Chapters authored
Noncoding RNAs in Lung Cancer Angiogenesis
Lung cancer is the major death-related cancer in both men and women, due to late diagnostic and limited treatment efficacy. The angiogenic process that is responsible for the support of tumor progression and metastasis represents one of the main hallmarks of cancer. The role of VEGF signaling in angiogenesis is well‐established, and we summarize the role of semaphorins and their related receptors or hypoxia‐related factors role as prone of tumor microenvironment in angiogenic mechanisms. Newly, noncoding RNA transcripts (ncRNA) were identified to have vital functions in miscellaneous biological processes, including lung cancer angiogenesis. Therefore, due to their capacity to regulate almost all molecular pathways related with altered key genes, including those involved in angiogenesis and its microenvironment, ncRNAs can serve as diagnosis and prognosis markers or therapeutic targets. We intend to summarize the latest progress in the field of ncRNAs in lung cancer and their relation with hypoxia‐related factors and angiogenic genes, with a particular focus on ncRNAs relation to semaphorins.
Part of the book: Physiologic and Pathologic Angiogenesis
i-Biomarker CaDx: Multi-Cancer Early Detection as a Data Science with AI Problem
Cancer is one of the most common and deadly diseases worldwide, claiming millions of lives yearly. Despite significant advances in treatment, the overall survival rate remains low, primarily due to late-stage diagnosis. In the high-throughput, high-dimensional omics data era, Biomedical Knowledge should be combined with Data Science best practices for real progress toward precision and personalized medicine. We intuitively and non-technically formulated the main problems or traps and suggested solutions. To illustrate them, we used our Biomedical Data Science platform, i-Biomarker, and its application to circulating miRNA for personalized Multi-Cancer Early Detection and treatment response monitoring, i-Biomarker CaDx. i-Biomarker combines and automates bioinformatics and Explainable AI/ML pipelines. i-Biomarker CaDx works on 32 types of cancer with 99–100% accuracy and is based on more than 30,000 cases.
Part of the book: Molecular Diagnostics of Cancer