ArticleOncotarget2017
Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning-based early diagnosis and proposes novel diagnostic and prognostic biomarkers.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification.Bioengineering (Basel, Switzerland) · 2024Article
- Microbiota changes: the unseen players in cervical cancer progression.Frontiers in microbiology · 2024Review
- Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation.Scientific reports · 2023Article
- Weakly supervised label propagation algorithm classifies lung cancer imaging subtypes.Scientific reports · 2023Article
- Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells.Frontiers in oncology · 2023Review
- Circular RNA hsa_circ_0008003 facilitates tumorigenesis and development of non-small cell lung carcinoma via modulating miR-488/ZNF281 axis.Journal of cellular and molecular medicine · 2022Article
- Bone mesenchymal stem cell-derived exosomal microRNA-7-5p inhibits progression of acute myeloid leukemia by targeting OSBPL11.Journal of nanobiotechnology · 2022Article
- Signature based on RNA-binding protein-related genes for predicting prognosis and guiding therapy in non-small cell lung cancer.Frontiers in genetics · 2022Article
- Molecular perturbations in pulmonary tuberculosis patients identified by pathway-level analysis of plasma metabolic features.PloS one · 2022Article
- The Importance of Sex in the Discovery of Colorectal Cancer Prognostic Biomarkers.International journal of molecular sciences · 2021Article
- Structure of the EphB6 receptor ectodomain.PloS one · 2021Article
- Artificial Intelligence in Health Care: Current Applications and Issues.Journal of Korean medical science · 2020Review
- Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis.Metabolites · 2019Review
- Recognition and Clinical Diagnosis of Cervical Cancer Cells Based on our Improved Lightweight Deep Network for Pathological Image.Journal of medical systems · 2019Article
- Omics Approaches in Pancreatic Adenocarcinoma.Cancers · 2019Review
- Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.American journal of obstetrics and gynecology · 2019Article
- Article
- High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer.International journal of molecular sciences · 2019Article
- Spheroid-Induced Epithelial-Mesenchymal Transition Provokes Global Alterations of Breast Cancer Lipidome: A Multi-Layered Omics Analysis.Frontiers in oncology · 2019Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although many outstanding achievements in the management of cervical cancer (CxCa) have obtained, it still imposes a major burden which has prompted scientists to discover and validate new CxCa biomarkers to improve the diagnostic and prognostic assessment of CxCa. In this study, eight different gene expression data sets containing 202 cancer, 115 cervical intraepithelial neoplasia (CIN), and 105 normal samples were utilized for an integrative systems biology assessment in a multi-stage carcinogenesis manner. Deep learning-based diagnostic models were established based on the genetic panels of intrinsic genes of cervical carcinogenesis as well as on the unbiased variable selection approach. Survival analysis was also conducted to explore the potential biomarker candidates for prognostic assessment. Our results showed that cell cycle, RNA transport, mRNA surveillance, and one carbon pool by folate were the key regulatory mechanisms involved in the initiation, progression, and metastasis of CxCa. Various genetic panels combined with machine learning algorithms successfully differentiated CxCa from CIN and normalcy in cross-study normalized data sets. In particular, the 168-gene deep learning model for the differentiation of cancer from normalcy achieved an externally validated accuracy of 97.96% (99.01% sensitivity and 95.65% specificity). Survival analysis revealed that ZNF281 and EPHB6 were the two most promising prognostic genetic markers for CxCa among others. Our findings open new opportunities to enhance current understanding of the characteristics of CxCa pathobiology. In addition, the combination of transcriptomics-based signatures and deep learning classification may become an important approach to improve CxCa diagnosis and management in clinical practice.
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