ArticleCancers2023
Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Article
- Advancements in nanobiosensors for early cancer detection, challenges, treatment, and future prospects: a comprehensive review.Discover nano · 2026Review
- Translating Data Into Clinical Tools: An Integrative Strategy for Precision Biomarker Identification in Soft Tissue Sarcoma Diagnosis and Prognosis.Cancer informatics · 2026Article
- Gene Expression Profiling to Unveil Novel Biomarkers for Early Diagnosis and Therapies for Breast Cancer.Current topics in medicinal chemistry · 2026Article
- Harnessing plasma transcriptomics for non-invasive cancer biomarker identification: a comprehensive review.Discover oncology · 2025Review
- Machine Learning-Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity.Cancer medicine · 2025Article
- AI-driven discovery of novel extracellular matrix biomarkers in pelvic organ prolapse.PLoS computational biology · 2025Article
- The role and therapeutic value of NUSAP1 in human cancers.Journal of translational medicine · 2025Review
- Multiple roles of S100P in pan carcinoma: Biological functions and mechanisms (Review).Oncology reports · 2025Review
- Evaluating the Diagnostic Potential of Biomarker Panels in Breast Cancer and Prostate Adenocarcinoma.Health science reports · 2025Article
- Artificial Intelligence and Breast Cancer Management: From Data to the Clinic.Cancer innovation · 2025Review
- Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.Discover oncology · 2025Article
- Identification of Prognostic and Diagnostic Biomarkers for Glioma Utilizing Immune System Gene Profiling.Medical journal of the Islamic Republic of Iran · 2025Article
- In silico analysis of CSF2RB from cancer genomic databases reveals a heterogeneous role in different breast cancer subtypes.Frontiers in bioinformatics · 2025Article
- Identification and validation of CCN family genes to predict the prognosis in gastric cancer.Discover oncology · 2024Article
- Review
- An overview of CCN4 (WISP1) role in human diseases.Journal of translational medicine · 2024Review
- AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.Briefings in bioinformatics · 2024Article
- Unveiling Collagen's Role in Breast Cancer: Insights into Expression Patterns, Functions and Clinical Implications.International journal of general medicine · 2024Review
- Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundBreast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis.
methodsA cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP.
resultsWe identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (
conclusionThe ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.
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