Evidence map›Paper›PMID 36276092›Full record

ArticleFrontiers in oncology2022

A cuproptosis-related genes signature associated with prognosis and immune cell infiltration in osteosarcoma.

Weiguang Yang, Haiyang Wu, Linjian Tong, Yulin Wang, Qiang Guo, Lixia Xu, Hua Yan, Chengliang Yin, Zhiming Sun

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

13 citing papers in PubMed, 17 citations in OpenAlex.

  1. Musculoskeletal disorders: does cuproptosis hold the key?Frontiers in cell and developmental biology · 2026
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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

9 authors at 3 institutions in 2 countries.

Weiguang YangClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Haiyang WuClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Linjian TongClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Yulin WangClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Qiang GuoDepartment of Orthopaedics, Baodi Clinical College of Tianjin Medical University, Tianjin, China.
Lixia XuTianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin Huanhu Hospital, Tianjin, China.
Hua YanClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Chengliang YinFaculty of Medicine, Macau University of Science and Technology, Taipa, Macao SAR, China.
Zhiming SunClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Tianjin Medical University · CNMacau University of Science and Technology · MOTianjin Huanhu Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteosarcoma (OS) is one of the most prevalent primary bone tumors at all ages of human development. The objective of our study was to develop a model of Cuproptosis-Related Genes (CRGs) for predicting prognosis in OS patients. All datasets of OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database. We obtained the gene set (81 CRGs) related to cuproptosis by accessing the database and previous literature. All the CRGs were analyzed by univariate COX regression, least absolute shrinkage and selection operator (LASSO) COX regression analysis to screen for CRGs associated with prognosis in OS patients. Then these CRGs were used to construct a prognostic signature, which was further verified by independent cohort (GSE21257) and clinical correlation analysis. Afterward, to identify underlying mechanisms, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for the high-risk group by using the GSEA method. The association between the prognostic signature and 28 types of immune infiltrating cells in the tumor microenvironment was assessed. Ultimately, Lipoic Acid Synthetase (LIAS) (HR=0.632, P=0.004), Lipoyltransferase 1 (LIPT1) (HR=0.524, P=0.011), BCL2 Like 1 (BCL2L1/BCL-XL) (HR=0.593, P=0.022), and Pyruvate Dehydrogenase Kinase 1 (PDK1) (HR=0.662, P=0.025) were identified. Subsequently, they were used to calculate the risk score and build a prognostic model. In the training cohort, risk score (HR=1.878, P=0.003) could be considered as an independent prognostic factor, and OS patients with high-risk scores showed lower survival rates. Biological pathways related to substance metabolism and transport were enriched. There were significant differences in immune infiltrating cells in the tumor microenvironment. All in all, The CRGs signature is related to the tumor immune microenvironment and could be used as a credible predictor of the prognostic status in OS patients.

Indexed as

bioinformatics analysiscuproptosisosteosarcomaprognosistumor microenvironment

Identifiers

PMID36276092
PMCPMC9582135
OpenAlexW4302283365

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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.