Hematological malignancies are a diverse group of cancers developing in the peripheral blood, the bone marrow or the lymphatic system. Due to their heterogeneity, the identification of novel and advanced molecular signatures is essential for enhancing their characterization and facilitate its translation to new pharmaceutical solutions and eventually to clinical applications. In this study, we collected publicly available microarray data for more than five thousand subjects, across thirteen hematological malignancies. Using PANDA to estimate gene regulatory networks (GRNs), we performed hierarchical clustering and network analysis to explore transcription factor (TF) interactions and their implications on biological pathways. Our findings reveal distinct clustering patterns among leukemias and lymphomas, with notable differences in gene and TF expression profiles. Gene Set Enrichment Analysis (GSEA) identified 57 significantly enriched KEGG pathways, highlighting both common and unique biological processes across HMs. We also identified potential drug targets within these pathways, emphasizing the role of TFs such as CEBPB and NFE2L1 in disease pathophysiology. Our comprehensive analysis enhances the understanding of the molecular landscape of HMs and suggests new avenues for targeted therapeutic strategies. These findings also motivate the adoption of regulatory networks, combined with modern biotechnological possibilities, for insightful pan-cancer exploratory studies.

Proton beam therapy is considered a step forward with respect to electromagnetic radiation, thanks to the reduction in the dose delivered. Among unwanted effects to healthy tissue, cardiovascular complications are a known long-term radiotherapy complication. The transcriptional response of cardiac tissue from xenografted BALB/c nude mice obtained at 3 and 10 days after proton irradiation covering both the tumor region and the underlying healthy tissue was analyzed as a function of dose and time. Three doses were used: 2 Gy, 6 Gy, and 9 Gy. The intermediate dose had caused the greatest impact at 3 days after irradiation: at 2 Gy, 219 genes were differently expressed, many of them represented by zinc finger proteins; at 6 Gy, there were 1109, with a predominance of genes involved in energy metabolism and responses to stimuli; and at 9 Gy, there were 105, mainly represented by zinc finger proteins and molecules involved in the regulation of cardiac function. After 10 days, no significant effects were detected, suggesting that cellular repair mechanisms had defused the potential alterations in gene expression. The nonlinear dose–response curve indicates a need to update the models built on photons to improve accuracy in health risk prediction. Our data also suggest a possible role for zinc finger protein genes as markers of proton therapy efficacy.

Single-cell transcriptomics has redefined our understanding of cancer by exposing the complexity of tumor ecosystems and their therapeutic vulnerabilities. scRNA-seq studies have identified lineage hierarchies, immune evasion programs, and resistance-associated states across solid and liquid tumors, informing biomarker development and drug discovery. Advanced computational frameworks integrate these data with longitudinal profiling, RNA velocity, and network diffusion to prioritize targets and predict therapeutic response. Emerging multi-omics approaches further expand the scope of precision oncology by linking genetic alterations, protein-level markers, and spatial context to functional states. This narrative review aims to synthesize current applications of single-cell transcriptomics for target discovery, highlight computational frameworks that translate high-dimensional data into actionable insights, and explore how multi-omics integration is shaping future directions. By bridging molecular complexity with target prioritization, these approaches hold promise for translating single-cell insights into clinically actionable biomarkers and therapeutic strategies for personalized cancer treatment and rational drug development.

Purpose: Deauville Score (DS) assessment from [ ¹⁸ F]FDG PET/CT in multiple myeloma (MM) relies on visual interpretation, limiting reproducibility. This study aimed to develop an automated pipeline for standardised DS computation, evaluate the prognostic value of DS at five anatomical sites, and predict progression-free survival (PFS) by integrating DS with copy number alterations (CNA) and clinical variables. Methods: A retrospective cohort of 165 newly diagnosed MM patients with baseline FDG PET/CT, CNA profiling, and blood tests was analysed. An automated pipeline computed DS fully automatically for vertebral bone marrow (BM) and long bones (LB), and semi-automatically for focal (FL), paramedullary (PM), and extramedullary (EM) lesions. DS were subdivided into absent (1), low (2–3), and high (4–5) groups and compared via log-rank test. A penalised Cox model with 12 covariates (five DS, three CNAs, haemoglobin, platelet count, age, sex) was evaluated via nested cross-validation for PFS prediction. For inference on individual prognostic contributions, an unpenalised multivariable Cox model was fitted on the full cohort. Results: In univariate analyses, high LB, PM and EM DS were significantly associated with shorter PFS. The penalised Cox model achieved a C-index of 0.710 [95% CI: 0.689–0.732] in predicting the risk of progression. In the multivariable analysis, age, haemoglobin, BM DS, PM DS and amp(1q) were identified as independent prognostic factors. Conclusion: Automated DS computation from baseline FDG PET/CT is feasible and, combined with genomic and clinical data, enables a reproducible multimodal approach to prognostic stratification in MM. The pipeline for DS computation is publicly available as an open-source tool (autoDS-PET) at https://github.com/Sara-Peluso/autoDS-PET .