Oral cancer is a public health problem with an incidence of almost 50,000 and a mortality of 10,000 each year in the USA alone. Black raspberries (BRBs) have been shown to inhibit oral carcinogenesis in several preclinical models, but our understanding of how BRB phytochemicals affect the metabolic pathways during oral carcinogenesis remains incomplete. We used a well-established rat oral cancer model to determine potential metabolic pathways impacted by BRBs during oral carcinogenesis. F344 rats were exposed to the oral carcinogen 4-nitroquinoline-1-oxide in drinking water for 14 weeks, then regular drinking water for six weeks. Carcinogen exposed rats were fed a 5% or 10% BRB supplemented diet or control diet for six weeks after carcinogen exposure. RNA-Seq transcriptome analysis on rat tongue, and mass spectrometry and NMR metabolomics analysis on rat urine were performed. We tentatively identified 57 differentially or uniquely expressed metabolites and over 662 modulated genes in rats being fed with BRB. Glycolysis and AMPK pathways were modulated during BRB-mediated oral cancer chemoprevention. Glycolytic enzymes Aldoa, Hk2, Tpi1, Pgam2, Pfkl, and Pkm2 as well as the PKA-AMPK pathway genes Prkaa2, Pde4a, Pde10a, Ywhag, and Crebbp were downregulated by BRBs during oral cancer chemoprevention. Furthermore, the glycolysis metabolite glucose-6-phosphate decreased in BRB-administered rats. Our data reveal the novel metabolic pathways modulated by BRB phytochemicals that can be targeted during the chemoprevention of oral cancer.

Question Collective evidence for the bidirectional association between depression and autoimmune diseases (ADs) is scarce, especially for subgroups of patients with specific ADs. We conducted a meta-analysis to determine the incidence rates and relative risks (RRs) of depression among patients with ADs, and vice versa. Study selection and analysis PubMed, Embase, Web of Science, Ovid, PsycNet and Cochrane were searched up to 10 September 2024. Cohort studies evaluating longitudinal risks between ADs and depression were included. Incidence rates and RRs of depression among patients with ADs and vice versa were pooled. Findings The analysis included 47 studies, involving over 40.77 million participants. The pooled incidence rate of depression among patients with ADs was 6.71% (95% CI 5.10% to 8.77%), with an RR of 1.85 (95% CI 1.57 to 2.19), higher in patients aged over 45 (2.30; 95% CI 1.62 to 3.26) and females (1.88; 95% CI 1.61 to 2.20). Conversely, the pooled incidence rate of ADs among depression was 0.54% (95% CI 0.24% to 1.19%), with an RR of 1.84 (95% CI 1.10 to 3.09). The incidence rate and RRs also varied across subgroups with the highest incidence rate in the musculoskeletal system and connective tissue (1.36; 95% CI 0.50 to 3.63) and RR in the genitourinary system (2.23; 95% CI 1.98 to 2.51). Conclusions This study identified a bidirectional association between depression and ADs, with higher RRs among patients aged over 45 and females. Especially higher risks were also found for specific types of ADs including endocrine, nutritional, and metabolic diseases, genitourinary system, and skin and subcutaneous tissue. PROSPERO registration number CRD42024541053.

Intrinsically disordered proteins (IDPs) participate in many biological processes by interacting with other proteins, including the regulation of transcription, translation, and the cell cycle. With the increasing amount of disorder sequence data available, it is thus crucial to identify the IDP binding sites for functional annotation of these proteins. Over the decades, many computational approaches have been developed to predict protein-protein binding sites of IDP (IDP-PPIS) based on protein sequence information. Moreover, there are new IDP-PPIS predictors developed every year with the rapid development of artificial intelligence. It is thus necessary to provide an up-to-date overview of these methods in this field. In this paper, we collected 30 representative predictors published recently and summarized the databases, features and algorithms. We described the procedure how the features were generated based on public data and used for the prediction of IDP-PPIS, along with the methods to generate the feature representations. All the predictors were divided into three categories: scoring functions, machine learning-based prediction, and consensus approaches. For each category, we described the details of algorithms and their performances. Hopefully, our manuscript will not only provide a full picture of the status quo of IDP binding prediction, but also a guide for selecting different methods. More importantly, it will shed light on the inspirations for future development trends and principles.

Background: Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC). Methods: A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC. Results: We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816–0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716–0.805). Conclusions: These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC.