Breast cancer remains the most frequently diagnosed malignant tumor among women worldwide, and the limited selectivity as well as the emerging resistance to currently used therapies highlight the need to search for new therapeutic compounds. Aromatase, a key enzyme in the estrogen biosynthesis pathway, represents a recognized molecular target in the treatment of hormone-dependent cancers. In this study, six new 1,3,4-thiadiazole derivatives containing two halogen-substituted aromatic rings were designed and synthesized as potential nonsteroidal aromatase inhibitors. The cytotoxic activity of the obtained compounds was evaluated against two breast cancer cell lines: MCF-7 (estrogen-dependent) and MDA-MB-231 (estrogen-independent). All tested compounds exhibited concentration-dependent cytotoxic activity against MCF-7 cells, with the strongest effects observed for compounds A2, A3, B1, and B3 (IC₅₀ ≈ 52-55 µM). In contrast, none of the tested compounds showed significant activity against MDA-MB-231 cells (IC₅₀ > 100 µM), suggesting their selectivity toward estrogen-dependent cancer cells. Compound B3, identified as the most promising, was further subjected to in silico analyses. Molecular docking and molecular dynamics simulations revealed that B3 occupies a binding site similar to that of the co-crystallized native inhibitor and forms interactions characteristic of strong aromatase inhibitors. The obtained results confirm a mechanism of action related to aromatase inhibition and indicate that fluorophenyl-substituted 1,3,4-thiadiazole derivatives represent a promising scaffold for the design of new, selective, and less toxic aromatase inhibitors.

Personalized cancer vaccines are a key strategy for training the immune system to recognize and respond to tumor-specific antigens. Our earlier software release, AutoEpiCollect 1.0, was designed to accelerate the vaccine design process, but the identification of tumor-specific genetic variants remains a manual process and is highly burdensome. In this study, we introduce AutoEpiCollect 2.0, an improved version with integrated genetic analysis capabilities that automate the identification and prioritization of tumorigenic variants from individual tumor samples. AutoEpiCollect 2.0 connects with RNA sequencing and cross-references the resulting RNAseq data for efficient determination of cancer-specific and prognostic gene variants. Using AutoEpiCollect 2.0, we conducted two case studies to design personalized peptide vaccines for two distinct cancer types: cervical squamous cell carcinoma and breast carcinoma. Case 1 analyzed five cervical tumor samples from different stages, ranging from CIN1 to cervical cancer stage IIB. CIN3 was selected for detailed analysis due to its pre-invasive status and clinical relevance, as it is the earliest stage where patients typically present symptoms. Case 2 examined five breast tumor samples, including HER2-negative, ER-positive, PR-positive, and triple-negative subtypes. In three of these breast samples, the same epitope was identified and was synthesized by identical gene variants. This finding suggests the presence of shared antigenic targets across subtypes. We identified the top MHC class I and class II epitopes for both cancer types. In cervical carcinoma, the most immunogenic epitopes were found in proteins expressed by HSPG2 and MUC5AC. In breast carcinoma, epitopes with the highest potential were derived from proteins expressed by BRCA2 and AHNAK2. These epitopes were further validated through pMHC-TCR modeling analysis. Despite differences in cancer type and tumor subtype, both case studies successfully identified high-potential epitopes suitable for personalized vaccine design. The integration of AutoEpiCollect 2.0 streamlined the variant analysis workflow and reduced the time required to identify key tumor antigens. This study demonstrates the value of automated data integration in genomic analysis for cancer vaccine development. Furthermore, by applying RNAseq in a standardized workflow, the approach enables both patient-specific and population-level vaccine design, based on statistically frequent gene variants observed across tumor datasets. AutoEpiCollect 2.0 is freely available as a website based tool for user to design vaccine.

