Volume 55, Number 3, July-September 2020
|Page(s)||173 - 178|
|Published online||15 May 2020|
Identification of potential molecular mechanisms of radiation pneumonitis development in non-small-cell lung cancer treatment by data mining
Department of Oncology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou Cancer hospital,
310006, PR China
2 Department of Oncology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, No. 6 Xiaonv Road, Shangcheng District, Hangzhou 310006, PR China
3 Department of Oncology, Jiande Second People’s Hospital, Zhejiang 311604, PR China
* Correspondences: firstname.lastname@example.org
Accepted: 21 April 2020
Introduction: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity in patients receiving thoracic radiotherapy. The underlying mechanisms of RP are still inconclusive. Our objective was to determine the genes and molecular pathways associated with RP using computational tools and publicly available data. Methods: RP-associated genes were determined by text mining, and the intersection of the two gene sets was selected for Gene Ontology analysis using the GeneCodis program. Protein-protein interaction network analysis was performed using STRINGdb to identify the final genes. Results: Our analysis identified 256 genes related to RP with text mining. The enriched biological process annotations resulted in 47 sets of annotations containing a total of 156 unique genes. KEGG analysis of the enriched pathways identified 24 pathways containing a total of 41 unique genes. The protein-protein interaction analysis yielded 23 genes (mostly the PI3K family). Conclusion: Gene discovery using in silico text mining and pathway analysis tools can facilitate the identification of the underlying mechanisms of RP.
Key words: radiation pneumonitis / text mining / data mining
© SFRP, 2020
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