Colorectal cancer (CRC), like all other cancers, results from genetic and epigenetic alterations of the genome. The mechanisms leading to epigenetic alterations include DNA methylation, histone modifications, and small non-coding RNAs. As shown in many studies, some histone modifications such as acetylation, methylation, and phosphorylation are reported to be altered in CRC. Since these epigenetic alterations are reversible, they can be targeted as a strategy for CRC treatment. Numerous studies demonstrate the effects of molecules (both natural and synthetic) as inhibitors of enzymes responsible for histone acetylation, methylation, and phosphorylation in CRC cell lines. Some of these molecules have reached clinical trial stages. Vorinostat and belinostat, as histone deacetylase inhibitors; pinometostat and ribavirin, as histone methyltransferase inhibitors; and staurosporine and barasertib, which target histone phosphorylation, are among the promising epigenetic modifiers targeting histone alterations. Some of these modifiers can be used alone or in combination with other anticancer drugs or radiotherapy to increase efficacy. This review aims to identify molecules that target enzymes responsible for altering acetylation, methylation, and phosphorylation of histones in CRC.

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.

Systemic light chain (AL) amyloidosis is a rare and potentially fatal disease characterized by the abnormal deposition of homogeneous, amorphous amyloid proteins in tissues and organs. This deposition leads to varying degrees of structural and functional abnormalities, ultimately causing organ dysfunction and failure. The disease often involves multiple systems and organs, including the heart, kidneys, gastrointestinal tract, liver, and nervous system, with cardiac and renal involvement being the most common. Due to its rarity, multisystem involvement, and rapid progression, a comprehensive summary of the diagnosis and treatment of AL amyloidosis is crucial for guiding clinical practice and advancing research in this field. This article reviews the progress in diagnosis and discusses future treatment of AL amyloidosis, aiming to provide expanded options for clinical practice.

The association between chronic inflammation and cancer has reshaped our understanding of tumorigenesis and cancer therapy. Inflammatory responses can both promote and suppress cancer, depending on the context and timing. Key molecular players, such as nuclear factor kappa-light-chain-enhancer of activated B cells, interleukin-6, signal transducer and activator of transcription 3, and a variety of immune cell types, including tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, orchestrate an environment conducive to tumor survival, angiogenesis, metastasis, and immune evasion. In recent years, immunotherapy, particularly immune checkpoint inhibitors, has revolutionized cancer treatment. However, its success varies across tumor types and patients, underscoring the need to understand the tumor microenvironment and inflammatory context. This review examines the mechanistic underpinnings of inflammation-driven cancer, discusses translational research efforts targeting inflammatory pathways, and explores clinical applications, including the integration of immunotherapy with anti-inflammatory agents and biomarkers for personalized treatment. Future directions in the field include the application of artificial intelligence, microbiome research, single-cell technologies, and gene editing tools to further tailor therapies and overcome resistance mechanisms.

Artificial intelligence (AI) is transforming the diagnosis, treatment, monitoring, and research of soft tissue disorders, which include muscles, tendons, ligaments, fascia, nerves, and blood vessels. Traditional diagnostic methods often rely on imaging, histopathology, and clinical evaluation, which can be time-consuming and prone to human error. This review aims to explore the impact of AI on enhancing soft tissue care. The review examines the application of deep learning algorithms in medical imaging, pathology, predictive analytics, and treatment planning. It also evaluates AI’s role in monitoring and rehabilitation, as well as its contributions to research in soft tissue disorders. AI significantly improves the accuracy of medical imaging analysis, facilitating the detection of abnormalities such as tumors and tears. AI-powered pathology tools automate slide analysis, enhancing diagnostic consistency and efficiency. Predictive analytics enable early risk assessment and personalized patient management. In surgical contexts, AI supports preoperative simulations and robotic-assisted procedures, leading to improved outcomes. Additionally, AI enhances patient monitoring through wearable devices and telemedicine. The integration of AI into soft tissue diagnostics and therapeutics presents transformative potential for personalized and efficient healthcare. However, challenges related to data security, algorithm bias, interpretability, and ethical considerations must be addressed. Overall, AI holds promise for improving patient outcomes and advancing medical science in the field of soft tissue disorders.

It is established that administration of the COVID-19 vaccine may be associated with an exaggerated immune response leading to enlargement of several lymph nodes. Although most cases are benign and self-limiting, some have been reported in the literature as B-cell or T-cell lymphomas, with no reported cases of chronic lymphocytic leukaemia (CLL). We report two cases of follicular lymphoma and CLL that occurred a few weeks after COVID-19 vaccination. Case 1 is a 48-year-old woman who noticed two significantly palpable masses, one in each axilla, 48 h after receiving the first dose of the Pfizer-BioNTech BNT162b2 vaccine for COVID-19. Seven days later, she noticed another mass on the right side of her neck, which was biopsied within 48 hours. Case 2 is a 75-year-old man who presented with localized swellings in the axilla and on the neck, noted 24 h after the first dose of the Moderna messenger RNA-1273 COVID-19 vaccine. Neither patient reported any constitutional or associated symptoms. Surgical biopsy of the axillary lymph node in case 1 revealed a non-Hodgkin lymphoma, confirmed via immunohistochemistry as CD20-positive B-cell follicular lymphoma. The patient also had multiple pre- and para-aortic lymph nodes. In case 2, complete blood count showed lymphocytosis (total white blood cell – 148 × 109/L; lymphocyte differential – 92%), while peripheral blood film showed lymphocytosis with a predominance of small, mature-looking lymphocytes, both suggesting CLL. Although requested, immunophenotyping and molecular testing were not performed due to patient-related challenges. Although a chance occurrence is possible, lymphoid malignancies should be considered a strong differential. The vaccination history of patients presenting with clinical manifestations suggestive of a lymphoid malignancy should be thoroughly investigated, while ruling out other possible differentials such as a benign, self-limiting inflammatory process.

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.

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.