Identification of Bone Metastasis-associated Genes of Gastric Cancer by Genome-wide Transcriptional Profiling

Lin, Ming-Zhe; Li, Xin; Guo, Haizhou; Ji, Faxiang; Ye, Linhan; Ma, Xuemei; Cheng, Wen

doi:10.2174/1574893612666171121154017

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…Cancer is still a severe health problem globally, and lots of people have died from cancer ( Liao et al, 2018 ; Cheng et al, 2019a ; Zeng W. et al, 2019 ; Zhang Y. et al, 2019 ; Zhou et al, 2019 ; Yang et al, 2020 ). Traditional cancer treatments kill not only cancer cells but also normal cells, and the medical costs are very high ( Feng, 2019 ; Lin et al, 2019 ; Li Y.H. et al, 2020 ; Zhang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model

Zhou

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features of anticancer peptides.

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Section: Introductionmentioning

confidence: 99%

Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model

Zhou

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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“…Gastric cancer (GC) is a very common malignant tumor, and its prognosis is relatively poor. In 2015, it was found that the incidence rate of GC was second in all cancers ( Chen et al, 2015 ; Lin et al, 2019 ; Mottini et al, 2019 ). Due to the hidden nature of the disease, the early symptoms are not obvious.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Cyclin-Dependent Kinase 1 as an Independent Prognostic Factor for Gastric Cancer Based on Statistical Methods

Zhang

Zou

et al. 2020

Front. Cell Dev. Biol.

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ObjectiveThe aim of this study was to investigate the expression of cyclin-dependent kinase 1 (CDK1) in gastric cancer (GC), evaluate its relationship with the clinicopathological features and prognosis of GC, and analyze the advantage of CDK1 as a potential independent prognostic factor for GC.MethodsThe Cancer Genome Atlas (TCGA) data and corresponding clinical features of GC were collected. First, the aim gene was selected by combining five topological analysis methods, where the gene expression in paracancerous and GC tissues was analyzed by Limma package and Wilcox test. Second, the correlation between gene expression and clinical features was analyzed by logistic regression. Finally, the survival analysis was carried out by using the Kaplan–Meier. The gene prognostic value was evaluated by univariate and multivariate Cox analyses, and the gene potential biological function was explored by gene set enrichment analysis (GSEA).ResultsCDK1 was selected as one of the most important genes associated with GC. The expression level of CDK1 in GC tissues was significantly higher than that in paracancerous tissues, which was significantly correlated with pathological stage and grade. The survival rate of the CDK1 high expression group was significantly lower than that of the low expression group. CDK1 expression was significantly correlated with overall survival (OS). CDK1 expression was mainly involved in prostate cancer, small cell lung cancer, and GC and was enriched in the WNT signaling pathway and T cell receptor signaling pathway.ConclusionCDK1 may serve as an independent prognostic factor for GC. It is also expected to be a new target for molecular targeted therapy of GC.

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“…Gene is merely a “formulation” of the cell, and the protein encoded by the gene is ultimately a functional participant in normal and cancer physiology [23] . Thus, while the genetic background contributes in part to the susceptibility and development of cancer [24] , cancer can now be considered as a proteome disease and has more links to the post-transcriptional steps [25] , [26] . In terms of patient prognosis, there is an urgent need for protein markers distinguishing cancer patients from normal individuals.…”

Section: Introductionmentioning

confidence: 99%