Evidence map›Paper›PMID 36895971›Full record

ArticleAmerican journal of cancer research2023

Identification of necroptosis-related genes as prognostic indicators for lower-grade glioma.

Xiaowan Huang, Somayeh Vafaei, Lingxia Li, Yunjiu Wang, Jian Sun, Yuzhen Gao, Jue Zhang

Abstract read
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In one paragraph

Article in American journal of cancer research, 2023. 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
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

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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. 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.

Xiaowan HuangDepartment of Laboratory Medicine, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine Shanghai 200021, China.
Somayeh VafaeiDepartment of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences Tehran, Iran.
Lingxia LiDepartment of Laboratory Medicine, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine Shanghai 200021, China.
Yunjiu WangDepartment of Laboratory Medicine, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine Shanghai 200021, China.
Jian SunDepartment of Laboratory Medicine, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine Shanghai 200021, China.
Yuzhen GaoDepartment of Clinical Laboratory, Sir Run Run Shaw Hospital of Zhejiang University School of Medicine Hangzhou 310016, Zhejiang, China.
Jue ZhangDepartment of Laboratory Medicine, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine Shanghai 200021, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this research is to develop a predictive model based on necroptosis-related genes to predict the prognosis and survival of lower grade gliomas (LGGs) efficiently. To achieve this goal, we searched for differentially expressed necrotizing apoptosis-related genes using the TCGA and CGGA databases. To construct a prognostic model, LASSO Cox and COX regression analyses were conducted on the differentially expressed genes. In this study, three genes were used to develop a prognostic model of necrotizing apoptosis, and all samples were split into high- and low-risk groups. We observed that patients with a high-risk score had a worse overall survival rate (OS) than those with a low-risk score. In the TCGA and CGGA cohorts, the nomogram plot showed a high capacity to predict overall survival of LGG patients. GSEA analysis revealed that the high-risk group was enriched for inflammatory responses, tumor-related pathways, and pathological processes. Additionally, the high-risk score was associated with invading immune cell expression. In conclusion, our predictive model based on necroptosis-related genes in LGG was shown to be effective in the diagnosis and could predict the prognosis of LGG. In addition, we identified possible targets related to necroptosis-related genes for glioma therapy in this study.

Indexed as

Low-grade gliomasnecroptosisprognosisssGSEATCGATMB

Identifiers

PMID36895971
PMCPMC9989601

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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.