Evidence map›Paper›PMID 36353119›Full record

ArticleFrontiers in genetics2022

Development of necroptosis-related gene signature to predict the prognosis of colon adenocarcinoma.

Miaomiao Li, Tianyang Zhang, Wei Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 89% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 2 institutions in 1 country.

Miaomiao LiSchool of Life Sciences, North China University of Science and Technology, Tangshan, China.
Tianyang ZhangSchool of Life Sciences, North China University of Science and Technology, Tangshan, China.
Wei ChenSchool of Life Sciences, North China University of Science and Technology, Tangshan, China.
North China University of Science and Technology · CNChengdu University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colon adenocarcinoma (COAD) is a common malignancy and has a high mortality rate. However, the current tumor node metastasis (TNM) staging system is inadequate for prognostic assessment of COAD patients. Therefore, there is an urgent need to identify reliable biomarkers for the prognosis COAD patients. The aberrant expression of necroptosis-related genes (NRGs) is reported to be associated with tumorigenesis and metastasis. In the present work, we compared the expression profiles of NRGs between COAD patients and normal individuals. Based on seven differentially expressed NRGs, a risk score was defined to predict the prognosis of COAD patients. The validation results from both training and independent external cohorts demonstrated that the risk score is able to distinguish the high and low risk COAD patients with higher accuracies, and is independent of the other clinical factors. To facilitate its clinical use, by integrating the proposed risk score, a nomogram was built to predict the risk of individual COAD patients. The C-index of the nomogram is 0.75, indicating the reliability of the nomogram in predicting survival rates. Furthermore, two candidate drugs, namely dapsone and xanthohumol, were screed out and validated by molecular docking, which hold the potential for the treatment of COAD. These results will provide novel clues for the diagnosis and treatment of COAD.

Indexed as

colon adenocarcinomagene signaturemolecular dockingnecroptosisnomogramsurvival analysis

Identifiers

PMID36353119
PMCPMC9639779
OpenAlexW4307265855

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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.