Search Results (3,188)

Biological invasions provide a unique window into community assembly. While classic theory predicts that native species must differentiate their niches to coexist with an invader, the actual outcomes under intense pressure are complex. Our study examines community reassembly under extreme pressure from the invasive ant Solenopsis invicta. We found that while native species do differentiate themselves from the invader, the overwhelming competition constrains this process, forcing survivors into a narrow, shared functional space. This constrained niche differentiation produces a pattern of community-level functional convergence, a process where functionally dissimilar communities become more similar under intense environmental filtering, as survivors are forced into a narrow, shared niche space. The capacity for these rapid, adaptive niche shifts is rooted in intraspecific trait variation (ITV). We also identified a dynamic feedback loop through density-dependent phenotypic plasticity in the invader. By showing how the foundational process of niche differentiation leads to a convergent outcome under extreme pressure, our work clarifies the rules of community assembly in an increasingly invaded world. Full article

Background/Objectives: The factors contributing to residual atherosclerotic cardiovascular disease (ASCVD) risk in individuals are not fully understood, but knowledge of the specific type of dyslipoproteinemia may help further refine risk assessment. We developed a novel phenotyping and risk assessment system that may be applied automatically using standard lipid panel parameters. Methods: NHANES data collected from 37,056 individuals during 1999–2018 were used to develop a three-dimensional dyslipidemia phenotype classification system. ARIC data from 14,632 individuals were used to train and validate the risk model. Three-dimensional Cartesian coordinates were converted to spherical coordinates, which were used as features in a logistic regression model that provides a probability of ASCVD. UK Biobank data from 354,344 individuals were used to further validate and test the model. Results: Nine lipidemia phenotypes were defined based on the concentrations of HDLC, non-HDLC and TG. These phenotypes were related to the prevalence of metabolic syndrome, pooled cohort equation (PCE) score and ASCVD-free survival. A logistic regression model including age, sex and the spherical coordinates of the phenotype provided a composite risk score with predictive accuracy comparable to that of the PCEs. Conclusions: We provided an example of how a multidimensional coordinate system may be used to define a novel lipoprotein phenotyping system to examine disease associations. When applied to an ASCVD risk model, the composite spherical coordinate risk marker, which can be fully automated, provided an F1 performance score almost as good as the PCEs, which requires other risk factors besides lipids. Full article

Introduction: Cigarette smoking is unquestionably associated with an increase in morbidity and mortality worldwide, exerting significant adverse effects on respiratory health. The impact of tobacco persists in the epigenome long after smoking cessation. Furthermore, the offspring of smokers may also be affected by the detrimental effects of smoking. Material and methods: The modifications made to the body, such as DNA methylation, histone modification, and regulation by non-coding RNAs, do not change the DNA sequence but can influence gene expression. In respiratory disease, multigenerational effects have been reported in humans, with an increased risk of asthma or COPD and decreased lung function in offspring, despite them not being exposed to smoke. Prenatal nicotine exposure leads to pulmonary pathology that persists across three consecutive generations, supported by animal studies conducted by Rehan et al. Significant advances in high-throughput genomic and epigenomic technologies have enabled the discovery of molecular phenotypes. These either reflect or are influenced by them. Due to the hidden environmental effects and the rise of artificial intelligence (AI) in biomedical research, new predictive models are emerging that not only explain complex data but also enable earlier detection and prevention of smoking-related diseases. In this narrative review, we synthesise the latest research on how smoking affects gene regulation and chromatin structure, emphasising how tobacco can increase vulnerability to multiple diseases. Discussion: For many years, it was widely believed that diseases are solely inherited through genetics. However, recent research in epigenetics has led to a significant realisation: environmental factors play a crucial role in an individual’s life. External influences leave a mark on DNA that can influence future health and offer insights into potential illnesses. In this context, it is possible that in the future, doctors might treat people not as a whole but as individual beings, with personalised medication, tests, and other approaches. Conclusions: The accumulated evidence suggests that exposure to various environmental factors is associated with multigenerational changes in gene expression patterns, which may contribute to increased disease risk. The application of artificial intelligence in this domain is currently a crucial tool for researching potential future health issues in individuals, and it holds a powerful prospect that could transform current medical and scientific practice. Full article

