Medicine and Pharmacology

Abstract: Background/Objectives: Recent World Health Organization (WHO) classifications of gliomas emphasize integrated molecular diagnosis, improving diagnostic precision but increasing the need for immunohistochemistry and genetic testing. As a result, increasing costs and a decrease in diagnostic rates in developing countries have become issues. Consequently, there is growing interest in the development of image-based approaches that can extract biological information from routine histopathological slides. In this study, we investigated whether nuclear morphometric features derived from hematoxylin–eosin (HE) images of tumors can predict the molecular subtype, pathological tumor type, WHO grade, and prognosis of gliomas using machine learning (ML). Methods: A total of 157 specimens were analyzed. All HE slides were scanned and 15 regions of interest (ROIs) were chosen. About 960 features of cell nuclei in the ROIs were analyzed using support vector machine and random forest models to predict molecular alterations (isocitrate dehydrogenase 1 [IDH-1], alpha thalassemia/mental retardation syndrome X-linked, p53, and the 1p/19q codeletion), pathological diagnosis, WHO grade, and prognosis. Results: The tumor type classification and prognosis were predicted with 96% and 94% respectively. IDH-1 mutation status was predicted with 95% accuracy. Feature importance analysis indicated that parameters associated with nuclear shape, particularly orientation, eccentricity, and solidity, contributed substantially to the prediction models. Conclusions: Quantitative nuclear morphometric analysis of HE images using ML enables accurate prediction of the molecular characteristics and prognosis of gliomas. This approach may complement conventional diagnostics, and demonstrates the potential to reduce diagnostic costs. Furthermore, with the incorporation of additional genetic analyses, this approach may lead to personalized medicine in the future.

Abstract: Background: Reliable molecular biomarkers that complement cytomorphological assessment for early identification of high-risk oral epithelial lesions remain limited. Although p53 immunocytochemistry (ICC) is widely used as a surrogate marker of TP53 abnormalities, p53 protein expression does not always reflect TP53 transcriptional activity. This study investigated the relationships between p53 ICC, TP53 mRNA expression, and p53 immunohistochemical (IHC) staining patterns in oral high-grade squamous intraepithelial lesions (OHSIL) and oral squamous cell carcinoma (SCC). Methods: A total of 504 liquid-based cytology (LBC) specimens were classified according to the Bethesda System for Reporting Oral Cytology as NILM (n = 394), OLSIL (n = 72), OHSIL (n = 18), and SCC (n = 20). p53 ICC was performed on cytological specimens, TP53 mRNA expression was quantified by quantitative real-time PCR using residual LBC samples, and corresponding biopsy specimens were evaluated for p53 IHC staining patterns. Associations between p53 ICC and clinicopathological variables were also analyzed. Results: The p53 labeling index significantly increased with cytological severity, and TP53 mRNA expression was significantly higher in OHSIL and SCC than in NILM and OLSIL. In OHSIL and SCC, lesions showing a null-type p53 IHC staining pattern showed significantly lower TP53 mRNA expression than lesions with non-null staining patterns. No significant associations between p53 ICC and clinicopathological variables were observed in OHSIL. In contrast, p53 ICC positivity in SCC was significantly associated with the tumor size, depth of invasion, and p53 IHC staining patterns. Conclusions: Negative p53 ICC may result from either sampling limitations or null-type TP53 expression. TP53 mRNA analysis provides complementary molecular information that distinguishes false-negative p53 ICC caused by sampling limitations from biologically reduced TP53 expression. Integrating cytomorphology, p53 ICC, histopathology, and TP53 mRNA analysis may improve diagnostic accuracy and risk stratification of high-risk oral epithelial lesions.

