This review is designed to highlight recent research efforts to optimize treatment strategies in men with advanced prostate cancer.

Oligometastatic prostate cancer (oligoPCa) represents a clinical state of limited metastatic spread in which metastasis-directed therapy (MDT) may offer meaningful disease control either alone or with systemic therapy. As imaging, systemic therapy, and biologic characterization evolve, management strategies for both synchronous and metachronous presentations continue to undergo significant refinement.

Body-composition indices beyond body mass index (BMI) are increasingly being investigated as prognostic cancer markers. In pancreatic cancer, the prognostic relevance of skeletal muscle and adipose tissue compartments remains uncertain, with studies yielding inconsistent findings. We examined whether early on-treatment changes in subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle index (SMI) are associated with unresectable pancreatic cancer prognosis.

Globally, breast cancer (BC) continues to be the primary cause of cancer-related death for women, underscoring the need for reliable biomarkers for early detection and improved patient outcomes. Because of their stability in biological fluids and their functions in controlling gene expression, microRNAs (miRNAs) have become attractive diagnostic tools.

Head and neck cancers (HNC) significantly impact patients' quality of life (QOL), with oral health often being significantly compromised, especially in advanced stages. Green tea, with its known anti-inflammatory and antioxidant properties, may help to improve oral health in these patients. Therefore, this study aims to investigate the effect of green tea-based mouth rinse on oral health status, pain, and QOL of advanced HNC patients.

Breast cancer remains the most prevalent malignancy among women worldwide, driven by complex interactions between tumor-intrinsic factors and the immune microenvironment. Understanding immune-related molecular regulators is essential to improve diagnostic and therapeutic strategies. Transcriptomic data from the TCGA-BRCA dataset (1109 tumor and 113 normal samples) were analyzed to identify differentially expressed immune-related genes by cross-referencing ImmPort and InnateDB gene sets. Protein-protein interaction networks were constructed using STRING and Cytoscape to identify hub genes. Validation analyses were conducted using GSCA, cBioPortal, and GEO datasets. miRNA-mRNA regulatory interactions were explored using TargetScan, miRDB, miRWalk, and miRTarBase databases, followed by dual-luciferase reporter assays. Functional characterization of CCL5 and CD74 was performed through siRNA-mediated knockdown in breast cancer cell lines, proliferation, colony formation, and wound healing assays, as well as xenograft mouse models. Four key immune-related hub genes, including CXCL9, CXCL13, CCL5, and CD74 were identified as significantly upregulated in breast cancer. Epigenetic and prognostic analyses revealed that these genes were influenced by promoter methylation and correlated with poor survival outcomes. miRNA-mRNA network analysis identified hsa-miR-146a-5p and hsa-miR-200c-3p as central regulators, validated by luciferase assays. Functional studies showed that silencing CCL5 and CD74 significantly reduced cell proliferation, migration, and tumor growth in vivo, confirming their oncogenic roles. This integrative multi-omics and experimental study identified CCL5 and CD74 as key immune-oncogenic drivers of breast cancer progression. Their modulation of the MIF-CD74 signaling axis highlights potential therapeutic targets for immunomodulatory interventions and combination therapy in breast cancer.

Pancreatic ductal adenocarcinoma (PDAC) is among the most devastating malignancies worldwide, characterized by late diagnosis, rapid progression, and dismal survival rates. The discovery of reliable biomarkers for early detection and disease monitoring remains an urgent clinical need. Owing to its proximity to tumor cells and protein-rich composition, pancreatic juice represents a compelling reservoir for biomarker discovery.

