ArticleBMC bioinformatics2015
Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Increased Expression of NXPH4 Correlates with Immune Cell Infiltration and Unfavorable Prognosis in Hepatocellular Carcinoma.Journal of oncology · 2022Article
- CCL16 maintains stem cell-like properties in breast cancer by activating CCR2/GSK3β/β-catenin/OCT4 axis.Theranostics · 2021Article
- Systematic profiling of immune signatures identifies prognostic predictors in lung adenocarcinoma.Cellular oncology (Dordrecht, Netherlands) · 2020Article
- Stress-induced changes in CARF expression determine cell fate to death, survival, or malignant transformation.Cell stress & chaperones · 2020Article
- Predictive Analytics in Spine Oncology Research: First Steps, Limitations, and Future Directions.Neurospine · 2019Article
- A growth hormone receptor SNP promotes lung cancer by impairment of SOCS2-mediated degradation.Oncogene · 2018Article
- A multi-gene expression profile panel for predicting liver metastasis: An algorithmic approach.PloS one · 2018Article
- Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC.Translational oncology · 2017Article
- Gene Expression Profiling of Bronchoalveolar Lavage Cells Preceding a Clinical Diagnosis of Chronic Lung Allograft Dysfunction.PloS one · 2017Article
- Systematic analysis of mRNA expression profiles in NSCLC cell lines to screen metastasis-related genes.Molecular medicine reports · 2016Article
- Serum anti-MDM2 and anti-c-Myc autoantibodies as biomarkers in the early detection of lung cancer.Oncoimmunology · 2016Article
- Nucleic Acid Aptamers: An Emerging Tool for Biotechnology and Biomedical Sensing.Sensors (Basel, Switzerland) · 2015Review
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9 authors.
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Abstract
backgroundFew studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients' survival.
resultsThis work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies.
conclusionsThe proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples.
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