ArticleBMC genomics2019
Putative biomarkers for predicting tumor sample purity based on gene expression data.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- SynthCraft: An AI partner for synthetic data generation to support data access and augmentation in healthcare.PLOS digital health · 2026Article
- A generalized higher-order correlation analysis framework for multi-omics network inference.PLoS computational biology · 2025Article
- Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP.Briefings in bioinformatics · 2024Article
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- OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics.Computers in biology and medicine · 2024Article
- Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.Cell reports. Medicine · 2024Article
- Identification of cancer risk groups through multi-omics integration using autoencoder and tensor analysis.Scientific reports · 2024Article
- High expression of CCDC69 is correlated with immunotherapy response and protective effects on breast cancer.BMC cancer · 2023Article
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- AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples.Experimental & molecular medicine · 2023Article
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- Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.Patterns (New York, N.Y.) · 2022Article
- CCDC69 is a prognostic marker of breast cancer and correlates with tumor immune cell infiltration.Frontiers in surgery · 2022Article
- Knockoff boosted tree for model-free variable selection.Bioinformatics (Oxford, England) · 2021Article
- Study on <em>CCDC69</em> interfering with the prognosis of patients with breast cancer through PPAR signal pathway.European journal of histochemistry : EJH · 2021Article
- The Analysis of Gene Expression Data Incorporating Tumor Purity Information.Frontiers in genetics · 2021Article
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6 authors.
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Abstract
backgroundTumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. The ability to determine tumor purity is important to understand the roles of cancerous and non-cancerous cells in a tumor.
methodsWe applied a supervised machine learning method, XGBoost, to data from 33 TCGA tumor types to predict tumor purity using RNA-seq gene expression data.
resultsAcross the 33 tumor types, the median correlation between observed and predicted tumor-purity ranged from 0.75 to 0.87 with small root mean square errors, suggesting that tumor purity can be accurately predicted υσινγ expression data. We further confirmed that expression levels of a ten-gene set (CSF2RB, RHOH, C1S, CCDC69, CCL22, CYTIP, POU2AF1, FGR, CCL21, and IL7R) were predictive of tumor purity regardless of tumor type. We tested whether our set of ten genes could accurately predict tumor purity of a TCGA-independent data set. We showed that expression levels from our set of ten genes were highly correlated (ρ = 0.88) with the actual observed tumor purity.
conclusionsOur analyses suggested that the ten-gene set may serve as a biomarker for tumor purity prediction using gene expression data.
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