The metabolic activities of cancer cells undergo complete transformation because they need to maintain their growth while resisting metabolic challenges and environmental dangers from their tumour surroundings. The metabolic changes that occur in cells depend on specific oncogenes together with tumour suppressor genes and stress-response pathways, which control essential bioenergetic and biosynthetic functions. This review presents the current scientific knowledge about genetic regulators, which include MYC, KRAS, PI3K-AKT-mTOR, EGFR, p53, PTEN, and LKB1-AMPK, that control glucose, amino acid, lipid, nucleotide, and mitochondrial metabolism in different human cancers. The research demonstrates that these pathways connect through common metabolic pathways, which produce metabolic flexibility and create complex metabolic patterns that drive tumour diversity and development and resistance to treatment. We present new systems-level frameworks that exceed pathway-based descriptions to show the intricate nature of cancer metabolism. The review investigates how artificial intelligence (AI) and machine learning methods, combined with multi-omics data and genome-scale metabolic models, enable scientists to enhance metabolic phenotyping and discover specific tumour weaknesses and forecast treatment results and combination methods. The study begins with a discussion of present-day obstacles that impede clinical application of research results, which include data inconsistency and the challenges of understanding and testing models. Then it presents upcoming research paths that will develop AI-powered metabolic assessment into biologically understandable and clinically usable tools. The review creates a comprehensive framework that connects genetic control mechanisms with metabolic network functions and AI-driven precision oncology.

Programmed cell death protein 1 (PD-1) and programmed death ligand 1 (PD-L1) constitute a core immune checkpoint axis that restrains T cell activation. Under physiological conditions, the interaction between PD-1 and PD-L1 maintains peripheral tolerance and prevents tissue damage from excessive immune responses. In tumor microenvironment, this axis is frequently activated through upregulation of PD-L1 on cancer cells, causing T cell dysfunction and tumor immune escape. PD-1/PD-L1 blockades reactivate exhausted T cells to kill cancer cells. However, some cancer patients fail to respond to PD-1/PD-L1 inhibitors, and the underlying mechanisms remain incompletely revealed. Thus, it is important to unveil the regulatory mechanisms that govern PD-1 and PD-L1 expression in tumor microenvironment. This review systematically summarizes the regulation of PD-1/PD-L1 expression at the genetic, epigenetic, transcriptional, and posttranscriptional levels. Beyond expression levels, posttranslational modifications and membrane trafficking determine PD‑L1 surface availability and sensitivity to PD-1/PD-L1 inhibitors. In this review, the rapidly expanding clinical trials of PD-1/PD-L1 blockades and future research directions are also discussed.

Clostridioides difficile infection (CDI) is a frequent and consequential healthcare-associated complication in patients with hematologic malignancies (HMs), particularly in the setting of bone marrow transplant (BMT); however, national data comparing the impact of CDI across HM hospitalizations with and without BMT remain significantly limited. To evaluate associations between CDI and key inpatient outcomes like in-hospital mortality, length of stay (LOS), and total hospital charges, this retrospective cross-sectional analysis was conducted using the Nationwide Inpatient Sample. Multivariable models were fitted using survey-weighted logistic regression for mortality and Poisson regression with a log link for LOS and charges, adjusting for demographic and clinical covariates to calculate adjusted odds ratios and incidence rate ratios. Among an estimated 2.86 million adult HM hospitalizations, most involved neither exposure, while smaller proportions involved BMT only, CDI only, or both. In adjusted analyses, CDI was independently associated with higher odds of in-hospital death and substantially increased LOS and hospital charges. BMT was associated with markedly higher mortality and pronounced increases in LOS and charges. Hospitalizations complicated by concurrent CDI and BMT demonstrated the greatest burden across outcomes, with significantly increased mortality risk and the largest increases in LOS and charges (all p < 0.001). These findings indicate that CDI was associated with worse clinical and economic outcomes in hospitalized HM patients, with the highest risk observed when CDI occurs in post-transplant settings. Targeted prevention, early diagnosis, and prompt treatment of CDI therefore represent high-yield opportunities to reduce inpatient morbidity, mortality, and resource utilization in this vulnerable population.

Exercise is widely recommended in oncology; however, its role in orthopedic oncology remains insufficiently defined within a clinical context where tumor biology, skeletal fragility, muscle loss, systemic therapy, and reconstructive biomechanics converge. Existing rehabilitation frameworks primarily emphasize functional recovery, while the biological integration between mechanical loading and tumor-host systems remains incompletely characterized in clinical decision-making contexts. This narrative review synthesizes mechanistic evidence across tumor mechanobiology and its integration with bone-muscle endocrine crosstalk, marrow niche regulation, immunometabolism, and vascular-metabolic adaptation. Key pathways, including integrin-FAK/Src signaling, RhoA/ROCK dynamics, YAP/TAZ and β-catenin transcriptional regulation, Piezo-mediated mechanosensing, and load-sensitive RANKL/OPG balance, are examined within hybrid movement strategies that integrate aerobic, resistance, neuromotor, isometric, eccentric, and blood flow-restricted modalities. Emerging evidence indicates that mechanical stimuli may be associated with coordinated changes in extracellular matrix organization, perfusion dynamics, and immune cell trafficking, with secondary effects on mitochondrial function and marrow adiposity. These effects appear to remain context-dependent, with implications for fracture risk, cachexia, neuropathy, and treatment tolerance. However, the boundary between adaptive remodeling and pro-invasive signaling remains incompletely defined, supporting a threshold-dependent rather than linear dose-response relationship. Accordingly, this review proposes a safety-calibrated, phase-sensitive framework for translational movement dosing in orthopedic oncology. Future research should prioritize biomarker-driven clinical trials and temporally resolved dosing strategies to clarify how structured movement may support musculoskeletal integrity while remaining aligned with oncologic treatment constraints.

