Maria Rosaria De Miglio

Background and Objectives: Major Depressive Disorder (MDD) and Bipolar Disorder (BD) lack objective molecular stratification despite partial clinical overlap, particularly during depressive phases. This cross-sectional study explored whether coordinated peripheral biomarker patterns could be identified using an integrated multivariate analytical framework. Materials and Methods: A total of 151 participants (MDD n = 41; BD n = 40; HC (healthy controls) n = 70) were profiled for 42 blood-derived parameters including composite inflammatory indices, hematological markers, trace elements measured by ICP-MS, and circulating BDNF and NLRP3 quantified by ELISA. Data were analyzed using univariate testing, unsupervised dimensionality reduction (PCA, t-SNE), and supervised classification (PLS-DA with cross-validation and permutation testing). Results: Thirty-seven of 42 parameters showed significant inter-group differences (p < 0.05). Circulating NLRP3 concentrations were markedly reduced in both psychiatric groups compared with HC. Composite inflammatory indices (NLR, SIRI, SII) were elevated in MDD. Zinc levels were modestly reduced, while manganese levels were increased in psychiatric cohorts. BDNF showed lower concentrations in MDD and higher concentrations in BD relative to HC. Cross-validated PLS-DA classification for psychiatric disorder vs. controls yielded an accuracy of 89.4% (AUC-ROC 0.947), with permutation testing indicating performance above chance. However, the sample-to-variable ratio and exploratory design warrant cautious interpretation. Conclusions: Multidomain peripheral biomarker profiling identified coordinated biochemical differences across diagnostic groups. These findings suggest the presence of multidimensional peripheral signatures associated with mood disorders within an exploratory framework.

Background: An increasing number of studies have shown that ferroptosis is related to the initiation and development of small cell lung cancer (SCLC). The systematic review aimed to summarize the characteristics of ferroptosis from its pathogenetic role to translational therapeutic implications in SCLC. Methods: This systematic review, registered in PROSPERO (CRD420251090058), followed PRISMA 2020 guidelines. Comprehensive research of PubMed, Scopus, and Web of Science was performed for studies published between January 2010 and July 2025 investigating ferroptosis mechanisms, genetic or pharmacological modulation, or molecular profiling in SCLC. Two reviewers independently performed data extraction and quality assessment. Results: Nineteen preclinical studies met the inclusion criteria. Key regulators included solute carrier family 7 member 11 (SLC7A11), glutathione peroxidase 4 (GPX4), ferroptosis suppressor protein 1 (FSP1), and acyl-CoA synthetase long chain family member 4 (ACSL4). The molecular subtypes of SCLC, achaete-scute homolog 1 (ASCL1), neuronal differentiation 1 (NEUROD1), POU class 2 homeobox 3 (POU2F3), and Yes1 associated transcriptional regulator (YAP1) exhibit differential ferroptosis gene expressions, influencing therapeutic responsiveness. Non-neuroendocrine subtypes are more ferroptosis-prone, whereas neuroendocrine variants display enhanced antioxidant defenses. Ferroptosis induction also promotes immune activation through stimulator of interferon genes (STING)-mediated CD8+ T-cell recruitment. Conclusions: Ferroptosis constitutes a promising therapeutic axis in SCLC. Integrating ferroptosis biomarkers into molecular stratification frameworks could refine patient selection and support precision oncology strategies, warranting further translational and clinical validation.

Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers, largely due to late diagnosis and the lack of reliable non-invasive biomarkers. Altered trace element homeostasis has been implicated in tumor biology and systemic inflammation, but comprehensive metallomic profiling in PDAC is still limited. Methods: Using inductively coupled plasma mass spectrometry (ICP-MS), we quantified 20 serum and 15 urinary metals in 71 PDAC patients and 69 matched controls. Statistical analyses included univariate Wilcoxon testing, correlation with systemic inflammatory indices (NLR, MLR, SIRI, AISI, HGB/RDW, PCT), and multivariate chemometric modeling (PCA-LDA). K-means clustering was applied to identify patient subgroups with distinct biochemical signatures. Results: PDAC patients showed significantly elevated urinary antimony, chromium, cadmium, and vanadium, whereas controls exhibited higher serum selenium, zinc, barium, vanadium, and cobalt (all p < 10⁻⁵). The PCA-LDA model achieved 99% classification accuracy (Monte Carlo cross-validation, 1000 iterations), highlighting complementary diagnostic contributions of serum and urinary profiles. Serum selenium was inversely associated with SIRI and NLR, while urinary cobalt correlated positively with NLR. Clustering revealed three PDAC subgroups with different inflammatory and metallomic patterns, suggesting underlying biological heterogeneity. Conclusions: PDAC is characterized by opposite serum-urine metal signatures, indicating altered absorption-excretion dynamics. Selenium depletion may represent a protective biomarker, whereas urinary cobalt excretion reflects systemic inflammation. This integrative ICP-MS–chemometric approach provides a promising diagnostic tool for improving early detection and patient stratification in clinical practice.

