This integrative review investigates perceptions, educational expectations, and knowledge gaps of patients with breast cancer (BC) regarding radiotherapy (RT).

The included studies were analysed using a thematic analysis approach. Each segment of data was coded with open coding. The codes were gathered into subthemes as they emerged and into overarching themes, after which the data was analysed again.

22 studies were included: 11 qualitative, 10 cross-sectional, and 1 case study. Our findings indicate that breast cancer patients perceptions of radiation therapy (RT) are influenced by their understanding of RT, its side effects, the treatment burden, emotional state or feelings, effectiveness if RT, prognosis, and viewing RT as the end-of-care phase. They expect education on the treatment pathway, psychosocial support, personalised aspects of RT, planning, delivery, follow-up, and side effects. Additionally, women undergoing RT have knowledge gaps related to preparedness and support, unforeseen risks and side effects, as well as daily practical issues.

Tailored, culturally sensitive education is essential to bridge gaps in understanding, manage anxiety, and build trust. It requires individualized communication strategies and psychosocial support. By integrating personalized information and leveraging technological solutions, healthcare providers can empower patients, improve adherence, and enhance outcomes, particularly in resource-limited settings.

The treatment outcomes of lung cancer are highly variable, and machine learning (ML) models provide valuable insights into how clinical and biochemical factors influence survival across different treatments. This study will investigate the survival of patients after four major treatments for lung cancer by interpreting the impact of biomarkers on survival using SHapley Additive exPlanations (SHAP). We analyzed 23,658 lung cancer patient records derived from a Kaggle dataset. Using the most relevant clinical and biochemical variables, ML models were employed to study survival outcomes for different treatments. SHAP analysis revealed major survival predictors in each treatment. Survival outcomes are visualized as f(x) (predicted survival) and E[f(x)] (baseline expectation) in SHAP waterfall plots. The most performed model is Gradient Boosting with an accuracy of 88.99%, precision of 89.06%, recall of 88.99%, F1-score of 88.91%, and Receiver Operating Characteristic Curve (AUC-ROC) score of 0.9332. Chemotherapy treatment was positive for survival, the key for survival was phosphorus levels (+0.05), low Alanine Aminotransferase levels (+0.04) and low glucose levels (+0.04). Targeted therapy and radiation had worse survival, while surgery was favorable, especially in cases with high white blood cell and Lactate Dehydrogenase (LDH) levels. SHAP-based ML analysis aptly underlines how clinical and biochemical factors influence the survival rate. It indicates that ML-driven interpretability might drive personalized treatment approaches in lung cancer.

Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, with Gleason grading critical for prognosis and treatment decisions. Machine learning (ML) models offer potential for automated grading but are limited by dataset biases, staining variability, and data scarcity, reducing their generalizability. This study employs generative adversarial networks (GANs) to generate high-quality synthetic histopathological images to address these challenges. A conditional GAN (dcGAN) was developed and validated using expert pathologist review and Spatial Heterogeneous Recurrence Quantification Analysis (SHRQA), achieving 80% diagnostic quality approval. A convolutional neural network (EfficientNet) was trained on original and synthetic images and validated across TCGA, PANDA Challenge, and MAST trial datasets. Integrating synthetic images improved classification accuracy for Gleason 3 (26%, p = 0.0010), Gleason 4 (15%, p = 0.0274), and Gleason 5 (32%, p < 0.0001), with sensitivity and specificity reaching 81% and 92%, respectively. This study demonstrates that synthetic data significantly enhances ML-based Gleason grading accuracy and improves reproducibility, providing a scalable AI-driven solution for precision oncology.

Non-Hodgkin lymphoma (NHL) is one of the most prevalent disorders worldwide, with a variety range of etiology from environmental to genetic factors. H19 is a non-coding RNA that codes no protein while playing regulatory roles and is hypothesized to be involved in susceptibility to NHL.

209 NHL patients and 259 healthy subjects were studied. The salting out method was used for genomic DNA extraction, followed by the Refractory fragment length polymorphism polymerase chain reaction (RFLP-PCR) technique for genotyping. SPSS package V.22 software was used for statistical analysis. Several in silico tools were used to predict the probable consequences of studied H19 genetic variants on the different aspects of non-coding RNAs.

The results revealed that statistically, both rs3741219T>C and rs217727C>T variants increased the susceptibility to NHL. The T allele of rs3741219T>C in the codominant model caused the most enhancement in the incidence of NHL (OR = 2.33, 95% CI = 1.28-4.25, p = 0.005). Moreover, The CC genotype of rs217727C>T compared to TT had the sharpest impact on the susceptibility to NHL (OR = 2.27, 95% CI = 1.21-4.23, p = 0.009). In silico predictions revealed that the studied variants seem to alter the binding sites of miRNAs on the H19 long non-coding RNA and change its targets. Furthermore, nucleotide substitution in both rs3741219T>C and rs217727C>T may prepare a new binding site for a transcription factor called Y-Box-binding protein-1 (YB-1).

The rs217727C>T and rs3714219T>C were responsible for elevating the likelihood of NHL in our population. These substitutions alter the RNA folding of H19 and alter the miRNA binding sites on the H19 transcript.

The increasing global prevalence of cancer, particularly breast cancer, necessitates the development of innovative therapeutic strategies. Probiotics, proficient in promoting gut health, have emerged as promising candidates for cancer treatment due to their immunomodulatory and potential anticancer properties. This review focuses on the therapeutic mechanisms of probiotics in breast cancer, examining their anticancer efficacy through metabolic, immune, and molecular mechanisms. Probiotics enhance cancer therapies, minimize side effects, and offer new adjuvant approaches in oncology. Recent advancements discussed in the review include the utilization of probiotics as oncolytic gene expression systems and drug delivery vectors, as well as personalized probiotic interventions aimed at optimizing cancer therapy. Clinical studies are critically evaluated, highlighting both the outcomes and limitations of probiotic use in cancer patients, particularly those suffering from breast cancer. Additionally, the review explores factors influencing anticancer effects of probiotics, focusing on their role in modulating the tumor microenvironment. Challenges in translating preclinical findings to clinical practice are discussed, along with future research directions, focusing on the relationship between probiotics, the microbiome, and cancer treatment. Ultimately, this review advocates for further investigation into the therapeutic potential of probiotics in breast cancer, aiming to harness their benefits in oncology.

Patient-level simulation (PLS) models overcome some major limitations of conventional cohort models and have broad applicability in healthcare, yet limited knowledge exists about their potential in cancer care.

