Search Results (2,996)

Liquid biopsy has emerged as a valuable tool for the detection and monitoring of colorectal cancer (CRC), providing minimally invasive insights into tumor biology through circulating biomarkers such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). Additional biomarkers, including tumor-educated platelets (TEPs) and exosomal RNAs, offer further potential for early detection and prognostic role, although ongoing clinical validation is still needed. This review summarizes the current evidence on the diagnostic, prognostic, and predictive capabilities of liquid biopsy in both metastatic and non-metastatic CRC. In the non-metastatic setting, liquid biopsy is gaining traction in early detection through screening and in identifying minimal residual disease (MRD), potentially guiding adjuvant treatment and reducing overtreatment. In contrast, liquid biopsy is more established in metastatic CRC for monitoring treatment responses, clonal evolution, and mechanisms of resistance. The integration of ctDNA-guided treatment algorithms into clinical practice could optimize therapeutic strategies and minimize unnecessary interventions. Despite promising advances, challenges remain in assay standardization, early-stage sensitivity, and the integration of multi-omic data for comprehensive tumor profiling. Future efforts should focus on enhancing the sensitivity of liquid biopsy platforms, validating emerging biomarkers, and expanding multi-omic approaches to support more targeted and personalized treatment strategies across CRC stages. Full article

Hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC) are aggressive malignancies characterized by a poor prognosis and resistance to conventional therapies. Mounting evidence suggests the pivotal role of epithelial–mesenchymal transition (EMT) in tumor progression, metastasis, and therapeutic resistance in these cancers. Protein induced by vitamin K absence II (PIVKA-II)—a valuable HCC detector—has ultimately emerged as a potentially relevant biomarker in PDAC, serving as both a serum biomarker and a prognostic indicator. This study investigates the putative link between PIVKA-II expression and the EMT process in HCC and PDAC. Using a Western blot analysis and electrochemiluminescence immunoassay (ECLIA), we quantified PIVKA-II serum levels alongside two canonical EMT markers—Vimentin and E-cadherin—in selected cohorts. Emerging data suggest a dual, context-dependent role for PIVKA-II. Beyond its diagnostic value in both malignancies, its co-expression with EMT markers points to a potential mechanistic involvement in tumor invasiveness and phenotypic plasticity. Notably, the selective detection of E-cadherin in HCC implies limited EMT activation and a preservation of the epithelial phenotype, whereas the higher expression of Vimentin in PDAC reflects a more substantial shift toward EMT. We provide a comprehensive analysis of key molecular markers, their involvement in EMT-driven pathophysiological mechanisms, and their potential as novel diagnostic tools. Full article

Background: RiBi is integral to cell proliferation, and its dysregulation is increasingly recognized as a hallmark of aggressive cancers. We sought to develop and validate a composite “PanRibo-515 score” reflecting RiBi activity across multiple tumor types, assess its prognostic significance, and explore its relationship with immune checkpoint therapy outcomes. Methods: We curated 515 RiBi–associated genes (PanRibo-515) and used a LASSO regression-based strategy on a training dataset (GSE202203) to select the prognostically most relevant subset of 68 genes (OncoRibo-68). Directionality (positive or negative impact on survival) was assigned based on the sign of the LASSO coefficients. We integrated a forward selection approach to identify a refined subset of genes for computing the OncoRibo-68 score. For validation, patients in The Cancer Genome Atlas (TCGA) were stratified into high or low OncoRibo-68 score groups for survival analyses. Additional validation for immunotherapy response was conducted using bioinformatic platforms used for immunotherapy response analysis. Results: A higher OncoRibo-68 score consistently correlated with poorer overall and progression-free survival across multiple cancers. Elevated OncoRibo-68 score was linked to an immunosuppressive tumor microenvironment, but interestingly to increased response to checkpoint inhibitors. Conclusions: Our findings highlight RiBi as an important determinant of tumor aggressiveness and identify the OncoRibo-68 score as a promising biomarker for risk stratification and therapy selection. Future research may evaluate whether targeting RiBi pathways could enhance treatment efficacy, particularly in combination with immunotherapy. 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