Background: Prostate cancer (PCa) remains one of the most prevalent malignancies in men, with diagnostic challenges arising from the limited specificity of current biomarkers, like PSA. Improved stratification tools are essential to reduce overdiagnosis and guide personalized patient management. Objective: This study aimed to identify and validate clinical and hematological biomarkers capable of differentiating PCa from benign prostatic hyperplasia (BPH) and precancerous lesions (PL) using univariate and multivariate statistical methods. Methods: In a cohort of 514 patients with suspected PCa, we performed a univariate analysis (Kruskal–Wallis and ANOVA) with preprocessing via adaptive Box–Cox transformation and missing value imputation through probabilistic principal component analysis (PPCA). LASSO regression was used for variable selection and classification. An ROC curve analysis assessed diagnostic performance. Results: Five variables—age, PSA, Index %, hemoglobin (HGB), and the International Index of Erectile Function (IIEF)—were consistently significant across univariate and multivariate analyses. The LASSO regression achieved a classification accuracy of 70% and an AUC of 0.74. Biplot and post-hoc analyses confirmed partial separation between PCa and benign conditions. Conclusions: The integration of multivariate modeling with reconstructed clinical data enabled the identification of blood-based biomarkers with strong diagnostic potential. These routinely available, cost-effective indicators may support early PCa diagnosis and patient stratification, reducing unnecessary invasive procedures.

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disorder associated with an increased risk of cardiovascular disease (CVD), largely driven by peripheral endothelial dysfunction (ED). Humanin, a mitochondrial-derived peptide, has been suggested to play a protective role in endothelial function. However, the relationship between Humanin levels and ED in RA, as well as the interaction between Humanin and non-coding RNAs such as Long Non-Coding RNA GAS5, microRNA-21 (miR-21), and microRNA-103 (miR-103), remains unclear. Objective: This study aimed to investigate the relationship between circulating Humanin levels, non-coding RNAs (GAS5, miR-21, miR-103), and endothelial dysfunction (ED) in patients with RA. Additionally, we explored the correlation between Humanin expression and specific non-coding RNAs (GAS5, miR-21, and miR-103) to better understand their potential role in vascular health. Methods: Peripheral ED was assessed using flow-mediated pulse amplitude tonometry, with Ln-RHI values p = 0.0196). Kaplan–Meier analysis demonstrated that higher Humanin levels correlated with improved survival (p < 0.0001). The non-coding RNAs (GAS5, miR-21, and miR-103) did not show significant associations with ED. Conclusions: Humanin is a potential protective biomarker for endothelial dysfunction and survival in RA patients. Further research is needed to explore the interaction between Humanin and non-coding RNAs in the context of vascular health.

Breast cancer is a heterogeneous disease characterized by several subgroups that can be identified through molecular biomarkers, which may serve as predictive indicators (1). Globally, female breast cancer ranks as the second most common cancer, with 2,308,897 new cases diagnosed, and the fourth leading cause of cancer-related deaths, with 665,684 fatalities in 2022 (2). Furthermore, the GLOBOCAN Cancer Tomorrow prediction tool estimates that the incidence of breast cancer will increase by more than 46% by 2040 (3). Early cancer detection is critical for improving overall survival rates, as it enables the initiation of appropriate treatments before metastasis occurs (4). The identification of biomarkers, such as miRNAs, is emerging as a promising strategy for the early diagnosis of breast cancer. F. Wang et al. conducted a meta-analysis on the association between circulating miR-155 and breast cancer diagnosis and suggested that further large-scale clinical studies on this miRNA are warranted.The primary cause of death in patients with breast cancer is disease progression due to metastasis and drug resistance. To address this challenge, there is a critical need for reliable molecular biomarkers that can predict disease response.In a meta-analysis and systematic review, Sang et al. found that low absolute lymphocyte counts and elevated neutrophil-lymphocyte ratios were associated with poor outcomes in metastatic breast cancer (mBC) patients. These findings underscore the significant prognostic value of these biomarkers in this patient population.Trophoblast cell-surface antigen 2 (TROP2) overexpression is associated with aggressive subtypes of breast cancer and drug resistance (5), and its silencing has been shown to reduce tumor growth, underscoring its oncogenic relevance Overall, the articles compiled in this research topic not only consolidate the latest advancements but also provide new insights into breast cancer research. This collection serves as a valuable resource for researchers, clinicians, and policymakers dedicated to enhancing the diagnosis and treatment outcomes for patients with breast cancer.

