Evidence map›Paper›PMID 36072666›Full record

ArticleFrontiers in genetics2022

A Novel Approach: Combining Prognostic Models and Network Pharmacology to Target Breast Cancer Necroptosis-Associated Genes.

Congzhi Yan, Conghui Liu, Zhixuan Wu, Yinwei Dai, Erjie Xia, Wenjing Hu, Xuanxuan Dai

Open access · goldAbstract read
PubMed Full text at PMC Publisher
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 88% 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 2 institutions in 1 country.

Congzhi YanKey Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Conghui LiuKey Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Zhixuan WuKey Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Yinwei DaiKey Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Erjie XiaKey Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Wenjing HuKey Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Xuanxuan DaiKey Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Wenzhou Medical University · CNFirst Affiliated Hospital of Wenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer (BC) accounts for the highest proportion of the all cancers among women, and necroptosis is recognized as a form of caspase-independent programmed cell death. We created prognostic signatures using univariate survival analysis, and lasso regression, to assess immune microenvironments between subgroups. We then used network pharmacology to bind our drugs to target differentially expressed genes (DEGs). A signature comprising a set of necroptosis-related genes was established to predict patient outcomes based on median risk scores. Those above and below the median were classified as high-risk group (HRG) and low-risk group (LRG), respectively. Patients at high risk had lower overall survival, and poorer predicted tumor, nodes, and metastases stages (TNM). The novel prognostic signature can effectively predict the prognosis of breast cancer patients docking of

Indexed as

breast cancerimmune microenvironmentnecroptosisnetworktreatment

Identifiers

PMID36072666
PMCPMC9441943
OpenAlexW4292583110

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.