Abstract: Aging and glycative stress are major contributors to skeletal fragility, yet they affect bone through partially overlapping and distinct mechanisms. Aging is characterized by progressive deterioration of bone mass, microarchitecture, and remodeling balance, driven by hallmarks such as cellular senescence, mitochondrial dysfunction, oxidative stress, chronic low-grade inflammation, impaired autophagy, and altered intercellular communication. These processes disrupt the function of osteocytes, osteoblasts, and osteoclasts, leading to reduced bone formation, increased bone resorption, and impaired adaptation to mechanical loading. In parallel, glycative stress results from the accumulation of advanced glycation end-products (AGEs) and advanced glycoxidation end-products, which accumulate during aging and are accelerated in metabolic disorders such as type 2 diabetes mellitus. Unlike aging, glycative stress predominantly compromises bone quality rather than bone mass by altering collagen cross-linking, matrix mechanics, and cellular signaling through activation of the AGE–RAGE axis. Both conditions converge on shared pathways involving oxidative stress, inflammation, mitochondrial dysfunction, autophagy impairment, senescence, and defective mechanotransduction, ultimately reducing osteocyte viability and disrupting bone remodeling. Glycative stress additionally impairs bone mechanosensitivity by modifying extracellular matrix properties and altering key signaling networks, including Wnt/β-catenin, connexin 43, and PTH1R-dependent pathways. Emerging evidence identifies NLRP3 inflammasome activation, ferroptosis, and metabolic reprogramming as important downstream mediators linking aging, inflammation, and glycation-induced skeletal deterioration. Understanding the convergent and divergent mechanisms underlying bone aging and glycative stress may facilitate the development of targeted therapeutic strategies combining anti-resorptive, anabolic, senolytic, antiglycative, and mechanoprotective approaches to reduce fracture risk and preserve skeletal health.

Abstract: Background. Routine laboratory tests provide multidimensional information about systemic physiology, but their interpretation is usually analyte-specific and may not capture cumulative abnormality burden across biological systems. A laboratory-derived Restorative Index (RI) framework has been formulated to transform normalized laboratory deviations into a bounded 0–100 score, where higher values indicate lower laboratory abnormality burden. This study applied the global and domain-specific RI framework to de-identified longitudinal clinical laboratory data and evaluated its computability, longitudinal behavior, domain decomposition, and exploratory predictability. Methods. This retrospective application study analyzed de-identified longitudinal laboratory and treatment/dose data. Eligible numeric laboratory values were transformed into normalized abnormality distances relative to reference intervals. Subject-date panels with at least five RI-eligible analytes were used to compute global RI. Domain-specific RIs were computed for predefined biological domains. Subject-date panels were classified as baseline, intermediate, follow-up, or single-record observations. Exploratory predictive models included regularized linear models, Bayesian regression, tree-based ensemble models, boosting models, support-vector regression, neural networks, and domain-to-global models. Cross-validation used subject-level grouping where repeated observations were present. Results. The RI framework was applied to 2,001 eligible subject-date panels. Baseline RI was available in 902 subjects, follow-up RI in 551 subjects, and intermediate RI in 306 subjects, contributing 548 intermediate RI rows. Median global RI increased from 59.66 at baseline to 61.72 at follow-up, with a median change of +2.52 and mean change of +3.28 RI points. Using a 5-point threshold, 227 subjects improved, 167 remained stable, and 157 declined. Global RI prediction was feasible but modest; the strongest global model predicted final RI from the latest known pre-follow-up RI using ExtraTrees, with cross-validated R²=0.417, MAE = 12.24, and RMSE = 15.23. Domain-specific prediction was stronger for renal/uric intermediate RI (R²=0.598) and hematology/CBC intermediate RI (R²=0.570). Same-date global RI could be partially estimated from domain-specific RI features, with the best model achieving R²=0.639, whereas future final global RI prediction from domain RI dynamics was weaker (R²=0.237). Conclusion. The RI framework was applicable to heterogeneous longitudinal laboratory data and provided a computable, trackable, and biologically decomposable summary of laboratory abnormality burden. Domain-specific RI improved interpretability and selected-domain predictability. These findings support RI as a retrospective laboratory-informatics framework, but RI changes should not be interpreted as treatment efficacy and require prospective external validation against clinical outcomes.

