The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the gray matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype-phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype-phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.

Circular RNAs (circRNAs) are regulatory RNAs that play a crucial role in various biological activities and have been identified as potential biomarkers for neurological disorders and cancer. CircRNAs have emerged as significant regulators of gene expression through different mechanisms, including regulation of transcription and splicing, modulation of translation, and post-translational modifications. Additionally, some circRNAs operate as microRNA (miRNA) sponges in the cytoplasm, boosting post-transcriptional expression of target genes by inhibiting miRNA activity. Although existing pipelines can reconstruct circRNAs, identify miRNAs sponged by them, retrieve cascade-regulated mRNAs, and represent the regulatory interactions as complex circRNA-miRNA-mRNA networks, none of the state-of-the-art approaches can discriminate the biological level at which the mRNAs involved in the interactions are regulated, avoiding considering potential target mRNAs not regulated at the post-transcriptional level. EasyCircR is a novel R package that combines circRNA detection and reconstruction with post-transcriptional gene expression analysis (exon-intron split analysis) and miRNA response element prediction. The package enables estimation and visualization of circRNA-miRNA-mRNA interactions through an intuitive Shiny application, leveraging the post-transcriptional regulatory nature of circRNA-miRNA relationship and excluding unrealistic regulatory interactions at the biological level. EasyCircR source code, Docker container and user guide are available at: https://github.com/InfOmics/EasyCircR.

Background: Mantle Cell Lymphoma (MCL) is an aggressive malignancy with variable clinical behavior, largely reflecting the underlying molecular heterogeneity. The genomic landscape of MCL encompasses gene mutations with strong prognostic implications and secondary genetic events, which are also implicated in the pathogenesis and prognosis of the disease. Methods: We evaluated the diagnostic samples of 73 patients with relapsed/refractory MCL that were enrolled in the Fondazione Italiana Linfomi Mantle First-BIO study. All patients had available data for correlating CNVs with the presence of TP53 mutation. Time to first relapse or progression of disease (POD) was used as the primary outcome measure. Results: We identified CNVs associated with MCL, with Del 9p21.3 (CDKN2A) being the strongest predictor of shorter time to POD (p = 0.01), independently of TP53 mutation in multivariable analysis. Unsupervised clustering identified molecularly defined clusters that were associated with significantly different times to POD (p = 0.01). Pairwise log-rank tests confirmed TP53 mutated vs. wild-type (WT) as the strongest prognostic factor, with cluster assessment improving the prognostic predictivity among patients: clusters TP53-mut vs. TP53-WT, p = 0.001, HR = 3.92; and p = 0.014, HR = 2.23, respectively. In conclusion, CNV-based molecular clusters might represent a novel approach to identify patients at higher risk of treatment failure, further contributing to the prognostic predictivity of TP53 mutation.

This study aimed to identify possible early biomarkers of mortality among clinical and biochemical parameters, iron metabolism parameters, and cytokines detected within 24 h from admission in hospitalized COVID-19 patients. We enrolled 80 hospitalized patients (40 survivors and 40 non-survivors) with COVID-19 pneumonia and acute respiratory failure. The median time from the onset of COVID-19 symptoms to hospital admission was lower in non-survivors than survivors (p < 0.05). Respiratory failure, expressed as the ratio of arterial oxygen partial pressure to the fraction of inspired oxygen (P/F), was more severe in non-survivors than survivors (p < 0.0001). Comorbidities were similar in both groups. Among biochemical parameters and cytokines, eGFR and interleukin (IL)-1β were found to be significantly lower (p < 0.05), while LDH, IL-10, and IL-8 were significantly higher in non-survivors than in survivors (p < 0.0005, p < 0.05 and p < 0.005, respectively). Among other parameters, LDH values distribution showed the most significant difference between study groups (p < 0.0001). LASSO feature selection combined with Cox proportional hazards and logistic regression models was applied to identify features distinguishing between survivors and non-survivors. Both approaches highlighted LDH as the strongest predictor, with IL-22 and creatinine emerging in the Cox model, while IL-10, eGFR, and creatinine were influential in the logistic model (AUC = 0.744 for Cox, 0.723 for logistic regression). In a similar manner, we applied linear regression for predicting LDH levels, identifying the P/F ratio as the top predictor, followed by IL-10 and eGFR (NRMSE = 0.128). Collectively, these findings underscore LDH’s critical role in mortality prediction, with P/F and IL-10 as key determinants of LDH increases in this Italian COVID-19 cohort.

Transforming growth factor-β (TGFβ) receptor ALK5 inhibition has shown promise in pancreatic ductal adenocarcinoma (PDAC), but predictive biomarkers remain undefined. We identify angiogenin (ANG) as a negative prognostic yet positive predictive biomarker for ALK5 inhibition combined with chemotherapy. In the randomized phase II H9H-MC-JBAJ trial, high baseline ANG predicted poor survival with gemcitabine alone but significant benefit from galunisertib addition. Mechanistic studies revealed that tumor-derived ANG binds epidermal growth factor receptor (EGFR) on tumor-associated macrophages (TAMs), activating RhoA-dependent cytoskeletal remodeling and autocrine ALK5/TGFβ signaling. This drives M2-like polarization and Smad2-mediated transcription of tumor necrosis factor-α (Tnf-α), which activates Nf-κB in neighboring tumor cells, conferring chemoresistance. ALK5 inhibition suppressed TAM-derived Tnf-α, reduced M2 polarization, prevented Nf-κB activation, and restored chemosensitivity in ANG-high models. Clinically, elevated ANG correlated with systemic TNF-α, and galunisertib reduced TNF-α exclusively in ANG-high patients, with reductions associated with markedly improved survival. These findings define an ANG–EGFR–TGFβ–TNF-α axis in TAMs as a stromal driver of PDAC chemoresistance, and provide a mechanistic rationale for the development of combination strategies targeting ALK5 signaling in ANG-high PDAC patients.