This systematic review aims to: (1) describe the application areas of PLS models in cancer care, (2) identify commonly used model structures, (3) evaluate the quality of reporting based on established guidelines, and (4) critically discuss the potential and limitations of PLS models in this context.

A systematic literature search was completed in Web of Science, PubMed, EMBASE and EconLit. Reasons underlying the use of PLS models were identified with a conventional inductive content analysis and reporting quality was assessed with an 18-item checklist based on the ISPOR-SMDM guidelines.

The number of publications increased over time and most studies used state-transition microsimulation (49.25%) or discrete event simulation (48.51%). Two main application areas could be discerned, namely disease progression modelling (DPM) (78.36%) and health and care systems operation (HCSO) (21.64%). In the DPM domain, the use of PLS models was mainly motivated by the need to represent patient heterogeneity and history. In the HCSO domain, PLS models were used to better understand and improve cancer care delivery. Average reporting quality was 65.2% and did not improve over time.

PLS models can be used to simulate the progression of cancer and to model cancer care delivery. In the DPM domain more direct comparisons with cohort models are required to establish the relative advantages of PLS models and in the HCSO domain the impact on clinical practice needs to be systematically assessed. Furthermore, adherence to the ISPOR-SMDM guidelines should be improved.

Personalized medicine in cancer treatment has the potential to enhance therapeutic efficacy while simultaneously reducing adverse effects. Molecular characterization of circulating tumor cells (CTCs) offers invaluable insight into metastatic tumor heterogeneity, making them a perfect candidate for metastatic cancer drug screening. However, they are extremely rare. This study presents the development of melt-electrowritten membrane filters designed for the capture, culture, and drug testing of CTCs. By varying the collector speeds, filters with optimized pore sizes and polymer densities were produced, enabling selective capture of CTCs while minimizing co-capture of white blood cells. Biocompatibility tests showed that the filter supported the proliferation of multiple cancer cell lines. The filter successfully captured and cultured colorectal cancer patient-derived CTC44 and CTC45 cells, which formed 3D clusters observable over several weeks. Drug testing with chemotherapeutic agents 5-fluorouracil/oxaliplatin (FOX) and 5-fluorouracil/irinotecan (FIRI) revealed that CTCs in 3D clusters on the filters exhibited significantly higher drug resistance compared to 2D monolayers. These findings demonstrate the potential of the filter as a versatile platform for studying CTC biology and for screening anticancer drugs, providing a more physiologically relevant environment than traditional 2D cultures.

Breast cancer is one of the main causes of morbidity and mortality among women around the world. In Peru, it has recently surpassed cervical cancer as the most commonly reported cancer. Studying the relationship between intrinsic breast cancer subtypes and disease staging can optimize diagnosis, prognosis, and treatment. Therefore, there is a need for better risk stratification, selection of personalized treatment, and improved early detection strategies. We conducted this study to address the lack of data on underrepresented populations such as the Peruvian population. The objective of the study was to analyze the distribution of intrinsic subtypes of breast cancer and their correlation with prognostic factors and demographic characteristics among women in Peru.

A descriptive, retrospective observational study was conducted, analyzing 67 cases of breast cancer of various intrinsic subtypes diagnosed at a referral hospital in Peru. Clinical, demographic, and pathological data were collected, including histological type, intrinsic subtype, tumor stage, and geographic origin of the patients. Intrinsic subtypes were classified through immunohistochemistry, and the data were processed to determine their distribution and correlation with prognostic factors such as disease stage.

The mean age of the 67 patients included in the study was 54.2 years. The majority of cases originated from the city of Cajamarca (56.7%, n = 38). Invasive breast carcinoma of no special type was the most common histological type (62.7%, n = 42). Among the intrinsic subtypes, luminal B was the most common (31.3%, n = 21), followed by luminal A and triple-negative (22.4%, n = 15), both with the same frequency. Furthermore, 16.4% (n = 11) of patients presented with metastasis at the time of evaluation. A high frequency of tumors was observed in Tumor, Nodes, Metastasis (TNM) stages 3 and 4, accounting for 49.2% (n = 33).

This study describes the heterogeneity of breast cancer based on the identification of intrinsic subtypes within the analyzed population. The high frequency of luminal B, luminal A, and triple-negative subtypes is notable. The highest frequency of identified cases was in the advanced stages, highlighting the need for personalized treatments and improved early detection strategies.

The widespread use of next-generation sequencing (NGS) panels to guide targeted therapies in advanced malignancies raises questions regarding their real-world impact on clinical outcomes and affordability.

The study aims to find trends in targetable genetic alterations among late-stage cancer patients in the Jordanian population and to assess the clinical effectiveness of NGS testing in Jordan.

This article is a retrospective observational analysis. Data extracted from patient files were used to determine genetic alteration trends and for a comparative assessment of progression-free survival (PFS) between patients receiving NGS-guided personalized treatment compared to those receiving conventional treatment.

Two hundred thirteen nonselected late-stage cancer patients from three Jordanian hospitals (2019-2024) were included in this study. Key data points included demographics, genetic variations, and treatment details. Statistical analysis involved the use of the Cox proportional hazards model to evaluate PFS between the two groups of patients.

Clinically significant genetic alterations were found in 89 patients (42%), with the most common genes being KRAS (20%), PIK3CA (9%), ERBB2 (7%), EGFR (6%), and BRCA1 and 2 (1.5% each). Microsatellite instability-high status was seen in 8 patients (3.7%). Out of the 89 patients with targetable genetic variations, 80.9% received their recommended NGS-guided treatments, while 19.1% did not due to cost or drug unavailability. PFS was significantly improved for patients receiving NGS-guided targeted treatment compared to conventional treatment in the general population with a hazard ratio (HR) of 0.216, the colorectal cancer population (HR of 0.185), the breast cancer population (HR of 0.239), and the lung cancer population (HR of 0.089).

NGS testing is a very beneficial tool for patients with advanced malignancies to select targeted therapies and potentially improve survival. However, the incorporation of NGS testing in Jordan faces challenges such as high testing and treatment costs and drug unavailability.

