Evidence map›Paper›PMID 36675706›Full record

ArticleJournal of personalized medicine2022

Comprehensive Analysis of Necroptosis-Related Genes as Prognostic Factors and Immunological Biomarkers in Breast Cancer.

Yingkun Xu, Qiulin Wu, Zhenrong Tang, Zhaofu Tan, Dongyao Pu, Wenhao Tan, Wenjie Zhang, Shengchun Liu

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 37% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

8 authors at 1 institution in 1 country.

Yingkun XuDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.ORCID 0000-0002-7308-735X
Qiulin WuDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.
Zhenrong TangDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.
Zhaofu TanDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.
Dongyao PuDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.
Wenhao TanDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.
Wenjie ZhangDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.
Shengchun LiuDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.
The Affiliated Yongchuan Hospital of Chongqing Medical University · CN

Funding

Doctoral Research Innovation Project of the First Affiliated Hospital of Chongqing Medical University CYYY-BSYJSCXXM-202213Key Research and Development Project of Chongqing's Technology Innovation and Application Development Special Big Health Field CSTC2021jscx-gksb-N0027National Natural Science Foundation of China 81772979
6 · The paper itself

Abstract

Breast cancer (BC) is a lethal malignancy with a poor prognosis. Necroptosis is critical in the progression of cancer. However, the expression of genes involved in necroptosis in BC and their association with prognosis remain unclear. We investigated the predictive potential of necroptosis-related genes in BC samples from the TCGA dataset. We used LASSO regression to build a risk model consisting of twelve necroptosis-related genes in BC. Using the necroptosis-related risk model, we were able to successfully classify BC patients into high- and low-risk groups with significant prognostic differences (

Indexed as

breast cancerGSEAnecroptosisrisk modelTCGA

Identifiers

PMID36675706
PMCPMC9863352
OpenAlexW4313260286

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.