Search Results (829)

Soft tissue sarcomas (STSs) are a heterogeneous group of rare mesenchymal malignancies with overlapping morphological and immunohistochemical features, often making definitive diagnosis challenging. Recent advances in next-generation sequencing (NGS) have enabled the identification of recurrent molecular alterations that contribute to tumor classification, prognostic stratification, and precision oncology approaches. This retrospective study aimed to evaluate the diagnostic and clinical impact of molecular profiling in pediatric soft tissue sarcomas using the Oncomine Childhood Cancer Research Assay (OCCRA) panel. Fifty-five frozen tumor samples representing 24 distinct soft tissue sarcoma subtypes were obtained from the Pediatric Oncology Institute -IOP/GRAACC/UNIFESP Biobank (B-053). Molecular analysis was performed using NGS to identify gene fusions, single nucleotide variants (SNVs), copy number variations (CNVs), and insertions/deletions (InDels). Clinically relevant molecular alterations were identified in 70% (37/55) of cases, including 18 fusion transcripts, 13 SNVs, 8 CNVs, and 6 InDels. Recurrent and diagnostically relevant alterations included BCOR::CCNB3, ASPSCR1::TFE3, NFR1::BRAF, FUS::DDIT3, EML4::NTRK3, ETV6::NTRK3, CIC::DUX4, NAB2::STAT6 and SS18::SSX1/2 fusions, as well as amplifications involving PDGFRA, FGFR1, GLI1, CDK4, ERBB3, and KIT. Pathogenic variants affecting genes involved in tumor suppression and chromatin remodeling, including TP53, NF1, DICER1, SMARCA4, PTEN, and PIK3CA, were also detected. Importantly, molecular profiling had significant diagnostic impact in several histologically ambiguous tumors, enabling molecular reclassification and refinement of previously inconclusive or inaccurate pathological diagnoses. In multiple cases, NGS transformed descriptive histopathological interpretations into genetically defined sarcoma entities, including NTRK-rearranged spindle cell neoplasms, CIC-rearranged sarcomas, synovial sarcoma, low-grade fibromyxoid sarcoma, and clear cell sarcoma. Furthermore, the identification of actionable alterations highlighted potential opportunities for targeted therapies and precision medicine approaches. Our findings demonstrate that comprehensive molecular profiling significantly enhances diagnostic accuracy in pediatric soft tissue sarcomas, particularly in morphologically challenging cases. The integration of NGS into routine sarcoma diagnostics enables biologically informed tumor classification and supports personalized therapeutic strategies. Full article