Tumor lysis syndrome (TLS) arises from the uncontrolled release of intracellular contents into the bloodstream. Generally associated with hematologic malignancies, TLS has also been reported in aggressive solid cancers. Corticosteroids are known triggers of cell lysis in leukemias and lymphomas, but less evidence exists to suggest steroids cause TLS in solid malignancies. We present a case of a 43-year-old smoker with dyspnea, cough, and chest pain. Endobronchial biopsy confirmed the diagnosis of extensive-stage small-cell carcinoma. He remained intubated due to airway edema and was started on dexamethasone. Prior to steroid administration, his blood chemistry values were within reference ranges. The following day, he met both laboratory and clinical Cairo-Bishop criteria for TLS. Despite IV hydration and rasburicase, he worsened and was transitioned to comfort care. This case suggests one of the clearest associations between steroid administration and TLS in a solid tumor. We systematically explored PubMed for cases of TLS in solid tumors and evaluated these for steroid use prior to diagnosis. Of the 252 included articles, 20 described corticosteroid use before the onset of TLS, with only 10 explicitly suggesting steroids may have been causative. While rare in solid tumors, TLS should be considered in any aggressive solid malignancy. A high index of suspicion is essential as early recognition and management of TLS are associated with improved outcomes. We encourage particular attention to be paid after steroids have been administered.

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are a well-known group of malignancies characterized by hypervascularization, and much of the focus in this field has been on angiogenesis. Rather than simply an increase in microvessel density, angiogenesis of GEP-NENs is a far more sophisticated process regulated by angiogenic factors, genes, and cells. These angiogenesis-related factors are not only involved in tumorigenesis and progression but also serve as biomarkers with prognostic values and targets for antiangiogenic therapy. Extensive research has been conducted on the role of various angiogenesis-related factors in angiogenesis, their prognostic values, and antiangiogenic therapy has become a critical part of the treatment of GEP-NENs. However, the occurrence of drug resistance and unsatisfactory prognosis reminds us that our understanding of angiogenesis of GEP-NENs is insufficient, and we are in urgent need to optimize existing antiangiogenic therapies. This review focuses on angiogenesis-related factors, their roles in angiogenesis, as well as their values in predicting the prognosis of patients, and introduces the corresponding angiogenic therapies. By summarizing current research findings, we aim to shed light on the mechanism of angiogenesis of GEP-NENs and to elucidate prospects for more accurate prediction of prognosis and individualized antiangiogenic therapies.

Hormone-related cancers represent a significant global health concern, contributing to substantial mortality rates worldwide. The intricate interplay of steroidogenesis, the biochemical pathway governing hormone synthesis, plays a pivotal role in the development and progression of these malignancies. Dysregulation of key steroidogenic enzymes can disrupt physiological and biological processes, thereby driving malignant transformation. Post-translational modifications of these enzymes play an important role in modulating their activity, stability, and subcellular localization, thereby potentially influencing cancer progression. This study provides a comprehensive analysis of the functionality and regulatory mechanisms of four key steroidogenic enzymes, Desmolase, 17α-hydroxylase, 3β-HSD (3β-hydroxysteroid dehydrogenase), and 17β-HSD (17β-hydroxysteroid dehydrogenase), along with a transporter protein (steroidogenic acute regulatory protein [StAR]). We present an integrated overview of their expression patterns, domain architectures, reported post-translational modification sites, and the kinases involved. By elucidating these molecular details, our work highlights novel avenues for targeted therapy in hormone-related cancer research.

