Fucoidans are sulphated polysaccharides that can be obtained from brown seaweed and marine invertebrates. They have anti-cancer properties, through their targeting of several signaling pathways and molecular mechanisms within malignant cells. This review describes the chemical structure diversity of fucoidans and their similarity with other molecules such as glycosaminoglycan, which enable them to participation in diverse biological processes. Furthermore, this review summarizes their influence on the development of metastasis and drug resistance, which are the main obstacles to cure cancer. Finally, this article discusses how fucoidans have been used in clinical trials to evaluate their potential synergy with other anti-cancer therapies.

• cancer
• epithelial mesenchymal transition
• fucoidan
• metastasis
• nanoparticles

• Antineoplastic Agents/therapeutic use
• Drug Resistance
• Humans
• Marine Biology
• Neoplasm Metastasis
• Neoplasms/drug therapy
• Polysaccharides/therapeutic use
• Seaweed/chemistry

Identifying the health benefits of phytochemicals is an essential step in drug and functional food development. While many in vitro screening methods have been developed to identify the health effects of phytochemicals, there is still room for improvement because of high cost and low productivity. Therefore, researchers have alternatively proposed in silico methods, primarily based on three types of approaches; utilizing molecular, chemical or ethnopharmacological information. Although each approach has its own strength in analyzing the characteristics of phytochemicals, previous studies have not considered them all together. Here, we apply an integrated in silico analysis to identify the potential health benefits of phytochemicals based on molecular analysis and chemical properties as well as ethnopharmacological evidence. From the molecular analysis, we found an average of 415.6 health effects for 591 phytochemicals. We further investigated ethnopharmacological evidence of phytochemicals and found that on average 129.1 (31%) of the predicted health effects had ethnopharmacological evidence. Lastly, we investigated chemical properties to confirm whether they are orally bio-available, drug available or effective on certain tissues. The evaluation results indicate that the health effects can be predicted more accurately by cooperatively considering the molecular analysis, chemical properties and ethnopharmacological evidence.

• chemical property
• ethnopharmacology
• health benefits
• herbal medicine
• molecular analysis
• network medicine
• phytochemical

• Algorithms
• Data Mining/methods
• Databases, Factual
• Ethnopharmacology/methods
• Humans
• Molecular Structure
• Phytochemicals/adverse effects
• Phytochemicals/chemistry
• Phytochemicals/therapeutic use
• Plant Preparations/adverse effects
• Plant Preparations/chemistry
• Plant Preparations/therapeutic use
• Structure-Activity Relationship
• Systems Integration

• Computational Biology/methods
• Drug Interactions
• Drug-Related Side Effects and Adverse Reactions
• Humans
• Pharmaceutical Preparations
• Pharmacology
• Sensitivity and Specificity

• Cell Proliferation
• Epidermal Growth Factor/metabolism
• ErbB Receptors/metabolism
• Humans
• Logic
• Models, Biological
• Signal Transduction

• R21 GM068146 NIGMS NIH HHS
• R33 GM068146 NIGMS NIH HHS
• U54 CA112970 NCI NIH HHS

As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

Gastric cancer (GC) is the fourth most common cancer worldwide and ranks second in global cancer mortality statistics. Perioperative chemotherapy plays an important role in the management and treatment of advanced stage disease. However, response to chemotherapy varies widely, with some patients presenting no or only minor response to treatment. Hence, chemotherapy resistance is a major clinical problem that impacts on outcome. Unfortunately, to date there are no reliable biomarkers available that predict response to chemotherapy before the start of the treatment, or that allow modification of chemotherapy resistance. MicroRNAs (miRNAs) could provide an answer to this problem. miRNAs are involved in the initiation and progression of a variety of cancer types, and there is evidence that miRNAs impact on resistance towards chemotherapeutic drugs as well. This current review aims to provide an overview about the potential clinical applicability of miRNAs as biomarkers for chemoresistance in GC. The authors focus in this context on the potential of miRNAs to predict sensitivity towards different chemotherapeutics, and on the potential of miRNAs to modulate sensitivity and resistance towards chemotherapy in GC.

• MicroRNAs
• Biomarker
• Chemoresistance
• Gastric cancer
• Diagnostic
• Therapeutic

• Animals
• Antineoplastic Agents/therapeutic use
• Biomarkers, Tumor/genetics
• Drug Resistance, Neoplasm/genetics
• Gene Expression Regulation, Neoplastic
• Genetic Testing
• Genetic Therapy/methods
• Humans
• MicroRNAs/genetics
• Patient Selection
• Precision Medicine
• Predictive Value of Tests
• Stomach Neoplasms/genetics
• Stomach Neoplasms/pathology
• Stomach Neoplasms/therapy
• Treatment Outcome

This study computationally determines the contribution of clinicopathologic factors correlated with 5-year survival in oral squamous cell carcinoma (OSCC) patients primarily treated by surgical operation (OP) followed by other treatments. From 2004 to 2010, the program enrolled 493 OSCC patients at the Kaohsiung Medical Hospital University. The clinicopathologic records were retrospectively reviewed and compared for survival analysis. The Apriori algorithm was applied to mine the association rules between these factors and improved survival. Univariate analysis of demographic data showed that grade/differentiation, clinical tumor size, pathology tumor size, and OP grouping were associated with survival longer than 36 months. Using the Apriori algorithm, multivariate correlation analysis identified the factors that coexistently provide good survival rates with higher lift values, such as grade/differentiation = 2, clinical stage group = early, primary site = tongue, and group = OP. Without the OP, the lift values are lower. In conclusion, this hospital-based analysis suggests that early OP and other treatments starting from OP are the key to improving the survival of OSCC patients, especially for early stage tongue cancer with moderate differentiation, having a better survival (>36 months) with varied OP approaches.