Search Results (2,531)

Background/Objectives: Pancreatic cancer (PC) is a diagnosis with a poor prognosis which can be associated with significant distress and may hinder a patient’s ability to understand treatment details. Educating patients based on their learning preferences (LPs) and emotions may allow for personalized, enhanced care. Methods: This prospective project enrolled patients with non-metastatic PC. Phase 1 utilized the Learning Preference Barometer (LPB) and Emotional Journey Barometer (EJB), which are digital instruments co-designed by CANCER101 (C101) and the Health Collaboratory, to assess patient LPs and emotional states. Phase 2 provided information prescriptions aligned with LPs through C101’s Prescription to Learn^® (P2L) platform. Collected data included demographics, treatment, LPs (auditory, kinesthetic, linguistic, visual), patient engagement with P2L, and patient emotional states with qualitative verbal validation. Descriptive variables were used to report outcomes. Results: Primary LPs in the 47 participating patients were as follows: linguistic 45%, visual 34%, auditory 11%, and kinesthetic 9%, with secondary preferences in the majority (53%). Those patients (66%) who accessed P2L had linguistic and visual preferences; the majority accessed 1- 2 resources out of the 25 provided. Resources accessed aligned to 88% of patient LPs. The majority of patients (60%) initiated treatment prior to initial EJB, and 40% were treatment naive. Common baseline emotions were optimistic (47% vs. 36%, respectively), satisfied (11% vs. 25%), acceptance (11% vs. 11%), and overwhelmed (5% vs. 11%). Conclusions: Assessing LPs and emotional state allows for personalized patient education and clinical encounters for PC patients. Future work includes examining the effects of personalized approaches on patient satisfaction, decision-making, health outcomes, and the overall patient–clinician relationship. Full article

Exosomes are membranous vesicles that play a crucial role as intercellular messengers. Cells secrete exosomes, which can be found in a variety of bodily fluids such as amniotic fluid, semen, breast milk, tears, saliva, urine, blood, bile, ascites, and cerebrospinal fluid. Exosomes have a distinct bilipid protein structure and can be as small as 30–150 nm in diameter. They may transport and exchange multiple cellular messenger cargoes across cells and are used as a non-invasive biomarker for various illnesses. Due to their unique features, exosomes are recognized as the most effective biomarkers for cancer and other disease detection. We give a review of the most current applications of exosomes derived from various sources in the prognosis and diagnosis of multiple diseases. This review also briefly examines the significance of exosomes and their applications in biomedical research, including the use of aptamers and antibody–antigen functionalized biosensors. Full article

Background/Objectives: Pancreatic cancer (PC) is the seventh leading cause of cancer-related deaths worldwide, with its incidence rising each year. Despite its relatively low incidence, the aggressiveness of pancreatic cancer results in high mortality, with only 12% of patients surviving five years post-diagnosis. Surgical resection remains the only potentially curative treatment, but the tumor is often diagnosed at an advanced stage. The goal of this work is to identify vulnerabilities that can affect the efficacy of treatments and improve the efficacy of therapy. Methods: MAP17 overexpression in pancreatic cancer cell lines, RT-qPCR analysis, xenografts, in vitro and in vivo treatments, analysis of data from pancreatic tumors in transcriptomic patient databases. Results: We studied the prognostic and predictive value of MAP17 (PDZK1IP1) expression in pancreatic cancer, and we found that high MAP17 mRNA expression was associated with poor prognosis. In addition, single-cell analysis revealed that high MAP17 expression was present only in tumor cells. We investigated whether the response to various antitumor agents depended on MAP17 expression. In 2D culture, MAP17-expressing pancreatic cancer cells responded better to gemcitabine and 5-fluorouracil. However, in vivo xenograft tumors with MAP17 expression showed resistance to all treatments. Additionally, MAP17-expressing cells had a high NAD pool, which seems to be effectively depleted in vivo by NAMPT inhibitors, the primary enzyme for NAD biosynthesis. Conclusions: Our findings suggest that MAP17 expression could enhance the prognostic stratification of pancreatic cancer patients. Moreover, the coadministration of NAMPT inhibitors with current treatments may sensitize tumors with high MAP17 expression to chemotherapy and improve the efficacy of chemotherapy. Full article

