ArticlePloS one2015
Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 50 citations in OpenAlex.
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- When breast cancer patients participate in ritual interactive activities: the mechanism of perceived emotional synchrony on health information avoidance.Frontiers in psychology · 2025Article
- A Critical Review on microRNAs as Prognostic Biomarkers in Laryngeal Carcinoma.International journal of molecular sciences · 2024Review
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- StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling.PLoS computational biology · 2024Article
- Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture.Medical & biological engineering & computing · 2023Article
- Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach.Iranian journal of pharmaceutical research : IJPR · 2022Article
- A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma.BMC gastroenterology · 2022Article
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- Scalable network estimation withStatistical analysis and data mining · 2021Article
- An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets.BMC bioinformatics · 2019Article
- BioTarget: A Computational Framework Identifying Cancer Type Specific Transcriptional Targets of Immune Response Pathways.Scientific reports · 2019Article
- Trimodal distribution of arylamine N-acetyltransferase 1 mRNA in breast cancer tumors: association with overall survival and drug resistance.BMC genomics · 2018Article
- An efficient biomarker panel for diagnosis of breast cancer using surface-enhanced laser desorption ionization time-of-flight mass spectrometry.Biomedical reports · 2018Article
- Angiogenesis and evading immune destruction are the main related transcriptomic characteristics to the invasive process of oral tongue cancer.Scientific reports · 2018Article
- Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images.Biomedical informatics insights · 2018Article
- A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.BMC genomics · 2016Article
- Determining breast cancer histological grade from RNA-sequencing data.Breast cancer research : BCR · 2016Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The grade of a cancer is a measure of the cancer's malignancy level, and the stage of a cancer refers to the size and the extent that the cancer has spread. Here we present a computational method for prediction of gene signatures and blood/urine protein markers for breast cancer grades and stages based on RNA-seq data, which are retrieved from the TCGA breast cancer dataset and cover 111 pairs of disease and matching adjacent noncancerous tissues with pathologists-assigned stages and grades. By applying a differential expression and an SVM-based classification approach, we found that 324 and 227 genes in cancer have their expression levels consistently up-regulated vs. their matching controls in a grade- and stage-dependent manner, respectively. By using these genes, we predicted a 9-gene panel as a gene signature for distinguishing poorly differentiated from moderately and well differentiated breast cancers, and a 19-gene panel as a gene signature for discriminating between the moderately and well differentiated breast cancers. Similarly, a 30-gene panel and a 21-gene panel are predicted as gene signatures for distinguishing advanced stage (stages III-IV) from early stage (stages I-II) cancer samples and for distinguishing stage II from stage I samples, respectively. We expect these gene panels can be used as gene-expression signatures for cancer grade and stage classification. In addition, of the 324 grade-dependent genes, 188 and 66 encode proteins that are predicted to be blood-secretory and urine-excretory, respectively; and of the 227 stage-dependent genes, 123 and 51 encode proteins predicted to be blood-secretory and urine-excretory, respectively. We anticipate that some combinations of these blood and urine proteins could serve as markers for monitoring breast cancer at specific grades and stages through blood and urine tests.
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