Genomic and epigenomic instability are defining features of cancer, driving tumor progression, heterogeneity, and therapeutic resistance. Central to this process are epigenetic echoes, persistent and dynamic modifications in DNA methylation, histone modifications, non-coding RNA regulation, and chromatin remodeling that mirror underlying genomic chaos and actively influence cancer cell behavior. This review delves into the complex relationship between genomic instability and these epigenetic echoes, illustrating how they collectively shape the cancer genome, affect DNA repair mechanisms, and contribute to tumor evolution. However, the dynamic, context-dependent nature of epigenetic changes presents scientific and ethical challenges, particularly concerning privacy and clinical applicability. Focusing on lung cancer, we examine how specific epigenetic patterns function as biomarkers for distinguishing cancer subtypes and monitoring disease progression and relapse.

Familial adenomatous polyposis (FAP) is caused by pathogenic germline variants in the tumor suppressor gene APC. Confirmation of diagnosis was not achieved by cancer gene panel and exome sequencing or custom array-CGH in a family with suspected FAP across five generations. Long-read genome sequencing (PacBio), short-read genome sequencing (Illumina), short-read RNA sequencing, and further validations were performed in different tissues of multiple family members. Long-read genome sequencing resolved a 6 kb full-length intronic insertion of a heterozygous LINE-1 element between exons 7 and 8 of APC that could be detected but not fully resolved by short-read genome sequencing. Targeted RNA analysis revealed aberrant splicing resulting in the formation of a pseudo-exon with a premature stop codon. The variant segregated with the phenotype in several family members allowing its evaluation as likely pathogenic. This study supports the utility of long-read DNA sequencing and complementary RNA approaches to tackle unsolved cases of hereditary disease.

The Cancer Genome Atlas (TCGA) provides comprehensive genomic data across various cancer types. However, complex file naming conventions and the necessity of linking disparate data types to individual case IDs can be challenging for first-time users. While other tools have been introduced to facilitate TCGA data handling, they lack a straightforward combination of all required steps. To address this, we developed a streamlined pipeline using the Genomic Data Commons (GDC) portal’s cart system for file selection and the GDC Data Transfer Tool for data downloads. We use the Sample Sheet provided by the GDC portal to replace the default 36-character opaque file IDs and filenames with human-readable case IDs. We developed a pipeline integrating customizable Python scripts in a Jupyter Notebook and a Snakemake pipeline for ID mapping along with automating data preprocessing tasks (https://github.com/alex-baumann-ur/TCGADownloadHelper). Our pipeline simplifies the data download process by modifying manifest files to focus on specific subsets, facilitating the handling of multimodal data sets related to single patients. The pipeline essentially reduced the effort required to preprocess data. Overall, this pipeline enables researchers to efficiently navigate the complexities of TCGA data extraction and preprocessing. By establishing a clear step-by-step approach, we provide a streamlined methodology that minimizes errors, enhances data usability, and supports the broader utilization of TCGA data in cancer research. It is particularly beneficial for researchers new to genomic data analysis, offering them a practical framework prior to conducting their TCGA studies.

Background: Climate change is leading to an increase in acute heatwaves, thereby raising risks to human health and healthcare systems. The aim of this study was to quantify the relationship between heat and hospital admissions, and to develop a predictive model for patient admissions. Methods: A retrospective secondary data analysis was conducted for the period 2018–2023, using data from Chemnitz Hospital and the Regional Climate Information Service of TU Dresden. Hospital admissions with the following diagnoses were analyzed: E86 (Volume depletion), I20 (Angina pectoris), I21 (Acute myocardial infarction), I63 (Cerebral infarction), I64 (Stroke, unspecified as hemorrhagic or ischemic), J44 (COPD), L55 (Sunburn), T67 (Effects of heat and light). Meteorological variables considered included temperature, humidity, air pressure, and daily sunshine hours. Analyses included correlations between daily maximum temperatures (≥ 23°C and ≥ 30°C) and hospital admissions, as well as linear and non-linear regression analyses., both univariate (maximum temperature) and multivariate (humidity, air pressure, sunshine hours). Results: A positive correlation between maximum temperature and hospital admissions was observed: r = 0.44 ( p > 0.05) for ≥ 23°C and r = 0.88 ( p < 0.01) for ≥ 30°C. Regression analyses suggested a non-linear relationship (U-shaped pattern with alternating curvature) between temperature and hospital admissions for days with a maximum temperature between 23°C and 29°C, and a linear effect for days above 30°C, additionally influenced by air pressure, humidity, and sunshine hours. Conclusion: The results indicate a complex association between heat and hospital admissions. Based on these findings, a predictive model was developed to forecast patient admissions. The study highlights the importance of primary preventive measures and the consideration of moderate heat stress in healthcare planning.