Oral squamous cell carcinoma (OSCC) is an aggressive malignancy characterized by high invasiveness and poor prognosis. This study investigated the anticancer mechanisms of eupatilin, a pharmacologically active flavonoid derived from Artemisia species, in human OSCC YD-10B cells. Eupatilin significantly reduced cell viability in a dose-dependent manner, with an IC₅₀ of approximately 50 μM. Flow cytometric analysis revealed G0/G1 phase arrest accompanied by downregulation of Cyclin D1 and CDK2, and upregulation of p21. Annexin V/Propidium Iodide staining and Western blotting confirmed apoptosis induction through activation of Bax, cleaved caspase-3/9, and poly ADP-ribose polymerase (PARP) cleavage, alongside suppression of Bcl-2. Furthermore, eupatilin markedly decreased both the mRNA expression and enzymatic activities of matrix metalloproteinases (MMP)-2 and MMP-9, indicating its potential to inhibit cancer cell invasion. Collectively, these findings demonstrate that eupatilin exerts potent antiproliferative and anti-invasive effects on OSCC cells via cell-cycle modulation and mitochondrial-mediated apoptosis. This study provides the first evidence of eupatilin's therapeutic potential against OSCC, suggesting its promise as a natural compound for the development of safer and more effective treatments for oral cancer.

Early detection of breast cancer commonly relies on imaging technologies such as ultrasound, mammography and MRI. Among these, breast ultrasound is widely used by radiologists to identify and assess lesions. In this study, we developed image segmentation techniques and multiclass classification artificial intelligence (AI) tools based on pretrained models to segment lesions and detect breast cancer. The proposed workflow includes both the development of segmentation models and development of a series of classification models to classify ultrasound images as normal, benign or malignant. The pretrained models were trained and evaluated on the Breast Ultrasound Images (BUSI) dataset, a publicly available collection of grayscale breast ultrasound images with corresponding expert-annotated masks. For segmentation, images and ground-truth masks were used to pretrained encoder (ResNet18, EfficientNet-B0 and MobileNetV2)-decoder (U-Net, U-Net++ and DeepLabV3) models, including the DeepLabV3 architecture integrated with a Frequency-Domain Feature Enhancement Module (FEM). The proposed FEM improves spatial and spectral feature representations using Discrete Fourier Transform (DFT), GroupNorm, dropout regularization and adaptive fusion. For classification, each image was assigned a label (normal, benign or malignant). Optuna, an open-source software framework, was used for hyperparameter optimization and for the testing of various pretrained models to determine the best encoder-decoder segmentation architecture. Five different pretrained models (ResNet18, DenseNet121, InceptionV3, MobielNetV3 and GoogleNet) were optimized for multiclass classification. DeepLabV3 outperformed other segmentation architectures, with consistent performance across training, validation and test images, with Dice Similarity Coefficient (DSC, a metric describing the overlap between predicted and true lesion regions) values of 0.87, 0.80 and 0.83 on training, validation and test sets, respectively. ResNet18:DeepLabV3 achieved an Intersection over Union (IoU) score of 0.78 during training, while ResNet18:U-Net++ achieved the best Dice coefficient (0.83) and IoU (0.71) and area under the curve (AUC, 0.91) scores on the test (unseen) dataset when compared to other models. However, the proposed Resnet18: FrequencyAwareDeepLabV3 (FADeepLabV3) achieved a DSC of 0.85 and an IoU of 0.72 on the test dataset, demonstrating improvements over standard DeepLabV3. Notably, the frequency-domain enhancement substantially improved the AUC from 0.90 to 0.98, indicating enhanced prediction confidence and clinical reliability. For classification, ResNet18 produced an F1 score-a measure combining precision and recall-of 0.95 and an accuracy of 0.90 on the training dataset, while InceptionV3 performed best on the test dataset, with an F1 score of 0.75 and accuracy of 0.83. We demonstrate a comprehensive approach to automate the segmentation and multiclass classification of breast cancer ultrasound images into benign, malignant or normal transfer learning models on an imbalanced ultrasound image dataset.

