The Repertoire of Glycan Alterations and Glycoproteins in Human Cancers

A Differential Transcriptional Regulome Approach to Unpack Cancer Biology: Insights on Renal Cell Carcinoma Subtypes

Cancer research calls for new approaches that account for the regulatory complexities of biology. We present, in this study, the differential transcriptional regulome (DIFFREG) approach for the identification and prioritization of key transcriptional regulators and apply it to the case of renal cell carcinoma (RCC) biology. Of note, RCC has a poor prognosis and the biomarker and drug discovery studies to date have tended to focus on gene expression independent from mutations and/or post-translational modifications. DIFFREG focuses on the differential regulation between transcription factors (TFs) and their target genes rather than differential gene expression and integrates transcriptome profiling with the human transcriptional regulatory network to analyze differential gene regulation between healthy and RCC cases. In this study, RNA-seq tissue samples (n = 1020) from the Cancer Genome Atlas (TCGA), including healthy and tumor subjects, were integrated with a comprehensive human TF-gene interactome dataset (1122603 interactions between 1289 TFs and 25177 genes). Comparative analysis of DIFFREG profiles, consisting of perturbed TF-gene interactions, from three common subtypes (clear cell RCC, papillary RCC and chromophobe RCC) revealed subtype-specific alterations, supporting the hypothesis that these signatures in the transcriptional regulome profiles may be considered potential biomarkers that may play an important role in elucidating the molecular mechanisms of RCC development and translating knowledge about the genetic basis of RCC into the clinic. In addition, these indicators may help oncologists make the best decisions for diagnosis and prognosis management.

HS, an Ancient Molecular Recognition and Information Storage Glycosaminoglycan, Equips HS-Proteoglycans with Diverse Matrix and Cell-Interactive Properties Operative in Tissue Development and Tissue Function in Health and Disease

Heparan sulfate is a ubiquitous, variably sulfated interactive glycosaminoglycan that consists of repeating disaccharides of glucuronic acid and glucosamine that are subject to a number of modifications (acetylation, de-acetylation, epimerization, sulfation). Variable heparan sulfate chain lengths and sequences within the heparan sulfate chains provide structural diversity generating interactive oligosaccharide binding motifs with a diverse range of extracellular ligands and cellular receptors providing instructional cues over cellular behaviour and tissue homeostasis through the regulation of essential physiological processes in development, health, and disease. heparan sulfate and heparan sulfate-PGs are integral components of the specialized glycocalyx surrounding cells. Heparan sulfate is the most heterogeneous glycosaminoglycan, in terms of its sequence and biosynthetic modifications making it a difficult molecule to fully characterize, multiple ligands also make an elucidation of heparan sulfate functional properties complicated. Spatio-temporal presentation of heparan sulfate sulfate groups is an important functional determinant in tissue development and in cellular control of wound healing and extracellular remodelling in pathological tissues. The regulatory properties of heparan sulfate are mediated via interactions with chemokines, chemokine receptors, growth factors and morphogens in cell proliferation, differentiation, development, tissue remodelling, wound healing, immune regulation, inflammation, and tumour development. A greater understanding of these HS interactive processes will improve therapeutic procedures and prognoses. Advances in glycosaminoglycan synthesis and sequencing, computational analytical carbohydrate algorithms and advanced software for the evaluation of molecular docking of heparan sulfate with its molecular partners are now available. These advanced analytic techniques and artificial intelligence offer predictive capability in the elucidation of heparan sulfate conformational effects on heparan sulfate-ligand interactions significantly aiding heparan sulfate therapeutics development.

Overview of omics biomarkers in pituitary neuroendocrine tumors to design future diagnosis and treatment strategies

Pituitary neuroendocrine tumors (PitNETs) are the second most common type of intracranial neoplasia. Since their manifestation usually causes hormone hypersecretion, effective management of PitNETs is indisputably necessary. Most of the non-functioning PitNETs pose a real challenge in diagnosis as they grow without giving any signs. Despite the good response of prolactinomas to dopamine agonist therapy, some of these tumors persist or recur; also, about 20% are resistant and 10% behave aggressively. The silent corticotropinomas may not cause symptoms until the tumor mass causes a complication. In somatotropinomas, the possibility of recurrence after transsphenoidal resection is more common in pediatric patients than in adult patients. Therefore, detection of tumors at early stages or identification of recurrence and remission after transsphenoidal surgery would allow wiser management of the disease. Extensive studies have been performed to uncover potential signatures that can be used for preventive diagnosis and/or prognosis of PitNETs as well as for targeted therapy. These molecular signatures at multiple biological levels hold promise for the convergence of preventive approaches and patient-centered disease management and offer potential therapeutic strategies. In this review, we provide an overview of the omics-based biomarker research and highlight the multi-omics signatures that have been proposed as pitNET biomarkers. In addition, understanding the multi-omics data integration of current biomarker discovery strategies was discussed in terms of preventive, predictive, and personalized medicine. The topics discussed in this review will help to develop broader visions for pitNET research, diagnosis, and therapy, particularly in the context of personalized medicine.

Current State of "Omics" Biomarkers in Pancreatic Cancer

Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different "omics" levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.