Genomics and Epigenomics in the Molecular Biology of Melanoma-A Prerequisite for Biomarkers Studies.

Melanoma is a common and aggressive tumor originating from melanocytes. The increasing incidence of cutaneous melanoma in recent last decades highlights the need for predictive biomarkers studies. Melanoma development is a complex process, involving the interplay of genetic, epigenetic, and environmental factors. Genetic aberrations include BRAF, NRAS, NF1, MAP2K1/MAP2K2, KIT, GNAQ, GNA11, CDKN2A, TERT mutations, and translocations of kinases. Epigenetic alterations involve microRNAs, non-coding RNAs, histones modifications, and abnormal DNA methylations. Genetic aberrations and epigenetic marks are important as biomarkers for the diagnosis, prognosis, and prediction of disease recurrence, and for therapeutic targets. This review summarizes our current knowledge of the genomic and epigenetic changes in melanoma and discusses the latest scientific information.

Diagnostic Accuracy of Machine-Learning Models on Predicting Chemo-Brain in Breast Cancer Survivors Previously Treated with Chemotherapy: A Meta-Analysis.

We performed a meta-analysis of chemo-brain diagnostic, pooling sensitivities, and specificities in order to assess the accuracy of a machine-learning (ML) algorithm in breast cancer survivors previously treated with chemotherapy. We searched PubMed, Web of Science, and Scopus for eligible articles before 30 September 2022. We identified three eligible studies from which we extracted seven ML algorithms. For our data, the χ2 tests demonstrated the homogeneity of the sensitivity's models (χ2 = 7.6987, df = 6, p-value = 0.261) and the specificities of the ML models (χ2 = 3.0151, df = 6, p-value = 0.807). The pooled area under the curve (AUC) for the overall ML models in this study was 0.914 (95%CI: 0.891-0.939) and partial AUC (restricted to observed false positive rates and normalized) was 0.844 (95%CI: 0.80-0.889). Additionally, the pooled sensitivity and pooled specificity values were 0.81 (95% CI: 0.75-0.86) and 0.82 (95% CI: 0.76-0.86), respectively. From all included ML models, support vector machine demonstrated the best test performance. ML models represent a promising, reliable modality for chemo-brain prediction in breast cancer survivors previously treated with chemotherapy, demonstrating high accuracy.

Adenocarcinoma versus pancreatic neuroendocrine tumor - case report.

Pancreatic cancer represents one of the most aggressive types of cancer, resulting in a late diagnosis and rapid death (poor overall survival). After adenocarcinoma (counting almost 80% of cases of pancreatic cancer), the second category, as frequency, is represented by the family of gastroenteropancreatic neuroendocrine tumors (GEP-NETs). Pancreatic cancer is characterized by genetic heterogeneity and may results in different evolution among metastases, which may acquire driver mutations with the ability to transform under the action of several cancer treatments. Here we report a case of a 64-year-old patient diagnosed with pancreatic tumor localized on the body and tail, invasive in the splenic and portal vein, pT3pN0M0 (adenocarcinoma pancreatic cancer), treated with a multimodal approach: surgery (splenectomy and distal pancreatectomy, with suture of the portal vein), chemotherapy, in 2010, that relapsed in 2015, with local recurrence that was resected and distant liver metastases. Immunohistochemistry of the recurrence tumor showed a neuroendocrine transformation of the tumor, with major implications in treatment and prognosis. Computed tomography examination, as well as histopathological and immunohistochemically testing, sustained positive and differential diagnosis.