ArticleJournal of Cancer2019
Co-Expression Network Analysis Identified Gene Signatures in Osteosarcoma as a Predictive Tool for Lung Metastasis and Survival.
Article in Journal of Cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Statistical genomics in rare cancer.Seminars in cancer biology · 2020Pooled it
- Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis.Heliyon · 2024Article
- Identification of AGXT2, SHMT1, and ACO2 as important biomarkers of acute kidney injury by WGCNA.PloS one · 2023Article
- Multi-omics data identified TP53 and LRP1B as key regulatory gene related to immune phenotypes via EPCAM in HCC.Cancer medicine · 2022Article
- Outstanding prognostic value of novel ferroptosis-related genes in chemoresistance osteosarcoma patients.Scientific reports · 2022Article
- Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis.Frontiers in genetics · 2022Article
- Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments.BioMed research international · 2022Article
- Identification of a seven-gene prognostic signature using the gene expression profile of osteosarcoma.Annals of translational medicine · 2022Article
- Expression of immune-related genes as prognostic biomarkers for the assessment of osteosarcoma clinical outcomes.Scientific reports · 2021Article
- Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics.Cancer cell international · 2021Article
- Identification of a 3-gene signature based on differentially expressed invasion genes related to cancer molecular subtypes to predict the prognosis of osteosarcoma patients.Bioengineered · 2021Article
- Gene correlation network analysis to identify regulatory factors in sciatic nerve injury.Journal of orthopaedic surgery and research · 2021Article
- Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis.Experimental biology and medicine (Maywood, N.J.) · 2021Article
- High expression of COL5A2, a member of COL5 family, indicates the poor survival and facilitates cell migration in gastric cancer.Bioscience reports · 2021Article
- Comprehensive analysis of immune-related prognostic genes in the tumour microenvironment of hepatocellular carcinoma.BMC cancer · 2021Article
- A ceRNA-associated risk model predicts the poor prognosis for head and neck squamous cell carcinoma patients.Scientific reports · 2021Article
- Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma.Frontiers in genetics · 2021Article
- Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma.Frontiers in cell and developmental biology · 2021Article
- Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data.Scientific reports · 2020Article
- Screening and evaluation of key genes in contributing to pathogenesis of hepatic fibrosis based on microarray data.European journal of medical research · 2020Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteosarcoma (OS) is the most common primary bone tumor, whose poor prognosis is mainly due to lung metastasis. The aim of this study is to build a practical and valid diagnostic test that can predict the risk of OS metastasis and progression. We performed weighted gene co-expression network analysis (WGCNA) on GSE21257 from the Gene Expression Omnibus (GEO) database, which contains microarray data of biopsies from OS patients. In these modules, the highest association was found between the blue module and metastasis stage (r = -0.52) by Pearson's correlation analysis. Based on Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we derived eight clinically significant genes and constructed an eight-gene signature for metastasis status. It showed great efficacy to distinguish metastasis from non-metastasis (AUC = 0.886) and the results were validated in The Cancer Genome Atlas (TCGA) database. Functional enrichment analysis of hub genes showed that their biological processes focused on immune-related pathways, suggesting the important roles of immune cells, immune pathways and the tumor microenvironment in metastasis development. In conclusion, we discovered an efficient gene signature with great efficacy to distinguish metastasis status, which may help improve early diagnosis and treatment, enhancing the clinical outcomes of OS patients. Besides we created an effective protocol to seek for several hub genes in high-throughput data by combining WGCNA and LASSO Cox regression.
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