Background and Objectives: The hemoglobin (Hb)/red cell distribution width (RDW) ratio has emerged as an accessible, repeatable, and inexpensive prognostic factor that may predict survival in cancer patients. The focus of this systematic review is to investigate the prognostic role of the Hb/RDW ratio in cancer and the implications for clinical practice. Materials and Methods: A literature search of PubMed, Scopus, and Web of Science databases was performed by an independent author between 18 March and 30 March 2023 to collect relevant literature that assessed the prognostic value of the Hb/RDW ratio in cancer. Overall survival (OS), progression-free survival (PFS), and the association of these with the Hb/RDW ratio were considered to be the main endpoints. Results: Thirteen retrospective studies, including 3818 cancer patients, were identified and involved in this review. It was observed that, when patients with a high vs. low Hb/RDW ratio were compared, those with a lower Hb/RDW ratio had significantly poorer outcomes (p < 0.05). In lung cancer patients, a one-unit increase in the Hb/RDW ratio reduces mortality by 1.6 times, whilst in esophageal squamous-cell carcinoma patients, a lower Hb/RDW ratio results in a 1.416-times greater risk of mortality. Conclusions: A low Hb/RDW ratio was associated with poor OS and disease progression in patients with cancer. This blood parameter should be considered a standard biomarker in clinical practice for predicting OS and PFS in cancer patients. Future searches will be necessary to determine and standardize the Hb/RDW cut-off value and to assess whether the Hb/RDW ratio is optimal as an independent prognostic factor or if it requires incorporation into risk assessment models for predicting outcomes in cancer patients.

Minimal Residual Disease (MRD) detection has emerged as an independent factor in clinical and pathological cancer assessment offering a highly effective method for predicting recurrence in colorectal cancer (CRC). The ongoing research initiatives such as the DYNAMIC and CIRCULATE-Japan studies, have revealed the potential of MRD detection based on circulating tumor DNA (ctDNA) to revolutionize management for CRC patients. MRD detection represents an opportunity for risk stratification, treatment guidance, and early relapse monitoring. Here we overviewed the evolving landscape of MRD technology and its promising applications through the most up-to-date research and reviews, underscoring the transformative potential of this approach. Our primary focus is to provide a point-to-point perspective and address key challenges relating to the adoption of ctDNA-based MRD detection in the clinical setting. By identifying critical areas of interest and hurdles surrounding clinical significance, detection criteria, and potential applications of basic research, this article offers insights into the advancements needed to evaluate the role of ctDNA in CRC MRD detection, contributing to favorable clinical options and improved outcomes in the management of CRC.

Aging is a complex process influenced by genetics and the environment, leading to physiological decline and increased susceptibility to diseases. Cognitive decline is a prominent feature of aging, with implications for different neurodegenerative disorders. The gut microbiome has gained attention for its potential impact on health and disease, including cognitive function. This systematic review and meta-analysis aimed to investigate the relationship between the gut microbiome and cognitive function in the context of aging. Following PRISMA guidelines, a comprehensive search strategy was employed in PubMed, Scopus, and Web of Science databases. Studies exploring the role of the microbiome in cognition and neurodegenerative disorders, published between 2013 and 2023, were included. Data extraction and quality assessment were performed. Quantitative synthesis using statistical analyses was performed to examine microbial diversity and relative abundance in various cognitive conditions. Sixteen studies involving a total of 1303 participants were included in the analysis. The gut microbiota’s relative abundance was different in individuals with cognitive impairments such as Alzheimer’s disease, Parkinson’s disease, and dementia, compared to the healthy controls. The most prevalent phyla affected were Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria. Meta-analyses indicated substantial heterogeneity among studies focusing on Alzheimer’s disease. The overall quality of evidence related to microbial analysis was moderate. The gut microbiome’s role in cognitive decline and neurodegenerative disorders warrants investigation. Altered microbial abundance, particularly in specific phyla, is associated with cognitive impairments. However, variations in study findings and methodologies highlight the complexity of the relationship between the gut microbiome and cognitive function. Further studies are needed to better understand the mechanisms underlying this connection and its potential implications for aging and cognitive health.

The aim is better to understand and critically explore and present the available data from observational studies on the pathogenetic role of the microbiome in the development of rheumatoid arthritis (RA). The electronic databases PubMed, Scopus, and Web of Science were screened for the relevant literature published in the last ten years. The primary outcomes investigated included the influence of the gut microbiome on the pathogenesis and development of rheumatoid arthritis, exploring the changes in microbiota diversity and relative abundance of microbial taxa in individuals with RA and healthy controls (HCs). The risk of bias in the included literature was assessed using the GRADE criteria. Ten observational studies were identified and included in the qualitative assessment. A total of 647 individuals with RA were represented in the literature, in addition to 16 individuals with psoriatic arthritis (PsA) and 247 HCs. The biospecimens comprised fecal samples across all the included literature, with 16S rDNA sequencing representing the primary method of biological analyses. Significant differences were observed in the RA microbiome compared to that of HCs: a decrease in Faecalibacterium, Fusicatenibacter, Enterococcus, and Megamonas and increases in Eggerthellales, Collinsella, Prevotella copri, Klebsiella, Escherichia, Eisenbergiella, and Flavobacterium. There are significant alterations in the microbiome of individuals with RA compared to HCs. This includes an increase in Prevotella copri and Lactobacillus and reductions in Collinsella. Collectively, these alterations are proposed to induce inflammatory responses and degrade the integrity of the intestinal barrier; however, further studies are needed to confirm this relationship.