To deliver the most effective cancer treatment, clinicians require rapid and accurate diagnoses that delineate tumor type, stage, and prognosis. Consequently, minimizing the need for repetitive and invasive procedures like biopsies and myelograms, along with their associated risks, is a critical challenge. Non-invasive monitoring offers a promising avenue for tumor detection, screening, and prognostication. While the identification of oncogenes and biomarkers from circulating tumor cells or tissue biopsies is currently standard practice for cancer diagnosis and classification, accumulating evidence underscores the significant role of epigenetics in regulating stem cell fate, including proliferation, self-renewal, and malignant transformation. This highlights the importance of analyzing the methylome, exosomes, and circulating RNA for detecting cellular transformation. The development of diagnostic assays that integrate liquid biopsies with epigenetic analysis holds immense potential for revolutionizing tumor management by enabling rapid, non-invasive diagnosis, real-time monitoring, and personalized treatment decisions. This review covers current studies exploring the use of epigenetic regulation, specifically the methylome and circulating RNA, as diagnostic tools derived from liquid biopsies. This approach shows promise in facilitating the differentiation between primary central nervous system lymphoma and other central nervous system tumors and may enable the detection and monitoring of acute myeloid/lymphoid leukemia. We also discuss the current limitations hindering the rapid clinical translation of these technologies. Full article

This review delves into the characteristics of lipid metabolism reprogramming in cancer cells and immune cells within the tumor microenvironment (TME), discussing its role in tumorigenesis and development and analyzing the value of lipid metabolism-related molecules in tumor diagnosis and prognosis. Cancer cells support their rapid growth through aerobic glycolysis and lipid metabolism reprogramming. Lipid metabolism plays distinct roles in cancer and immune cells, including energy supply, cell proliferation, angiogenesis, immune suppression, and tumor metastasis. This review focused on shared lipid metabolic enzymes and transporters, lipid metabolism-related oncogenes and non-coding RNAs (ncRNAs) involved in cancer cells, and the influence of lipid metabolism on T cells, dendritic cells (DCs), B cells, tumor associated macrophages (TAMs), tumor associated neutrophils (TANs), and natural killer cells (NKs) within TME. Additionally, the role of lipid metabolism in tumor diagnosis and prognosis was explored, and lipid metabolism-based anti-tumor treatment strategies were summarized, aiming to provide new perspectives for achieving precision medicine. Full article

Background: Delays in breast cancer treatment negatively affect prognosis and have increased over time. Data on waiting times in Switzerland are limited. Patients and Methods: This study analyzed cancer registry data from 2003 to 2005 (2628 patients) and 2015 to 2017 (421 patients) to evaluate waiting times for diagnosis, surgery, and radiotherapy; temporal trends; and survival in women with stage I–III invasive breast cancer treated with surgery without chemotherapy. Associations with demographic/clinical factors and overall survival (OS) were assessed using ANOVA, uni-/multivariable regression, Kaplan–Meier, and Cox regression. Results: From 2003 to 2005, mean intervals were biopsy-to-diagnosis 4.3 days, diagnosis-to-surgery 18.8 days, biopsy-to-surgery 26.8 days, and surgery-to-radiotherapy 56.7 days. Longer diagnosis-to-surgery times were associated with metropolitan areas, public hospitals, basic insurance, mastectomy, and older age (all p < 0.001). Radiotherapy delays were also longer in metropolitan areas and after mastectomy (p < 0.001). Between 2003–2005 and 2015–2017, diagnosis-to-surgery times rose in Eastern Switzerland (from 21.3 to 31.2 days), while radiotherapy timing remained stable. Five-year overall survival improved (from 76.7% to 88.4%), but was not significantly impacted by diagnosis-to-surgery intervals. Conclusions: Despite timely surgery in Switzerland (2003–2005), disparities existed, and time to surgery increased by 2015–2017. Reducing waiting times remains important despite no significant short-term OS impact. Full article

