Genomic instability-related twelve-microRNA signatures for predicting the prognosis of gastric cancer

Is Chronic Ice Water Ingestion a Risk Factor for Gastric Cancer Development? An Evidence-Based Hypothesis Focusing on East Asian Populations

This article introduces a novel risk factor for gastric cancer (GC) by analyzing available epidemiological data from East Asian populations. A significantly higher age-standardized GC rate was observed in Japanese and Korean populations than in Chinese populations, despite nearly identical ethnicity, food habits, obesity rates, and alcohol consumption. Given the pivotal role of environmental factors in GC development, particularly for the intestinal type, a thorough evaluation of the lifestyles of these three populations was conducted to identify commonalities and disparities. It was observed that Japanese and Korean individuals prefer consuming ice water, while Chinese individuals tend to drink warm water, potentially influenced by traditional Chinese medicine disciplines. Considering the key features of GC development, a literature review was conducted to investigate the mechanisms through which the consumption of ice water might contribute to GC initiation and progression. Mechanistically, exposing gastric cells to hypothermia can increase the risk of carcinogenesis through multiple pathways. This includes the promotion of Helicobacter pylori colonization, prolonged gastric inflammation, and mitochondrial dysfunction in gastric cells. Furthermore, drinking ice water can enhance the survival, proliferation, and invasion of GC cells by releasing cold shock proteins, increasing gastric acid secretion, and delaying gastric emptying. Additionally, hypothermia can boost the immune evasion of cancer cells by weakening the antitumor immune system and activating different components of the tumor microenvironment. This paper also explores the association between exposure of GC cells to hypothermia and current insights into cancer hallmarks. These findings may partially elucidate the higher incidence of GC in Japanese and Korean populations and provide a clue for future experimental studies.Graphical abstract available for this article.

Molecular Morbidity Score-Can MicroRNAs Assess the Burden of Disease?

Multimorbidity refers to the presence of two or more chronic diseases and is associated with adverse outcomes for patients. Factors such as an ageing population have contributed to a rise in prevalence of multimorbidity globally; however, multimorbidity is often neglected in clinical guidelines. This is largely because patients with multimorbidity are systematically excluded from clinical trials. Accordingly, there is an urgent need to develop novel biomarkers and methods of prognostication for this cohort of patients. The hallmarks of ageing are now thought to potentiate the pathogenesis of multimorbidity. MicroRNAs are small, regulatory, noncoding RNAs which have been implicated in the pathogenesis and prognostication of numerous chronic diseases; there is a substantial body of evidence now implicating microRNA dysregulation with the different hallmarks of ageing in the aetiology of chronic diseases. This article proposes using the hallmarks of ageing as a framework to develop a panel of microRNAs to assess the prognostic burden of multimorbidity. This putative molecular morbidity score would have many potential applications, including assessing the efficacy of clinical interventions, informing clinical decision making and facilitating wider inclusion of patients with multimorbidity in clinical trials.

Identification of a novel apoptosis-related genes signature to improve gastric cancer prognosis prediction

Dysregulation of apoptosis occurs in different types of malignant tumors and is likely to influence the tumor evolution, as well as clinical prognosis. However, the limited number of studies investigating the predictive power of apoptosis-related genes (ARGs) in gastric cancer indicates a gap in the current research. 174 ARGs who differentially expressed were screened using public databases, including the Gene Expression Omnibus and the Molecular Signatures Database. Univariate and LASSO regression analyses were rigorous approaches to recognize the 12 optimal genes (CTHRC1, PDGFRL, VCAN, GJA1, LOX, UPP1, ANGPT2, CRIM1, HIF1A, APOD, RNase1, and ID1) that make up the prognostic risk model. Molecular mutations, related signaling pathways, and immune system characteristics in different subgroups defined by the risk model were analyzed using different R packages. Moreover, based on the database of Genomics of Drug Sensitivity in Cancer, chemotherapy sensitivity was predicted among the risk subgroups. As a result, there were differences in mutation profiles, signaling pathways, and infiltrated immune cells between patients in various risk groups. Moreover, the low-risk group displayed greater sensitivity to chemotherapy than the high-risk group. Risk model provided a better prognostic value than the T, N, and M stages, according to the receiver operating characteristic curve. Finally, in a nomogram, the risk model and clinical factors were combined to visualize the survival rates of patients with GC. In response to the differential expression of apoptosis-related genes, a novel model for predicting the prognosis of GC patients was developed. This model may be highly valuable for guiding doctors to deliver treatment plans tailored to the need of patients with GC.

Aptamer-functionalized MOFs and AI-driven strategies for early cancer diagnosis and therapeutics

Metal-Organic Frameworks (MOFs) have exceptional inherent properties that make them highly suitable for diverse applications, such as catalysis, storage, optics, chemo sensing, and biomedical science and technology. Over the past decades, researchers have utilized various techniques, including solvothermal, hydrothermal, mechanochemical, electrochemical, and ultrasonic, to synthesize MOFs with tailored properties. Post-synthetic modification of linkers, nodal components, and crystallite domain size and morphology can functionalize MOFs to improve their aptamer applications. Advancements in AI and machine learning led to the development of nonporous MOFs and nanoscale MOFs for medical purposes. MOFs have exhibited promise in cancer therapy, with the successful accumulation of a photosensitizer in cancer cells representing a significant breakthrough. This perspective is focused on MOFs' use as advanced materials and systems for cancer therapy, exploring the challenging aspects and promising features of MOF-based cancer diagnosis and treatment. The paper concludes by emphasizing the potential of MOFs as a transformative technology for cancer treatment and diagnosis.