Background/Objectives: This study aimed to characterize the expression patterns of B7 homolog 3 (B7-H3) and cluster of differentiation 155 (CD155), two immune-related transmembrane glycoproteins, in resectable gastric adenocarcinoma and to elucidate their clinicopathological, prognostic, and molecular implications. Methods: The study included 112 patients who underwent gastrectomy for gastric adenocarcinoma between 2020 and 2025, along with 30 samples of normal gastric tissue obtained from sleeve gastrectomy specimens. Histological subtype, grade of differentiation, TNM stage, and invasion parameters were re-evaluated. Immunohistochemical expression of B7-H3 and CD155 was quantified for membranous, stromal and membranous/cytoplasmic staining patterns. Quantitative reverse transcription polymerase chain reaction (RT-PCR) was performed on 29 tumor and 25 normal samples to confirm mRNA expression levels, with fold change ≥2 considered biologically significant upregulation and ≤0.5 considered downregulation. Machine learning models were developed to predict metastasis and mortality based on clinical and immunohistochemical features. Results: 78.5% of tumors were at an advanced stage (T3–T4), and metastasis was present in 22.3% of patients. Perineural invasion (PNI) and lymphovascular invasion (LVI) were observed in 67.9% and 88.4% of cases, respectively. Increased B7-H3 and CD155 expression were significantly associated with advanced tumor stage, metastasis, and the presence of PNI and LVI (all p < 0.05). In metastatic tumors, median membranous B7-H3, stromal B7-H3, and CD155 scores were 60, 130, and 190, respectively, compared with 20, 90, and 120 in non-metastatic tumors. A significant positive correlation was found between stromal B7-H3 and CD155 expression (r = 0.384, p < 0.001), indicating parallel upregulation. Quantitative RT-PCR confirmed significant overexpression of both genes in tumor tissues relative to normal controls. B7-H3 was upregulated in 75.9% and CD155 in 58.6% of samples, with co-upregulation in 55.2%. Fold-change levels were markedly higher in metastatic versus non-metastatic cases (B7-H3: 7.69-fold vs. 3.04-fold; CD155: 7.44-fold vs. 1.79-fold). ML analysis using the XGBoost model achieved 91.1% accuracy for metastasis prediction (F1-score 0.800). Key variables included pathological T4b stage, perineural invasion, N3b status, T4a stage, and CD155 score. The mortality model yielded 86.7% accuracy (F1-score 0.864), with metastasis, differentiation status, nodal involvement, age, lymph node ratio, and perineural invasion emerging as principal predictors. Conclusions: Combined evaluation of B7-H3 and CD155, supported by immunohistochemical staining and RT-PCR quantification of B7-H3 and CD155 mRNA expression levels, provides meaningful prognostic insights and supports their potential as dual molecular biomarkers for aggressive gastric adenocarcinoma phenotypes. Full article

Background/Objectives: The mTOR serine/threonine kinase coordinates protein translation, cell growth, and metabolism, and its dysregulation promotes tumorigenesis. We present a reproducible, pan-cancer, network-aware framework that integrates curated resources with genomics to move beyond pathway curation, yielding falsifiable hypotheses and prioritized candidates for mTOR axis biomarker validation. Materials and Methods: We assembled MTOR-related genes and interactions from GeneCards, KEGG, STRING, UniProt, and PathCards and harmonized identifiers. We formulated a concise working model linking genotype → pathway architecture (mTORC1/2) → expression-level rewiring → phenotype. Three analyses operationalized this model: (i) pan-cancer alteration mapping to separate widely shared drivers from tumor-specific nodes; (ii) expression-based activity scoring to quantify translational/nutrient-sensing modules; and (iii) topology-aware network propagation (personalized PageRank/Random Walk with Restart on a high-confidence STRING graph) to nominate functionally proximal neighbors. Reproducibility was supported by degree-normalized diffusion, predefined statistical thresholds, and sensitivity analyses. Results: Gene ontology analysis demonstrated significant enrichment for mTOR-related processes (TOR/TORC1 signaling and cellular responses to amino acids). Database synthesis corroborated disease associations involving MTOR and its partners (e.g., TSC2, RICTOR, RPTOR, MLST8, AKT1 across selected carcinomas). Across cohorts, our framework distinguishes broadly shared upstream drivers (PTEN, PIK3CA) from lineage-enriched nodes (e.g., RICTOR-linked components) and prioritizes non-mutated, network-proximal candidates that align with mTOR activity signatures. Conclusions: This study delivers a transparent, pan-cancer framework that unifies curated biology, genomics, and network topology to produce testable predictions about the mTOR axis. By distinguishing shared drivers from tumor-specific nodes and elevating non-mutated, topology-inferred candidates, the approach refines biomarker discovery and suggests architecture-aware therapeutic strategies. The analysis is reproducible and extensible, supporting prospective validation of prioritized candidates and the design of correlative studies that align pathway activity with clinical response. Full article