Pediatric high-grade glioma (pHGG) is a devastating group of childhood cancers associated with poor outcomes. Traditionally, diagnosis was based on histologic and immunohistochemical characteristics, including high mitotic activity, presence of necrosis, and presence of glial cell markers (e.g., GFAP). With advances in molecular tumor profiling, these tumors have been recategorized based on specific molecular findings that better lend themselves to prediction of treatment response and prognosis. pHGG is now categorized into four subtypes: H3K27-altered, H3G34-mutant, H3/IDH-WT, and infant-type high-grade glioma (iHGG). Molecular profiling has not only increased the specificity of diagnosis but also improved prognostication. Additionally, these molecular findings provide novel targets for individual tumor-directed therapy. While these therapies are largely still under investigation, continued investigation of distinct molecular markers in these tumors is imperative to extending event-free survival (EFS) and overall survival (OS) for patients with pHGG. Full article

MicroRNA-211 (miR-211) is a versatile regulatory molecule that plays critical roles in cellular homeostasis and disease progression through the post-transcriptional regulation of gene expression. This review comprehensively examines miR-211’s multifaceted functions across various biological systems, highlighting its context-dependent activity as both a tumor suppressor and oncogene. In physiological contexts, miR-211 regulates cell cycle progression, metabolism, and differentiation through the modulation of key signaling pathways, including TGF-β/SMAD and PI3K/AKT. miR-211 participates in retinal development, bone physiology, and protection against renal ischemia–reperfusion injury. In pathological conditions, miR-211 expression is altered in various diseases, particularly cancer, where it may be a useful diagnostic and prognostic biomarker. Its stability in serum and differential expression in various cancer types make it a promising candidate for non-invasive diagnostics. The review also explores miR-211’s therapeutic potential, discussing both challenges and opportunities in developing miRNA-based treatments. Understanding miR-211’s complex regulatory interactions and context-dependent functions is crucial for advancing its clinical applications for diagnosis, prognosis, and targeted therapy in multiple diseases. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Background: The identification and clinical implementation of robust biomarkers are essential for improving diagnosis, prognosis, and treatment across a wide range of diseases. Pancreatic stone protein (PSP) has recently emerged as a promising candidate biomarker. Objective: This narrative review aims to provide an updated and comprehensive overview of the clinical applications of PSP in infectious, oncological, metabolic, and surgical contexts. Methods: We conducted a structured literature search using PubMed^®, applying the SANRA framework for narrative reviews. Boolean operators were used to retrieve relevant studies on PSP in a wide range of clinical conditions, including sepsis, gastrointestinal cancers, diabetes, and ventilator-associated pneumonia. Results: PSP has shown strong diagnostic and prognostic potential in sepsis, where it may outperform traditional markers such as CRP and PCT. It has also demonstrated relevance in gastrointestinal cancers, type 1 and type 2 diabetes, and perioperative infections. PSP levels appear to rise earlier than other inflammatory markers and may be less affected by sterile inflammation. Conclusion: PSP represents a versatile and clinically valuable biomarker. Its integration into diagnostic protocols could enhance early detection and risk stratification in critical care and oncology settings. However, widespread adoption is currently limited by the availability of point-of-care assay platforms. Full article

