The two well-known quality-assessment metrics of head and neck oncosurgery are the status of surgical margin (SM) and lymph node yield (LNY). While the clinical importance of LNY has been well-established, several unresolved controversies around the SM have deterred its practical application.

Renal cell carcinoma (RCC) represents nearly 90% of kidney cancers. With improvements in treatment and longer survival, reliable prognostic markers are increasingly important. This study investigated the prognostic value of systemic inflammatory markers and clinicopathological features in RCC patients undergoing nephrectomy.

The advent of molecular classification has ushered breast cancer treatment into the era of precision medicine. Nevertheless, clinical management continues to face significant challenges posed by drug resistance and tumor heterogeneity. Recent advances have identified novel programmed cell death (PCD) mechanisms-including ferroptosis, cuproptosis, disulfidptosis, and pyroptosis-as critical regulators of breast cancer progression, therapeutic responsiveness, and immune microenvironment remodeling. This review systematically elucidates the mechanistic foundations of these PCD pathways and investigates their molecular regulatory networks within breast cancer pathogenesis. Furthermore, we evaluate current targeted intervention strategies and assess their clinical translation potential, ultimately providing a theoretical framework for pioneering therapeutic approaches.

Accurate staging is essential in cancer care. The American Joint Committee on Cancer (AJCC) TNM staging system is commonly used but is subject to the risk of stage migration. Recent literature suggests that the log odds of positive lymph nodes (LODDS) and positive lymph node ratio (LNR) may have superior predictive values and are considered alternatives to the N-category. However, their predictive performance in gastric signet ring cell carcinoma (GSRC) remains vague. This study aims to explore the association between three lymph node (LN) staging systems (AJCC N-category, LODDS, and LNR) and outcomes in GSRC, and assess the predictive power.

Individuals diagnosed with Fanconi anemia (FA) present an incidence of 500- to 700-fold higher to develop head and neck squamous carcinomas (HNSCCs) compared to the general population. Effective anticancer treatments for FA-HNSCCs are missing. Several studies demonstrated that FA-HNSCCs overexpress the epithelial growth factor receptor (EGFR) and their viability is highly dependent on this pathway, as FA-HNSCCs cells are highly sensitive to EGFR inhibitors such as afatinib in preclinical models, which led to an orphan drug designation by EMA in 2018.

The importance of epithelial‒mesenchymal transition (EMT) in tumour invasion and metastasis in high-grade serous ovarian cancer (HGSOC) has been highlighted in numerous studies, but genetic biomarkers for predicting EMT in HGSOC are still lacking.

Hepatocellular carcinoma (HCC) with lymph node metastases (LNM) is an uncommon neoplasm and has an ambivalent prognosis compared to the common type of HCC with macrovascular invasion (MaVI).

Patients with various malignancies can develop malignant pleural effusion (MPE). MPE is the abnormal accumulation of fluid in the pleural cavity. This fluid usually contains immune cells, tumor cells, and high levels of plasma proteins. This condition is associated with impaired pleural fluid drainage and is considered an immune and angiogenic manifestation of tumors metastasized in the pleura from other thoracic and non-thoracic areas. Tumor-induced pleural vascular hyperpermeability and neoangiogenesis are crucial to pleural fluid accumulation and could be therapeutically targeted to minimize MPE development. Here, we present two methodologies required for assessing the angiogenic properties of MPE fluid-Evans' blue pulse-and-chase for evaluating increased vascular permeability and the chorioallantoic membrane (CAM) assay to assess tumor-triggered angiogenesis. We describe the steps for experimental MPE formation in mice, including MPE fluid collection, evaluation of MPE-induced vascular permeability, and assessment of MPE-induced angiogenesis.