Although endocrine therapies prevent recurrence and progression of estrogen receptor alpha (ER)-positive breast cancer, approximately one-third of patients experience recurrent disease that is rarely cured in the advanced/metastatic setting. A subpopulation of endocrine-tolerant breast cancer cells persists as residual disease that confers risk for the eventual emergence of drug resistance. An analysis of persisters that continue to proliferate despite endocrine therapy revealed the activation of pathways related to metabolism and E2F transcription factor signaling. E2F signaling is driven by cyclin-dependent kinases 4 and 6 (CDK4/6), and CDK4/6 inhibitors (CDK4/6i) are used clinically to prevent and manage endocrine resistance. CDK4/6i slowed the cycling of endocrine-tolerant persisters. Analyzing metabolic alterations induced by CDK4/6i, we found that CDK4/6i-tolerant persisters had upregulation of mitochondrial content, mitochondrial membrane potential, respiration, and reactive oxygen species (ROS). Inhibition of mitochondrial complex I further increased ROS levels and enhanced growth inhibition in both endocrine-sensitive and -resistant cell lines and patient-derived xenografts. These findings collectively offer mitochondrial respiration as a therapeutic target in CDK4/6-tolerant persister breast cancer cells to help eradicate residual disease.

Despite the remarkable success of immune checkpoint inhibitor (ICI) therapy in solid tumors, immune-related adverse events (irAEs) have posed great challenges in the whole-course management of ICI immunotherapy. Reliable biomarkers helping to predict irAEs are still limited and lacking.

Tumor dynamic models are vital for evaluating oncology treatments and guiding clinical drug development decisions. However, few studies rigorously assess their predictive capabilities, especially when forecasting tumor trajectories from clinical trials with short or inconsistent follow-up across treatment arms. Poor predictive performance or biases related to follow-up time could potentially limit the general utility of tumor growth inhibition (TGI) models. This study quantitatively evaluates prediction bias across several established tumor dynamic models, comparing five classical pharmacometric TGI models with the deep learning-based Tumor Dynamic Neural-ODE (TDNODE) framework. Using time-truncated clinical trial data from 3106 patients with non-small cell lung cancer (NSCLC) across four completed atezolizumab phase III studies, we consistently observed moderate-to-high positive bias in the predictions from pharmacometric models, particularly with more limited follow-up. By examining the structures of these models and comparing them to observed data, we highlight how the assumed kinetic patterns potentially lead to biased parameter estimation and systemic overestimation of tumor size when applied to immature datasets. In contrast, the TDNODE framework, using deep learning, demonstrated promising early results, exhibiting improved predictive performance in the same evaluations. These findings underscore the critical need to address prediction bias in tumor dynamic modeling with immature data and to consider alternative approaches to established paradigms for certain drug development applications. This study also generally demonstrates the potential of novel methods, such as deep learning, to potentially enhance the reliability of tumor dynamics modeling, especially in challenging early-phase clinical decision-making scenarios.

The prognostic value of TP53 mutations in metastatic colorectal cancer remains unclear owing to inconsistent findings in the literature. Given its central role in tumor biology, clarifying the impact of TP53 status on survival outcomes is clinically relevant.

Solid tumors are organized pathophysiologic systems in which vascular dysfunction, stromal remodeling, and diffusion-consumption imbalance partition lesions into recurrent microenvironmental microdomains. These regions regulate not only whether agents enter tumors, but also where they localize and function within tissue. This review presents a microdomain-centered framework linking tumor biology and microenvironment heterogeneity to the behavior of microenvironment-responsive nanomaterials, with emphasis on clinically relevant tissue exposure rather than therapeutic outcome. We first distinguish trans-vascular entry from intratumoral transport, demonstrating why tumor-level accumulation does not ensure uniform cellular exposure. Vascular heterogeneity, including endothelial-mediated transport pathways and perfusion variability, is examined as a determinant of delivery efficiency across tumor types. We then analyze stromal and interstitial constraints-extracellular matrix organization, interstitial fluid pressure, solid stress, and cellular sequestration-that dominate postentry distribution and bias localization toward perivascular compartments. Hypoxic, acidic, protease-active, fibroblast-remodeled, and receptor-defined niches are interpreted as spatially structured metabolic and signaling environments whose clinical relevance depends on accessibility and residence time. We propose morphology-based evidence standards centered on compartment-resolved mapping of localization and in situ material state transitions, together with minimum reporting practices to improve reproducibility and cross-study translation. We identify recurring mismatches between tumor architecture and material design that contribute to heterogeneous distribution and variable response across lesions and patients. Standardized spatial metrics, including vessel-distance stratification and penetration-depth profiling, are recommended to support comparison across studies. Future work should prioritize 3D coregistration of microenvironmental markers, localization, and activation, and incorporate lesion-level heterogeneity as a measurable biological variable to improve clinical translation.

