Breast tumor metastasis: analysis via proteomic profiling

The ability to predict the metastatic behavior of a patient’s cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.     

Neuroproteomics and microRNAs studies in multiple sclerosis: transforming research and clinical knowledge in biomarker research

Multiple sclerosis (MS) is a complex disease characterized by extensive phenotypic variability. Biomarkers to capture the different aspects of MS heterogeneity, and to help make a diagnosis and monitor disease progression, while providing insights into etiopathogenesis and response to treatment, are urgently needed. Omics technologies and research efforts with microRNAs have provide unparalleled opportunities for exploring altered protein profiles associated with molecular mechanisms of disease, substantially expanding the list of candidate biomarkers for MS. This review presents evidence from proteomic studies that have focused on identification of biomarkers released in biofluids as a result of the different pathophysiological processes of MS. Also discussed is the emerging role of miRNAs as complementary biomarkers related to cellular processes occurring in MS patients. Also provided is an overview of candidate biomarkers that have been proposed for elucidating pathophysiological processes and disease activity and for guiding clinical diagnosis and/or therapeutic interventions in MS.     

Partial Distributional Canonical Discriminant Analysis

A canonical discriminant technique is proposed which is unrelated to particular distributional assumptions. This approach allows for a data generated choice between ordered, partially ordered and unordered outcomes and remains identifiable in each dimension of the canonical model. The concepts of allocatability and distinguishability of categories are investigated. Maximum likelihood parameter estimation is conducted and inferential tests for the assessment of the dimensionality of the canonical model are derived. The model is applied to the perioperative prediction of risk of death from peritonitis, using real data.     

A 20-Gene Set Predictive of Progression to Severe Dengue

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Evaluation and characterization of HSPA5 (GRP78) expression profiles in normal individuals and cancer patients with COVID-19

HSPA5 (BiP, GRP78) has been reported as a potential host-cell receptor for SARS-Cov-2, but its expression profiles on different tissues including tumors, its susceptibility to SARS-Cov-2 virus and severity of its adverse effects on malignant patients are unclear. In the current study, HSPA5 has been found to be expressed ubiquitously in normal tissues and significantly increased in 14 of 31 types of cancer tissues. In lung cancer, mRNA levels of HSPA5 were 253-fold increase than that of ACE2. Meanwhile, in both malignant tumors and matched normal samples across almost all cancer types, mRNA levels of HSPA5 were much higher than those of ACE2. Higher expression of HSPA5 significantly decreased patient overall survival (OS) in 7 types of cancers. Moreover, systematic analyses found that 7.15% of 5,068 COVID-19 cases have malignant cancer coincidental situations, and the rate of severe events of COVID-19 patients with cancers present a higher trend than that for all COVID-19 patients, showing a significant difference (33.33% vs 16.09%, p<0.01). Collectively, these data imply that the tissues with high HSPA5 expression, not low ACE2 expression, are susceptible to be invaded by SARS-CoV-2. Taken together, this study not only indicates the clinical significance of HSPA5 in COVID-19 disease and cancers, but also provides potential clues for further medical treatments and managements of COVID-19 patients.