Abstract 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) signalling 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 signalling pathway inhibitors as a new treatment option for patients with advanced disease. In this review, we summarise BCC’s clinical presentation and histopathology and present knowledge on the most studied Hh signalling inhibitors, vismodegib and sonidegib, and other inhibitors of this signalling, such as itraconazole, patidegib, taladegib, and arsenic trioxide, in the treatment of BCC. We also present the most common Hh signalling inhibitor adverse events and their management options, which could improve patients’ quality of life during treatment.

BACKGROUND HER2-positive breast cancer (BC) is highly aggressive with a poor prognosis. It is driven by HER2 oncoprotein activation/crosstalk with other receptors like EGFR/(HER1), HER3, and HER4, in addition to IGF-1R, making these receptors ideal therapeutic targets as they are expressed/overexpressed in this subtype. We postulated that targeting HER2 and IGF-1R together is a promising therapy for HER2-positive BC. Thus, we explored the outcome of a novel combination treatment using neratinib, a pan-HER inhibitor, and metformin, an IGF-1R inhibitor, on HER2-positive BC cells. METHODS In this investigation, we used cellular and molecular biology techniques in addition to an angiogenesis model and tissue microarray analysis. RESULTS Our data revealed that this combination therapy significantly reduced cell viability compared to individual treatments and exhibited a synergistic effect in HER2-positive BC cells. Moreover, the combination disrupted cell cycle progression and inhibited colony formation, and invasion of HER2-positive BC cells; this is accompanied by the deregulation of HER1-3 and IGF-1R expression patterns, in addition to Caspase-3, BCL2, Fascin, and Vimentin. Moreover, key regulator molecular pathways, including, ERK1/2, AKT, p38 MAPK, and mTOR, were significantly downregulated upon treatment with neratinib and metformin combination. Additionally, our data pointed out that neratinib and metformin combination inhibited angiogenesis, in-ovo, an important biological event in cancer progression. Finally, using a cohort of 55 HER2-positive BC samples, we revealed that HER2 and IGF-1R are co-expressed in most of the cases. CONCLUSIONS These findings suggest that neratinib and metformin combination can present a promising strategy for targeting multiple pathways in HER2-positive BC.

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 1000 × 1000 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.

Breast cancer, marked by uncontrolled cell growth in breast tissue, is the most common cancer among women and a second-leading cause of cancer-related deaths. Among its types, ductal and lobular carcinomas are the most prevalent, with invasive ductal carcinoma accounting for about 70–80% of cases and invasive lobular carcinoma for about 10–15%. Accurate identification is crucial for effective treatment but can be time-consuming and prone to interobserver variability. AI can rapidly analyze pathological images, providing precise, cost-effective identification, thus reducing the pathologists’ workload. This study utilizes a deep learning framework for advanced, automatic breast cancer detection and subtype identification. The framework comprises three key components: detecting cancerous patches, identifying cancer subtypes (ductal and lobular carcinoma), and predicting patient-level outcomes from whole slide images (WSI). The validation process includes visualization using Score-CAM to highlight cancer-affected areas prominently. Datasets include 111 WSIs (85 malignant from the Warwick HER2 dataset and 26 benign from pathologists). For subtype detection, there are 57 ductal and 8 lobular carcinoma cases. A total of 28,428 annotated patches were reviewed by two expert pathologists. Four pre-trained models—DenseNet-201, MobileNetV2, an ensemble of these two, and a Vision Transformer-based model—were fine-tuned and tested on the patches. Patient-level results were predicted using a majority voting technique based on the percentage of each patch type in the WSI. The Vision Transformer-based model outperformed other models in patch classification, achieving an accuracy of 96.74% for cancerous patch detection and 89.78% for cancer subtype classification. For WSI-based cancer classification, the majority voting method attained an F1-score of 99.06 and 96.13% for WSI-based cancer subtype classification. The proposed deep learning-based framework for advanced breast cancer detection and subtype identification yielded promising results. This advanced framework shows great promise in medical practice, offering an economical, efficient solution for generating accurate, clinically relevant results and enhancing diagnostic accuracy in hospitals, research centers, and pathology laboratories. Nonetheless, further studies are needed to validate its effectiveness across various environments and larger datasets.

Cyclosporine A (CsA) is widely used as an immunosuppressant in organ transplantation to improve graft survival and prevent tissue rejection. The impact of CsA on cancer progression is highly complex, influenced by the intricate relationship between immunosuppression and malignancy. While individuals with compromised immune systems, notably organ transplant recipients, face an elevated risk of cancer invasion and progression due to immunosuppressive regimens, CsA’s role in either promoting or inhibiting cancer remains elusive. Divergent outcomes from in vitro and in vivo studies suggest suppression of cancer progression under CsA treatment and complicate the translation of findings to clinical scenarios. Despite promising in vitro and in vivo results, the clinical application of CsA in oncology necessitates careful consideration of its toxicity profile in in vivo models, starting at 50–200 mg/kg/d. The divergence between preclinical and clinical findings highlights the need for further research to elucidate the true nature of CsA’s impact on cancer, providing a foundation for more informed and targeted therapeutic approaches.