Physics-informed machine learning (PIML) is rapidly emerging as a transformative paradigm for designing tumor microenvironment (TME)-responsive nanomedicines. While existing reviews have surveyed PIML in broader biomedical contexts, this work provides a focused, critical synthesis specifically at the intersection of PIML, multiscale TME biophysics, and clinically actionable nanomedicine design. We argue that the unique value of PIML lies not merely in combining physics with data, but in its capacity to resolve the "personalization paradox" in oncology: the tension between the need for patient-specific models and the scarcity of patient-specific data. By embedding governing physical laws-such as Darcy's flow, reaction-diffusion kinetics, and Navier-Stokes equations-as soft constraints, PIML models can generate physically plausible, patient-tailored predictions even with sparse clinical inputs. This review uniquely articulates a translational roadmap that systematically links fundamental PIML methodologies to concrete nanomedicine optimization tasks: predicting nanoparticle transport in heterogeneous TMEs, designing stimulus-responsive nanocarriers, and integrating multi-omics/imaging for personalized therapy. We further introduce a novel comparative framework evaluating PIML against purely physics-based and purely data-driven approaches, highlighting its superior data efficiency and interpretability for TME applications. However, significant challenges remain, including data standardization, computational scalability, and regulatory adaptation. Looking forward, we identify under-explored yet high-impact frontiers, such as quantum-informed PIML for molecular-scale nanocarrier design, real-time adaptive nanomedicine guided by patient digital twins, and the ethical-regulatory frameworks needed for clinical deployment. By synthesizing cross-disciplinary insights and proposing a clear path from bench to bedside, this review aims to not only summarize the state-of-the-art but also to catalyze the next generation of intelligent, patient-centric cancer nanotherapeutics.

R0 resection and maximal preservation of anal function without a permanent stoma remains challenging in patients with a rectal neuroendocrine neoplasm (R-NEN) within 5 cm of the anal verge and lymph node metastasis in the iliac vascular region. A 22-year-old man presented with a 1-year history of paroxysmal palpitations and cold sweats. Colonoscopy revealed a 2-cm mass with well-defined margins located 5 cm from the anal verge. Biopsy revealed a neuroendocrine tumor. Endoscopic submucosal dissection and pathology revealed a G2 R-NEN with negative margins but vascular invasion. Enhanced MRI and whole-body PET/CT identified metastases in the left iliac and right common iliac vascular regions with presacral lymph node involvement. The patient underwent precision functional sphincter-preserving surgery combined with colonic J-pouch anal anastomosis without a stoma. The patient started oral short-peptide enteral nutrition on postoperative day (POD)3 and advanced to a liquid diet on POD8 and a semi-liquid diet on POD10. He was discharged on POD12, with no anastomotic leakage observed within 30 days. Histopathology confirmed stage T1bN1M0 disease. At the 7-month follow-up, the patient reported satisfactory anal, urinary, and sexual function and had maximal global health and quality of life scores. The favorable outcomes in this young case of G2 R-NEN suggests that precision functional sphincter-preserving surgery integrated with a colonic J-pouch may be a clinical reference for achieving oncological radicality while optimizing physiological outcomes. This strategy offers an alternative to traditional radical surgery for young patients and those with high functional demands. However, its safety, functional durability, and oncological efficacy require confirmation in larger, prospective multicenter studies with longer follow‑up.

This study evaluates the ability of generative artificial intelligence (AI) models to standardize tumor-node-metastasis (TNM) staging data in lung cancer. It assesses how well two large language models, ChatGPT and Gemini, convert information from historical editions of the American Joint Committee on Cancer (AJCC) staging system into the eighth edition. ChatGPT and Gemini were applied to retrospectively standardize TNM staging across multiple AJCC editions using lung cancer data from the Surveillance, Epidemiology, and End Results cancer registry. We used a zero-shot prompting method, employing Python to generate prompts dynamically for each case, and a few-shot variant with examples added to the same inputs. The AI-generated outputs were compared with expert-derived staging to evaluate their accuracy. ChatGPT achieved a micro-accuracy of 52.26% across T, N, M, and stage, slightly outperforming Gemini (50.72%), whereas few-shot prompting improved micro-accuracy to 71.35% and 77.24%, respectively. Both models showed mixed agreement with expert-derived T, N, and M components but often failed to perform the final stage assignment. These results indicate that, although generative AI models may support digital transformation and clinical data standardization, they still face challenges in tasks requiring detailed medical reasoning. Further improvements and validation are needed before relying on generative AI for TNM staging standardization.

Pancreatic cancer remains one of the most lethal of malignancies with a 5-year survival rate of approximately 10% in the USA, largely owing to its late diagnosis, intrinsic chemoresistance, and high incidence of metastatic disease. Although gemcitabine-based chemotherapy is the standard treatment for advanced pancreatic cancer, inherent tumor heterogeneity and highly desmoplastic and immunosuppressive tumor microenvironment result in conventional cancer treatments having little impact on patient's disease course. Therefore, the development of novel and effective therapies is urgently required. Triterpenoids are naturally occurring secondary metabolites biosynthesized following the isoprene rule of Ruzicka, with a basic nucleus of 30 carbon atoms. Growing evidence from preclinical studies indicates natural triterpenoids as potential anticancer agents against pancreatic cancer. In this review, we synthesize research findings from available preclinical studies that investigated the therapeutic potential of natural triterpenoids on pancreatic cancer, particularly focusing on anticancer mechanisms involving activation of apoptosis, induction of autophagy, regulation of immune responses, inhibition of metastasis, and enhancement of chemosensitivity. We also discuss the key challenges and envisage future research directions in this rapidly evolving field. We conclude that natural triterpenoids are a valuable resource for developing novel therapeutic agents against pancreatic cancer. We also hope that the knowledge gained from this narrative review will be helpful in accelerating the translation of triterpenoid-based therapeutics into clinical practice.