Immunotherapy emerges as a promising approach, marked by recent substantial progress in elucidating how the host immune response impacts tumor development and its sensitivity to various treatments. Immune checkpoint inhibitors have revolutionized cancer therapy by unleashing the power of the immune system to recognize and eradicate tumor cells. Among these, inhibitors targeting the programmed cell death protein 1 (PD-1) and its ligand (PD-L1) have garnered significant attention due to their remarkable clinical efficacy across various malignancies. This review delves into the mechanisms of action, clinical applications, and emerging therapeutic strategies surrounding PD-1/PD-L1 blockade. We explore the intricate interactions between PD-1/PD-L1 and other immune checkpoints, shedding light on combinatorial approaches to enhance treatment outcomes and overcome resistance mechanisms. Furthermore, we discuss the expanding landscape of immune checkpoint inhibitors beyond PD-1/PD-L1, including novel targets such as CTLA-4, LAG-3, TIM-3, and TIGIT. Through a comprehensive analysis of preclinical and clinical studies, we highlight the promise and challenges of immune checkpoint blockade in cancer immunotherapy, paving the way for future advancements in the field.

Hematological malignancies, such as leukemia, lymphoma, and multiple myeloma, contribute significantly to global cancer diagnoses. Despite progress in conventional therapies, such as chemotherapy and immunotherapy, these treatments face limitations, including nonspecific targeting, side effects, and drug resistance. The aim of this review is to explore the potential of nanotechnology, particularly nanoparticles (NPs), to improve therapeutic outcomes for these cancers by enhancing drug delivery and reducing toxicity.

This review examines recent advancements in NP-based therapies, focusing on their application in hematological malignancies. We discuss different types of NPs, including liposomes, polymeric, and inorganic NPs, for their potential in targeted drug delivery. The review also evaluates the current state of clinical trials and highlights challenges in the translation of nanomedicines from preclinical research to clinical practice.

Nanoparticles, with their unique properties, offer significant advantages in drug delivery systems, such as enhanced stability, extended circulation time, and targeted tumor delivery. Various NP formulations have shown promise in clinical trials, including liposomal formulations like Vyxeos for acute myeloid leukemia and Marqibo for Ph-negative acute lymphoblastic leukemia. However, challenges in toxicity, regulatory hurdles, and large-scale production still remain.

Nanomedicine holds transformative potential in the treatment of hematological malignancies, offering more effective and specific therapies compared to conventional treatments. Continued research is necessary to overcome the clinical challenges and maximize the benefits of NP-based therapies for patients with blood cancers.

Background/Objectives: Competing endogenous RNAs (ceRNA) are molecules that compete for the binding to a microRNA (miR). Usually, there are two ceRNA, one of which is a protein-coding RNA (mRNA), with the other being a long non-coding RNA (lncRNA). The miR role is to inhibit mRNA expression, either promoting its degradation or impairing its translation. The lncRNA can "sponge" the miR, thus impeding its inhibitory action on the mRNA. In their easier configuration, these three molecules constitute a regulatory axis for protein expression. However, each RNA can interact with multiple targets, creating branched and intersected axes that, all together, constitute what is known as a competing endogenous RNA network (ceRNET). Methods: In this systematic review, we collected all available data from PubMed about experimentally verified (by luciferase assay) regulatory axes in endometrial cancer (EC), excluding works not using this test; Results: This search allowed the selection of 172 bibliographic sources, and manually building a series of ceRNETs of variable complexity showed the known axes and the deduced intersections. The main limitation of this search is the highly stringent selection criteria, possibly leading to an underestimation of the complexity of the networks identified. However, this work allows us not only to hypothesize possible gap fillings but also to set the basis to instruct artificial intelligence, using adequate prompts, to expand the EC ceRNET by comparing it with ceRNETs of other cancers. Moreover, these networks can be used to inform and guide research toward specific, though still unidentified, axes in EC, to complete parts of the network that are only partially described, or even to integrate low complexity subnetworks into larger more complex ones. Filling the gaps among the existing EC ceRNET will allow physicians to hypothesize new therapeutic strategies that may either potentiate or substitute existing ones. Conclusions: These ceRNETs allow us to easily visualize long-distance interactions, thus helping to select the best treatment, depending on the molecular profile of each patient, for personalized medicine. This would yield higher efficiency rates and lower toxicity levels, both of which are extremely relevant factors not only for patients' wellbeing, but also for the legal, regulatory, and ethical aspects of miR-based innovative treatments and personalized medicine as a whole. This systematic review has been registered in PROSPERO (ID: PROSPERO 2025 CRD420251035222).

Background: Advancements in pharmacogenomics, artificial intelligence (AI), and CRISPR gene-editing technology are revolutionizing precision medicine by enabling highly individualized therapeutic strategies. Artificial intelligence-driven computational techniques improve biomarker discovery and drug optimization while pharmacogenomics helps to identify genetic polymorphisms affecting medicine metabolism, efficacy, and toxicity. Genetically editing based on CRISPR presents a precise method for changing gene expression and repairing damaging mutations. This review explores the convergence of these three fields to enhance improved precision medicine. Method: A methodical study of the current literature was performed on the effects of pharmacogenomics on drug response variability, artificial intelligence, and CRISPR in predictive modeling and gene-editing applications. Results: Driven by artificial intelligence, pharmacogenomics allows clinicians to classify patients and select the appropriate medications depending on their DNA profiles. This reduces the side effect risk and increases the therapeutic efficacy. Precision genetic modifications made feasible by CRISPR technology improve therapy outcomes in oncology, metabolic illnesses, neurological diseases, and other fields. The integration of artificial intelligence streamlines genome-editing applications, lowers off-target effects, and increases CRISPR specificity. Notwithstanding these advances, issues including computational biases, moral dilemmas, and legal constraints still arise. Conclusions: The synergy of artificial intelligence, pharmacogenomics, and CRISPR alters precision medicine by letting customized therapeutic interventions. Clinically translating, however, hinges on resolving data privacy concerns, assuring equitable access, and strengthening legal systems. Future research should focus on refining CRISPR gene-editing technologies, enhancing AI-driven pharmacogenomics, and developing moral guidelines for applying these tools in individualized medicine going forward.

Given that only a small fraction of patients with cancer exhibits specific markers making them eligible for effective targeted therapies, this paper investigates the justification of treating cancer differently in terms of resource allocation when it comes to the application of novel precise therapies-the so-called onco-exceptionalism. Specifically, it assesses whether the reimbursement of expensive drugs is equitable. To do so, we first contextualise healthcare resource allocation concerning immunotherapeutic treatments for cancer, then explore arguments for and against onco-exceptionalism and finally conclude by advocating for a proactive health approach.

