ArticleGenes2019
A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Functional roles and clinical implications of pseudogenes signatures as emerging biomarkers in cancer.Discover oncology · 2026Review
- Common crosstalk genes and molecular mechanisms in Helicobacter pylori infection and colorectal cancer.Discover oncology · 2026Article
- Pseudogene pair-based prognostic model reveals LAT as a biomarker of immune response in head and neck squamous cell carcinoma.Discover oncology · 2025Article
- Repair mechanisms of bone system tissues based on comprehensive perspective of multi-omics.Cell biology and toxicology · 2025Review
- Research on the application of a multi-model cascaded deep learning framework in the pathological diagnosis of osteosarcoma.Oncology reviews · 2025Article
- Deep radiomics-based prognostic prediction of oral cancer using optical coherence tomography.BMC oral health · 2024Article
- Targeted sequencing of DNA/RNA combined with radiomics predicts lymph node metastasis of papillary thyroid carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- System analysis based on Anoikis-related genes identifies MAPK1 as a novel therapy target for osteosarcoma with neoadjuvant chemotherapy.BMC musculoskeletal disorders · 2024Article
- Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.BMC musculoskeletal disorders · 2024Article
- A hypoxia-glycolysis-lactate-related gene signature for prognosis prediction in hepatocellular carcinoma.BMC medical genomics · 2024Article
- Advances in prognostic models for osteosarcoma risk.Heliyon · 2024Review
- Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma.Scientific reports · 2024Article
- The immune cell infiltration-associated molecular subtypes and gene signature predict prognosis for osteosarcoma patients.Scientific reports · 2024Article
- A nomogram for predicting adverse pathologic features in low-risk papillary thyroid microcarcinoma.BMC cancer · 2024Article
- Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.Bioinformatics and biology insights · 2024Article
- Article
- A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron.Diagnostics (Basel, Switzerland) · 2023Article
- Regulation of the Epithelial to Mesenchymal Transition in Osteosarcoma.Biomolecules · 2023Review
- The pseudogenes of eukaryotic translation elongation factors (EEFs): Role in cancer and other human diseases.Genes & diseases · 2022Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteosarcoma is a common malignancy with high mortality and poor prognosis due to lack of predictive markers. Increasing evidence has demonstrated that pseudogenes, a type of non-coding gene, play an important role in tumorigenesis. The aim of this study was to identify a prognostic pseudogene signature of osteosarcoma by machine learning. A sample of 94 osteosarcoma patients' RNA-Seq data with clinical follow-up information was involved in the study. The survival-related pseudogenes were screened and related signature model was constructed by cox-regression analysis (univariate, lasso, and multivariate). The predictive value of the signature was further validated in different subgroups. The putative biological functions were determined by co-expression analysis. In total, 125 survival-related pseudogenes were identified and a four-pseudogene (RPL11-551L14.1, HR: 0.65 (95% CI: 0.44-0.95); RPL7AP28, HR: 0.32 (95% CI: 0.14-0.76); RP4-706A16.3, HR: 1.89 (95% CI: 1.35-2.65); RP11-326A19.5, HR: 0.52(95% CI: 0.37-0.74)) signature effectively distinguished the high- and low-risk patients, and predicted prognosis with high sensitivity and specificity (AUC: 0.878). Furthermore, the signature was applicable to patients of different genders, ages, and metastatic status. Co-expression analysis revealed the four pseudogenes are involved in regulating malignant phenotype, immune, and DNA/RNA editing. This four-pseudogene signature is not only a promising predictor of prognosis and survival, but also a potential marker for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.
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