Interleukin 17 (IL17) is a cytokine involved in immune regulation and has been increasingly recognized for its role in cancer progression. This systematic review aims to integrate data on IL17's role in various tumors to better understand its implications for cancer prognosis and treatment. The review included 105 studies (27.6% experimental and 72.4% clinical). Clinical studies involved 9,266 patients: 31.2% males, 60.0% females, and 8.8% with undefined gender. IL17A and IL17 were the most studied subtypes (36.2% and 33.3%, respectively). Breast cancer (26.7%), colorectal carcinoma (13.3%), and hematologic malignancies (10.5%) were the most researched neoplasms. IL17A promoted tumor growth in breast cancer and correlated with poor outcomes in colorectal, breast, and lung cancers. IL17 also played a significant role in immune modulation in gliomas and other tumors. IL17A significantly influences tumor growth and prognosis across various cancers, with notable roles in immune modulation and poor outcomes in multiple cancer types.

This review assesses the burden of human papillomavirus (HPV)-related cancers in Bosnia and Herzegovina (BH), aiming to inform strategies for prevention and early detection. Despite the availability of highly effective HPV vaccines and screening programs, HPV-related cancers remain a significant public health burden worldwide. We conducted a comprehensive search of PubMed and GLOBOCAN to identify all available data on HPV prevalence/genotype and HPV-related malignancies in BH, including information on HPV vaccination and cervical cancer screening. A comprehensive literature search revealed limited data on HPV prevalence and HPV-related cancers, as well as the absence of a national HPV vaccination or cervical cancer screening program in BH. In the largest study with available data from BH, HPV prevalence was 43% among women undergoing routine gynecologic exams. HPV-16 was identified as the most common cause of cervical cancer. The HPV prevalence was 50% in head and neck cancer, with HPV-18 being the most prevalent subtype. HPV was detected in 80% of patients with colorectal cancer, and HPV-16 was the most common subtype. Conclusions. HPV-related cancers, particularly cervical cancer, represent a significant public health problem in BH. Implementation of a national HPV vaccination program, along with organized cervical cancer screening is essential to reduce HPV-related morbidity and mortality. Addressing systemic challenges, such as establishing a comprehensive cancer registry, is essential for effective HPV prevention and control. Raising public awareness about HPV infection, its consequences, and the importance of prevention is essential for vaccine acceptance and promoting healthy behaviors. By investing in HPV prevention, BH can significantly improve the health and well-being of its population, particularly women.

Abstract Duplication of the vermiform appendix is a rare anomaly observed in patients undergoing appendectomy. A 27-month-old male toddler presented with a 9-day history of abdominal pain, vomiting, and diarrhea, progressing to an acute abdomen with signs of severe peritonitis. Intraoperative findings revealed a periappendicular infiltrate from a perforated vermiform appendix of the tenia coli type. A second, inflamed appendix was incidentally discovered in its typical location during the procedure. Vermiform appendix duplication presents a clinical challenge due to its rarity and potential for complications. According to the Cave–Wallbridge classification, this case represents Type B2, or the tenia coli variant, characterized by a perforated appendix originating at the tenia coli convergence and a smaller, secondary appendix in a retrocecal position. This case emphasizes the importance of thorough distal and proximal exploration during initial appendectomy when this anomaly is suspected, particularly in cases of Type B2.

Simple Summary This study investigates lung cancer detection by combining metabolomics and advanced machine learning to identify small cell lung cancer (SCLC) with high accuracy. We analyzed 461 serum samples from publicly available data to create a stacking-based ensemble model that can distinguish between SCLC, non-small cell lung cancer (NSCLC), and healthy controls. The model has 85.03% accuracy in multi-class classification and 88.19% accuracy in binary classification (SCLC vs. NSCLC). This innovation relies on sophisticated feature selection techniques to identify significant metabolites, particularly positive ions. SHAP analysis identifies key predictors such as benzoic acid, DL-lactate, and L-arginine, shedding new light on cancer metabolism. This non-invasive approach presents a promising alternative to traditional diagnostic methods, with the potential to transform early lung cancer detection. By combining metabolomics and machine learning, the study paves the way for faster, more accurate, and patient-friendly cancer diagnostics, potentially improving treatment outcomes and survival rates.

OBJECTIVES Testicular torsion (TT) is an emergency requiring timely surgery to prevent testicular loss. There is a lack of reports on the clinical significance of the time of admission (on-hours vs. off-hours) on the long-term surgical outcome of TT. METHODS We retrospectively reviewed all consecutive patients <18 years who were admitted to the hospital and treated for TT during the ten years. Patients were classified according to their admission time: weekday (on-hours), outside working hours, and weekends (off-hours). They were also classified based on their testicular outcome: salvaged and non-salvaged testis. RESULTS Seventy-two patients were included. Their median age was 14.2 years. Thirty-three patients (46 %) were admitted during on-hours, whereas 39 patients (54 %) were admitted during off-hours. Forty-three patients (59.7 %) required orchidopexy and, out of those, during the long-term follow-up, only 27 (37.5 %) had definitive testicular salvage. Forty-five patients (62.5 %) were with no testicular salvage. On-hours vs. off-hours admission had no impact on the clinical outcome (p = 0.25). However, significant differences in the duration of symptoms (DoS) between the orchidopexy and orchidectomy groups were observed (p < 0.001). CONCLUSION Testicular torsion is a time-dependent diagnosis, and any delay in treatment could cause testicular loss. Our data suggest that the DoS before admission, rather than the admission time, influences the testicular outcome. The efficient management of emergencies regardless of the time of day is a key factor for the reduced probability that admission timing affects outcomes.