Accurate and real-time detection of polyps in colonoscopy is a critical task for the early prevention of colorectal cancer. The primary difficulties include insufficient extraction of multi-scale contextual cues for polyps of different sizes, inefficient fusion of multi-level features, and a reliance on hand-crafted anchor priors that require extensive tuning and compromise generalization performance. Therefore, we introduce a one-stage anchor-free detector that achieves state-of-the-art accuracy whilst running in real-time on a GTX 1080-Ti GPU workstation. Specifically, to enrich contextual information across a wide spectrum, our Cross-Stage Pyramid Pooling module efficiently aggregates multi-scale contexts through cascaded pooling and cross-stage partial connections. Subsequently, to achieve a robust equilibrium between low-level spatial details and high-level semantics, our Weighted Bidirectional Feature Pyramid Network adaptively integrates features across all scales using learnable channel-wise weights. Furthermore, by reconceptualizing detection as a direct point-to-boundary regression task, our anchor-free head obviates the dependency on hand-tuned priors. This regression is supervised by a Scale-invariant Distance with Aspect-ratio IoU loss, substantially improving localization accuracy for polyps of diverse morphologies. Comprehensive experiments on a large dataset comprising 103,469 colonoscopy frames substantiate the superiority of our method, achieving 98.8% mAP@0.5 and 82.5% mAP@0.5:0.95 at 35.8 FPS. Our method outperforms widely used CNN-based models (e.g., EfficientDet, YOLO series) and recent Transformer-based competitors (e.g., Adamixer, HDETR), demonstrating its potential for clinical application.

Glioblastoma (GB) is a highly aggressive brain tumor with a very poor prognosis. Treatment usually consists of surgery, followed by radiotherapy and chemotherapy, but the prognosis remains poor due to its resistance to therapies and a high recurrence rate. Multiple studies have reported the presence of human cytomegalovirus (HCMV) proteins and/or nucleic acids in GB tissues, suggesting its possible implication. These findings have led to the hypothesis that HCMV may contribute to tumor progression, immune evasion, angiogenesis, and resistance to therapy. Clinical trials using anti-HCMV therapies have shown promising preliminary results, indicating a potential therapeutic benefit. This review aims to provide a comprehensive overview of the current evidence linking HCMV to GB and the therapeutic implications. A deeper understanding of this complex interaction could unveil novel strategies for GB treatment.

For decades, cancer risk has been explained mainly by local factors. However, emerging evidence shows that the oral microbiome acts as a systemic modifier of oncogenesis well beyond the head and neck. This review synthesizes clinical and mechanistic data linking dysbiotic oral communities, especially Porphyromonas gingivalis, Fusobacterium nucleatum, and Treponema denticola, to malignancies across gastrointestinal, respiratory, hepatobiliary, pancreatic, breast, and urogenital systems. We summarize organ-specific associations from saliva, tissue, and stool studies, noting the recurrent enrichment of oral taxa in tumor and peri-tumoral niches of oral, esophageal, gastric, colorectal, lung, pancreatic, liver, bladder, cervical, and breast cancers. Convergent mechanisms include the following: (i) persistent inflammation (lypopolysacharide, gingipains, cytolysins, and collagenases); (ii) direct genotoxicity (acetaldehyde, nitrosation, and CDT); (iii) immune evasion/suppression (TLR/NLR signaling, MDSC recruitment, TAN/TAM polarization, and TIGIT/CEACAM1 checkpoints); and (iv) epigenetic/signaling rewiring (NF-κB, MAPK/ERK, PI3K/AKT, JAK/STAT, WNT/β-catenin, Notch, COX-2, and CpG hypermethylation). Plausible dissemination along an oral-gut-systemic axis, hematogenous, lymphatic, microaspiration, and direct mucosal transfer enables distal effects. While causality is not yet definitive, cumulative data support oral dysbiosis as a clinically relevant cofactor, motivating biomarker-based risk stratification, saliva/stool assays for early detection, and microbiome-targeted interventions (periodontal care, antimicrobials, probiotics, and microbiota modulation) alongside conventional cancer control.