Abstract: Background Bloodstream infection (BSI) complicates 20–50% of ICU sepsis cases and independently increases mortality. Conventional biomarkers — procalcitonin and C-reactive protein — lose discriminative power amid the high background inflammation of critical illness. Cell population data (CPD) from automated hematology analyzers offer morphological neutrophil metrics at no added cost. We evaluated NE-WY (neutrophil side scatter width) and NE-SFL (neutrophil fluorescence intensity) for confirmed BSI identification in ICU patients. Methods This STARD 2015-compliant prospective study enrolled 72 adult ICU patients at Dr. Soetomo General Academic Hospital, Surabaya. The primary analysis compared G1 (Sepsis-3 criteria, NHSN LCBI-confirmed BSI; n=26) against G2 (SOFA < 2, negative culture; n=21). NE-WY and NE-SFL were measured on Sysmex XN-3000; MDW on Beckman Coulter DxH 900. ROC analysis with DeLong AUC comparison assessed diagnostic accuracy. A pre-specified sensitivity analysis included G1 versus G2 plus 25 suspected-sepsis patients with non-diagnostic cultures (n=72). Results NE-WY was higher in G1 (median 789.5 [IQR 758.8–867.3]) than G2 (686.0 [IQR 650.0–747.0]; p=0.001, r=0.47). At cutoff 766, NE-WY AUC was 0.775 (95%CI 0.632–0.917): sensitivity 73.1% (95%CI 53.9–86.3%), specificity 85.7% (95%CI 65.3–95.0%), PPV 86.4%, NPV 72.0%, and accuracy 78.7%. NE-WY outperformed MDW (AUC 0.533; DeLong p=0.047). In the sensitivity analysis (n=72), NE-WY AUC fell to 0.620 (95%CI 0.485–0.756), indicating that performance depends on comparator composition. Conclusions NE-WY identifies confirmed BSI against non-septic ICU controls with AUC=0.775 and specificity 85.7%, outperforming MDW. Performance falls to AUC=0.620 against a mixed comparator that includes suspected-sepsis patients. Future studies should compare culture-confirmed BSI against culture-negative Sepsis-3 patients to isolate the bacteraemia-specific signal.

Abstract: Endometrial cancer is one of the most rapidly increasing gynaecological malignancies worldwide. The clinically adapted molecular classification of endometrial carcinoma, derived from The Cancer Genome Atlas, comprises four major subtypes: POLE-mutated, mismatch repair deficiency, p53-abnormal expression, and no specific molecular profile. Its clinical implementation has improved prognostic stratification, risk assessment, and treatment decision-making in patients with endometrial carcinoma. However, current workflows rely on immunohistochemistry and targeted sequencing, which increase costs, turnaround times, and infrastructure requirements, thereby limiting their universal adoption in routine practice. In this context, recent advances in artificial intelligence (AI), particularly deep learning models capable of predicting molecular features directly from H&E-stained whole-slide images, have emerged as promising tools for precision oncology. In addition to reproducing established molecular classification, these approaches may reveal previously unrecognised biomarker-defined histologic patterns that are difficult to detect using conventional methods. This article synthesises the current evidence on AI-based molecular classification in endometrial carcinoma from a pathologist-centred perspective, with an emphasis on the biological rationale, methodological limitations, and future directions for clinical translation.

Abstract: Diffuse large B-cell lymphoma (DLBCL) is an aggressive lymphoma characterized by a diffuse proliferation of large neoplastic B lymphocytes. DLBCL is histologically and clinically heterogeneous. Skeletonization reduces binary objects to 1-pixel wide curved line representations without changing the essential structure of the image. This is useful for feature extraction and represents an object’s topology. This study used the skeletonization function to analyze the topological heterogeneity of DLBCL in a series of 153 patients, including 110 patients with DLBCL and 43 patients with reactive lymphoid tissue. In comparison with reactive tissue, DLBCL was characterized by lower skeletonization: 5665.20 ± 1012.82 vs 6341.16 ± 548.23, respectively (P < 0.001). Within the DLBCL diagnostic category, high skeletonization correlated with poor overall survival (hazard risk = 2.5, P = 0.003). High skeletonization also correlated with higher EBER positivity and lower CD5, E2F1, BCL2, ISY1, and TNFAIP8 protein levels (all P values < 0.05). Gene expression was available in a subset of 30 cases, including 18 cases with high skeletonization and 12 cases with low skeletonization. The analysis showed that 166 immuno-oncology genes were upregulated in the high group and only 8 genes were upregulated in the low skeletonization group. In conclusion, DLBCL is characterized by lower skeletonization than reactive lymphoid tissue. In DLBCL, high skeletonization is associated with poor prognosis and enrichment of immuno-oncology markers.