Background: Incomplete microwave ablation (iMWA) of lung cancer often leads to rapid recurrence and metastasis, yet the underlying mechanisms remain unclear. This study explored whether iMWA promotes tumor progression by activating the cyclic GMP–AMP synthase–stimulator of interferon genes (cGAS–STING) signaling pathway and its downstream effector ZEB1 in tumor cells. Materials and Methods: An in vivo iMWA model was established in nude mice bearing H1650 lung tumors, and an in vitro sublethal heat treatment model was used to mimic incomplete ablation. Transcriptomic profiling, molecular assays and functional analyses assessed cellular behavior and signaling activity changes post-iMWA; genetic and pharmacologic interventions modulated STING signaling and autophagy. Results: Post-iMWA residual cells exhibited enhanced proliferation and invasion. Thermal injury induced necrosis and inhibited mitophagy, causing cytosolic mtDNA accumulation that activated the intrinsic cGAS–STING pathway. This upregulation of ZEB1 drove epithelial–mesenchymal transition and dissemination. Notably, silencing STING or ZEB1, or pharmacologically restoring autophagy, significantly suppressed tumor growth and metastasis. Conclusions: iMWA drives malignant progression of lung cancer through an mtDNA–cGAS–STING–ZEB1 signaling axis. Targeting this pathway—by inhibiting STING or enhancing autophagy—may represent a promising therapeutic strategy to mitigate recurrence and metastasis following microwave ablation. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide despite substantial advances in screening, surgical techniques, systemic therapies, and multidisciplinary care. The increasing implementation of precision oncology has fundamentally transformed CRC management by enabling molecularly guided therapeutic strategies based on tumor-specific genetic alterations. In recent years, the molecular landscape of CRC has expanded considerably beyond traditional histopathological classification, incorporating a growing number of clinically actionable biomarkers with prognostic, predictive, and therapeutic significance. This review provides a comprehensive and up-to-date overview of the genetic landscape of CRC, focusing on established biomarkers currently integrated into clinical practice, including microsatellite instability/mismatch repair deficiency (MSI/dMMR), KRAS, NRAS, BRAF, HER2, and NTRK alterations. In addition, emerging biomarkers such as tumor mutational burden (TMB), POLE/POLD1 mutations, circulating tumor DNA (ctDNA), DNA damage repair (DDR) alterations, transcriptomic signatures, and artificial intelligence-based molecular prediction models are critically discussed. Particular emphasis is placed on their biological significance, diagnostic methodologies, prognostic and predictive value, and potential role in treatment selection. A structured, database-informed narrative review identified 140 relevant publications, primarily published between January 2020 and June 2026, supplemented by earlier seminal studies and major clinical guidelines. Based on the available evidence, we propose a Clinical Actionability Framework for CRC, categorizing biomarkers into three hierarchical tiers according to their level of clinical validation and therapeutic relevance: established standard-of-care biomarkers, emerging clinical biomarkers, and future precision oncology biomarkers. Collectively, current evidence supports a progressive transition from single-gene testing toward integrated multi-omics precision medicine. Advances in comprehensive genomic profiling, liquid biopsy technologies, transcriptomics, radiogenomics, and artificial intelligence are expected to further refine patient stratification, optimize therapeutic decision-making, and facilitate the development of adaptive precision oncology models. Understanding the evolving genetic landscape of CRC is therefore essential for maximizing treatment efficacy and improving patient outcomes in the era of personalized cancer care. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers, with a five-year survival rate of less than 12%. Oncolytic viruses are considered a promising immunotherapeutic approach, but their effectiveness is often limited by the innate interferon response, which is the main antiviral barrier in resistant tumors. A combination of an oncolytic virus with JAK/STAT pathway inhibitors has been proposed to overcome this resistance. Methods: In this study, we investigated the oncolytic and immunotherapeutic potential of the attenuated yellow fever vaccine strain YFV 17D in combination with the JAK1/JAK2 inhibitor ruxolitinib in a panel of six PDAC cell lines with diverse genetic profiles and JAK/STAT signaling activities and in a syngeneic immunocompetent PAN02 mouse model. Results: In vitro, we identified three distinct response patterns: synergistic enhancement of viral replication and cytopathic effect, lack of synergism due to absent interferon signaling, and increased viral replication without cytopathic effect. Despite different cell mutation profiles, the cell response patterns were determined by the basal JAK/STAT activity of each cell line rather than by specific KRAS or TP53 mutation. In vivo, the combination therapy significantly extended median survival compared to YFV 17D monotherapy and the control group and induced tumor regression in 62.5% of animals. Since no difference in intratumoral viral RNA was detected between the combination and monotherapy groups, we suggest that the antitumor effect in vivo was mediated not by enhanced viral replication, but by activation of adaptive immunity. This is indirectly suggested by increased intratumoral IL-12, a fourfold increase in CD8⁺ T cell infiltration, a reduction in CD4⁺ cells, and the generation of virus-neutralizing antibodies. No systemic cytokine surge, neurovirulence, or viral dissemination was observed, suggesting the safety of the proposed regimen. Conclusions: These findings clarify the immune mechanisms determining the efficacy of YFV 17D combined with ruxolitinib and establish the basis for personalized patient stratification by tumor interferon signaling status. Full article

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide and remains a major therapeutic challenge due to its marked inter- and intratumoral heterogeneity, diverse etiologies, and high propensity for therapeutic resistance. This review summarizes the biological complexity of HCC and current therapeutic challenges, with a particular focus on the RNA-binding protein human antigen R (HuR) as an emerging therapeutic target. Methods: A comprehensive narrative review of peer-reviewed literature was conducted, focusing on HCC pathogenesis, molecular heterogeneity, tumor microenvironment, mechanisms of therapeutic resistance, and recent advances in treatment. Emphasis was placed on studies investigating the biological functions of HuR and its therapeutic potential in HCC. Results: HCC progression is driven by complex interactions among genetic, epigenetic, metabolic, and environmental factors, resulting in substantial tumor heterogeneity and variable therapeutic responses. Dysregulated oncogenic signaling and immunosuppressive tumor microenvironment collectively contribute to resistance against current therapies, including multikinase inhibitors and immune checkpoint inhibitors. Although emerging strategies, such as combination immunotherapy, metabolic targeting, epigenetic modulation, and precision medicine, have shown encouraging preclinical and clinical results, their efficacy remains limited by tumor complexity and adaptive resistance. HuR functions as a master post-transcriptional regulator that stabilizes and promotes the translation of numerous mRNAs encoding oncogenic, inflammatory, and pro-survival factors. Accumulating preclinical evidence demonstrates that pharmacological inhibition of HuR suppresses multiple tumor-promoting pathways and enhances therapeutic sensitivity, supporting its potential as a novel therapeutic strategy for HCC. Conclusions: The biological complexity of HCC necessitates multifaceted, precision-based therapeutic approaches. Although additional HCC-specific mechanistic and translational studies are needed, targeting HuR represents a promising strategy to overcome tumor heterogeneity, therapeutic resistance, and disease progression. Continued integration of molecular profiling, advanced omics technologies, and rational combination therapies will be essential for translating these advances into improved clinical outcomes for patients with HCC. Full article