Background: Pleural mesothelioma (PM) is a type of cancer that is difficult to diagnose and treat. Patients may have vastly varying prognoses, and prognostic factors may help guide the clinical approach. As a recently identified biomarker, the pan-Immune-Inflammation-Value (PIV) is a simple, comprehensive, and peripheral blood cell-based biomarker. Methods: The present study represents a retrospective observational analysis carried out within a single-center setting. Ninety-five patients with PM stages I–IV were enrolled in the study. We analyzed the correlation between patients’ demographic characteristics, clinicopathological factors such as histological subtypes, surgery status, tumor thickness, blood-based parameters, and treatment options with their prognoses. PIV was calculated by the following formula: (neutrophil count × monocyte count × platelet count)/lymphocyte count. Additionally, blood-based parameters were used to calculate the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and systemic immune inflammation index (SII). Results: We categorized the patients into two groups, low PIV group (PIV ≤ 732.3) and high PIV group (PIV > 732.3) according to the determined cut-off value, which was defined as the median. It was revealed that high PIV was associated with poor survival outcomes. The median follow-up period was 15.8 months (interquartile range, IQR, 7.1 to 29.8 months). The median overall survival (OS) was significantly longer in patients in the low PIV group (median 29.8 months, 95% confidence interval (CI), 15.6 to 44) than the high PIV group (median 14.7 months, 95% CI, 10.8 to 18.6 p < 0.001). Furthermore, the study revealed that patients with low PIV, NLR, and SII values were more likely to be eligible for surgery and were diagnosed at earlier stages. Additionally, these markers were identified as potential predictors of disease-free survival (DFS) in the surgical cohort and of treatment response across the entire patient population. Conclusions: In addition to well-established clinical factors such as stage, histologic subtype, resectability, and Eastern Cooperative Oncology Group (ECOG) performance status (PS), PIV emerged as an independent and significant prognostic factor of overall survival (OS) in patients with PM. Moreover, PIV also demonstrated a remarkable independent prognostic value for disease-free survival (DFS) in this patient population. Additionally, some clues are provided for conditions such as treatment responses, staging, and suitability for surgery. As such, in this cohort, it has outperformed the other blood-based markers based on our findings. Given its ease of calculation and cost-effectiveness, PIV represents a promising and practical prognostic tool in the clinical management of pleural mesothelioma. It can be easily calculated using routinely available laboratory parameters for every cancer patient, requiring no additional cost or complex procedures, thus facilitating its integration into everyday clinical practice. Full article

Growing interest in the future applications of nanotechnology in medicine has led to groundbreaking developments in nanosensors. Nanosensors are excellent platforms that provide reliable solutions for continuous monitoring and real-time detection of clinical targets. Nanosensors have attracted great attention due to their remarkable sensitivity, portability, selectivity, and automated data acquisition. The exceptional nanoscale properties of nanomaterials used in the nanosensors boost their sensing potential even at minimal concentrations of analytes present in a clinical sample. Along with applications in diverse sectors, the beneficial aspects of nanosensors have been exploited in healthcare systems to utilize their applications in diagnosing, treating, and preventing diseases. Hence, in this review, we have presented an overview of the disease-prognostic applications of nanosensors in chronic diseases through a detailed literature analysis. We focused on the advances in various nanosensors in the field of major diseases such as cancer, cardiovascular diseases, diabetes mellitus, and neurodegenerative diseases along with other prevalent diseases. This review demonstrates various categories of nanosensors with different nanoparticle compositions and detection methods suitable for specific diagnostic applications in clinical settings. The chemical properties of different nanoparticles provide unique characteristics to each nanosensors for their specific applications. This will aid the detection of potential biomarkers or pathological conditions that correlate with the early detection of various diseases. The potential challenges and possible recommendations of the applications of nanosensors for disease diagnosis are also discussed. The consolidated information present in the review will help to better understand the disease-prognostic potentials of nanosensors, which can be utilized to explore new avenues in improved therapeutic interventions and treatment modalities. Full article