Abstract: The analysis of non-blood biological fluids, including cerebrospinal fluid (CSF), serous effusions, and synovial fluid, plays a central role in laboratory medicine by providing essential diagnostic and prognostic information for neurological, infectious, inflammatory, and neoplastic diseases. However, the interpretation of these specimens remains challenging because it requires the integration of heterogeneous biochemical, cytological, microbiological, molecular, and clinical data, often in the absence of standardized analytical workflows. Artificial intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is emerging as a powerful approach for extracting clinically relevant information from complex multidimensional datasets beyond the capabilities of conventional analytical methods. AI-driven Clinical Decision Support Systems (CDSS) can integrate laboratory findings with clinical, demographic, imaging, and multi-omics data, supporting diagnostic interpretation, patient stratification, and personalized clinical decision-making. At the same time, the convergence of AI with proteomics, metabolomics, metagenomics, and other omics technologies is accelerating biomarker discovery and advancing precision laboratory medicine. Current evidence indicates different levels of maturity across biological fluids. AI-assisted interpretation of CSF biomarkers and digital cytology of serous effusions currently show the strongest clinical evidence, whereas applications involving synovial fluid and integrated multi-omics remain largely exploratory. Although important technical, methodological, and regulatory challenges still limit widespread clinical implementation, AI has the potential to improve diagnostic accuracy, reduce interpretative variability, and support more integrated diagnostic workflows. This mini-review summarizes current and emerging AI applications in non-blood biological fluid analysis, with particular emphasis on biomarker discovery, CDSS, multi-omics integration, current evidence, existing limitations, and future perspectives for precision laboratory medicine.

Abstract: Background. Asthma pathophysiology spans immune mechanisms, epithelial biology, environmental exposures, microbiome, metabolism, neuroimmune circuits, and comorbidities, but the recent review literature is highly compartmentalized by domain. A systems-level representation of the current consensus on human asthma biology is lacking. Methods. A scoping review was conducted following PRISMA-ScR guidelines. Five databases (PubMed/MEDLINE, Scopus, Web of Science, Embase, Cochrane) were searched with seventeen thematic queries for reviews and systematic reviews published 2021–2026. After screening and full-text assessment, 251 human-evidence sources were included. Entities (nodes) and mechanistic connections (edges) were extracted, normalized, and classified by role (pathogenic, protective, mixed, therapeutic), biological level, and asthma subtype. Nodes were filtered by a pre-specified priority classification; the final network included Priority A nodes reported in ≥3 independent studies and all edges connecting included nodes reported in at least one study. Results. The network comprises 265 nodes and 1,632 edges, organized around 30 high-degree hubs and ten thematic clusters. Edges were 88.7% pathogenic, 9.7% protective, 1.5% mixed, and 0.2% therapeutic. The network is dominated by an epithelium-to-outcome axis rather than the canonical T2/non-T2 dichotomy; environmental, microbiome, metabolic, and comorbidity clusters are structurally integrated into core disease mechanism. Conclusions. The literature-derived network provides a reusable scaffold for systems-level research on asthma, operationalizes the treatable-traits framework, and makes explicit the integration of comorbidities and environmental determinants into core disease mechanism.

Abstract: Histopathologic melanoma diagnosis extends beyond melanocytic cytology to encompass non-melanocytic features: solar elastosis patterns, stromal regression, adnexal relationships, epidermal reaction patterns, and the host inflammatory response. These “old school” low-power clues are particularly valuable on sun-damaged skin, where benign nevi, reactive melanocytic hyperplasia, and melanoma in situ share overlapping features. Quantitative data support two elastosis-based signs: the “umbrella sign” (reduced elastosis beneath the lesion’s central third; PPV for nevus 96%, NPV for melanoma 74%; calculated from raw cohort data) and the “purple fiber sign” (100% specificity, 30% sensitivity for nevus), both from a cohort of 81 actinically damaged lesions. Regression-identified by compressed elastic layers displaced to the reticular dermis, fibrosis, melanophages, and inflammation-aids diagnosis but complicates distinction from surgical scar. The maturation state of tertiary lymphoid structures (TLS) within the regression zone, ranging from immunosuppressive immature aggregates to anti-tumoral mature structures with germinal centers, may explain the variable prognostic significance of histologic regression. Epidermal hyperplasia over thick melanomas reflects angiogenesis-related changes, while effacement is a practical red flag in spitzoid lesions. Ancillary tests are most productive when morphology has already framed the differential. These non-melanocytic clues remain indispensable as the foundation for rational ancillary testing.