Over the past decades, oncology treatment paradigms have developed significantly. Yet, the often unstructured nature of substance-related documentation in medical records presents a time-consuming challenge for analyzing treatment patterns and outcomes. To advance oncological research further, clinical data science must offer solutions that facilitate research and analysis with real-world data (RWD). The present contribution introduces a user-friendly R-tool designed to transform free-text medication entries into the structured Anatomical Therapeutic Chemical (ATC) Classification System by applying a dictionary-based approach. The resulting output is a structured data frame containing columns for antineoplastic medication, other medications, and supplementary information. For accuracy validation, 561 data entries from an evaluation data set were reviewed, consisting of 935 tokens. 88.5% of these tokens were successfully transformed into their respective ATC codes. Additional information was extracted from 129 data entries (23%), while 23 entries (4.1%) presented no usable information. All tokens underwent a manual review; 8.9% (84 tokens) failed transformations. This approach improves the standardization and analysis of systemic anti-cancer treatment data in German-speaking regions by optimizing efficiency while maintaining relevant accuracy.

The numerous side effects and adverse health implications associated with chemotherapies have long plagued the field of cancer care. Whilst in some cases a curative measure, this highly toxic intervention consistently scores poorly on quantitative measures of tolerability and safety. Of these side effects, cardiac and microvascular defects pose the greatest health risk and are the leading cause of death amongst cancer survivors who do not succumb to relapse. In fact, in many low-grade cancers, the risk of recurrence is far outweighed by the cardiovascular risk of morbidity. As such, there is a pressing need to improve outcomes within these populations. Polyphenols are a group of naturally occurring metabolites that have shown potential vasoprotective effects. Studies suggest they possess antioxidant and anti-inflammatory activities, in addition to directly modulating vascular signalling pathways and gene expression. Leveraging these properties may help counteract the vascular toxicity induced by chemotherapy. In this review, we outline the main mechanisms by which the endothelium is damaged by chemotherapeutic agents and discuss the ability of polyphenols to counteract such side effects. We suggest future considerations that may help overcome some of the published limitations of these compounds that have stalled their clinical success. Finally, we briefly explore their pharmacological properties and how novel approaches could enhance their efficacy while minimising treatment-related side effects.

The human microbiome, a complex ecosystem of microbial communities, plays a crucial role in physiological processes, and emerging research indicates a potential link between it and reproductive cancers. This connection highlights the significance of understanding the microbiome's influence on cancer development and treatment. A comprehensive review of current literature was conducted, focusing on studies that investigate the relationship between microbiome composition, reproductive cancer progression, and potential therapeutic approaches to modulate the microbiome. Evidence suggests that imbalances in the microbiome, known as dysbiosis, may contribute to the development and progression of reproductive cancers. Specific microbial populations have been associated with inflammatory responses, immune modulation, and even resistance to conventional therapies. Interventions such as probiotics, dietary modifications, and fecal microbiota transplantation have shown promise in restoring healthy microbiome function and improving cancer outcomes in pre-clinical models, with pilot studies in humans indicating potential benefits. This review explores the therapeutic potential of microbiome modulation in the management of reproductive cancers, discussing the mechanisms involved and the evidence supporting microbiome-targeted therapies. Future research is warranted to unravel the complex interactions between the microbiome and reproductive cancer pathophysiology, paving the way for innovative approaches.

Background/Objectives: Cancer classification using microarray datasets presents a significant challenge due to their extremely high dimensionality. This complexity necessitates advanced optimization methods for effective gene selection. Methods: This study introduces and evaluates the Nuclear Reaction Optimization (NRO)-drawing inspiration from nuclear fission and fusion-for identifying informative gene subsets in six benchmark cancer microarray datasets. Employed as a standalone approach without prior dimensionality reduction, NRO was assessed using both Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN). Leave-One-Out Cross-Validation (LOOCV) was used to rigorously evaluate classification accuracy and the relevance of the selected genes. Results: Experimental results show that NRO achieved high classification accuracy, particularly when used with SVM. In select datasets, it outperformed several state-of-the-art optimization algorithms. However, due to the absence of additional dimensionality reduction techniques, the number of selected genes remains relatively high. Comparative analysis with Harris Hawks Optimization (HHO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) shows that while NRO delivers competitive performance, it does not consistently outperform all methods across datasets. Conclusions: The study concludes that NRO is a promising gene selection approach, particularly effective in certain datasets, and suggests that future work should explore hybrid models and feature reduction techniques to further enhance its accuracy and efficiency.

Immunotherapy in the treatment of cancer, with immune inhibitors helps in many cancer types. Many patients still encounter resistance to these treatments, though. This resistance is mediated by metabolic changes in the tumour microenvironment and cancer cells. The development of novel treatments to overcome resistance and boost immunotherapy's effectiveness depends on these metabolic changes.

This review concentrates on the molecular mechanisms through which metabolic transformation contributes to cancer immunotherapy resistance. Additionally, research therapeutic approaches that target metabolic pathways to enhance immunotherapy for resistance.

We used databases available on PubMed, Scopus, and Web of Science to perform a thorough review of peer-reviewed literature. focusing on the tumor microenvironment, immunotherapy resistance mechanisms, and cancer metabolism. The study of metabolic pathways covers oxidative phosphorylation, glycolysis, lipid metabolism, and amino acid metabolism.

An immunosuppressive tumour microenvironment is produced by metabolic changes in cancer cells, such as dysregulated lipid metabolism, enhanced glutaminolysis, and increased glycolysis (Warburg effect). Myeloid-derived suppressor cells and regulatory T cells are promoted, immune responses are suppressed, and T cell activity is impaired when lactate and other metabolites build up. changes in the metabolism of amino acids in the pathways for arginine and tryptophan, which are nutrients crucial for immune function. By enhancing their function in the tumour microenvironment, these metabolic alterations aid in resistance to immune checkpoint inhibitors.

Metabolic change plays a key role in cancer immunotherapy resistance. Gaining knowledge of metabolic processes can help develop efficient treatments that improve immunotherapy's effectiveness. In order to determine the best targets for therapeutic intervention, future studies should concentrate on patient-specific metabolic profiling.