Breast cancer remains one of the most prevalent malignancies affecting women worldwide and continues to be a leading cause of cancer-related morbidity and mortality. Patients may present with either localized or advanced disease, with clinical outcomes increasingly influenced by molecular subtype and genetic profile. This review highlights the key genetic factors involved in breast cancer, current diagnostic and therapeutic strategies, and promising emerging approaches that may shape future clinical management. Breast cancer diagnosis typically involves clinical breast examination, imaging techniques such as mammography and ultrasound, and confirmatory biopsies. Genetic mutations in specific genes are strongly linked to the development, progression, and metastasis of the disease. Treatment options for localized breast cancer continue to include surgery (lumpectomy or mastectomy) and radiotherapy, combined with systemic therapies tailored to tumor biology, such as endocrine therapy, human epidermal growth factor receptor 2 (HER2)-targeted therapy, and cyclin-dependent kinase (CDK)4/6 inhibitors. For advanced or metastatic breast cancer, recent therapeutic advances include the use of immunotherapy (e.g., immune checkpoint inhibitors), Poly (ADP-ribose) polymerase (PARP) inhibitors for Breast Cancer gene (BRCA)-mutated cancers, antibody–drug conjugates, and novel targeted agents, which have significantly improved patient outcomes in selected populations. Recent findings in breast cancer genetics have highlighted the critical role of germline and somatic mutations, particularly in genes such as BRCA1, BRCA2, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), and TP53, in driving tumor initiation, progression, and therapeutic response. Molecular profiling and next-generation sequencing technologies have enabled more precise tumor classification and facilitated the development of personalized treatment strategies. Despite these advances, treatment resistance and disease recurrence remain major challenges, particularly in aggressive subtypes such as triple-negative breast cancer. Consequently, ongoing research is exploring alternative and complementary approaches, including nanotechnology-based drug delivery systems, gene editing techniques such as clustered regularly interspaced short palindromic repeats-Cas9 (CRISPR-associated protein 9) (CRISPR-Cas9), cancer vaccines, and the integration of traditional and plant-derived compounds. These strategies aim to enhance therapeutic efficacy, reduce systemic toxicity, and overcome resistance mechanisms. Full article

Background: Drug repurposing offers a cost-effective strategy to accelerate therapeutic discovery, but most computational approaches do not model noncoding genetic variation. Because over 90% of genome-wide association study (GWAS) risk variants reside in noncoding regions, linking regulatory variation to therapeutic hypotheses remains a major challenge. Methods: We developed an integrative deep learning framework that links allele-specific enhancer prediction to candidate therapeutics through two complementary prioritization strategies, a transcription factor (TF)-based and a gene-based approach. We used MCF7-breast cancer context as a proof-of-concept system. Results: GWAS heritability was significantly enriched in MCF7 enhancers. Allele-specific variant scoring identified 1537 breast cancer risk variants with strong predicted regulatory effects, and attribution-based motif discovery revealed enrichment of FOXA1-associated motif features, consistent with FOXA1 upregulation in primary tumors. TF-based prioritization, integrating FOXA1 knockdown-induced and drug-induced gene expression profiles, identified 63 candidate compounds, including 18 approved drugs, and recovered fulvestrant, an established breast cancer therapy. Gene-based prioritization, mapping candidate regulatory variants to 347 target genes, identified 140 candidate compounds, including approved breast cancer drugs toremifene and raloxifene. Both strategies identified compounds with anti-correlated transcriptional signatures across core breast cancer hallmark pathways, and integration of pathway anti-correlation, drug-gene interactions, and supporting experimental or clinical evidence yielded 15 high-confidence repurposing candidates. Conclusions: Recovery of approved breast cancer therapeutics supports the biological relevance of deep learning-predicted regulatory variants. This study establishes a regulatory variant-guided drug repurposing framework that connects noncoding genetic variation to candidate therapeutics and provides a scalable strategy for generating pharmacologically relevant hypotheses from the noncoding genome. Full article