Abstract: Background: Renal cell carcinomas (RCC) represent neoplasms with variable biological behaviour. Some remain difficult to classify within the current diagnostic framework. Clear cell papillary renal cell tumour (CCPRCT) is now recognized as an indolent entity, whereas renal cell carcinoma with fibromyomatous stroma (RCCFMS) remains a provisional subtype with partially overlapping morphological features. Methods: We analysed a multifocal RCC with clear cell morphology and prominent fibromyomatous stroma using whole-genome and RNA sequencing. The molecular profile was compared with the Cancer Genome Atlas (TCGA) pan-cancer dataset including 885 RCC cases. Histological re-evaluation of 10 identified similar cases was performed. Transcriptional data was mined for potential markers which were validated in an independent cohort. Results: The ten TCGA cases with similar transcriptomic features were characterized by diploid genomes, absence of recurrent chromosomal alterations and lack of VHL gene mutations. Reduced VHL mRNA expression was observed, with increased methylation at selected CpG sites consistent with possible epigenetic down-regulation. Histological re-evaluation by three urological pathologists identified diagnostic variability. Differential expression analysis highlighted cytokeratin 17 (KRT17) and collagen 17A1 (COL17A1) as candidate markers. Immunohistochemical evaluation in a small (n = 6) independent cohort of CCPRCT demonstrated expression of both markers, whereas tissue microarrays of 257 clear cell and 68 papillary RCC cases were found to be negative. Conclusions: These findings suggest that a subset of renal tumours with overlapping morphological features of CCPRCT and RCCFMS may share common molecular characteristics. These observations are exploratory and hypothesis-generating, and further studies in larger, well characterized cohorts are required to clarify the biological and diagnostic significance of this subgroup.

Abstract: Cutaneous metastases represent a relatively rare but clinically significant manifestation of advanced internal malignant neoplasms, occurring in approximately 0.7% to 10.4% of all cancer patients. Because cutaneous metastases can occasionally serve as the first indication of an occult internal malignant neoplasm, their accurate and timely diagnosis is of paramount importance. However, distinguishing cutaneous metastases from primary cutaneous neoplasms, particularly malignant cutaneous adnexal tumors, poses a diagnostic challenge in dermatopathology due to significant clinical and histomorphologic overlap. Here, we review the current literature on the epidemiology and clinical presentation of cutaneous metastases, emphasizing the importance of clinicopathologic correlation. While histologic features such as a purely dermal/subcutaneous location, intravascular tumor emboli, and a "bottom-heavy" architecture suggest metastasis, the presence of an in situ component or morphologic transition from a benign precursor lesion strongly supports a primary cutaneous origin. Furthermore, we detail the utility of optimized immunohistochemical panels, highlighting the diagnostic value of markers such as p63, cytokeratin 15, calretinin, and D2-40 (podoplanin) in confirming primary adnexal lineage. We also explore recent advances in molecular biology and comprehensive genomic profiling, discussing how the identification of specific gene fusions (e.g., MYB::NFIB, CRTC1::MAML2, YAP1 fusions) and mutational signatures can resolve ambiguous cases and provide critical insights into lineage. Finally, we provide a comprehensive, practical diagnostic algorithm for differentiating primary cutaneous adnexal carcinomas from cutaneous metastases of adenocarcinomas. By integrating traditional histopathologic techniques with modern immunohistochemical and molecular techniques, pathologists and clinicians can successfully navigate this complex differential diagnosis and thereby facilitate appropriate patient management and therapeutic intervention.