The tumor microenvironment (TME) emerges as a formidable actor in the cancer treatment landscape, wielding the power to thwart therapeutic efficacy across various modalities, including chemotherapy, radiotherapy, immunotherapy, targeted therapy, and hormonal therapy. This intricate ecosystem comprising diverse cellular constituents, signaling molecules, and the extracellular matrix fosters a dynamic interplay that profoundly influences tumor behavior and treatment outcomes. This review explores the mechanisms through which the TME drives resistance to standard therapies, emphasizing key factors such as hypoxia, immune evasion, and metabolic reprogramming. Furthermore, we illuminate innovative strategies aimed at reprogramming this hostile environment, including the application of therapeutic vaccines, CAR T cell therapy, and combination immunotherapies designed to enhance anti-tumor responses. By advocating for multidimensional approaches that dismantle the TME's barriers to effective treatment, this review calls for a transformative shift in cancer treatment paradigms. By bridging the gap between the TME's complexities and targeted therapeutic strategies, we pave the way for targeted interventions that promise to enhance clinical outcomes and improve patient prognosis in the relentless battle against cancer.

Oropharyngeal dysphagia and pain are prevalent concerns in the geriatric population. Therefore, this study investigates advances in the development of cannabidiol (CBD) gummies using 3D printing technology and compares them to commercially available molded gummies for pain management. A gelatin-based CBD formulation was prepared and printed using a syringe-based extrusion 3D printer. The formulation's rheological properties were assessed, and the printed gummies were characterized using a texture analyzer. Drug content was analyzed using HPLC, and in vitro dissolution studies were conducted in phosphate buffer (pH 1.2 and 6.8). Our results demonstrated that the gelatin-based formulation had shear-thinning rheological properties for 3D printing at a temperature of 38.00 °C, filament diameter of 26 mm and flow of 110%. The optimized printing parameters produced gummies with higher elasticity compared to marketed gummies and comparable toughness. Drug content analysis showed 98.14 ± 1.56 and 97.97 ± 2.14% of CBD in 3D-printed and marketed gummies, respectively. Dissolution studies revealed that both gummy types released 100% of the drug within 30 min in both pH 1.2 and 6.8 buffers. Overall, 3D printing enables customizable CBD gummies with optimized release and offer a personalized and patient-friendly alternative to traditional oral forms for geriatric care.

Glycosylation is a critical quality attribute in biopharmaceuticals that influences crucial properties, such as biological activity and blood clearance. Current methods for modeling glycosylation typically rely on imprecise or limited data on nucleotide sugar donor (NSD) dynamics. These methods use in vitro transporter kinetics or flux balance analysis, which overlook the key aspects of metabolic regulation. We devised an integrative workflow for absolute subcellular NSD quantification in both cytoplasm and secretory organelles. Using subcellular fractionation, exhaustive sample extraction, and liquid chromatography triple-quadrupole tandem mass spectrometry, we accurately measured NSD concentrations ranging from 1.6 amol/cell to 3 fmol/cell. As expected, NSD concentration profiles aligned closely with the glycan distributions on antibodies, particularly after nutrient pulsing to stimulate NSD production, showcasing method validity. This method enables empirical observation of compartment-specific NSD dynamics. Thus, this study provides novel insights indicating that N-glycosylation, which governs NSD supply, is primarily regulated within the Golgi apparatus (GA). This method offers a novel tool to obtain sophisticated data for a more efficient optimization of glycosylation processes in production cell lines.

Background/Objectives: The accurate prediction of adverse drug reactions (ADRs) to oncological treatments still poses a clinical challenge. Chemotherapy is usually selected based on clinical trials that do not consider patient variability in ADR risk. Consequently, many patients undergo multiple treatments to find the appropriate medication or dosage, enhancing ADR risks and increasing the chance of discontinuing therapy. We first aimed to develop a pharmacogenetic model for predicting chemotherapy-induced ADRs in cancer patients (the ANTIBLASTIC DRUG MULTIPANEL PLATFORM) and then to assess its feasibility and validate this model in patients with non-small-cell lung cancer (NSCLC) undergoing oncological treatments. Methods: Seventy NSCLC patients of all stages that needed oncological treatment at our facility were enrolled, reflecting the typical population served by our institution, based on geographic and demographic characteristics. Treatments followed existing guidelines, and patients were continuously monitored for adverse reactions. We developed and used a multipanel platform based on 326 SNPs that we identified as strongly associated with response to cancer treatments. Subsequently, a network-based algorithm to link these SNPs to molecular and biological functions, as well as efficacy and adverse reactions to oncological treatments, was used. Results: Data and blood samples were collected from 70 NSCLC patients. A bioinformatic analysis of all identified SNPs highlighted five clusters of patients based on variant aggregations and the associated genes, suggesting potential susceptibility to treatment-related toxicity. We assessed the feasibility of the platform and technically validated it by comparing NSCLC patients undergoing the same course of treatment with or without ADRs against the cluster combination. An odds ratio analysis confirmed the correlation between cluster allocation and increased ADR risk, indicating specific treatment susceptibilities. Conclusions: The ANTIBLASTIC DRUG MULTIPANEL PLATFORM was easily applicable and able to predict ADRs in NSCLC patients undergoing oncological treatments. The application of this novel predictive model could significantly reduce adverse drug reactions and improve the rate of chemotherapy completion, enhancing patient outcomes and quality of life. Its potential for broader prescription management suggests significant treatment improvements in cancer patients.

Chemotherapy-resistant tumors present a significant challenge in oncology, often leading to treatment failures owing to mechanisms such as genetic mutations, drug efflux, altered metabolism, and adaptations within the tumor microenvironment. These factors limit the effectiveness of treatment and contribute to tumor resistance. This review highlights the role of alternative therapies aimed at overcoming resistance mechanisms. Several alternative strategies with high efficacy rate against tumor resistance are being explored, including targeted therapies (58-64%), immunotherapy (80%), hormone therapy (22-61%), and emerging approaches such as herbal therapies (90%), probiotics (34-90%), metabolic therapies (> 50%), epigenetic therapies (51-89%), microbiome-based therapies (50%), gene therapy (67-80%), photodynamic therapy/hypothermia (86-99%), and nanotechnology (50-67%). Integrating these alternative strategies with conventional treatments has the potent-al to augment the therapeutic efficacy and patient outcomes. Despite this progress, limitations in cancer therapeutics include the lack of predictive biomarkers, resistance mechanisms, and tumor heterogeneity, all of which contribute to treatment failure and relapse. To address these limitations, advancements in molecular diagnostics, as well as early detection through liquid biopsies, and the use of biomarkers to monitor resistance and guide treatment are crucial. Additionally, expanding clinical trials is essential to validate new therapies and improve patient outcomes.