Bladder cancer pathogenesis is closely linked to epigenetic alterations, particularly DNA methylation and demethylation processes. Environmental carcinogens and persistent inflammatory stimuli—such as recurrent urinary tract infections—can induce aberrant DNA methylation, altering gene expression profiles and contributing to malignant transformation. This review synthesizes current evidence on the role of DNA methyltransferases (DNMT1, DNMT3a, DNMT3b) and the hypermethylation of key tumour suppressor genes, including A2BP1, NPTX2, SOX11, PENK, NKX6-2, DBC1, MYO3A, and CA10, in bladder cancer. It also evaluates the therapeutic application of DNA-demethylating agents such as 5-azacytidine and highlights the impact of chronic inflammation on epigenetic regulation. Promoter hypermethylation of tumour suppressor genes leads to transcriptional silencing and unchecked cell proliferation. Urine-based DNA methylation assays provide a sensitive and specific method for non-invasive early detection, with single-target approaches offering high diagnostic precision. Animal models are increasingly employed to validate these findings, allowing the study of methylation dynamics and gene–environment interactions in vivo. DNA methylation represents a key epigenetic mechanism in bladder cancer, with significant diagnostic, prognostic, and therapeutic implications. Integration of human and experimental data supports the use of methylation-based biomarkers for early detection and targeted treatment, paving the way for personalized approaches in bladder cancer management. Full article

Background/Objectives: Although numerous prognostic biomarkers have been proposed for colorectal cancer (CRC), their longitudinal evaluation remains limited. The aim of this study was to investigate longitudinal changes in biomarkers calculated from routinely used laboratory markers and their relationships to common chronic diseases (comorbidities). Methods: A retrospective longitudinal observational study was completed with the inclusion of 817 CRC patients and a total of 4542 measurement points. Pan-immune inflammation value (PIV), prognostic nutritional index (PNI), and systemic immune-inflammation index (SII) were calculated based on complete blood count and albumin measurement data. Results: Longitudinal data analyses confirmed the different values and slopes of the parameters tested at the different endpoints. Survivors had the lowest and most constant PIVs and SII values, and the highest and most slowly decreasing PNI values. Those patients with non-cancerous death had similar values to the previous cohort, but an increase/decrease occurred towards the death event. Patients with CRC-related death had significantly higher PIVs and SII values and significantly lower PNI values (p < 0.0001), and a significant increase/decrease was observed at the early observational periods. The presence of lymph node and/or distant metastases, adjuvant chemotherapy, and hypertension significantly affected PIVs and SII and/or PNI values. The changes in PIVs and SII and PNI values toward pathological values are poor prognostic signs (p < 0.0001). Conclusions: Each of the three calculated markers demonstrates suitability for longitudinal patient follow-up, and their pathological alterations over time serve as valuable prognostic indicators. They may also be useful to detect certain clinicopathological parameters early. 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

Locally advanced rectal cancer (LARC) remains a major clinical challenge due to its high risk of local recurrence and distant metastasis. Although total mesorectal excision (TME) has been established as the gold standard surgical approach, high recurrence rates associated with surgery alone have driven the development of multimodal preoperative strategies, such as radiotherapy and chemoradiotherapy. More recently, total neoadjuvant therapy (TNT)—which integrates systemic chemotherapy and radiotherapy prior to surgery—and non-operative management (NOM) for patients who achieve a clinical complete response (cCR) have further expanded treatment options. These advances aim not only to improve oncologic outcomes but also to enhance quality of life (QOL) by reducing long-term morbidity and preserving organ function. However, several unresolved issues persist, including the optimal sequencing of therapies, precise risk stratification, accurate evaluation of treatment response, and effective surveillance protocols for NOM. The advent of molecular biomarkers, next-generation sequencing, and artificial intelligence (AI) presents new opportunities for individualized treatment and more accurate prognostication. This narrative review provides a comprehensive overview of the current status of preoperative treatment for LARC, critically examines emerging strategies and their supporting evidence, and discusses future directions to optimize both oncological and patient-centered outcomes. By integrating clinical, molecular, and technological advances, the management of rectal cancer is moving toward truly personalized medicine. 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: Colorectal liver metastasis (CRLM) represents a major clinical challenge in oncology, affecting 25–50% of colorectal cancer patients and significantly impacting survival. While multimodal therapies—including surgical resection, systemic chemotherapy, and local ablative techniques—have improved outcomes, prognosis remains heterogeneous due to variations in tumor biology, patient factors, and institutional practices. Methods: This review synthesizes current evidence on prognostic factors influencing CRLM management, encompassing clinical (e.g., tumor burden, anatomic distribution, timing of metastases), biological (e.g., CEA levels, inflammatory markers), and molecular (e.g., RAS/BRAF mutations, MSI status, HER2 alterations) determinants. Results: Key findings highlight the critical role of molecular profiling in guiding therapeutic decisions, with RAS/BRAF mutations predicting resistance to anti-EGFR therapies and MSI-H status indicating potential responsiveness to immunotherapy. Emerging tools like circulating tumor DNA (ctDNA) and radiomics offer promise for dynamic risk stratification and early recurrence detection, while the gut microbiome is increasingly recognized as a modulator of treatment response. Conclusions: Despite advancements, challenges persist in standardizing resectability criteria and integrating multidisciplinary approaches. Current guidelines (NCCN, ESMO, ASCO) emphasize personalized strategies but lack granularity in terms of incorporating novel biomarkers. This exhaustive review underscores the imperative for the development of a unified, biomarker-integrated framework to refine CRLM management and improve long-term outcomes. Full article

