Metabolic syndrome (MetS) is associated with a plethora of different comorbidities. Exploring its key molecular mechanisms, such as advanced glycation end product and its receptor (AGE/RAGE) pathway, holds great potential. Numerous sources agree that targeting the AGE/RAGE pathway is a potential therapeutic strategy for MetS. However, the regulation of AGE/RAGE by microRNAs (miRNAs) in the context of MetS is still poorly understood. This review aimed to provide a systematic picture of the influence of miRNAs on AGE/RAGE in the context of MetS, with a particular focus on its ligands and receptors. This review achieves this in two ways: through an inductive “bottom-up” approach realized by a classical descriptive literature search, and through a deductive/synthetic “top-down” approach based on carefully selected miRNA profiling studies in MetS and its comorbidities. Although the initial inductive approach allowed the identification of some miRNAs of interest, almost all articles on this topic focus on the regulation of processes exclusively involved in atherogenesis. The new deductive approach has broadened the research horizon: It has enabled the discovery of new promising miRNAs and allowed for ranking different comorbid pathologies in MetS according to the degree of miRNA dysregulation of AGE/RAGE. Thus, in addition to atherosclerosis, significant miRNA dysregulation of AGE/RAGE was also described in MetS, particularly in immune cells, as well as in subcutaneous adipose tissue in obesity. This review, along with the novel approaches to systematizing the data contained therein may contribute to the understanding of MetS pathogenesis and the search for targets for the treatment of MetS.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with marked phenotypic differences observed among its major histological subtypes, adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small cell lung cancer (SCLC), in both clinical presentation and therapeutic response. In recent years, metabolomics has emerged as a powerful tool for studying cancer metabolic reprogramming, providing new insights into the metabolic distinctions among lung cancer subtypes. This review summarizes recent research advances in the metabolomics of ADC, SCC, and SCLC. Studies have revealed that ADC and SCC display distinct metabolic profiles in lipid metabolism, amino acid metabolism, and cell membrane synthesis, while SCLC demonstrates a unique metabolic pattern. Through metabolomic technologies, particularly mass spectrometry and liquid chromatography, it is possible to effectively differentiate lung cancer subtypes and identify potential biomarkers for early diagnosis and personalized treatment. This review also explores the clinical potential of metabolomics in lung cancer, emphasizing its critical role in early diagnosis and subtype stratification. These methodological advances establish a robust foundation for precision oncology paradigms in thoracic malignancies.

Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model.

Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator.

Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9–54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809–0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]).

The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.