Human AGEs: an interactive spatio-temporal visualization and database of human archeogenomics.

Archeogenomics is a rapidly growing interdisciplinary research field driven by the development of techniques that enable the acquisition and analysis of ancient DNA (aDNA). Recent advances in aDNA studies have contributed significantly to increasing our understanding of the natural history of humans. One of the most significant challenges facing archeogenomics is the integration of highly heterogeneous genomic, archeological, and anthropological data and their comprehensive analysis, considering changes that occur in time and space. Only this complex approach can explain the relationship between past populations in the context of migration or cultural development. To address these challenges, we developed a Human AGEs web server. It focuses on creating comprehensive spatiotemporal visualizations of genomic, archeogenomic, and archeological information, which can be provided by the user or loaded from a graph database. The interactive map application at the center of Human AGEs can display multiple layers of data in various forms, such as bubble charts, pie charts, heatmaps, or tag clouds. These visualizations can be modified using various clustering, filtering, and styling options, and the map state can be exported to a high-resolution image or saved as a session file for later use. Human AGEs, along with their tutorial, are accessible at https://archeogenomics.eu/.

Cancer Stem Cell Markers-Clinical Relevance and Prognostic Value in High-Grade Serous Ovarian Cancer (HGSOC) Based on The Cancer Genome Atlas Analysis.

Cancer stem cells (CSCs) may contribute to an increased risk of recurrence in ovarian cancer (OC). Further research is needed to identify associations between CSC markers and OC patients' clinical outcomes with greater certainty. If they prove to be correct, in the future, the CSC markers can be used to help predict survival and indicate new therapeutic targets. This study aimed to determine the CSC markers at mRNA and protein levels and their association with clinical presentation, outcome, and risk of recurrence in HGSOC (High-Grade Serous Ovarian Cancer). TCGA (The Cancer Genome Atlas) database with 558 ovarian cancer tumor samples was used for the evaluation of 13 CSC markers (ALDH1A1, CD44, EPCAM, KIT, LGR5, NES, NOTCH3, POU5F1, PROM1, PTTG1, ROR1, SOX9, and THY1). Data on mRNA and protein levels assessed by microarray and mass spectrometry were retrieved from TCGA. Models to predict chemotherapy response and survival were built using multiple variables, including epidemiological data, expression levels, and machine learning methodology. ALDH1A1 and LGR5 mRNA expressions indicated a higher platinum sensitivity (p = 3.50 × 10-3; p = 0.01, respectively). POU5F1 mRNA expression marked platinum-resistant tumors (p = 9.43 × 10-3). CD44 and EPCAM mRNA expression correlated with longer overall survival (OS) (p = 0.043; p = 0.039, respectively). THY1 mRNA and protein levels were associated with worse OS (p = 0.019; p = 0.015, respectively). Disease-free survival (DFS) was positively affected by EPCAM (p = 0.004), LGR5 (p = 0.018), and CD44 (p = 0.012). In the multivariate model based on CSC marker expression, the high-risk group had 9.1 months longer median overall survival than the low-risk group (p < 0.001). ALDH1A1, CD44, EPCAM, LGR5, POU5F1, and THY1 levels in OC may be used as prognostic factors for the primary outcome and help predict the treatment response.