Photodynamic therapy (PDT) is a minimally invasive treatment modality based on photochemical reactions that damage specific tumour tissues. Pyroptosis is a type of programmed death that relies on the activation of cysteine-containing aspartate proteolytic enzymes (caspases) and cleavage of the gasdermin protein family members. However, the mechanism of 5-aminolevulinic acid (5-ALA)-mediated PDT-induced pyroptosis in colorectal cancer is not well understood. Here, we found that 5-ALA-mediated PDT can induce pyroptosis in colorectal cancer cells, characterized by the appearance of pyroptotic bubbles and the detection of the release of cellular contents and inflammatory factors. Mechanistically, PDT activates caspase-1 and cleaves gasdermin D (GSDMD), and the release of N-GSDMD triggers pyroptosis in colorectal cancer cells. In addition, inhibition of this pathway in vivo can attenuate the therapeutic effect of PDT. In summary, we investigated the role of GSDMD in PDT-mediated pyroptosis in CRC cells, and the results showed that 5-ALA-mediated PDT induces pyroptosis in colorectal cancer cells by targeting the caspase-1/GSDMD pathway. This study may provide a reference for the clinical application of PDT in CRC treatment.

Digestive system tumors, including gastric and colorectal cancers, have notable global incidence and mortality rates. While 5-Fluorouracil (5-FU) is widely used in treating gastrointestinal (GI) cancers, resistance often limits its effectiveness. Recent research has focused on the potential of natural products, such as propolis, a resin produced by honeybees, as adjuncts in cancer therapy. This study examined whether water-based propolis (WBP) could enhance the therapeutic effects of 5-FU on AGS (p53-wild-type) and Caco-2 (p53-null) cancer cell lines, aiming to propose a new combined treatment strategy. The findings demonstrated that WBP and 5-FU exhibited dose- and time-dependent cytotoxicity, with WBP increasing the therapeutic efficiency of 5-FU by reducing its half-maximal inhibitory concentration in both cancer cell lines, and reducing 5-FU toxicity in non-cancerous cells. Notably, cancer cells expressing p53 showed greater sensitivity to 5-FU; however, WBP demonstrated similar effects in both cell lines. The combined therapy of WBP (100 µg/mL for 48-h) and 5-FU (10 µg/mL for 48-h) with synergistic effects significantly reduced cell proliferation and motility. Moreover, combined treatments caused increased reactive oxygen species production, collapse of mitochondrial membrane potential, endoplasmic reticulum stress, and autophagy, thus leading to cell cycle arrest and apoptosis compared to individual treatments and controls, regardless of p53 expression in both cancer cells. These findings suggest that WBP, a natural product, could supplement 5-FU chemotherapy by enhancing its antitumor effectiveness, warranting further investigation for treating GI cancers.

Ewing sarcoma is an aggressive cancer diagnosed in adolescents and young adults. Inhibition of TGFβ is being tested in limited clinical trials for relapsed Ewing sarcoma. TGFβ is an immunosuppressive cytokine that exists in latent and active states. The functional impact of TGFβ inhibition on the Ewing tumor microenvironment (TME) and on Ewing tumor behavior remains largely unknown. In this study, we use single-cell RNA sequencing analysis of human Ewing tumors to demonstrate that immune cells are the largest contributors of TGFB1 expression in the human Ewing TME. We utilize a humanized mouse model of Ewing sarcoma to demonstrate that TME signatures in these models differ significantly from Ewing sarcoma tumors developed in immunodeficient mice. Using this humanized model, we investigate the effect of TGFβ inhibition on the Ewing sarcoma TME during radiotherapy, a treatment that is commonly used to treat unresectable, metastatic, and relapsed/refractory Ewing sarcoma that is known to enhance TGFβ activation in multiple cancers. Utilizing a trivalent ligand TGFβ trap to inhibit TGFβ, we demonstrate that in combination with radiotherapy, TGFβ inhibition both increases Ewing sarcoma immune cell infiltration and decreases lung metastatic burden in vivo. These data demonstrate the value of immunocompetent models to address immune-biological preclinical questions in Ewing sarcoma and demonstrate that TGFβ inhibition during radiotherapy is a promising strategy to enhance antitumor immune response and improve treatment efficacy for metastatic Ewing sarcoma.