Cancer is a major global health challenge, with approximately 19.3 million new cases and 10 million deaths estimated by 2020. Laboratory advancements in cancer detection have transformed diagnostic capabilities, particularly through the use of biomarkers that play crucial roles in risk assessment, therapy selection, and disease monitoring. Tumor histology, single-cell technology, flow cytometry, molecular imaging, liquid biopsy, immunoassays, and molecular diagnostics have emerged as pivotal tools for cancer detection. The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. However, developing countries face significant challenges including financial constraints, inadequate healthcare infrastructure, and limited access to advanced diagnostic technologies. The impact of COVID-19 has further complicated cancer management in resource-limited settings. Future research should focus on precision medicine and early cancer diagnosis through sophisticated laboratory techniques to improve prognosis and health outcomes. This review examines the evolving landscape of cancer detection, focusing on laboratory research breakthroughs and limitations in developing countries, while providing recommendations for advancing tumor diagnostics in resource-constrained environments.

Cancer represents a significant societal, public health, and economic challenge. Conventional chemotherapy is based on systemic administration; however, it has current limitations, including poor bioavailability, high-dose requirements, adverse side effects, low therapeutic indices, and the development of multiple drug resistance. These factors underscore the need for innovative strategies to enhance drug delivery directly to tumours. However, local treatment also presents significant challenges, including the penetration of the drug through endothelial layers, tissue density in the tumour microenvironment, tumour interstitial fluid pressure, physiological conditions within the tumour, and permanence at the site of action. Nanotechnology represents a promising alternative for addressing these challenges. This narrative review elucidates the potential of nanostructured formulations for local cancer treatment, providing illustrative examples and an analysis of the advantages and challenges associated with this approach. Among the nanoformulations developed for the local treatment of breast, bladder, colorectal, oral, and melanoma cancer, polymeric nanoparticles, liposomes, lipid nanoparticles, and nanohydrogels have demonstrated particular efficacy. These systems permit mucoadhesion and enhanced tissue penetration, thereby increasing the drug concentration at the tumour site (bioavailability) and consequently improving anti-tumour efficacy and potentially reducing adverse effects. In addition to studies indicating chemotherapy, nanocarriers can be used as a theranostic approach and in combination with irradiation methods.

Metastatic cancer poses significant clinical challenges, necessitating effective immunotherapies with minimal systemic toxicity. Building on prior research demonstrating the rWTC-MBTA vaccine's ability to inhibit tumor metastasis and growth, this study focuses on its clinical translation by optimizing vaccine composition, dosing regimens, and freezing techniques. The vaccine formula components included three TLR ligands (LTA, Poly I:C, and Resiquimod) and an anti-CD40 antibody, which were tested in melanoma and triple-negative breast cancer (TNBC) models. The formulations were categorized as rWTC-MBT (Mannan-BAM with LTA, Poly I:C, Resiquimod), rWTC-MBL (LTA), rWTC-MBP (Mannan-BAM with Poly I:C), and rWTC-MBR (Resiquimod). In the melanoma models, all the formulations exhibited efficacy that was comparable to that of the full vaccine, while in the "colder" TNBC models, the formulations with multiple TLR ligands or Resiquimod alone performed the best. Vaccine-induced activation of dendritic cell (DC) subsets, including conventional DCs (cDCs), myeloid DCs (mDCs), and plasmacytoid DCs (pDCs), was accompanied by significant CD80+CD86+ population induction, suggesting robust innate immune stimulation. An initial three-dose schedule followed by booster doses (3-1-1-1 or 3-3-3-3) reduced the metastatic burden effectively. Gradual freezing (DMSO-based preservation) maintained vaccine efficacy, underscoring the importance of intact cell structure. These findings highlight the potential of simplified formulations, optimized dosing, and freezing techniques in developing practical, scalable immunotherapies for metastatic cancers.

Osteosarcoma is a highly aggressive tumor that originates in the bone and often infiltrates nearby bone cells. It is the most prevalent type of primary bone cancer among the various bone malignancies. Traditional cancer treatment methods such as surgery, chemotherapy, immunotherapy, and radiotherapy have had restricted success. However, the integration of nanotechnology into cancer research has led to notable progress. One promising area is the use of marine-derived polysaccharide-based nano formulations for treating various human diseases, including cancer. This study presents a straightforward method for synthesizing biocompatible gold nanoparticles (AuNPs), utilizing sodium borohydride as a reducing agent and a cost-effective, water-soluble chondroitin sulfate (CS) derived from shark cartilage as a stabilizing agent. The synthesized CS-Au NPs appeared purple and were mainly spherical, with 40.768 nm of average size. Cytotoxicity assays (MTT) indicated that CS-Au NPs significantly reduced the viability of human osteosarcoma cells (MG63) at 100 μg/mL, while it showed no cytotoxic effects on mouse embryonic fibroblast cells (NIH3T3) at the same concentration. The observed toxicity of the CS-Au NPs was linked to a rise in the production of reactive oxygen species (ROS) within damaged mitochondria. ROS generation and changes in mitochondrial membrane potential were detected in MG63 cells treated with CS-Au NPs. Furthermore, apoptotic analysis through ethidium bromide dual staining and flow cytometry demonstrated that CS-Au NPs at higher concentrations significantly increased the amount of apoptotic cells, as demonstrated by acridine orange/ethidium bromide staining. Flow cytometry also confirmed that CS-Au NPs activated the apoptotic pathway in MG63 cells.

The tumor microenvironment (TME) plays a crucial role in cancer progression and treatment response. It comprises a complex network of stromal cells, immune cells, extracellular matrix, and blood vessels, all of which interact with cancer cells and influence tumor behaviour. This review article provides an in-depth examination of the TME, focusing on stromal cells, blood vessels, signaling molecules, and ECM, along with commonly available therapeutic compounds that target these components. Moreover, we explore the TME as a novel strategy for discovering new anti-tumor drugs. The dynamic and adaptive nature of the TME offers opportunities for targeting specific cellular interactions and signaling pathways. We discuss emerging approaches, such as combination therapies that simultaneously target cancer cells and modulate the TME. Finally, we address the challenges and future prospects in targeting the TME. Overcoming drug resistance, improving drug delivery, and identifying new therapeutic targets within the TME are among the challenges discussed. We also highlight the potential of personalized medicine and the integration of emerging technologies, such as immunotherapy and nanotechnology, in TME-targeted therapies. This comprehensive review provides insights into the TME and its therapeutic implications. Understanding the TME's complexity and targeting its components offer promising avenues for the development of novel anti-tumor therapies and improved patient outcomes.