This review explores the role of bioinformatics in identifying novel biomarkers for immune cell exhaustion (ICE), a dysfunction state in T cells during chronic infections and cancer. ICE, marked by upregulation of inhibitory receptors such as PD-1 and CTLA-4, impairs immune responses, a critical barrier in chronic infection and cancer treatment. Understanding this state is crucial for developing therapies to reverse T cell exhaustion and improve immune function. The review highlights advanced bioinformatics tools that analyze high-throughput sequencing data, transcriptomics, proteomics, and metabolomics to identify biomarkers and therapeutic targets, enhancing diagnostics and treatments. Despite challenges like the complexity and heterogeneity of ICE, the integration of bioinformatics has advanced our molecular understanding and identification of key pathways. This facilitates the development of personalized immunotherapies, improving outcomes for patients with chronic infections and cancer. Additionally, this review emphasizes the tumor microenvironment's (TME) role in ICE, where factors such as the upregulation of immune checkpoint ligands, secretion of immunosuppressive cytokines like Transforming Growth Factor Beta (TGF-β) and Interleukin 10 (IL-10), and recruitment of regulatory immune cells create an immunosuppressive milieu fostering tumor growth. In conclusion, this review will also discuss the future directions for research in biomarker discovery and the integration of bioinformatics with clinical data to enhance the precision and effectiveness of therapies. By addressing these challenges, future research can lead to more targeted and efficient treatments for patients suffering from chronic infections and cancer.

Breast cancer (BC) is widely recognized as the most frequently diagnosed neoplasm among women worldwide. Despite significant advances, improved diagnosis and treatment of breast cancer have failed to translate into earlier detection or markedly better outcomes for patients, due to challenges including late-stage presentation, metastasis, recurrence, metabolic reprogramming, and drug resistance. Identifying reliable predictive indicators for early intervention and diagnostic markers remains a priority in breast cancer research, as this is crucial for improving patient prognosis. Circular RNAs (circRNAs), a class of non-coding RNAs (ncRNAs) abundant in various tissues and human cells, are established biomarkers for diagnosing and monitoring diseases such as those affecting the nervous, cardiovascular, and immune systems. CircRNAs contribute to tumorigenesis through the regulation of proliferation, metastasis, angiogenesis, and the tumor microenvironment. A growing body of evidence has established circRNAs as key regulators in breast cancer, elucidating their specific roles in driving tumor development and progression. Nevertheless, a more comprehensive understanding of the functional roles and molecular mechanisms of circRNAs in BC is essential and requires further research. This narrative review synthesizes the current knowledge on circRNAs encompassing their biogenesis, characteristics, methylation, tumor cell death, and selectively analyzes the translational potential of circRNAs in BC patients.

This review systematically evaluates the published literature on multiple fibroadenomas (MFAs) of the breast, focusing on clinical presentation, diagnostic challenges, and treatment strategies. A systematic search of PubMed, Medline, Scopus, and Embase from 1948 to June 2025 was conducted using a Boolean search strategy following the PRISMA 2020 guidelines. Of the 409 studies initially screened, 43 met the inclusion criteria. The level of evidence was assessed using the Oxford Centre for Evidence-Based Medicine guidelines, and 72% were found to be of Levels 4 and 5. MFA most commonly affects adolescents. Bilateral involvement was observed in 64.6% of patients, and more than 10 lesions were present in 52% of cases. Risk factors included hormonal contraceptive use, family history, and cyclosporine therapy. Ultrasonography was the most common diagnostic tool, followed by MRI and FNAC. Treatment was mainly surgical, with recurrence noted in 15.4% of patients. There was no evidence to suggest that the risk of cancer in MFA was greater than that of solitary FA. The review highlights inconsistencies in MFA definitions and calls for consensus on diagnostic criteria, prospective research, and the creation of standardized treatment algorithms to improve care and outcomes for this particularly distressing subset of benign breast diseases.

