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Strong epigenetic pan-cancer biomarkers are required to meet several current, urgent clinical needs and to further improve the present chemotherapeutic standard. We have concentrated on the investigation of epigenetic alteration of the hTERT gene, which is frequently epigenetically dysregulated in a number of cancers in specific developmental stages. Distinct DNA methylation profiles were identified in our data on early urothelial cancer. An efficient EpihTERT assay could be developed utilizing suitable combinations with sequence-dependent thermodynamic parameters to distinguish between differentially methylated states. We infer from this data set, the epigenetic context, and the related literature that a CpG-rich, 2800 bp region, a prominent CpG island, surrounding the transcription start of the hTERT gene is the crucial epigenetic zone for the development of a potent biomarker. In order to accurately describe this region, we have named it "Acheron" (Ἀχέρων). In Greek mythology, this is the river of woe and misery and the path to the underworld. Exploitation of the DNA methylation profiles focused on this region, e.g., idiolocal normalized Methylation Specific PCR (IDLN-MSP), opens up a wide range of new possibilities for diagnosis, determination of prognosis, follow-up, and detection of residual disease. It may also have broad implications for the choice of chemotherapy. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer treatment could be revolutionized by using particular CAR-T-cell therapies for all solid tumors. Finding appropriate CAR-T-cell antigens for every tumor entity would be necessary for this though. Our findings provide new insight into the possibility of employing CAR-T-cell therapy to treat nearly all cancers, as genome-wide screening following consistent occurring DNA hypomethylations may uncover novel antigens for every cancer entity. Based on the impressive success of Car-T-cell therapy in the treatment of hematological malignancies, a broad application for solid tumors also appears promising. However, some important hurdles need to be overcome. One of these is certainly the identification of specific target antigens on cancer cells. Hypomethylation is a characteristic epigenetic aberration in many tumor entities. Genome-wide screenings for consistent DNA hypomethylations in tumors enable the identification of aberrantly upregulated transcripts, which might result in cell surface proteins. Thus, this approach provides a new perspective for the discovery of potential new Car-T-cell target antigens for almost every tumor entity. First, we focus on this approach as a possible treatment for prostate cancer. [ABSTRACT FROM AUTHOR]

Introduction: Bipolar disorder (BD) is a chronically progressive mental condition, associated with a reduced quality of life and greater disability. Patient admissions are preventable events with a considerable impact on global functioning and social adjustment. While machine learning (ML) approaches have proven prediction ability in other diseases, little is known about their utility to predict patient admissions in this pathology. Aim: To develop prediction models for hospital admission/readmission within 5 years of diagnosis in patients with BD using ML techniques. Methods: The study utilized data from patients diagnosed with BD in a major healthcare organization in Colombia. Candidate predictors were selected from Electronic Health Records (EHRs) and included sociodemographic and clinical variables. ML algorithms, including Decision Trees, Random Forests, Logistic Regressions, and Support Vector Machines, were used to predict patient admission or readmission. Survival models, including a penalized Cox Model and Random Survival Forest, were used to predict time to admission and first readmission. Model performance was evaluated using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC) and concordance index. Results: The admission dataset included 2,726 BD patients, with 354 admissions, while the readmission dataset included 352 patients, with almost half being readmitted. The best-performing model for predicting admission was the Random Forest, with an accuracy score of 0.951 and an AUC of 0.98. The variables with the greatest predictive power in the Recursive Feature Elimination (RFE) importance analysis were the number of psychiatric emergency visits, the number of outpatient follow-up appointments and age. Survival models showed similar results, with the Random Survival Forest performing best, achieving an AUC of 0.95. However, the prediction models for patient readmission had poorer performance, with the Random Forest model being again the best performer but with an AUC below 0.70. Conclusion: ML models, particularly the Random Forest model, outperformed traditional statistical techniques for admission prediction. However, readmission prediction models had poorer performance. This study demonstrates the potential of ML techniques in improving prediction accuracy for BD patient admissions. [ABSTRACT FROM AUTHOR]

Cell-free extrachromosomal circular DNA (cf-eccDNA) has been proposed as a promising early biomarker for disease diagnosis, progression and drug response. Its established biomarker features are changes in the number and length distribution of cf-eccDNA. Another novel promising biomarker is a set of eccDNA excised from a panel of genes specific to a condition compared to a control. Deficiencies in two endonucleases that specifically target DNA, Dnase1 and Dnase1l3, are associated with systemic lupus erythematosus (SLE). To study the genic eccDNA profiles in the case of their deficiencies, we mapped sequenced eccDNA data from plasma, liver and buffy coat from Dnase1 and Dnase1l3 knockouts (KOs), and wild type controls in mouse. Next, we performed an eccDNA differential analysis between KO and control groups using our DifCir algorithm. We found a specific genic cf-eccDNA fingerprint of the Dnase1l3 group compared to the wild type controls involving 131 genes; 26% of them were associated with human chromosomal fragile sites (CFSs) and with a statistically significant enrichment of CFS-associated genes. We found six genes in common with the genic cf-eccDNA profile of SLE patients with DNASE1L3 deficiency, namely Rorb, Mvb12b, Osbpl10, Fto, Tnik and Arhgap10; all of them were specific and present in all human plasma samples, and none of them were associated with CFSs. A not so distinctive genic cf-eccDNA difference involving only seven genes was observed in the case of the Dnase1 group compared to the wild type. In tissue—liver and buffy coat—we did not detect the same genic eccDNA difference observed in the plasma samples. These results point to a specific role of a set of genic eccDNA in plasma from DNase KOs, as well as a relation with CFS genes, confirming the promise of the genic cf-eccDNA in studying diseases and the need for further research on the relationship between eccDNA and CFSs. [ABSTRACT FROM AUTHOR]