Background and Objectives: Acute metastatic cord compression (AMSCC) and femoral impending/pathological fracture negatively impact a patient’s quality of life, morbidity and survival, and are considered significant life events. This study aims to compare AMSCC and FMD as distinct yet overlapping metastatic orthopedic emergencies, addressing whether they represent sequential disease stages or distinct patient subpopulations—an analysis critical for prognosis and treatment planning. Materials and Methods: Records of all patients who underwent surgery for a femoral metastatic disease (FMD) over a decade (2004–2015) and patients who were treated for acute metastatic spinal compression (AMSCC) (2007–2017) were retrieved. There were no patients lost to follow-up. Results: The treatment cohorts were similar in terms of age, gender, tumour origin, and the number of spinal metastases. Fifty-four patients were diagnosed with AMSCC. Following treatment, the Frankel muscle grading improved by 0.5 ± 0.8 grades. Two hundred and eighteen patients underwent surgical intervention for FMD. Seventy percent of femoral metastases were located in the femoral neck and trochanteric area. Impending fractures accounted for 52% of the cohort. The FMD cohort, including impending and pathological fractures, was similar to the AMSCC cohort in terms of age and the time interval between cancer diagnosis and surgery (56.7 ± 74.2 vs. 51.6 ± 69.6, respectively, p = 0.646). The Karnofsky functional score was higher for the FMD cohort (63.3 ± 16.2) than for the AMSCC cohort (48.5 ± 19.5; p < 0.001). The mean survival time for the FMD cohort was double that of the AMSCC, at 18.4 ± 23.5 months versus 9.1 ± 13.6 months, respectively (p = 0.006). Conclusions: In conclusion, this study is novel in proposing that FMD and AMSCC are distinct clinical entities, differing in their impact on patient function and, most importantly, on patient survival. Full article

Endometrial cancer is one of the most prevalent gynecologic malignancies in developed countries, with its incidence steadily increasing each year. Early diagnosis is crucial for a favorable prognosis; however, certain patients experience recurrence and distant metastasis after surgery, similar to advanced cancer patients, with limited treatment options. Therefore, effective strategies for early screening, diagnosis, predicting local recurrence, and guiding rapid treatment interventions are essential for improving survival rates and prognosis. Liquid biopsy, a method known for being non-invasive, safe, and effective, has attracted widespread attention for cancer diagnosis and treatment. Although its clinical application in endometrial cancer is less established than in other cancers, research on biomarkers using liquid biopsy in endometrial cancer patients is currently in progress. This review examines the latest advancements in non-invasive biomarkers identified through liquid biopsy and provides a comprehensive overview of their clinical applications in endometrial cancer. Additionally, it discusses the challenges and future prospects of liquid biopsy, offering valuable insights into the diagnosis and personalized treatment of endometrial cancer. Full article

Background: Programmed cell death-related genes (PCDRGs) have been reported to play an important role in diagnosis, treatment and immunity regarding cancer, but their prognostic value and therapeutic potential in acute myeloid leukemia (AML) patients still need to be fully explored. Methods: Cox regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) analysis were used to identify PCDRGs significantly associated with the prognosis of AML patients. Furthermore, a prognostic risk model for AML patients was constructed based on the selected PCDRGs, and their immune microenvironment and biological pathways were analyzed. Cell experiments ultimately confirmed the potential role of PCDRGs in AML. Results: The results yielded four PCDRGs that were used to develop a prognostic risk model, and the prognostic significance of this model was confirmed using an independent external AML patient cohort. This prognostic risk model provides an independent prognostic risk factor for AML patients. This prognostic feature is related to immune cell infiltration in AML patients. The inhibition of solute carrier family 39 member 14 (SLC39A14) expression enhanced apoptosis and inhibited cell cycle progression in AML cells. Conclusions: This study integrates bioinformatics analysis and cellular experiments to reveal potential gene therapy targets and prognostic gene markers in AML. Full article