The vitamin D receptor (VDR) has been implicated in tumor progression, but its functional role in pancreatic ductal adenocarcinoma (PDAC) metastasis remains unclear. Here, using pharmacological modulation, gain- and loss-of-function approaches, transcriptomic profiling, and an experimental lung colonization model, we demonstrate that VDR acts as a suppressor of PDAC metastasis. Pharmacological activation of VDR by calcipotriol did not affect tumor cell proliferation but markedly inhibited the migratory and invasive capacities of multiple PDAC cell lines. In contrast, genetic deletion or pharmacological inhibition of VDR significantly enhanced metastatic phenotypes. To investigate the underlying mechanisms, we performed RNA sequencing on PDAC cells with differential VDR expression following calcipotriol treatment. Pathway enrichment analysis identified MAPK/ERK signaling as one of the most prominently altered pathways upon VDR activation. Functional studies further demonstrated that ERK inhibition abrogated the pro-metastatic effects induced by VDR loss or inhibition. In vivo lung colonization assays confirmed that VDR deficiency markedly promoted pulmonary metastatic colonization. Collectively, these findings identify VDR as a critical suppressor of PDAC cell metastatic colonization and reveal a previously unrecognized VDR–ERK regulatory axis that may represent a potential therapeutic target for limiting metastatic progression in pancreatic cancer. Full article

Background/Objectives: Chondrosarcoma is a clinically heterogeneous malignancy, and the prognostic role of its genetic features remains incompletely defined. While isocitrate dehydrogenase (IDH) mutations are well characterized, the significance of less common alterations—particularly telomerase reverse transcriptase (TERT) promoter mutations—remains unclear. This study aimed to define the genomic profile of chondrosarcoma and evaluate the prognostic relevance of TERT mutations, alone and in combination with IDH mutations. Methods: We retrospectively analyzed 91 patients with chondrosarcoma, including 54 patients with available next-generation sequencing data for genomic analyses. Tumors were assessed for mutations in IDH, TERT, TP53, and groups of genes involved in key cellular functions (e.g., cell cycle control, chromatin regulation). Associations with tumor characteristics were evaluated, recurrence outcomes were assessed using logistic regression, and overall survival was analyzed using Kaplan–Meier and Cox models adjusted for tumor subtype, grade, and size. Results: IDH mutations were present in 52% of tumors; other alterations included TP53 (16%), TERT (7.7%), chromatin-related genes (14%), and cell cycle genes (8.8%). TERT mutations were enriched in dedifferentiated tumors (p = 0.017) and occurred exclusively in grade 3 disease (“p < 0.001”). TERT-mutant tumors were associated with worse overall survival upon unadjusted analysis (HR 7.02, p = 0.024), but not after adjustment (HR 3.81, p = 0.160). All TERT mutations co-occurred with IDH mutations, and this subgroup had particularly poor survival (HR 7.76, p = 0.022), albeit limited by small numbers. Conclusions: These findings suggest that specific mutations, alone or in combination, may influence clinical outcomes by reflecting more aggressive tumor biology. TERT mutations, particularly with concurrent IDH mutations, may identify high-risk patients and complement established prognostic factors. Further validation is needed. Full article

