ArticleFrontiers in cell and developmental biology2021
Proliferation Cycle Transcriptomic Signatures are Strongly associated With Gastric Cancer Patient Survival.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Article
- Overcoming Clinical Resistance to EZH2 Inhibition Using Rational Epigenetic Combination Therapy.Cancer discovery · 2024Article
- Characterization of RNA Processing Genes in Colon Cancer for Predicting Clinical Outcomes.Biomarker insights · 2024Article
- Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer.Biomolecules · 2023Article
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6 authors.
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Abstract
Gastric cancer is one of the most heterogeneous tumors with multi-level molecular disturbances. Sustaining proliferative signaling and evading growth suppressors are two important hallmarks that enable the cancer cells to become tumorigenic and ultimately malignant, which enable tumor growth. Discovering and understanding the difference in tumor proliferation cycle phenotypes can be used to better classify tumors, and provide classification schemes for disease diagnosis and treatment options, which are more in line with the requirements of today's precision medicine. We collected 691 eligible samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, combined with transcriptome data, to explore different heterogeneous proliferation cycle phenotypes, and further study the potential genomic changes that may lead to these different phenotypes in this study. Interestingly, two subtypes with different clinical and biological characteristics were identified through cluster analysis of gastric cancer transcriptome data. The repeatability of the classification was confirmed in an independent Gene Expression Omnibus validation cohort, and consistent phenotypes were observed. These two phenotypes showed different clinical outcomes, and tumor mutation burden. This classification helped us to better classify gastric cancer patients and provide targeted treatment based on specific transcriptome data.
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