Background: Neurodegenerative diseases, including spastic paraplegia type 11 (SPG11), are complex disorders characterized by progressive neurological decline and significant metabolic disturbances. Spatacsin, the protein encoded by the SPG11 gene, plays a critical role in autophagy and lysosomal homeostasis, which are essential for neuronal health. Its impairment leads to defective cellular clearance and neurodegeneration. Recently, personalized and precision nutrition have emerged as promising approaches to enhance clinical outcomes by tailoring dietary interventions to individual genetic, metabolic, and phenotypic profiles. Objectives: This mini scoping review aimed to synthesize current evidence on the application of personalized and precision nutrition in SPG11 and to explore how insights from related neurodegenerative diseases could inform the development of future dietary and metabolic interventions for this rare disorder. Methods: Following PRISMA-ScR guidelines, a scoping review was conducted using PubMed, Scopus, and Web of Science databases (2020–2024). Eligible studies included investigations addressing nutritional, genomic, or metabolic interventions in neurodegenerative diseases. Of 30 screened papers, nine met the inclusion criteria, primarily focusing on nutritional and metabolic interventions related to neurodegenerative and neuromuscular conditions. Results: To date, no dietary intervention trials have been conducted specifically for SPG11. However, evidence from studies on related neurodegenerative diseases suggests that antioxidant, mitochondrial-supportive, and microbiota-targeted dietary approaches may beneficially influence key pathological processes such as oxidative stress, lipid dysregulation, and autophagy—core mechanisms that are also central to SPG11 pathophysiology. Conclusions: Although current evidence remains preliminary, personalized nutrition is a promising supplementary strategy for managing neurodegenerative diseases, including SPG11. Future research should incorporate systems-based approaches that combine dietary, metabolic, and neuroimaging assessments, with sex and comorbidity-stratified analyses, multi-omics profiling, and predictive modeling. These frameworks could help design safe, effective, and personalized nutritional interventions aimed at enhancing metabolic resilience and slowing disease progression in SPG11. Full article

Background: Wilson’s disease (WD) is a rare autosomal recessive disorder caused by mutations in the ATP7B gene, leading to copper accumulation in the liver and brain. Given the clinical heterogeneity of the disease, this study aimed to characterize the mutational spectrum of ATP7B and explore genotype–phenotype correlations in Turkish patients. Methods: Whole-exome sequencing (WES) was performed in 17 Turkish patients clinically diagnosed with WD. Variants were annotated and evaluated using five in silico prediction tools (REVEL, CADD, PolyPhen, SIFT, MutationTaster). Copy number variation (CNV) analysis was conducted using the CLC Genomics Server (Version 22.0.2). Results: A total of 14 distinct ATP7B variants were identified, comprising 12 missense, 1 nonsense, and 1 frameshift mutation. Variant distribution showed some phenotype-specific patterns: four variants were found more frequently in hepatic cases and three in neurological cases, although no statistically significant or consistent correlation between genotype and clinical presentation could be established. The most frequent mutation was p.His1069Gln, present in both phenotypes. All missense variants were predicted to be pathogenic by at least three computational tools, with high concordance among platforms. No pathogenic CNVs were detected. Conclusions: This study expands the mutational landscape of ATP7B in Turkish patients with WD and supports the utility of WES combined with in silico tools for accurate variant classification. The results emphasize the genetic heterogeneity of WD and suggest possible associations between certain mutations and clinical phenotypes. Full article