Background and Objectives: Clear cell renal cell carcinoma (ccRCC) demonstrates substantial molecular and clinical heterogeneity, limiting the prognostic accuracy of conventional staging system and complicating treatment selection. CT-based radiomics and radiogenomics have emerged as promising non-invasive approaches for predicting molecular biomarkers. This review aimed to evaluate the current evidence regarding CT-based radiogenomics for the prediction of molecular markers in ccRCC, with emphasis on methodological approaches, predictive performance, and clinical applicability. Materials and Methods: A comprehensive literature search of PubMed/MEDLINE, Scopus, and Cochrane Library databases was performed for original studies published between January 2012 and December 2025. Eligible studies included patients with histopathologically confirmed ccRCC, performed CT-based radiomics feature extraction, and investigated molecular or genetic biomarkers using machine learning (ML) methods. Data regarding CT acquisition phase, segmentation strategy, radiomics features, ML algorithms, investigated biomarkers, and model performance metrics were extracted. Results and Discussion: Twenty-five retrospective studies were included. CT-based radiomics demonstrated promising performance in predicting gene mutations, including Von Hippel–Lindau (VHL), Polybromo 1 (PBRM1), BRCA1-associated protein 1 (BAP1), SET domain containing 2 (SETD2), and Lysine demethylase 5C (KDM5C), with reported area under the curve (AUC) values reaching 0.987. Radiogenomic models also showed utility in assessing hypoxia-related pathways, lipid metabolism signatures, programmed cell death profiles, immune-related markers, and tumor microenvironment characteristics, including programmed death-ligand 1 (PD-L1), Cluster of Differentiation 68 (CD68+) tumor-associated macrophages (TAMs), Cytotoxic T-Lymphocyte–Associated Protein 4 (CTLA-4), Forkhead Box P3 (FOXP3), and Ki-67 proliferation index. Predictive performance varied across biomarkers, with AUCs generally ranging from 0.68 to 0.91. Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Gradient Boosting algorithms were most commonly applied. Conclusions: CT-based radiogenomics represents a promising non-invasive tool for molecular characterization and risk stratification in ccRCC. Standardized multicenter prospective studies, methodological homogeneity, and external validation are required before routine clinical implementation. Full article

Cancer accounts for approximately ten million deaths annually. The majority of these are attributable to resistance-driven tumor progression and metastasis. Although increasingly effective, precise, and selective therapeutic strategies are being developed, cancer cells retain the capacity to dynamically alter their phenotype and evade treatment. Traditional in vitro approaches rely heavily on cell line monocultures; however, their limited clinical translatability has driven the development of more advanced model systems. Three-dimensional in vitro models, including spheroids, organoids, and bioprinted tissues, provide more physiologically relevant and rapid insights, but fail to capture systemic pharmacodynamics and anatomical complexity. Emerging in vivo models, such as genetically engineered mouse models (GEMMs) of carcinogenesis and patient-derived xenografts (PDXs), as well as their derived organoids, provide a more comprehensive understanding of tumor biology. The preservation of tumor heterogeneity, microenvironment, and drug sensitivity profiles has positioned PDX models as widely used platforms in both drug development and therapy response prediction. Despite limitations—including variable engraftment rates, genetic drift, lack of fully functional immune systems, ethical concerns, and high costs—PDX models, when integrated with complementary techniques, contribute significantly to identifying novel therapeutic targets and combinations. Moreover, they support clinical decision-making by enabling drug response prediction based on genetic landscapes and co-clinical response data. Full article

Neurofibromatosis type 1 (NF1) is a genetic tumor predisposition syndrome characterized by a substantial risk of developing peripheral nerve sheath tumors, including malignant peripheral nerve sheath tumors (MPNSTs), which occur in 8–13% of patients. Approximately 50% arise from plexiform neurofibromas (PNs) and 40% develop de novo, making them a major cause of premature mortality. Current clinical management is limited by the intrinsic shortcomings of standard imaging modalities: magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT), and tissue biopsy in distinguishing benign PNs from early malignant transformation, which remains a major clinical challenge. This progression follows a stepwise molecular continuum marked by cumulative genetic alterations and widespread epigenetic dysregulation. In this setting, liquid biopsy has emerged as a promising non-invasive approach to help fill these diagnostic gaps by enabling real-time molecular monitoring through the analysis of circulating tumor DNA (ctDNA) and other blood-based biomarkers. This review examines the current evidence supporting liquid biopsy applications in NF1 management, including early detection of MPNST, discrimination between benign and malignant lesions, mutational profiling for therapeutic targeting, and disease monitoring before and during treatment. We also discuss the current evidence on fragmentomics, methylomics and driver mutation profiling as tools to distinguish PNs from MPNSTs. Recent evidence suggests that liquid biopsy may help detect molecular changes associated with malignant transformation before clear clinical signs emerge, potentially opening an important window for intervention and supporting a shift towards a more molecularly informed surveillance model. Finally, this review considers the possible extension of liquid biopsy to other tumor types, including NF1-deficient breast cancer, and outlines a future management framework aimed at improving early diagnosis and personalized therapeutic intervention in this high-risk population. Full article

Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous mature B-cell neoplasm whose classification, prognosis, and therapy have been reshaped by advances in genomic, transcriptomic, epigenomic, single-cell, and spatial profiling technologies. This review focuses on how these approaches have refined the molecular landscape of DLBCL, including recurrent chromosomal translocations, tumor-suppressor alterations, oncogenic signaling pathways, and tumor-microenvironment programs. Cell-of-origin (COO) frameworks remain clinically useful. However, contemporary models extend beyond conventional germinal center categories by incorporating probabilistic genetic subtypes, expression-defined high-risk states, and spatially resolved lymphoma-cell and immune-cell ecosystems. These high-resolution methods clarify intratumoral heterogeneity, identify biologically distinct subgroups, and inform prognosis and therapeutic selection. The review also summarizes how tumor-intrinsic biology and the tumor-microenvironment (TME) shape responses to frontline therapy, targeted agents, antibody-drug conjugates, bispecific antibodies, and CD19-directed CAR T-cell therapy. Particular emphasis is placed on product-specific evidence in relapsed/refractory disease, rational sequencing of immunotherapies, and emerging biomarkers such as circulating tumor DNA-based measurable residual disease (ctDNA-MRD). Together, these developments support a shift from COO-centric classification toward dynamic, biology-driven models that incorporate tumor-intrinsic and microenvironmental determinants to guide personalized therapy in DLBCL. Full article

Background: Cutaneous squamous cell carcinoma (cSCC) is commonly regarded as a sporadic malignancy primarily driven by ultraviolet exposure. However, the occurrence of multiple, aggressive tumors at a relatively young age suggests the presence of underlying genetic susceptibility. The role of germline variants affecting extracellular matrix organization, pigmentation pathways, and tumor metabolism in aggressive cSCC remains incompletely understood. Case Presentation: We describe a 53-year-old patient with a long-standing history of multiple aggressive cutaneous squamous cell carcinomas involving the scalp and facial regions, characterized by recurrent and multifocal disease. A comprehensive diagnostic approach was undertaken, including histopathological examination, fluorescence confocal microscopy, high-frequency cutaneous ultrasound, and genetic analysis using whole-exome sequencing (WES). Results: Histopathology confirmed high-risk features consistent with aggressive cSCC. Cutaneous ultrasound and fluorescence confocal microscopy provided complementary, non-invasive insights into tumor depth, architecture, and invasive patterns. Whole-exome sequencing identified a heterozygous truncating variant in COL6A3 (NM_004369.4:c.5645C>A, p.Ser1882Ter), classified as likely pathogenic according to ACMG criteria. Additionally, two heterozygous variants of uncertain significance were detected in TYR (NM_000372.5:c.1569C>A, p.Ser523Arg) and FH (NM_000143.4:c.1237-5_1237-4insTCTCCCTCCCTC). Although individually inconclusive, the combined germline genetic background may have contributed to the patient’s aggressive and multifocal cutaneous phenotype. Discussion: This case report supports a potential role of extracellular matrix remodeling, pigmentation-related susceptibility, and metabolic dysregulation in cutaneous carcinogenesis and tumor aggressiveness. This case illustrates how integrating WES with advanced non-invasive imaging techniques can enhance the understanding of biologically aggressive cSCC. Conclusions: This report highlights a unique case of multifocal aggressive cSCC characterized by a distinct germline genetic profile identified by WES and multimodal imaging assessment. Comprehensive molecular and imaging evaluation may be beneficial in selected patients with atypical or aggressive cutaneous squamous cell carcinoma, with implications for personalized surveillance and management. Full article

Epithelial ovarian cancers (EOCs) histotypes are characterized by marked molecular heterogeneity and limited effectiveness of current screening and monitoring strategies. Earlier identification of tumor-associated alterations may support timely intervention, especially in genetically predisposed or early-onset patient populations. While liquid biopsy approaches have primarily focused on circulating DNA, RNA, and proteins, increasing evidence indicates that cancer-associated metabolic reprogramming generates measurable informative signals in peripheral biofluids. This review summarizes recent progress in liquid biopsy-derived metabolomics in EOCs, covering analytical platforms applied to serum, plasma, urine, and ascites. Recurrent metabolic signatures linked to tumor burden, disease stage, treatment response, and clinical outcome are described, and their significance in discriminating malignant and non-malignant conditions is critically discussed. Collectively, these findings suggest that metabolomics may provide complementary functional information alongside genomic and histopathological profiling. Although its clinical implementation still requires further validation and methodological standardization, ongoing advances in analytical technologies and the integration of high-dimensional metabolic data into machine learning-based frameworks may progressively support the identification of early tumor-associated alterations and contribute to more accurate disease stratification and biologically informed clinical management. Full article