Abstract: Background/Objectives: Diffuse large B-cell lymphoma (DLBCL) is an aggressive lymphoma and one of the most common hematological neoplasia. Entropy is a statistical measure of randomness that can be used to characterize the texture of an input image and measure tissue complexity. Methods: Image processing and computer vision analysis were performed on a series of 114 diagnostic DLBCL cases and 44 reactive lymphoid tissues stained with hematoxylin & eosin (H&E). Histological entropy was measured to differentiate between reactive lymphoid tissue and DLBCL and predict clinical evolution. Gene expression analysis using the NanoString nCounter PanCancer Immune Profiling Panel was performed in 29 cases. Results: Comparison with reactive lymphoid tissue, DLBCL was characterized by lower entropy (7.3 ± 0.2 vs. 6.8 ± 0.6; P < 0.001, respectively). Within the DLBCL diagnostic category and at patient-level analysis, higher entropy was associated with poor overall survival and death events within the first 2 years (hazard-risk = 2.4, P = 0.004) and lower entropy with a moderate and more favorable outcome (hazard-risk = 0.4, P = 0.004). High entropy was also correlated with ECOG performance status ≥ 2, lower protein expression of apoptosis markers of cPARP and cCASP3, and upregulation and downregulation of specific immuno-oncology genes. Conclusion: The histological evaluation of entropy is useful for both the differential diagnosis of reactive lymphoid tissue and DLBCL and can be used as a predictor factor of DLBCL prognosis.

Abstract: TLS are ectopic, non-encapsulated aggregates of immune cells that develop de novo in non-lymphoid tissues in response to persistent antigenic stimulation, and have emerged as clinically relevant features of many solid tumours. However, conventional TLS assessment based on presence/absence, density, or simplified maturation scales does not adequately explain why TLS are associated with favourable, neutral, or even adverse clinical outcomes across tumour types and treatment settings. In this review, we synthesize current biological, spatial, and clinical evidence and argue that TLS should be interpreted not as static histologic findings, but as functional immune niches shaped by three interacting axes: structural maturity, spatial localization, and functional immune context. We discuss how mature germinal centre-positive TLS often reflect coordinated B-cell–T-cell cooperation and sustained antigen-driven antitumour immunity, whereas partially organised or suppressive TLS may display transitional or immunoregulatory properties. On this basis, we propose a pragmatic, pathology-oriented conceptual framework that groups TLS into three simplified functional states: TLS-A, representing mature effector TLS with germinal centre activity; TLS-B, representing organised but incompletely matured or functionally intermediate TLS; and TLS-C, representing TLS dominated by regulatory or suppressive immune programs. We further place these states within recurrent tumour microenvironment archetypes and outline the rationale for a “functional TLS score” integrating histopathologic and molecular readouts. Rather than introducing a definitive biological taxonomy, this framework is intended as a translational model for harmonising TLS interpretation, refining biomarker development, and supporting future studies on prognosis, immunotherapy response, and standardised pathology reporting in solid tumours.

Abstract: Background/Objectives: Despite Hematoxylin and eosin (H&E) staining remains the cornerstone of histopathological diagnosis, substantial intra- and inter-laboratory variability persists. This issue is increasingly relevant in Digital Pathology, where staining inconsistency may affect whole-slide image interpretation and the performance of image analysis algorithms. In the present work, we evaluated the diagnostic adequacy and non-inferiority of a novel tabs-based H&E staining method compared with conventional liquid reagents. Methods: Fifty formalin-fixed paraffin-embedded tissue samples from routine practice were sectioned in duplicate and stained either conventionally or using H&E Stain Tabs. After slide review, 14 representative tissue samples were selected, scanned at 40x magnification, and used to generate 24 matched image pairs at different magnifications. A blind online survey was completed by 13 expert pathologists using high-quality monitors. Participants assessed overall staining preference and rated stromal, epithelial, cytoplasmic, and nuclear staining quality. Non-inferiority was tested using a predefined margin of −0.10, and paired rating differences were analyzed using the Wilcoxon signed-rank test. Results: Across 312 paired evaluations, the tabs-based method was preferred in 120 cases (38.5%), conventional staining in 118 cases (37.8%), and no preference was expressed in 74 cases (23.7%). The tabs-based method met the criterion for non-inferiority compared with standard staining (z = 2.7). Rating-scale analysis showed significantly better stromal evaluation with the tablet-based method (z = 2.638; p = 0.008), whereas no significant differences were observed for epithelial, cytoplasmic, or nuclear staining. All evaluated images were considered diagnostically adequate. Conclusions: The tabs-based H&E stain was non-inferior to the conventional method and showed particularly favorable performance in the assessment of stromal components. These findings support its potential role in improving staining reproducibility and standardization, particularly in Digital Pathology workflows where pre-analytical and analytical consistency is critical.