Background: Patients with chronic lymphocytic leukemia (CLL) who were not receiving treatment were included in this experimental prospective correlation study. We aimed to elucidate the complex relationship between smudge cells, surface CD20, and soluble CD20 in CLL patients. Methods: We created blood smears from blood samples collected from our patients using a manual technique consistently performed by the same technician. The May–Grunwald Giemsa dye was used to stain all of the slides. The B-cell phenotypic was analyzed using the FacsCanto II flow cytometer (Becton Dickinson, CA, USA) at the time of diagnosis. Competitive Enzyme-Linked Immunoassay (ELISA) was used to quantitatively assess the amounts of soluble CD20/MS4A1. Results: The percentage of smudge cells and soluble CD20 antigen levels were shown to be significantly inversely correlated, suggesting a considerable link (correlation coefficient (r) = −0.51, p = 0.006). Similarly, a significant inverse relationship (r = −0.36, p = 0.04) was found by the Spearman correlation test between the smudge cell ratio and CD20 median fluorescence intensity (MFI) on cell surfaces. Soluble CD20/MS4A1 and surface CD20 MFI were shown to have a weakly positive association that was almost statistically significant (Spearman’s rho = 0.34, p = 0.064). With a sensitivity of 69% and specificity of 86%, we discovered that a cut-off value of 2.2 ng/dL for soluble CD20 predicted higher smudge cells (area under the curve (95% confidence interval (CI)): 0.75 (0.57 to 0.93), p = 0.021). Conclusions: We found a significant inverse association between smudge cells and both surface CD20 and soluble CD20/MS4A1 in our study examining the correlation between smudge cells, soluble CD20, and CD20/MS4A1 in CLL patients. Our findings indicate that soluble CD20 may contribute to understanding the pathophysiology of smudge cells and could be further investigated as a potential prognostic marker in CLL. Full article

Background: The role of CREBBP and EP300 mutations in hypermutation and immunotherapy response in gastroesophageal adenocarcinomas is poorly defined and needs further investigation. Methods: We conducted an in silico analysis of 12 publicly available studies (n = 1871; cBioPortal), stratifying samples by CREBBP/EP300 status to assess associations with TMB-High, MSI, co-mutation patterns, and mutation localization. Clinical validation was performed in an independent pan-cancer cohort treated with ICIs (n = 1610) and a gastric cancer cohort with WES data (n = 55). Results: Coding mutations in CREBBP and/or EP300 were significantly associated with TMB-high and MSI-high phenotypes (p < 0.001). All studied samples carrying coding mutations in both CREBBP and EP300 exhibited a TMB-high status. PTVs in functional HAT and bromodomain regions were exclusively associated with TMB-high. Incorporating CREBBP and/or EP300 mutation status improved identification of ultra-hypermutated tumors compared with single-gene biomarkers (p < 0.001). Clinically, these mutations predicted improved overall survival in the pan-cancer cohort (median OS 34 vs. 17 months; HR = 0.68, 95% CI 0.52–0.87, p = 0.0026), as well as in bladder (HR = 0.55, p = 0.0337) and gastrointestinal cancer cohorts (HR = 0.31, p = 0.0021) treated with ICIs. In the gastric cancer validation cohort, all tumors with PTVs demonstrated a partial response to anti-PD-1 therapy. Conclusions: We report CREBBP and EP300 coding mutations as novel potential surrogate biomarkers for hypermutation in gastroesophageal adenocarcinomas and demonstrate their association with favorable immunotherapy outcomes, supporting their potential clinical utility for patient stratification. Full article

Background/Objectives: Streptococcus uberis is a Gram-positive bacterium and a major cause of bovine mastitis. The use of antimicrobial treatments raises concerns about resistance. This study aimed to characterize S. uberis isolates from one of the ten largest milk-producing regions in Europe. Methods: Thirty-six isolates from 36 cows with mastitis were identified using MALDI-TOF and VITEK^®MS. Susceptibility to 9 antibiotics (penicillin G, ampicillin, tetracycline, erythromycin, clindamycin, cefotaxime, ceftriaxone, levofloxacin, and moxifloxacin) was determined with VITEK^®2. Whole-genome sequencing was performed using MinION Mk1C. Results: Alleles were identified for 7 loci: arcC, ddl, gki, recP, tdk, tpi, and yqiL. Only 10 isolates had alleles for all the loci. The loci with the highest number of alleles were ddl and tdk (33/36 strains), while arcC had the fewest (19/36). Four isolates were assigned to known sequence types (ST6, ST307, and ST184), and novel alleles were detected in 32 of the 36 isolates. Twelve isolates showed phenotypic resistance to one or more of the following antibiotics: tetracycline, erythromycin, clindamycin, and ceftriaxone. The lnu was the most frequently detected resistance gene (27 out of 102 total gene appearances). A total of 19 virulence factors were identified. All strains were predicted to be capable of infecting human hosts. Conclusions: Streptococcus uberis is a potential reservoir of antimicrobial resistance genes. The use of antimicrobials to treat bovine mastitis has reduced the susceptibility of this microorganism to several antibiotics, underscoring the importance of monitoring antimicrobial use in veterinary practice. The results also highlight the high genetic diversity of the isolates, suggesting a strong capacity to adapt to different environmental conditions. Full article