Reliable data on optimal fluid management in the perioperative period for patients with advanced ovarian cancer undergoing cytoreductive surgery is limited. These patients often present with malignant ascites and are prone to significant fluid shifts perioperatively. For this reason, our objective was to define clinical targets for optimal fluid balance and determine whether initial ascites should be included in fluid-loss calculations by examining the association between perioperative fluid balance and major postoperative complications.

This study aims to develop a robust and clinically applicable framework for preoperative grading of meningiomas using T1-contrast-enhanced and T2-weighted MRI images. The approach integrates radiomic feature extraction, attention-guided deep learning models, and reproducibility assessment to achieve high diagnostic accuracy, model interpretability, and clinical reliability.

Traditional methods for detecting tumor-reactive (TR) CD8 + tumor-infiltrating lymphocytes (TILs) in pancreatic cancer usually focus on neo-antigenic epitopes, which is limited by the narrow range of antigenic epitopes, and the lengthy and complex identification processes, resulting in an incomplete understanding of the biological characteristics of TR CD8 + TILs.

Muscle-invasive bladder cancer (MIBC) is a heterogeneous disease with variable outcomes, necessitating practical classification systems. Molecular subtyping using immunohistochemical (IHC) markers offers a cost-effective approach for therapeutic guidance and assessing survival. Moreover, MIBC molecular subclassification provides a practical approach for guiding immune checkpoint inhibitor therapy.

Skin cancer is a significant global health concern, and accurate and timely diagnosis is crucial for successful treatment. However, manual diagnosis can be challenging due to the subtle visual differences between benign and malignant lesions. This study introduces Skin-DeepNet, a novel deep learning-based framework designed for the automated early diagnosis and classification of skin cancer lesions from dermoscopy images. Skin-DeepNet incorporates a two-step pre-processing stage to enhance image contrast, followed by robust skin lesion segmentation using Mask R-CNN and GrabCut algorithm to achieve near-perfect segmentation accuracy (IOU up to 99.93%). Then a dual-feature extraction strategy is performed using a combination of a pre-trained high-resolution network (HRNet) model and attention block, which serve as feature descriptors. A deep belief networks (DBN) model is then trained on their outputs to capture high-level discriminative features. Finally, robust decision fusion strategies are employed to integrate the predictions of the proposed models using boosting and stacking to enhance overall Skin-DeepNet's accuracy. The Skin-DeepNet's performance has been validated on two challenging datasets: ISIC 2019 and HAM1000. The Skin-DeepNet system has outperformed the existing state-of-the-art systems by achieving an accuracy rate of 99.65% Precision of 99.51%, AUC of 99.94% on the ISIC 2019 dataset. Similarly, on the HAM1000 dataset, the Skin-DeepNet system demonstrated an accuracy rate, precision, and AUC of 100%, 99.92%, and 99.97%, respectively. These findings indicate that the developed Skin-DeepNet system can exhibit outstanding proficiency in accurately classifying skin lesions while aiding physicians in early diagnosis and treatment tasks in clinical settings.

In tumorous conditions, STAT1, traditionally recognized for its anti-tumor role in immunology, exhibits pro-survival characteristics, though unclear mechanisms. Investigating STAT1 function in isogenic colorectal tumor cells with wild-type or mutant KRAS, we found that STAT1 specifically promotes tumor survival and proliferation with mutant KRAS. Gene expression profiling revealed that STAT1 promotes the expression of sterol and lipid biosynthesis genes in these cells. This effect depends on STAT1 phosphorylation at S727, which upregulates SREBP1 and SREBP2 to drive de novo lipid production. In mutant KRAS cells, STAT1 amplifies the mevalonate pathway, maintaining its S727 phosphorylation and establishing a positive feedback loop through the transcription factors YAP1 and TEAD4, further driving lipid biosynthesis and tumor growth. This STAT1-YAP1 axis promotes mutant KRAS tumor cells' resistance to mevalonate pathway inhibitors, which can be overcome by pharmacologically targeting the YAP1-TEAD interaction. Moreover, this axis contributes to the inherent resistance of mutant KRAS colon cancer cells to EGFR-targeted therapy. Together, these findings identify the STAT1-YAP1 pathway as a critical mediator of therapy resistance and a promising therapeutic target in mutant KRAS colorectal cancer.