Abstract: Background/Objectives: Early prediction of clear cell renal cell carcinoma (ccRCC) metastasis helps differentiate patients for more appropriate therapies. MicroRNAs (miRNAs) - miR-10b-5p, miR-130b-3p, miR-139-5p and miR-199b-5p - have been found highly associated with ccRCC metastasis in frozen tumor tissue. In this study, we determine the risk status of ccRCC metastasis, specifically for FFPE tumor speci-mens. Methods: Using the risk score method and a formalin-fixed paraffin-embedded (FFPE) tissue training cohort (n=28) of localized and metastatic ccRCC samples, we built a quantitative PCR-based miRNA signature. With the defined risk score cutoffs, we stratified patients into high risk, low risk, and equivocal groups. We validated the signature in a 265-case test cohort of FFPE primary ccRCC specimens. Results: For all the patients predicted to be at high or low risk, the overall sensitivity and specificity for the signature were 80% and 76% (OR=6.60, 95%Cl=2.54-17.13, p=0.0001), respectively. The sensitivities and specificities were 72% and 78%, 70% and 67%, 78% and 75% for stages I, II and III patients, respectively. The signature was also well correlated with cancer-specific survival of patients (HR=3.06, 95%Cl=1.49-6.93, p=0.004). Conclusion: This FFPE specimen-specific miRNA signature can predict ccRCC metastasis and prognosis in routine FFPE tumor specimens, to help to stratify patients for more appropriate treatment.

Abstract: Background/Objectives: Chromosomal microarray analysis (CMA) is an essential tool in modern cytogenetics for detecting copy number alterations and copy-neutral loss of heterozygosity (CN-LOH). As optical genome mapping (OGM) emerges as a potential replacement for traditional cytogenetic methods, the extent to which CMA remains necessary in routine diagnostic workflows remains to be elucidated. Methods: We retrospectively reviewed 53 primary neoplastic cases in which CMA identified one or more CN-LOH events. Event size, genomic content, and correlation with next-generation sequencing (NGS) findings were assessed. A separate cohort of newly diagnosed B-cell acute lymphoblastic leukemia (B-ALL) was analyzed to evaluate disease-specific CN-LOH frequency. Results: Nearly half of CN-LOH events detected were &lt;25 Mb, below the current detection threshold of OGM. Many encompassed clinically relevant genes, including FLT3, JAK2, TET2, TP53, and RUNX1. Additionally, two-thirds of cases harbored pathogenic or likely pathogenic variants by NGS within the corresponding CN-LOH regions, further underscoring the clinical value of detecting these copy-neutral events. In contrast, CN-LOH was uncommon in B-ALL, and most alterations identified by CMA would be detectable by OGM. Many of these patients also harbored complex structural rearrangements that required multiple conventional assays for full characterization; these could be resolved by OGM in a single analysis. Conclusions: Our findings indicate that although OGM excels at resolving complex structural variants, CMA remains essential for detecting copy-neutral events. Until OGM achieves improved sensitivity for CN-LOH, an integrated approach utilizing conventional cytogenetics, CMA, NGS, and OGM provides the most reliable framework for comprehensive genomic assessment across cancer types.

Abstract: Fibroblast growth factor receptor 2b (FGFR2b) has become an increasingly important therapeutic target in gastric and gastroesophageal junction cancer, particularly with the clinical development of FGFR2b-directed antibody therapy. However, its translation into routine treatment selection is not straightforward. FGFR2b is usually assessed as a protein biomarker by immunohistochemistry, and a positive result may reflect different biological situations depending on staining intensity, percentage of positive tumor cells, sample type and spatial distribution. In addition, FGFR2b protein expression, FGFR2 amplification, transcript-level activity and true pathway dependency are related but not interchangeable. This review examines FGFR2b-positive gastric cancer from the perspective of biomarker reliability rather than target presence alone. We discuss the biological basis of FGFR2b targeting, the reasons for variability in reported positivity rates, the implications of intratumoral and inter-lesion heterogeneity, the current clinical evidence for FGFR2b-directed and broader FGFR-targeted approaches, and the emerging challenges of safety, resistance and treatment sequencing. Particular attention is given to the gap between detecting FGFR2b and identifying tumors in which this target is sufficiently expressed, representative and biologically relevant to guide therapy. We also consider how FGFR2b should be interpreted alongside HER2, CLDN18.2, immune biomarkers and other receptor tyrosine kinase alterations. As FGFR2b-directed strategies move forward, their success will depend not only on drug efficacy, but also on standardized testing, careful reporting, and selective reassessment when disease biology changes. FGFR2b therefore offers a useful model for how protein biomarkers can be developed in gastric cancer: not as isolated positive-or-negative la-bels, but as clinically interpreted variables within a changing therapeutic landscape.