Radiomics has emerged as a promising tool for non-invasive tumour phenotyping in breast cancer, providing valuable insights into tumour heterogeneity, response prediction, and risk stratification. However, traditional radiomic approaches often rely on correlative patterns of image analysis to clinical data and lack direct biological interpretability. Combining information provided by radiomics with genomics or other multi-omics data can be important to personalise diagnostic and therapeutic work up in breast cancer management. This review aims to explore the current progress in integrating radiomics with multi-omics data—genomics and transcriptomics—to establish biologically grounded, multidimensional models for precision management of breast cancer. We will review recent advances in integrative radiomics and radiogenomics, highlight the synergy between imaging and molecular profiling, and discuss emerging machine learning methodologies that facilitate the integration of high-dimensional data. Applications of radiogenomics, including breast cancer subtype and molecular mutation prediction, radiogenomic mapping of the tumour immune microenvironment, and response forecasting to immunotherapy and targeted therapies, as well as lymph nodes involvement, will be evaluated. Challenges in technical limitations including imaging modalities harmonization, interpretability, and advancing machine learning methodologies will be addressed. This review positions integrative radiogenomics as a driving force for next-generation breast cancer care. Full article

Edible mushrooms have gained global popularity due to their nutritional value, medicinal properties, bioactive compounds and industrial applications. Despite their long-standing roles in ecology, nutrition, and traditional medicine, their additional functions in cultivation, breeding, and classification processes are still in their infancy due to technological constraints. The advent of Artificial Intelligence (AI) technologies has transformed the cultivation process of mushrooms, genetic breeding, and classification methods. However, the analysis of the application of AI in the mushroom production cycle is currently scattered and unorganized. This comprehensive review explores the application of AI technologies in mushroom cultivation, breeding, and classification. Four databases (Scopus, IEEE Xplore, Web of Science, and PubMed) and one search engine (Google Scholar) were used to perform a thorough review of the literature on the utility of AI in various aspects of the mushroom production cycle, including intelligent environmental control, disease detection, yield prediction, germplasm characterization, genotype–phenotype integration, genome editing, gene mining, multi-omics, automatic species identification and grading. In order to fully realize the potential of these edge-cutting AI technologies in transforming mushroom breeding, classification, and cultivation, this review addresses challenges and future perspectives while calling for interdisciplinary approaches and multimodal fusion. Full article

The diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters. Traditional, single-dimensional two-dimensional imaging methods often struggle to balance precision with computational efficiency. This paper addresses the limitations of the YOLOv11n-seg model in the instance segmentation of floral disk fine structures by proposing the MBLA-YOLO instance segmentation model, achieving both lightweight efficiency and high accuracy. Building upon this foundation, a non-contact measurement method is proposed that combines an improved model with three-dimensional point cloud analysis to precisely extract key structural parameters of the flower head. First, image annotation is employed to eliminate interference from petals and sepals, whilst instance segmentation models are used to delineate the target region; The segmentation results for the disc surface (front) and edges (sides) are then mapped onto the three-dimensional point cloud space. Target regions are extracted, and following processing, separate models are constructed for the disc surface and edges. Finally, with regard to the differences between the surface and edge structures, targeted methods are employed for their respective calculations. Whilst maintaining lightweight characteristics, the proposed MBLA-YOLO model achieves simultaneous improvements in accuracy and efficiency compared to the baseline YOLOv11n-seg. The introduced CKMB backbone module enhances feature modelling capabilities for complex structural details, whilst the LADH detection head improves small object recognition and boundary segmentation accuracy. Specifically, the CKMB module integrates MBConv and channel attention to strengthen multi-scale feature extraction and representation, while the LADH module adopts a tri-branch design for classification, regression, and IoU prediction, structurally improving detection precision and boundary recognition. This research not only demonstrates superior accuracy and robustness but also significantly reduces computational overhead, thereby achieving an excellent balance between model efficiency and measurement precision. This method avoids the need for three-dimensional reconstruction of the entire plant and multi-view point cloud registration, thereby reducing data redundancy and computational resource expenditure. Full article

