Background/Objectives: This study aimed to evaluate the diagnostic and prognostic utility of B7-H3 expression in differentiating low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to examine its association with clinical outcomes. Methods: This retrospective study included 99 patients with histopathologically confirmed gliomas (42 LGGs and 57 HGGs). B7-H3 expression was assessed using immunohistochemistry and scored by immunoreactive score (IRS). Results: B7-H3 expression was significantly higher in HGG compared to LGG (p < 0.001). The total IRS (B7-H3 A × B) demonstrated strong discriminative power (AUC = 0.816). High B7-H3 expression independently predicted disease progression (OR = 4.9, 95% CI: 2.4–10.1; p < 0.001) and was associated with IDH wild-type status and elevated Ki-67 index. Patients with high B7-H3 had significantly shorter overall survival (median 6 months vs. 42 months) and progression-free survival (median 3 months vs. 25 months) (both p < 0.001). Cox regression confirmed high B7-H3 as an independent predictor of mortality (HR = 2.9, 95% CI: 1.7–4.7; p < 0.001) and progression (HR = 2.6, 95% CI: 1.6–4.2; p < 0.001). Conclusions: B7-H3 expression is a reliable biomarker for distinguishing HGG from LGG and is independently associated with worse survival outcomes. Its assessment may aid in glioma classification and prognostication.

A systematic review with meta-analysis (SRMA) represents the pinnacle of evidence, but its validity depends on methodological rigor. This narrative review synthesizes recommendations from major reporting frameworks- Preferred Reporting Items for Systematic Reviews and Meta‑Analyses 2020 (PRISMA‑2020), Meta‑Analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Overviews of Reviews (PRIOR)-into a concise checklist for peer reviewers. The checklist addresses common sources of bias that often escape editorial assessment. Initially, it outlines how reviewers should assess the rationale for an SRMA by identifying existing syntheses on the same topic and determining whether the new work provides substantive novelty or a significant update. Best practices are summarized for protocol registration, comprehensive search strategies, study selection and data extraction, risk-of-bias evaluation, and context-appropriate statistical modeling, with a specific focus on heterogeneity, small-study effects, and data transparency. Case examples highlight frequent pitfalls, such as unjustified pooling of heterogeneous designs and selective outcome reporting. Guidance is also provided for formulating balanced, actionable review comments that enhance methodological integrity without extending editorial timelines. This checklist equips editors and reviewers with a structured tool for systematic appraisal across clinical disciplines, ultimately improving the reliability, reproducibility, and clinical utility of future SRMAs.

The scientific community is continually evolving, driven by advancements, shifting priorities, and growing demands for global dissemination of knowledge. A clear example of successfully adapting to these demands is the transition from the Bosnian Journal of Basic Medical Sciences (BJBMS) to Biomolecules and Biomedicine (BB) in 2023. This strategic move symbolizes a significant step forward, expanding the journal's global reach and scientific scope.

Introduction: Tumor-infiltrating lymphocytes (TIL) are linked to responses to chemotherapy and immunotherapy and clinical outcomes, especially in high-risk breast carcinomas. MammaPrint® (MP) and BluePrint® (BP) are genomic tests designed to provide risk stratification and molecular classification for early-stage hormone receptor (HR)-positive breast carcinomas, which could include tumors with HER2-low expression. We investigated correlations between TIL measurements, HER2 status, and MP/BP assays in early-stage HR-positive breast carcinomas. Materials and Methods: 167 early-stage HR-positive breast carcinomas with known MP/BP risk categorization were evaluated for TIL using whole slide scanned images according to the International TILs Working Group 2014 guidelines. HER2-low breast cancers were identified by IHC scores of 1+ and 2+ without HER2 amplification. A subset of high-TIL, high-risk cases underwent TSO500 (Illumina) next-generation sequencing (NGS). Results: The patients had a mean age of 51 years, ranging from 26 to 75 years. Among the profiled cases, 97% were either luminal A (96/167) or luminal B (66/167) breast carcinomas, with only five cases classified as HER2-enriched (n = 2) or basal-like (n = 3) carcinomas. Tumor grade was strongly associated with recurrence risk (p<0.001). The prevalence of the HER2-low phenotype was 65%, including 46/69 (67%) high-risk cases. TIL levels ranged from 0 to 70% and were low (≤10%) in the majority (75%) of cases in the cohort. However, high TIL levels were more frequently observed in cases with high recurrence risk (56% vs. 39%, p = 0.03). Additionally, TIL-enriched high-recurrence risk carcinomas contained targetable genomic alterations, including PIK3CA, BRCA1, BRCA2, and HER2 mutations. Conclusions: TIL levels are higher in early-stage HR-positive breast carcinomas with a high recurrence risk. These tumors also harbor targetable genomic alterations, suggesting that TIL measurement and genomic profiling could enhance risk stratification and identify patients who might benefit from targeted therapies. Her-2 low expression in high-risk patients provides a consideration for including novel ADC therapies in this subset of patients. Citation Format: Zoran Gatalica, Inga Rose, Faruk Skenderi, Nataliya Kuzmova, Semir Beslija, Timur Ceric, Inga Marijanovic, Ilir Kurtishi, Semir Vranic. High Tumor-Infiltrating Lymphocyte Levels Correlate with High MammaPrint® Recurrence Risk in Early-Stage Breast Carcinomas [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-11-17.

