Evidence map›Paper›PMID 39717265›Full record

ArticleBioMed research international2024

Bioinformatics Analysis of Biomarkers and Therapeutic Targets Related to Necroptosis in Intervertebral Disc Degeneration.

Fan Zhang, Lei Yuan, Heng Ding, Zhenkai Lou, Xingguo Li

Abstract read
In one paragraph

Article in BioMed research international, 2024. 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
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.

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

5 authors.

Fan ZhangDepartment of Orthopedics, The First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan 650032, China.
Lei YuanDepartment of Orthopedics, The First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan 650032, China.
Heng DingDepartment of Orthopedics, The First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan 650032, China.
Zhenkai LouDepartment of Orthopedics, The First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan 650032, China.
Xingguo LiDepartment of Orthopedics, The First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan 650032, China.ORCID https://orcid.org/0000-0002-7470-3038

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Necroptosis is a critical process in intervertebral disc degeneration (IDD). This research is aimed at identifying key genes regulating necroptosis in IDD to provide a theoretical basis for early diagnosis and treatment. Transcriptome data from patients with IDD and normal samples were obtained from the GSE34095 and GSE124272 datasets of the Gene Expression Omnibus (GEO) public database. Necroptosis-related genes (NRGs) were sourced from the GeneCards database and literature. Differentially expressed necroptosis-related genes (DE-NRGs) in IDD were identified by intersecting these sources. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for gene annotation analysis. The receiver operating characteristic (ROC) curve and nomogram analyses assessed the diagnostic efficiency of DE-NRGs. The miRWalk and starBase databases helped construct the competing endogenous RNA (ceRNA) regulatory network of DE-NRGs. We identified 517 differential genes in tissue and 2974 in blood, with 62 genes in common. DE-NRGs (

Indexed as

BiomarkersComputational BiologyIntervertebral Disc DegenerationNecroptosisDatabases, GeneticGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansMaleROC CurveTranscriptomeBiomarkersceRNA networkdiagnostic markersimmune infiltrationintervertebral disc degenerationnecroptosistherapeutic target

Identifiers

PMID39717265
PMCPMC11666314

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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.