New androgen receptor (AR) pathway inhibitors (ARPIs) in clinical development, including AR degraders and CYP11A inhibitors, largely target ligand-dependent AR activation and have reported antitumor activity in metastatic castration-resistant prostate cancer (mCRPC) resistant to established ARPIs, predominately against tumors with AR mutations. We hypothesized that AR-mutated mCRPC exhibits lower AR splice variant 7 (AR-V7) expression and remains full-length-AR (FL-AR) driven, explaining, in part, the antitumor activity of these AR ligand-binding domain (LBD) targeting drugs. The data herein demonstrate that mCRPC tissue biopsies with detectable AR mutations express significantly lower levels of AR-V7 protein and associate with better overall survival and enhanced sensitivity to ARPIs. This is independent of differences in the total number of global splicing events but may be related to differences in splicing factor expression between AR-mutated and nonmutated mCRPC. In conclusion, AR-mutated mCRPC frequently exhibits low AR-V7 expression, arguably explaining the enhanced sensitivity to ARPIs observed in these cancers. Consequently, AR mutation status may serve as a biomarker to predict response to AR-directed therapies.

Treatment of locally advanced and metastatic prostate cancer (PC) with androgen receptor-targeting (AR-targeting) therapies has limited durability, with disease eventually progressing to castrate-resistant PC (CRPC). Constitutively active AR splice variants (AR-Vs), such as AR-V7, play a key role in driving treatment resistance and disease progression. Importantly, the failure to attenuate AR-V function represents a major unmet clinical need, and as such, defining how AR-Vs are generated is likely to yield new therapeutic targets. Our knowledge of factors that mediate splicing of AR-V-encoding mRNAs remains limited. Here, we have employed an RNA-targeting CasRx approach to identify selective protein interactors of AR-V7 mRNA in PC. TRA2B and its ortholog, TRA2A, were identified as splicing regulators of AR transcripts that facilitate AR-V synthesis at the expense of full-length AR isoforms. TRA2B expression correlated with AR-V7 transcript in CRPC and attenuation of TRA2-mediated splicing diminished PC cell growth. Exploiting TRA2B function may therefore provide new therapeutic opportunities in advanced disease.

The role of intensity-modulated radiotherapy (IMRT) and novel combination chemotherapy regimens in locally advanced pancreatic cancer (LAPC) remains unclear. In this study, we focused on comparing survival between first-line chemotherapy alone and first-line chemotherapy plus IMRT in patients with LAPC.

The progression from cirrhosis to hepatocellular carcinoma (HCC) is a key outcome in the management of chronic liver disease. This process has a long incubation period and significant individual differences, making early warning still difficult. Clinical follow-up mainly relies on imaging examinations and alpha fetoprotein, but the ability to identify high risk precancerous states is limited. The imbalance of gut microbiota and its metabolites may occur earlier than the visible stage of tumors. They can affect barrier integrity, chronic inflammation, immune surveillance, and metabolic homeostasis through the gut liver axis, and participate in the formation of a pro tumor microenvironment. Therefore, such changes may provide more upstream risk stratification clues for the population with cirrhosis. This article summarizes previous research evidence and summarizes the common microbiome and metabolite characteristics of cirrhosis and high-risk populations, including a decrease in short chain fatty acid (SCFA) related symbiotic bacteria, an increase in inflammation related bacteria, bile acid spectrum shift, and other intestinal derived metabolite abnormalities. This article also outlines the key mechanisms that these features may correspond to, such as barrier damage and microbial translocation, immune suppression, etc. There are still significant uncertainties at present. The effect of SCFA is context dependent. Different etiologies, diets, medications, and complications can lead to significant confounding and affect cross cohort consistency. Subsequent research requires longitudinal cohort validation and the promotion of multi omics integration and the construction of interpretable predictive models to support clinical translation.

To evaluate the effectiveness of different methods for segmenting tumor regions of interest in building prediction models for the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of clear cell renal cell carcinoma (ccRCC).