RESUMEN
Intratumoral heterogeneity has been a hot topic of cancer research in recent years, which has become a part of resolving cancer metastasis, recurrence and drug resistance. Intratumoral heterogeneity shows that cells undergo different division and proliferation during the process of tumor development, and their genomic cells exist in the process of tumor development. Protein and epigenetic changes can lead to differences in proliferation, migration and invasion, sensitivity and pharmacological prognosis of tumor cells, promote sustainable development and development of cancer cells, produce greater adaptability, and lead to metastasis, recurrence and drug resistance of malignant tumors. In recent years, the molecular mechanism and clinical application of intratumoral heterogeneity have captivated widespread attention from researchers. In the era of precision medicine, oncologists attempt to improve the clinical efficacy of targeted tumor therapy via intratumoral heterogeneity. In this article, recent advances in the study of intratumoral heterogeneity, molecular mechanism of intratumoral heterogeneity, systematic evolution and quantification and clinical significance of tumor heterogeneity were reviewed.
Asunto(s)
Relevancia Clínica , Neoplasias Ováricas , Humanos , Femenino , Neoplasias Ováricas/genética , PronósticoRESUMEN
Cholesterol metabolism is crucial for cell survival and cancer progression. The prognostic patterns of genes linked to cholesterol metabolism (CMAGs) in CESC, however, have received very little attention in research. From public databases, TCGA-CESC cohorts with mRNA expression patterns and the accompanying clinical information of patients were gathered. Consensus clustering was used to find the molecular subtype connected to cholesterol metabolism. In the TCGA-CESC cohort, a predictive risk model with 28 CMAGs was created using Lasso-Cox regression. The function enrichment analysis between groups with high-and low-risk were investigated by employing GO, KEGG, and GSVA software. The immune cell infiltration was analyzed using ESTIMATE, CIBERSORT, and MCPCOUNTER methods. Finally, we select 7 genes in risk model for further multivariate Cox analysis, and ultimately a hub gene, CHIT1, was identified. Meanwhile, the function of CHIT1 was preliminarily verified in cell and mice tumor model. In conclusion, the abundance of the CHIT1 gene might be beneficial for forecasting the prognosis of CESC, demonstrating that cholesterol metabolism could be a promising treatment target for CESC.