Background/Aim: Rheumatoid arthritis (RA) is an autoimmune inflammatory disease, characterized by the production of numerous pro-inflammatory cytokines, such as tumor necrosis factor α (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β), which lead to pathophysiological changes in innate and acquired immunity. The existing evidence shows that pro-inflammatory cytokines in rheumatoid arthritis impact monoaminergic neurotransmission, neurotropic factors, and synaptic activity, which may lead to the development of depression. Materials and Methods: In our study, we explored the association between TNF-α and IL-6, disease activity, and the degree of depression in patients with RA. The association between TNF-α and IL-6 and the Beck and Hamilton depression scales was analyzed in a group of 116 RA patients with depression. We investigated the same correlation in 45 patients with primary depression who represented the control group. Results: A Spearman test showed that IL-6 levels had a positive association with the Beck and Hamilton scales (p < 0.05) and that TNF-α had a positive association with the Hamilton scale (p < 0.05). Also, the Hamilton depression scale was the more sensitive scale in the detection of depressive symptoms. Conclusions: Our study indicates that elevated values of pro-inflammatory cytokines are associated with the degree of depression in patients with RA. Future preclinical and clinical studies will contribute to a better understanding of the pathophysiological mechanism of depression in patients with RA and may serve as the basis for new treatment modalities. By detecting depression promptly, with the help of the HAM-D as the more sensitive scale, we could influence the future modality of treatment, and with a multidisciplinary approach, we could ensure an improvement in the quality of life of patients with RA.

Despite the well-established role of human papillomavirus (HPV) as the primary cause of cervical cancer (CC) and the existence of an effective HPV vaccine, over half a million women are diagnosed with CC globally each year, with more than half of them dying from the disease. Immunotherapy has rapidly become a cornerstone of cancer treatment, offering substantial improvements in survival rates and reducing treatment-related side effects compared to traditional therapies. For the past 25 years, chemoradiotherapy (CRT) has been the standard treatment for locally advanced CC. However, while adjuvant chemotherapy has failed to improve outcomes in locally advanced CC, the integration of neoadjuvant chemotherapy (NACT) with CRT, as well as chemoimmunoradiotherapy followed by consolidation immunotherapy, has transformed treatment strategies, demonstrating superior efficacy compared to CRT alone. In the first-line treatment of CC, adding pembrolizumab to platinum-based chemotherapy, either with or without bevacizumab, has significantly improved outcomes compared to platinum-based chemotherapy and bevacizumab alone. This review explores the current landscape of immunotherapy and biomarker advancements in CC. Furthermore, we discuss promising future directions, including the potential of personalized immunotherapy approaches and novel combination therapies to further enhance treatment efficacy and improve prognoses for patients with CC.

Basal cell carcinoma (BCC) is the most common type of skin cancer that usually appears in sun-exposed body regions such as the head, trunk, and extremities. There are four main clinicopathological subtypes of BCC: nodular, superficial, morpheaform, and fibroepithelial. BCC's molecular basis includes inherited genetic susceptibility and somatic mutations, often induced by exposure to UV radiation. The aberrant activation of the Hedgehog (Hh) signaling pathway, caused by mutations in the Hh components, plays a central role in the molecular pathogenesis of this carcinoma. This led to the development of Hh signaling pathway inhibitors as a new treatment option for patients with advanced disease. In this review, we summarize BCC's clinical presentation and histopathology and present knowledge on the most studied Hh signaling inhibitors, vismodegib and sonidegib, and other inhibitors of this signaling, such as itraconazole, patidegib, taladegib, and arsenic trioxide, in the treatment of BCC. We also present the most common Hh signaling inhibitor adverse events and their management options, which could improve patients' quality of life during treatment.

