Mucin expression in gastric- and gastro-oesophageal signet-ring cell cancer: results from a comprehensive literature review and a large cohort study of Caucasian and Asian gastric cancer

Background: The literature on the prognostic relevance of signet-ring cell (SRC) histology in gastric cancer (GC) is controversial which is most likely related to inconsistent SRC classification based on haematoxylin–eosin staining. We hypothesised that mucin stains can consistently identify SRC-GC and predict GC patient outcome. Methods: We performed a comprehensive literature review on mucin stains in SRC-GC and characterised the mucin expression in 851 Caucasian GC and 410 Asian GC using Alcian Blue (AB)-Periodic Acid-Schiff (PAS), MUC2 (intestinal-type mucin), and MUC5AC (gastric-type mucin). The relationship between mucin expression and histological phenotype [poorly cohesive (PC) including proportion of SRCs, non-poorly cohesive (non-PC), or mucinous (MC)], clinicopathological variables, and patient outcome was analysed. Results: Depending on mucin expression and cut-offs, the positivity rates of SRC-GC reported in the literature varied from 6 to 100%. Patients with MUC2 positive SRC-GC or SRC-GC with (gastro)intestinal phenotype had poorest outcome. In our cohort study, PC with ≥ 10% SRCs expressed more frequently MUC2, MUC5AC, and ABPAS (p < 0.001, p = 0.004 and p < 0.001, respectively). Caucasians with AB positive GC or combined ABPAS-MUC2 positive and MUC5AC negative had poorest outcome (all p = 0.002). This association was not seen in Asian patients. Conclusions: This is the first study to suggest that mucin stains do not help to differentiate between SRC-GC and non-SRC-GC. However, mucin stains appear to be able to identify GC patients with different outcome. To our surprise, the relationship between outcome and mucin expression seems to differ between Caucasian and Asian GC patients which warrants further investigations

Assessment of Synergistic Contribution of Histone Deacetylases in Prognosis and Therapeutic Management of Sarcoma

Sarcomas are a rare group of neoplasms with a mesenchymal origin that are mainly characterized by the abnormal growth of connective tissue cells. The standard treatment for local control of sarcomas includes surgery and radiation, while for adjuvant and palliative therapy, chemotherapy has been strongly recommended. Despite the availability of multimodal therapies, the survival rate for patients with sarcoma is still not satisfactory. In recent decades, there has been a considerable effort to overcome chemotherapy resistance in sarcoma cells. This has led to the investigation of more cellular compounds implicated in gene expression and transcription processes. Furthermore, it has been discovered that histone acetylation/deacetylation equilibrium is affected in carcinogenesis, leading to a modified chromatin structure and therefore changes in gene expression. In addition, histone deacetylase inhibition is found to play a key role in limiting the tumor burden in sarcomas, as histone deacetylase inhibitors act on well-described oncogenic signaling pathways. Histone deacetylase inhibitors disrupt the increased cell motility and invasiveness of sarcoma cells, undermining their metastatic potential. Moreover, their activity on evoking cell arrest has been extensively described, with histone deacetylase inhibitors regulating the reactivation of tumor suppressor genes and induction of apoptosis. Promoting autophagy and increasing cellular reactive oxygen species are also included in the antitumor activity of histone deacetylase inhibitors. It should be noted that many studies revealed the synergy between histone deacetylase inhibitors and other drugs, leading to the enhancement of an antitumor effect in sarcomas. Therefore, there is an urgent need for therapeutic interventions modulated according to the distinct clinical and molecular characteristics of each sarcoma subtype. It is concluded that a better understanding of histone deacetylase and histone deacetylase inhibitors could provide patients with sarcoma with more targeted and efficient therapies, which may contribute to significant improvement of their survival potential. © 2020, Springer Nature Switzerland AG

Hysteresis identification using nonlinear state-space models

Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinear state-space models to identify hysteresis dynamics. This approach is shown to provide a general framework to hysteresis identification, featuring flexibility and parsimony of representation. Nonlinear model terms are constructed as a multivariate polynomial in the state variables, and parameter estimation is performed by minimising weighted least-squares cost functions. Technical issues, including the selection of the model order and the polynomial degree, are discussed, and model validation is achieved in both broadband and sine conditions. The study is carried out numerically by exploiting synthetic data generated via the Bouc-Wen equations