LAUSR

Cancer remains one of the leading causes of global morbidity and mortality. As an enormous health burden worldwide, cancer touches every geographic region and is growing at an alarming pace. It is projected that 21.7 million new cases and 13.0 million deaths will occur in 2030 alone. To tackle this vicious disease effectively, a concerted effort by both research and healthcare communities is required to yield significant advances in cancer research and therapy. With the recent developments of high-throughput biotechnologies for genomes, proteomes, and transcriptomes, it is essential to develop innovative computational methods for comprehensive data analysis to improve our understanding of cancer initiation, progression, and metastasis. Therefore, novel computational and statistical methods are needed to analyze each type of omics data, to integrate multiple types of data across platforms, and to discover potential cancer-related biomarkers that can shed light on early detection, monitor disease progression, and eventually facilitate the development of personalized therapy of cancer. The articles contained in the present issue include basic scientific studies focused on novel computational approaches and tools to analyze high-throughput multiplatform cancer data. In addition, image-based biomarkers were also discussed for early detection of the disease. Diagnosis of tumor and definition of tumor borders intraoperatively are primarily based on the visualization modalities. However, intraoperative fast histopathology is often not sufficient. The contribution by A. Kamen et al. in " Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery " proposes an automated endomicroscopic tissue differentiation algorithm based on the machine learning theory. This algorithm offers a useful component to an intraoperative pathology system for guiding the resection procedure based on cellular level information. Breast cancer is one of the most commonly diagnosed cancers in women all over the world. Osteopontin (OPN) is overexpressed in breast cancers, while its clinical and prognostic significance remain unclear. The contribution by C. Hao et al. in " Prognostic Value of Osteopontin Splice Variant-c Expression in Breast Cancers: A Meta-Analysis " proposes assessing the prognostic value of OPN, especially its splice variants, in breast cancers from eligible studies concerning the OPN and OPN-c expression. It concludes that the high level of OPN-c is suggested to be more reliably associated with poor survival in breast cancer patients. Apolipoprotein E (ApoE) í µí¼€4 allele has been proved to be a risk gene of late-onset Alzheimer's disease. It is very important to look for sensitive and reliable …