Signaling lymphocytic activation molecule (SLAM) family receptors are widely expressed on immune cells, often acting as self-ligands and playing crucial roles in cellular communication and adhesion, thereby modulating immune responses. Several studies have demonstrated that SLAM family receptors are associated with potential immune checkpoints on T cells and play a role in tumor immunity in various cancers. However, the effect of SLAMF1 expression in tumors has been rarely investigated. Here, we confirmed SLAMF1 expression using tissue microarray analysis in breast cancer tissues with diverse pathological characteristics and subtypes. Additionally, SLAMF1 expression in triple-negative breast cancer (TNBC) cells was analyzed using flow cytometry and real-time PCR. Public clinical data analysis suggests that a positive correlation exists between SLAMF1 expression and overall survival and that SLAMF1 levels are slightly increased in patients with breast cancer who received radiation therapy. Similarly, when TNBC cells were irradiated, SLAMF1 expression specifically increased compared to that in non-irradiated cells. To study the biological function of SLAMF1 in mice, we established 4T1-SLAMF1 overexpressing a stable cell line. In the 4T1 syngenetic tumor model, SLAMF1 overexpression triggered strong infiltrating-CD8+ T cell responses and significantly reduced the tumor growth. Our results provide clear evidence for SLAMF1 expression in breast cancer and provide insights into the recent advances in SLAM-based targeted immunotherapies.

Accurate prediction of breast cancer survival is critical for optimizing treatment strategies and improving clinical outcomes. This study evaluated a combination of parametric statistical models and machine learning algorithms to identify the most influential prognostic factors affecting the survival of patients. Two commonly used parametric models, log-gaussian regression and logistic regression, were applied to assess the relationship between survival and a set of clinical variables, including age at diagnosis, tumor grade, primary tumor site, marital status, American Joint Committee on Cancer (AJCC) stage, race, and receipt of radiation therapy or chemotherapy. Machine learning methods, such as neural networks, support vector machines (SVMs), random forests, gradient boosting machines (GBMs), and logistic regression classifiers, were employed to compare the predictive performance. Among these, the neural network model exhibited the highest predictive accuracy. The random forest model achieved the best balance between model fit and complexity, as indicated by its lowest akaike information criterion and bayesian information criterion values. Across all models, five variables consistently emerged as significant predictors of survival: age, tumor grade, ajcc stage, marital status, and radiation therapy use. These findings highlight the importance of combining traditional survival analysis techniques with machine learning approaches to enhance predictive accuracy and support evidence-based personalized treatment planning in breast cancer care.

Angiosarcoma is a rare and aggressive soft tissue sarcoma with a poor prognosis and limited treatment options. The role of interleukin-13 (IL-13) and its receptors in angiosarcoma pathogenesis has been largely unknown. We first reanalyzed transcriptomic data from a published angiosarcoma cohort and found IL13RA2 mRNA elevated in angiosarcoma versus normal tissue. In addition, high IL13RA2 expression was significantly associated with increased nonsynonymous mutations. We next detected high IL-13 receptor α2 (IL-13Rα2) expression in angiosarcoma cell lines and patient samples compared to other cell types and benign vascular tumors. Moreover, histological analysis showed the presence of IL-13 in the angiosarcoma microenvironment. Functional studies using angiosarcoma cell lines, MO-LAS-B cells, revealed the promoting effect of IL-13 on cell proliferation. The effect was inhibited by siRNA-mediated knockdown of IL13RA2 or neutralizing antibodies against IL-13, suggesting the impact of IL-13/IL-13Rα2 axis in the angiosarcoma proliferation. In addition, IL-13 stimulation increased mRNA levels of IL13RA2 and VEGFA, suggesting an underlying positive feedback mechanism, which was attenuated by a STAT6 inhibitor. These findings highlight the importance of the IL-13/IL-13Rα2 axis in angiosarcoma progression and its potential as a novel therapeutic target for this challenging malignancy.