Introduction Ovarian serous cystadenocarcinoma (SCA), a deadly gynecologic cancer, often goes undetected until the late stages. Tissue proteomics unveils disease heterogeneity, enhancing tumor classification and enabling personalized treatments tailored to individual expression profiles. Material and methods Tissue samples from 46 serous ovarian tumors were quantified using label-free liquid chromatography-tandem mass spectrometry. We identified 80 proteins differentiating SCA from borderline tumors, 277 distinguishing SCA from benign tumors, and 195 between borderline and benign tumors. Ingenuity pathway analysis revealed increased cell proliferation and RNA processing in SCA and borderline tumors compared to benign tumors, with SCA showing greater oxidative phosphorylation than borderline tumors. Results Our comparative analysis indicates that upregulated (ASS1 – argininosuccinate synthase 1, CAPS, PPA1, BCAT1, MCM4) and downregulated proteins (MUC5B, SLC4A1, tenascin-XB – TNXB, carbonic anhydrase 1, hemoglobin β) may offer a robust panel for distinguishing SCA from benign and borderline ovarian tumors, potentially aiding in early diagnosis and disease monitoring. The cancer-associated proteins pyridoxal dependent decarboxylase domain containing 1 (AUC: 0.83, 95% CI: 0.66–1), GFPT1 (AUC: 0.84, CI: 0.70–0.89), and HYOU1 (AUC: 0.84, CI: 0.70–0.98) significantly differentiated between low-grade (LGSCA) and high-grade serous cystadenocarcinoma (HGSCA). Low-grade SCA showed significantly greater levels of MZB1 (log2 fold change (FC): –1.951, p-value: 0.0258), CRABP2 (FC: –2.34, p-value: 0.0016), and BCAM (FC: –1.945, p-value: 0.0197) than borderline cancers. Conclusions Argininosuccinate synthase 1 and TNXB showed potential as markers of disease progression. Elevated ASS1 was observed in borderline, LGSCA, and HGSCA tumors compared to benign tumors, while TNXB levels progressively declined from benign to borderline, LGSCA, and HGSCA tumors. Our study pinpoints critical biomarkers in serous ovarian tumors for HGSCA progression.

A critical predictive marker for anti-PD-1/PD-L1 therapy is programmed death-ligand 1 (PD-L1) expression, assessed by immunohistochemistry (IHC). This paper explores a novel automated framework using deep learning to accurately evaluate PD-L1 expression from whole slide images (WSIs) of non-small cell lung cancer (NSCLC), aiming to improve the precision and consistency of Tumor Proportion Score (TPS) evaluation, which is essential for determining patient eligibility for immunotherapy. Automating TPS evaluation can enhance accuracy and consistency while reducing pathologists' workload. The proposed automated framework encompasses three stages: identifying tumor patches, segmenting tumor areas, and detecting cell nuclei within these areas, followed by estimating the TPS based on the ratio of positively stained to total viable tumor cells. This study utilized a Reference Medicine (Phoenix, Arizona) dataset containing 66 NSCLC tissue samples, adopting a hybrid human-machine approach for annotating extensive WSIs. Patches of size 1000x1000 pixels were generated to train classification models such as EfficientNet, Inception, and Vision Transformer models. Additionally, segmentation performance was evaluated across various UNet and DeepLabV3 architectures, and the pre-trained StarDist model was employed for nuclei detection, replacing traditional watershed techniques. PD-L1 expression was categorized into three levels based on TPS: negative expression (TPS < 1%), low expression (TPS 1-49%), and high expression (TPS ≥ 50%). The Vision Transformer-based model excelled in classification, achieving an F1-score of 97.54%, while the modified DeepLabV3+ model led in segmentation, attaining a Dice Similarity Coefficient of 83.47%. The TPS predicted by the framework closely correlated with the pathologist's TPS at 0.9635, and the framework's three-level classification F1-score was 93.89%. The proposed deep learning framework for automatically evaluating the TPS of PD-L1 expression in NSCLC demonstrated promising performance. This framework presents a potential tool that could produce clinically significant results more efficiently and cost-effectively.