Cancers, Vol. 11, Pages 1606: A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning
We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.
CONCLUSIONS: The matrices and the questionnaire have a great diagnostic performance which seems interesting for a use as a screening tool for occupational exposures. These results have yet to be confirmed by large-scale studies. PMID: 31727556 [PubMed - as supplied by publisher]
The objective of this paper is to synthesise current knowledge on ALK rearrangement and its impact on the management of advanced NSCLC. Several inhibitors of the tyrosine kinase of ALK (crizotinib, ceritinib, alectinib) have been approved as first line therapies in patients with advanced ALK positive NSCLC, which are associated with a better median progression-free survival than conventional chemotherapy. Unfortunately, the emergence of drug resistance leads to tumor progression. In patients with oligoprogressive disease if local ablative therapy can be effected, continuing with the same ALK tyrosine kinase inhibitor is on...
Publication date: Available online 15 November 2019Source: Biosensors and BioelectronicsAuthor(s): Liying Zhao, Huaixia Yang, Xiaoke Zheng, Jinge Li, Lihe Jian, Weisheng Feng, Jinming KongAbstractCytokeratin fragment antigen 21–1 (CYFRA 21–1) DNA is a crucial biomarker closely associated with non-small cell lung cancer. Here, we fabricated a novel electrochemical biosensor for ultrasensitive detection of CYFRA 21–1 DNA via polysaccharide and electrochemically mediated atom transfer radical polymerization (eATRP) dual signal amplification. Specifically, thiolated peptide nucleic acid (PNA) probes at 5&prim...
This study offers a great potential for ECL as an alternative safer radiosensitizer for increasing the RT efficiency against NSCLC. PMID: 31727206 [PubMed - as supplied by publisher]
Publication date: Available online 16 November 2019Source: Journal of Advanced ResearchAuthor(s): Jingyu Zhang, Yanyan Ba, Qianrui Liu, Liying Zhao, Dazhong Wang, Huaixia Yang, Jinming KongAbstractIn this paper, we reported a system for the ultrasensitive fluorescence detection of cytokeratin fragment antigen 21-1 DNA (CYFRA21-1 DNA) for the early diagnosis of lung cancer. The approach used electron transfer atom transfer radical polymerization (ARGET-ATRP) with ethylenediaminetetraacetic acid (EDTA) as the metal ligand. Firstly, thiolated peptide nucleic acid (PNA) was linked to aminated magnetic beads solutions (MBs) by ...
Conclusions: X-rays could induce IL-8 production in lung cancer cells, which may be related to the activation of p38/MAPK and NF-κB signaling pathway, providing a new point for elucidating the mechanism of radiation pneumonitis. PMID: 31729901 [PubMed - as supplied by publisher]
Analyst, 2019, Accepted Manuscript DOI: 10.1039/C9AN01524H, PaperDongliang Song, Tianming Chen, Shuang Wang, Shilin Chen, Heping Li, Fan Yu, Jingyuan Zhang, Zhe Zhang As a highly invasive and the most prevalent malignancy, lung cancer remains the leading cause of cancer-associated mortality worldwide, especially in China. Microwave ablation (MWA) is a type of effective,... The content of this RSS Feed (c) The Royal Society of Chemistry
ConclusionThis study reports development of a comprehensive analysis method to classify pulmonary nodules at multiple sections using GAN and DCNN. The effectiveness of the proposed discrimination method based on use of multiplanar images has been demonstrated to be improved compared to that realized in a previous study reported by the authors. In addition, the possibility of enhancing classification accuracy via application of GAN-generated images, instead of data augmentation, for pretraining even for medical datasets that contain relatively few images has also been demonstrated.