Background: Antimicrobial resistance (AMR) in Legionella pneumophila is emerging as a concern, particularly with resistance to macrolides and fluoroquinolones. Although clinically significant resistance in Legionella pneumophila remains uncommon, systematic genomic surveillance using whole-genome sequencing (WGS) is needed to anticipate treatment failure as metagenomic diagnostics move toward routine use. Objectives: We assessed the UK Health Security Agency AMR pipeline for predicting resistance in L. pneumophila by analysing 522 L. pneumophila isolates from England and Wales (2020–2023) together with nine database sequences that carry confirmed 23S rRNA mutations conferring high-level azithromycin resistance. The objective of the present study was to examine the presence of antimicrobial resistance genes (ARGs) in L. pneumophila isolates and to determine whether they exhibited phenotypic resistance through minimum inhibitory concentration (MIC) testing. Methods: Serogroups (sgs) were determined using an in-house qPCR assay, and L. pneumophila non-sg1 isolates were serogrouped using the Dresden monoclonal antibody (mAb) typing method. Sequence types were determined using the standard sequence-based typing method by Sanger sequencing. WGS reads were screened against standard AMR databases to identify resistance genes and resistance-mediating mutations. Agar dilution measured MICs for azithromycin, erythromycin, ampicillin, levofloxacin, tetracycline and spectinomycin in isolates possessing the bla_OXA-29, lpeAB or aph(9)-Ia gene. Results: AMR screening detected lpeAB, two allelic β-lactamase variants (bla_OXA-29 and bla_LoxA) and aph(9)-Ia in 165 of the 522 L. pneumophila isolates, while all high-azithromycin MIC reference sequences contained the expected 23S mutation. Only lpeAB was associated with a significant twofold elevation in macrolide MICs. Neither β-lactamase variant increased ampicillin MICs, and aph(9)-Ia carriage did not correlate with higher spectinomycin MICs. Conclusions: Advanced genomic analytics can now deliver timely therapeutic guidance, yet database-flagged genes may not translate into phenotypic resistance. Continuous pairing of curated mutation catalogues with confirmatory testing remains essential for distinguishing clinically actionable determinants such as 23S mutations and lpeAB from silent markers like bla_OXA-29 and aph (9)-Ia. Full article

High-grade serous carcinoma (HGSC) of the ovary is characterized by two major histological patterns: a classic papillary/micropapillary architecture and a solid pseudo-endometrioid transitional (SET) variant. We investigated whether the distinct morphologic subtypes are underpinned by transcriptomic differences in the tumor microenvironment (TME). We profiled 21 HGSC tumors (7 SET, 14 classic) using a 770-gene NanoString PanCancer Progression panel. Differential expression analysis revealed ~20 genes with significantly different expression (>4-fold, adjusted p < 0.01) between SET and classic tumors. Unsupervised clustering partially separated SET and classic tumors, suggesting that global gene expression patterns correlate with histologic subtype. SET tumors exhibited upregulation of cell-cycle and epithelial genes (e.g., PTTG1, TRAIL, HER3) and downregulation of genes involved in epithelial–mesenchymal transition (EMT), extracellular matrix (ECM) organization, and angiogenesis (e.g., TWIST2, FGF2, decorin) relative to classic tumors. Notably, PTTG1 and TRAIL were upregulated ~6–9-fold in SET tumors, whereas TWIST2 was ~7-fold downregulated, consistent with reduced EMT in SET tumors. Pathway analysis indicated that SET tumors appear to have an immune-active, stroma-poor microenvironment, in line with an “immunoreactive” phenotype, whereas classic tumors showed a mesenchymal, stroma-rich profile. These molecular distinctions could have diagnostic utility and may inform therapeutic stratification, with key dysregulated genes (e.g., HER3, TRAIL, FGF2) representing potential prognostic or predictive biomarkers. For example, high HER3 expression in SET tumors might predict sensitivity to ERBB3/PI3K inhibitors, whereas stromal factors (e.g., FGF2) enriched in classic HGSC could be targeted with microenvironment-modulating therapies. These preliminary findings require validation before translation into pathology practice via immunohistochemical (IHC) assays (e.g., for HER3 or TRAIL), potentially enabling improved classification and personalized treatment of HGSC. We report effect sizes as log₂ fold change with 95% confidence intervals and emphasize FDR-adjusted q-values. Given the small sample size and the absence of outcome data (OS/PFS/PFI), results are preliminary and hypothesis-generating. Orthogonal protein-level validation and replication in larger, independent cohorts are required before any translational inference. Full article