Background: The development of brain metastases (BM) is a relatively uncommon but significantly adverse event in the spread of colorectal cancer (CRC). Although management of CRC BM has improved with advances in imaging and systemic therapies, clinical outcomes remain poor. Methods: This retrospective cohort study used the U.S. National Cancer Database to evaluate survival outcomes, treatment patterns, and prognostic factors in CRC patients diagnosed with BM between 2010 and 2020. Patients with isolated brain-only metastases formed the primary analytic cohort, while those with additional extracranial metastases were included for descriptive comparison. Multivariable Cox proportional hazards and logistic regression models were used to assess factors associated with of survival. Proportional hazards assumptions were tested using Schoenfeld residuals. Accelerated failure time models were also employed. Results: From a cohort of 1,040,877 individuals with CRC, 795 had metastatic disease present along with relevant data, of which 296 had isolated BM. Median overall survival (mOS) in BM-only metastatic disease group was 7.82 months (95% CI: 5.82–9.66). The longest survival was observed among patients treated with stereotactic radiosurgery combined with systemic therapy (SRS+Sys), with a median OS of 23.26 months (95% CI: 17.51–41.95) and a 3-year survival rate of 35.8%. In adjusted Cox models, SRS, systemic therapy, and definitive surgery of the primary site were each independently associated with reduced hazard of death. Rectal cancer patients had longer survival than those with colon primaries (mOS: 10.35 vs. 6.08 months). Age, comorbidity burden, and insurance status were not associated with survival in adjusted analyses. Conclusions: SRS+Sys was associated with longer survival compared to other treatment strategies. However, treatment selection is highly dependent on individual clinical factors such as performance status, comorbidities, and disease extent; therefore, these findings must be interpreted with caution Future prospective studies incorporating molecular and biomarker data are warranted to better guide care in this rare and high-risk group. Full article

The Chromobox (CBX) family comprises key epigenetic regulators involved in transcriptional repression through chromatin modifications. Dysregulation of polycomb CBX proteins has been linked to epigenetic gene silencing and cancer progression. However, the specific roles and prognostic value of CBX family members in colorectal cancer (CC) remain unclear. In this study, we show that CBX genes are significantly dysregulated in CC tissues and cell models compared to normal colorectal tissue. Among them, CBX4 and CBX8 emerged as the most upregulated isoforms in tumors. Functional analyses revealed that CBX4 overexpression enhances CC cell proliferation, while its silencing reduces tumor growth. Similarly, pharmacological inhibition of CBX4 in patient-derived tumor organoids led to decreased proliferation, supporting its pro-tumorigenic role. Immunofluorescence analysis further revealed alterations in NF-κB signaling upon CBX4 inhibition, along with reduced mRNA levels of pathway components including NF-κB, TNF, IL-1, and c-Myc. These findings point to a potential interplay between CBX4 and inflammation-related pathways in CC. Overall, our study highlights the oncogenic role of CBX4 in colorectal cancer and supports its potential as a novel therapeutic target and early biomarker for disease progression. Full article