Background/Objectives. Prostate cancer (PCa) is among the leading causes of death from cancer in men. Frequent use of androgen receptor inhibitors induces PCa transdifferentiation, leading to poorly differentiated neuroendocrine PCa (NEPC). ROR2 is critical for NEPC pathogenesis by activating ASCL1, promoting lineage plasticity. Protein lysine methylation mediated by N-lysine methyltransferases SMYD2 and its downstream effector EZH2 upregulates the NEPC marker ASCL1 and enhances c-MET signaling, promoting PCa aggression. Epidemiological studies suggest a lower incidence of certain malignancies in Mediterranean populations due to their intake of an olive-phenolics-rich diet. Methods. Cell viability, gene knockdown, and immunoblotting were used for in vitro analyses. A nude mouse NEPC xenograft model evaluated the anti-tumor efficacy of purified and crude oleocanthal. Xenograft tumors were subjected to RNA-seq, qPCR, and Western blot analyses, with clinical validation performed using tissue microarrays. Results. A tissue microarray analysis showed that SMYD2 expression was significantly elevated in PCa tissues with higher IHS versus normal prostate tissue cores. The olive phenolic S-(-)-oleocanthal (OC) suppressed the de novo NEPC NCI-H660 cells proliferation. Male athymic nude mice xenografted with the NCI-H660-Luc cells were used to assess OC effects on de novo NEPC progression and recurrence. Male NSG mice transplanted with LuCaP 93 PDX tumor tissues generated a heterogeneous in vivo model used to assess OC effects against t-NEPC progression. Daily oral 10 mg/kg OC administration significantly suppressed the NCI-H660-Luc tumor progression and locoregional recurrence after primary tumor surgical excision. OC treatments effectively suppressed the progression of LuCaP 93 PDX tumors. OC-treated tumors revealed downregulation of ROR2, ASCL1, SMYD2, and EZH2, as well as activated c-MET levels versus the placebo control. RNA sequencing of the collected treated NEPC tumors showed that OC disrupted NEPC splicing, translation, growth factor signaling, and neuronal differentiation. Conclusions. This study's findings validate OC as a novel lead entity for NEPC management by targeting the ROR2-ASCL1-SMYD2-EZH2-c-MET axis.

Studies have shown that obesity contributes to colorectal tumors (CRC). Hyodeoxycholic acid (HDCA) has been found to have a therapeutic effect on obesity-related diseases such as nonalcoholic fatty liver (NAFLD). However, there are still no studies revealing whether HDCA has effects on CRC, which may suggest new uses for HDCA.

C57BL/6 mice fed with high-fat diet supplemented with 0.5% HDCA were injected with MC38 cells subcutaneously to construct the subcutaneous metastasis model of CRC. The trend of body weight and tumor volume were evaluated, and blood metabolites and gut microbiota sequencing were analyzed.

Compared with HFD-fed mice, HDCA-treated mice had higher fecal and serum HDCA levels. After tumor inoculation, the HDCA mice had smaller subcutaneous tumor volumes, as well as higher HDCA and THDCA levels in feces and blood. Blood metabolomics revealed significant enrichment in pathways of bile secretion, arachidonic acid metabolism, primary bile acid metabolism, and taurine and hypotaurine metabolism. Analysis of gut microbiota at the completion of obesity modeling revealed the Chao1 index of the feces being lower in the HDCA mice. The relative abundance of a total of nine genera were significantly higher and eighteen genera were lower. The KEGG results indicated significant upregulation of nine metabolic pathways and downregulation of sixteen metabolic pathways.

HDCA intake ameliorates HFD-induced obesity phenotype, inhibiting colorectal tumor growth in mice, and decreases the abundance of gut microbiota. Gut microbiota affected by HDCA may participate in metabolism-related effects through circulation, which might be one way that HDCA affects colorectal tumors.

Background: miRNA is linked to a variety of human diseases, including cancer. The expression levels and profiles can be related to disease prevention and the promotion of good health. Understanding the beneficial changes in miRNA expression mediated by micro- and macronutrients is vital for maintaining optimal health. However, it remains unknown which phytochemicals affect miRNA expression, thereby hindering the identification of novel dietary functions. Methods: We searched for and investigated novel phytochemicals that would regulate miRNAs in colon cancer using artificial intelligence. We comprehensively analyzed miRNA expression in colon cancer cell lines treated with new phytochemical candidates using next-generation sequencing. Results: We identified three phytochemicals (fisetin, glabridin, and silibinin) that suppressed cell proliferation and were associated with changes in cancer-related miRNA expression in colon cancer cells. The miRNA expression profiles observed in response to each phytochemical shared some common features while also displaying compound-specific miRNA signatures. Exploratory pathway analyses of fisetin, glabridin, or silibinin have shown that each affects pathways involved in tumor development, including the p53 signaling pathway, apoptosis, cellular senescence, and colorectal cancer. Conclusions: The use of artificial intelligence to explore candidate compounds is beneficial, leading to the discovery of new phytochemicals modulating tumor-related miRNAs. Investigating the mechanisms of action of miRNAs will be essential for understanding new functions of dietary nutrients, thereby providing further insights into the development of diet-based health promotion and disease prevention strategies.