Identification of the Diagnostic Biomarker VIPR1 in Hepatocellular Carcinoma Based on Machine Learning Algorithm

Synthesizing network pharmacology, bioinformatics, and in vitro experimental verification to screen candidate targets of Salidroside for mitigating Alzheimer's disease

Alzheimer's disease (AD) is a neurological disorder leading to cognitive deficits. Salidroside (Sal), a primary bioactive ingredient extracted from the roots of Rhodiola rosea L., has potent neuroprotective effects in AD. However, studies on potential targets for Sal-anchored AD are limited. In this study, we combined network pharmacology, bioinformatics, and experimental validation to identify potential targets of Sal treating AD. First, we screened 10 pyroptosis-related genes (PRGs) in Sal and AD using public databases. Then, we used Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes enrichment analysis to explore the biological functions of the shared PRGs (Sal and AD). This finding exhibited that pathways linked to inflammation, like the nucleotide oligomerization domain (NOD)-like receptors signaling pathway, are important for Sal to help fight AD. The GeneMANIA functional results subsequently revealed an association between AD and the processes of inflammasome complex and inflammatory response. Additionally, nine hub genes were identified in the protein-protein interaction network of these shared PRGs. Subsequent analysis of the genes and phenotypes confirmed that these nine hub genes were directly correlated with AD. Subsequently, an in vitro AD model was created using rat adrenal pheochromocytoma cell line (PC12) cells induced by amyloid β-peptide (Aβ) 25-35 (20 µM). Sal significantly reduced the pyroptosis caused by Aβ 25-35 in PC12 cells and decreased the expression levels of IL-1β, CASP1, IL-18, PYCARD, and NLRP3. Furthermore, molecular docking and molecular dynamics simulations confirmed that Sal could stably bind to NLRP3. Druggability analysis revealed that Sal had excellent druggability. These results demonstrated that Sal could alleviate AD by targeting IL-1β, CASP1, IL-18, PYCARD, and NLRP3 to regulate the NLRP3-mediated pyroptosis signaling pathway.

Predicting hepatocellular carcinoma survival with artificial intelligence

Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfactory. The aim of this research was to evaluate the ability of machine learning (ML) methods in predicting the survival probability of HCC patients. The study retrospectively analyzed cases of patients with stage 1-4 HCC. Demographic, clinical, pathological, and laboratory data served as input variables. The researchers employed various feature selection techniques to identify the key predictors of patient mortality. Additionally, the study utilized a range of machine learning methods to model patient survival rates. The study included 393 individuals with HCC. For early-stage patients (stages 1-2), the models reached recall values ​​of up to 91% for 6-month survival prediction. For advanced-stage patients (stage 4), the models achieved accuracy values ​​of up to 92% for 3-year overall survival prediction. To predict whether patients are ex or not, the accuracy was 87.5% when using all 28 features without feature selection with the best performance coming from the implementation of weighted KNN. Further improvements in accuracy, reaching 87.8%, were achieved by applying feature selection methods and using a medium Gaussian SVM. This study demonstrates that machine learning techniques can reliably predict survival probabilities for HCC patients across all disease stages. The research also shows that AI models can accurately identify a high proportion of surviving individuals when assessing various clinical and pathological factors.

Cancer Biomarkers and Precision Oncology: A Review of Recent Trends and Innovations

The discovery of cancer-specific biomarkers has resulted in major advancements in the field of cancer diagnostics and therapeutics, therefore significantly lowering cancer-related morbidity and mortality. Cancer biomarkers can be generally classified as prognostic biomarkers that predict specific disease outcomes and predictive biomarkers that predict disease response to targeted therapeutic interventions. As research in the area of predictive biomarkers continues to grow, precision medicine becomes far more integrated in cancer treatment. This article presents a general overview on the most recent advancements in the area of cancer biomarkers, immunotherapy, artificial intelligence, and pharmacogenomics of the Middle East.

Endocrine and cellular physiology and pathology of the insulin-like growth factor acid-labile subunit

The acid-labile subunit (ALS) of the insulin-like growth factor (IGF) binding protein (IGFBP) complex, encoded in humans by IGFALS, has a vital role in regulating the endocrine transport and bioavailability of IGF-1 and IGF-2. Accordingly, ALS has a considerable influence on postnatal growth and metabolism. ALS is a leucine-rich glycoprotein that forms high-affinity ternary complexes with IGFBP-3 or IGFBP-5 when they are occupied by either IGF-1 or IGF-2. These complexes constitute a stable reservoir of circulating IGFs, blocking the potentially hypoglycaemic activity of unbound IGFs. ALS is primarily synthesized by hepatocytes and its expression is lower in non-hepatic tissues. ALS synthesis is strongly induced by growth hormone and suppressed by IL-1β, thus potentially serving as a marker of growth hormone secretion and/or activity and of inflammation. IGFALS mutations in humans and Igfals deletion in mice cause modest growth retardation and pubertal delay, accompanied by decreased osteogenesis and enhanced adipogenesis. In hepatocellular carcinoma, IGFALS is described as a tumour suppressor; however, its contribution to other cancers is not well delineated. This Review addresses the endocrine physiology and pathology of ALS, discusses the latest cell and proteomic studies that suggest emerging cellular roles for ALS and outlines its involvement in other disease states.

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.