk groups according to the linear prediction of the model, and a Kaplan-Meier survival analysis was performed. In this study, a model was established to predict the postoperative recurrence of colon cancer, which exhibits a high prediction accuracy. Furthermore, the negative correlation between cuproptosis and angiogenesis was validated in colorectal cancer cell lines and the expression of lncRNAs

Indexed as

colorectal cancercuproptosisprediction modelprognosisrecurrence

Identifiers

PMID38264748
PMCPMC10805227

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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.