Early diagnosis of lung cancer is crucial for improving patient prognosis. In this study, we developed a diagnostic model for lung cancer based on serum proteomic data from the GSE168198 dataset using four machine learning algorithms (nnet, glmnet, svm, and XGBoost). The model’s performance was validated on datasets that included normal controls, disease controls, and lung cancer data containing both. Furthermore, the model’s diagnostic capability was further validated on an independent external dataset. Our analysis identified SLC16A4 as a key protein in the model, which was significantly downregulated in lung cancer serum samples compared to normal controls. The expression of SLC16A4 was closely associated with clinical pathological features such as gender, tumor stage, lymph node metastasis, and smoking history. Functional assays revealed that overexpression of SLC16A4 significantly inhibited lung cancer cell proliferation and induced cellular senescence, suggesting its potential role in lung cancer development. Additionally, correlation analyses showed that SLC16A4 expression was linked to immune cell infiltration and the expression of immune checkpoint genes, indicating its potential involvement in immune escape mechanisms. Based on multi-omics data from the TCGA database, we further discovered that the low expression of SLC16A4 in lung cancer may be regulated by DNA copy number variations and DNA methylation. In conclusion, this study not only established an efficient diagnostic model for lung cancer but also identified SLC16A4 as a promising biomarker with potential applications in early diagnosis and immunotherapy. Full article

Background/Objectives: The COVID-19 pandemic disrupted global cancer care. This study compared gastric cancer surgical outcomes before and during the pandemic in Turkey. We also aimed to analyze the impact of the pandemic and factors on survival and mortality in gastric cancer patients. Materials and Methods: This retrospective, multicenter cohort study included 324 patients from three tertiary centers in Turkey who underwent gastric cancer surgery between January 2018 and December 2022. Patients were stratified into Pre-COVID-19 (n = 150) and COVID-19 Era (n = 174) groups. Comprehensive demographic, surgical, pathological, and survival data were analyzed. To identify factors independently associated with postoperative mortality, a multivariable logistic regression model was applied. For evaluating predictors of long-term survival, multivariable Cox proportional hazards regression analysis was conducted. Results: The median time from diagnosis to surgery was comparable between groups, while the time from surgery to pathology report was significantly prolonged during the pandemic (p = 0.012). Laparoscopic surgery (p = 0.040) and near-total gastrectomy (p = 0.025) were more frequently performed in the Pre-COVID-19 group. Although survival rates between groups were similar (p = 0.964), follow-up duration was significantly shorter in the COVID-19 Era (p < 0.001). Comparison between survivor and non-survivor groups showed that several variables were significantly associated with mortality, including larger tumor size (p < 0.001), greater number of metastatic lymph nodes (p < 0.001), elevated preoperative CEA (p = 0.001), CA 19-9 (p < 0.001), poor tumor differentiation (p = 0.002), signet ring cell histology (p = 0.003), lymphovascular invasion (p < 0.001), and perineural invasion (p < 0.001). Multivariable logistic regression identified total gastrectomy (OR: 2.14), T4 tumor stage (OR: 2.93), N3 nodal status (OR: 2.87), and lymphovascular invasion (OR: 2.87) as independent predictors of postoperative mortality. Cox regression analysis revealed that combined tumor location (HR: 1.73), total gastrectomy (HR: 1.56), lymphovascular invasion (HR: 2.63), T4 tumor stage (HR: 1.93), N3 nodal status (HR: 1.71), and distant metastasis (HR: 1.74) were independently associated with decreased overall survival. Conclusions: Although gastric cancer surgery continued during the COVID-19 pandemic, some delays in pathology reporting were observed; however, these did not significantly affect the timing of adjuvant therapy or patient outcomes. Importantly, pandemic timing was not identified as an independent risk factor for mortality in multivariable logistic regression analysis, nor for survival in multivariable Cox regression analysis. Instead, tumor burden and aggressiveness—specifically advanced stage, lymphovascular invasion, and total gastrectomy—remained the primary independent determinants of poor prognosis. While pandemic-related workflow delays occurred, institutional adaptability preserved oncologic outcomes. Full article