Abstract: Neuroendocrine differentiation in tumors of the female genital tract is an uncommon but diagnostically consequential finding. Its interpretation is challenging because neuroendocrine marker expression does not necessarily define a neuroendocrine neoplasm. Focal or aberrant staining for synaptophysin, chromogranin A, CD56 or INSM1 may occur in otherwise conventional gynecologic carcinomas, whereas true poorly differentiated neuroendocrine carcinomas represent aggressive tumors with distinct prognostic and therapeutic implications. This narrative review examines neuroendocrine differentiation across the cervix, endometrium, ovary, vagina and vulva from an integrated clinicopathologic perspective. We emphasize that neuroendocrine differentiation should be approached as a diagnostic and biological spectrum, ranging from incidental immunophenotypic expression to carcinoma with neuroendocrine differentiation, mixed neuroendocrine/non-neuroendocrine tumors, well-differentiated neuroendocrine tumors and poorly differentiated neuroendocrine carcinomas. Morphology remains the diagnostic anchor, while immunohistochemistry, molecular context and clinicoradiologic correlation refine classification and help exclude mimics or metastatic disease. Site-specific interpretation is essential: cervical neuroendocrine car-cinoma is commonly HPV-associated and clinically aggressive; endometrial tumors require integration with p53, mismatch repair, POLE and SWI/SNF-related contexts; ovarian lesions demand distinction between primary well-differentiated neuroendocrine tumors, poorly differentiated carcinomas and metastases; and vaginal or vulvar tumors require careful exclusion of adjacent extension, cutaneous mimics and extragenital primaries. We propose a practical diagnostic framework that separates incidental marker expression from clinically meaningful neuroendocrine differentiation and links this distinction to reporting, prognosis and treatment. The central diagnostic question is not whether neuroendocrine markers are expressed, but whether their expression defines a morphologically, biologically and clinically meaningful tumor category.

Abstract: Background: Penile squamous cell carcinoma (pSCC) is a global health burden with poor systemic treatment efficacy for advanced disease, relying on pathway-agnostic regimens despite two distinct carcinogenic pathways (HPV-driven vs. HPV-independent). We conducted an integrative review to systematically compare the tumor immune microenvironment (TIME) of HPV-driven and HPV-independent pSCC to guide immunotherapy stratification. Methods: An integrative review of 21 studies, including single-cell/spatial transcriptomics and a Phase II clinical trial, synthesized evidence from over 4,500 pSCC patients published between January 2020 and April 2026. Results: HPV-positive pSCC presents an immunologically active but partially suppressed TIME, defined by significantly higher CD8+ T-cell infiltration and lower immune checkpoint co-expression and exhaustion (e.g., TIGIT). HPV-negative tumors exhibit a broadly immunosuppressive niche marked by elevated PD-L1 prevalence (51.4% pooled), increased regulatory T-cell and M2-macrophage polarization, and multi-checkpoint co-exhaustion (PD-1, TIM-3, LAG-3). PD-L1 overexpression is associated with shorter cancer-specific survival. Clinically, HPV positivity and CD8+ T-cell density independently predicted progression-free survival benefit from atezolizumab. Conclusion: These findings establish HPV status and TIME composition as actionable determinants of immunotherapy benefit. We recommend prospective integration of HPV testing and tumor-infiltrating lymphocyte quantification into future randomized trials to guide patient selection and explore combinatorial checkpoint blockade, particularly for the multi-exhausted HPV-negative disease subset.