Abstract
Because of high heterogeneity, a further classification should be made for diagnosis and treatment in gastric cancer. Biomarkers selected in subtypes are important for precision medicine. Based on gene expression level, we constructed genome-wide co-expression networks for invasive, proliferative and metabolic subtype in gastric cancer respectively. The hierarchical clustering was used to get sub-networks, and hub gene sets of subtypes were got by analysis in sub-networks. Unique differential expression genes as candidate targeted genes in subtype were gained by a comparative analysis between subtypes. These genes may be helpful for improving diagnosis and therapy methods and developing new drug in gastric cancer.
Highlights
Gastric cancer is a common tumor with high morbidity and mortality globally
In the comparative analysis between each pair of three sets, we identified some unique differential expression genes as the candidate targeted genes (Table 1)
It is noted that some unique differential expression genes appear in both results of analysis between one subtype and other subtypes, such as ARHGAP15, CAP2, COL14A1, DARC, FERMT2, FHL1, FLNA, RAB23, SMYD1, SPON1 and ZEB1 in invasive subtype, BUB1B, KIF11, KIF18B, NUSAP1 and SYNPO2 in proliferative subtype
Summary
Gastric cancer is a common tumor with high morbidity and mortality globally. It is a heterogeneous disease with multiple histopathologic features. For a better diagnosis and treatment, the subtype classification of gastric cancer should be made clinically. Tumor molecular classification was first proposed by National Cancer Institute. It divided tumor into subtypes using molecular classification technology. Classification of tumor based on the characteristics of molecular expression is more useful for individual therapy and more effective in prognosis than classification in pathology. LEI et al used a robust method of unsupervised clustering and consensus hierarchical clustering with iterative feature selection
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