Breast cancer remains the leading cause of mortality among women with cancer. This article delves into the intricate relationship between breast cancer and cancer stem cells (CSCs), emphasizing advanced methods for their identification and isolation. The key isolation techniques, such as the mammosphere formation assay, surface marker identification, Side Population assay, and Aldehyde Dehydrogenase assay, are critically examined. Furthermore, the review analyzes CSC-specific molecular signaling pathways, focusing on actionable targets like CD44/CD24, Nanog, and Oct4. The potential of targeted therapies and small molecules that disrupt these pathways is explored. Additionally, the review highlights immunotherapy strategies against CSCs, focusing on resistance mechanisms and the critical role of precision medicine. The study investigates how precision medicine enhances therapeutic outcomes by targeting specific CSC biomarkers. This comprehensive analysis offers insights into recent advancements and emerging strategies in breast cancer treatment, pointing toward future therapeutic innovations.

Gene editing techniques have emerged as powerful tools in biomedical research, offering precise manipulation of genetic material with the potential to revolutionize cancer treatment strategies. This review provides a comprehensive overview of the current landscape of gene editing technologies, including CRISPR-Cas systems, base editing, prime editing, and synthetic gene circuits, highlighting their applications and potential in cancer therapy. It discusses the mechanisms, advantages, and limitations of each gene editing approach, emphasizing their transformative impact on targeting oncogenes, tumor suppressor genes, and drug resistance mechanisms in various cancer types. The review delves into population-level interventions and precision prevention strategies enabled by gene editing technologies, including gene drives, synthetic gene circuits, and precision prevention tools, for controlling cancer-causing genes, targeting pre-cancerous lesions, and implementing personalized preventive measures. Ethical considerations, regulatory challenges, and future directions in gene editing research for cancer treatment are also addressed. This review highlights how gene editing could revolutionize precision medicine by enhancing patient care and advancing cancer treatments with targeted, personalized methods. For these benefits to be fully realized, collaboration among researchers, doctors, regulators, and patient advocates is crucial in fighting cancer and meeting clinical needs.

Oral squamous cell carcinoma (OSCC) is the most prevalent type of malignant epithelial neoplasm that affects the oral cavity. It has been a significant health concern in many countries for a long time since it was usually treated with surgery, radiation, and/or chemotherapy. Drug resistance is the primary issue in patient populations and scientific research, which promotes OSCC tumour cell invasion and migration. Thus, identifying highly specific therapeutic targets could be the potential approach for more successful treatment of OSCC. It is still challenging to understand the genetic causes of oral carcinogenesis due to its highly varied clinic-pathological parameters. It is important to remember that signaling channels and complexes that affect chromatin accessibility control gene expression, which in turn affects cell development and differentiation. Histones undergo post-translational alteration to give this platform. Understanding the processes of gene regulation through histone methylation and its modifications could enhance the early detection, prognostic prediction, and therapy of OSCC. To be properly used as a therapeutic target, histone methylation in OSCC requires more investigation. This review details the dysregulated histone methylation and the modifying enzymes linked to the development and aetiology of OSCC. Furthermore, the part that lysine methylation plays in cell migration, chemo-resistance, and OSCC invasion is also investigated.

The tumor microenvironment (TME) is integral to cancer progression, impacting metastasis and treatment response. It consists of diverse cell types, extracellular matrix components, and signaling molecules that interact to promote tumor growth and therapeutic resistance. Elucidating the intricate interactions between cancer cells and the TME is crucial in understanding cancer progression and therapeutic challenges. A critical process induced by TME signaling is the epithelial-mesenchymal transition (EMT), wherein epithelial cells acquire mesenchymal traits, which enhance their motility and invasiveness and promote metastasis and cancer progression. By targeting various components of the TME, novel investigational strategies aim to disrupt the TME's contribution to the EMT, thereby improving treatment efficacy, addressing therapeutic resistance, and offering a nuanced approach to cancer therapy. This review scrutinizes the key players in the TME and the TME's contribution to the EMT, emphasizing avenues to therapeutically disrupt the interactions between the various TME components. Moreover, the article discusses the TME's implications for resistance mechanisms and highlights the current therapeutic strategies toward TME modulation along with potential caveats.

Molecular characterization of tumors is essential to identify predictive biomarkers that inform treatment decisions and improve precision immunotherapy development and administration. However, challenges such as the heterogeneity of tumors and patient responses, limited efficacy of current biomarkers, and the predominant reliance on single-omics data, have hindered advances in accurately predicting treatment outcomes. Standard therapy generally applies a "one size fits all" approach, which not only provides ineffective or limited responses, but also an increased risk of off-target toxicities and acceleration of resistance mechanisms or adverse effects. As the development of emerging multi- and spatial-omics platforms continues to evolve, an effective tumor assessment platform providing utility in a clinical setting should i) enable high-throughput and robust screening in a variety of biological matrices, ii) provide in-depth information resolved with single to subcellular precision, and iii) improve accessibility in economical point-of-care settings. In this perspective, we explore the application of label-free Raman spectroscopy as a tumor profiling tool for precision immunotherapy. We examine how Raman spectroscopy's non-invasive, label-free approach can deepen our understanding of intricate inter- and intra-cellular interactions within the tumor-immune microenvironment. Furthermore, we discuss the analytical advances in Raman spectroscopy, highlighting its evolution to be utilized as a single "Raman-omics" approach. Lastly, we highlight the translational potential of Raman for its integration in clinical practice for safe and precise patient-centric immunotherapy.

Breast cancer research heavily relies on diverse model systems to comprehend disease progression, develop novel diagnostics, and evaluate new therapeutic strategies. This review offers a comprehensive overview of mammary cancer models, covering both ex vivo and in vivo approaches. We delve into established techniques, such as cell culture and explore cutting-edge advancements, like tumor-on-a-chip and bioprinting. The in vivo section encompasses spontaneous, induced, and transplanted models, genetically engineered models, chick chorioallantoic membrane assays, and the burgeoning field of in silico models. Additionally, this article briefly highlights the key discoveries made using these models, significantly enhancing our understanding of breast cancer. In essence, this article serves as a comprehensive compass, charting the trajectory of mammary cancer modeling from its early beginnings to the promising vistas of tomorrow.

Diabetes mellitus remains a global health challenge, demanding innovative therapeutic strategies. Herbal remedies have garnered attention for their potential in diabetes management, and recent advancements in nanotechnology have enabled the development of herbal nanoformulations with enhanced efficacy and bioavailability.

This review aimed to comprehensively analyze the mechanisms, formulations, and clinical impact of herbal nanoformulations in managing diabetes mellitus.

A systematic literature search was conducted to identify relevant studies exploring the mechanisms of action, various formulations, and clinical outcomes of herbal nanoformulations in diabetes management.

Herbal nanoformulations exert their anti-diabetic effects through multiple mechanisms, including enhanced bioavailability, improved tissue targeting, and potentiation of insulin signaling pathways. Various herbal ingredients, such as bitter melon, fenugreek, and Gymnema sylvestre, have been encapsulated into nanocarriers, like liposomes, polymeric nanoparticles, and solid lipid nanoparticles, to enhance their therapeutic potential. Clinical studies have demonstrated promising results, showing improvements in glycemic control, lipid profile, and antioxidant status with minimal adverse effects.

Herbal nanoformulations represent a promising avenue for the management of diabetes mellitus, offering improved therapeutic outcomes compared to conventional herbal preparations. Further research is warranted to optimize formulation strategies, elucidate long-term safety profiles, and explore the potential synergistic effects of herbal nanoformulations in combination therapies for diabetes management.

Precision medicine in oncology aims to identify an individualized treatment plan based on genomic alterations in a patient's tumor. It helps to select the most beneficial therapy for an individual patient. As it is now known that no patient's cancer is the same, and therefore, different patients may respond differently to conventional treatments, precision medicine, which replaces the one-size-fits-all approach, supports the development of tailored treatments for specific cancers of different patients. Patient-specific organoid or spheroid models as 3D cell culture models are very promising for predicting resistance to anti-cancer drugs and for identifying the most effective cancer therapy for high-throughput drug screening combined with genomic analysis in personalized medicine. Because tumor spheroids incorporate many features of solid tumors and reflect resistance to drugs and radiation, as in human cancers, they are widely used in drug screening studies. Testing patient-derived 3D cancer spheroids with some anticancer drugs based on information from molecular profiling can reveal the sensitivity of tumor cells to drugs and provide the right compounds to be effective against resistant cells. Given that many patients do not respond to standard treatments, patient-specific treatments will be more effective, less toxic. They will affect survival better compared to the standard approach used for all patients.

Cancer is a complex and heterogeneous disease characterised by uncontrolled cell growth and proliferation. One hallmark of cancer cells is their ability to undergo metabolic reprogramming, which allows them to sustain their rapid growth and survival. This metabolic reprogramming creates an immunosuppressive microenvironment that facilitates tumour progression and evasion of the immune system. In this article, we review the mechanisms underlying metabolic reprogramming in cancer cells and discuss how these metabolic alterations contribute to the establishment of an immunosuppressive microenvironment. We also explore potential therapeutic strategies targeting metabolic vulnerabilities in cancer cells to enhance immune-mediated anti-tumour responses. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02044861, NCT03163667, NCT04265534, NCT02071927, NCT02903914, NCT03314935, NCT03361228, NCT03048500, NCT03311308, NCT03800602, NCT04414540, NCT02771626, NCT03994744, NCT03229278, NCT04899921.

Despite the acknowledged merits of precision oncology (PO) and its increasing global implementation, its full potential for advancing care and prevention remains unrealized. The benefits are currently accessible to only limited patient segments because of multifaceted barriers. Successful implementation hinges on various factors-scientific complexities not limited to technical, clinical, regulatory, economic, administrative, and health care policy-related challenges. From building infrastructure to the associated costs, including research and development, testing, processing, and trained personnel, a lack of alignment persists. Administrative alignment with regulatory and payor acceptance is crucial. Health care policy must adapt to the ongoing shift from a one-size-fits-all treatment to a personalized approach. Without official endorsement of long-term gains over short-term costs and the health establishment's readiness for innovation, PO prospects, even in prosperous economies, may stagnate. Lower-income countries face exacerbated challenges, intensifying barriers to adoption. Nevertheless, growing awareness and utilization, driven by recognized potential for patients and public health, along with successful examples and advocacy, are progressively influencing policy for a more inclusive and beneficial approach to PO adoption.

Sleep is an essential biological function that is critical for a healthy and fulfilling life. Available sleep quality assessment tools contain long questionnaires covering a long period of time, not taking into account daily physical activity patterns and individual lifestyles.

In this paper we present SmartHypnos, an Android application that supports low-end devices. It enables users to report their sleep quality, monitor their physical activity and exercise intensity and gain personalized recommendations aimed at increasing sleep quality. The application functionalities are implemented through sleep quality evaluation questions, passive step counter, efficient data storage and Personal data are stored locally protecting user privacy. All these are integrated into a single interface that requires a single device, is of low learning difficulty and easy to use. SmartHypnos was evaluated during a pilot study that involved 48 adults (ages 18-50) that used the application for seven days and subsequently submitted their data, possible through the interface directly, and evaluated the application through an appropriate questionnaire.

SmartHypnos was rated positively by users, especially it terms of learnability, ease of use and stability, with a mean score over 8. Task completion time and ease, simplicity, user comfort and recommendation utility were scored with a mean over 7. The correlation between the features extracted were in accordance to prior works.

SmartHypnos has the potential to become a sleep monitoring and intervention tool readily available to the general public, including vulnerable populations of low socio-economic status.

Oral cancer is a major global health concern, with high incidence and mortality rates, especially in high-risk populations. Early diagnosis remains a challenge, and current treatments, such as surgery, radiation, and chemotherapy, have limited effectiveness, particularly in advanced stages. Recent advances in targeted therapies and immunotherapy offer promising alternatives, providing more precise and personalized treatment options. Targeted therapies, such as epidermal growth factor receptor inhibitors, aim to disrupt specific molecular pathways in tumor growth, while immunotherapies, including immune checkpoint inhibitors and chimeric antigen receptor-T cell therapy, enhance the body's immune response to fight cancer. Combination therapies, integrating both targeted and immune strategies, are being explored to overcome the limitations of single-agent treatments. This review highlights the current strategies in the prevention and treatment of oral cancer, discusses emerging therapies, explores future research directions, focusing on optimizing existing treatments, identifying new biomarkers, and developing innovative therapeutic approaches. The potential of personalized medicine and combination therapies offers new hope for improving survival rates and quality of life for oral cancer patients.