Search Results (1,107)

Mitochondria, pivotal organelles in cellular metabolism and energy production, have emerged as critical players in the pathogenesis of cancer. This review outlines the progress in mitochondrial profiling through mass spectrometry-based metabolomics and its applications in cancer research. We provide unprecedented insights into the mitochondrial metabolic rewiring that fuels tumorigenesis, metastasis, and therapeutic resistance. The purpose of this review is to provide a comprehensive guide for the implementation of mitochondrial metabolomics, integrating advanced methodologies—including isolation, detection, and data integration—with insights into cancer-specific metabolic rewiring. We first summarize current methodologies for mitochondrial sample collection and pretreatment. Furthermore, we then discuss the recent advancements in mass spectrometry-based methodologies that facilitate the detailed profiling of mitochondrial metabolites, unveiling significant metabolic reprogramming associated with tumorigenesis. We emphasize how recent technological advancements have addressed longstanding challenges in the field and explore the role of mitochondrial metabolism-driven cancer development and progression for novel drug discovery and translational research applications in cancer. Collectively, this review delineates emerging opportunities for therapeutic discovery and aims to establish a foundation for future investigations into the therapeutic modulation of mitochondrial pathways in cancer, thereby paving the way for innovative diagnostic and therapeutic approaches targeting mitochondrial pathways. Full article

Background: Colorectal liver metastasis (CRLM) represents a major clinical challenge in oncology, affecting 25–50% of colorectal cancer patients and significantly impacting survival. While multimodal therapies—including surgical resection, systemic chemotherapy, and local ablative techniques—have improved outcomes, prognosis remains heterogeneous due to variations in tumor biology, patient factors, and institutional practices. Methods: This review synthesizes current evidence on prognostic factors influencing CRLM management, encompassing clinical (e.g., tumor burden, anatomic distribution, timing of metastases), biological (e.g., CEA levels, inflammatory markers), and molecular (e.g., RAS/BRAF mutations, MSI status, HER2 alterations) determinants. Results: Key findings highlight the critical role of molecular profiling in guiding therapeutic decisions, with RAS/BRAF mutations predicting resistance to anti-EGFR therapies and MSI-H status indicating potential responsiveness to immunotherapy. Emerging tools like circulating tumor DNA (ctDNA) and radiomics offer promise for dynamic risk stratification and early recurrence detection, while the gut microbiome is increasingly recognized as a modulator of treatment response. Conclusions: Despite advancements, challenges persist in standardizing resectability criteria and integrating multidisciplinary approaches. Current guidelines (NCCN, ESMO, ASCO) emphasize personalized strategies but lack granularity in terms of incorporating novel biomarkers. This exhaustive review underscores the imperative for the development of a unified, biomarker-integrated framework to refine CRLM management and improve long-term outcomes. Full article

Background: Lysosomal-associated membrane protein 1 (LAMP1), typically localized to the lysosomal membrane, is increasingly implicated as a marker of cancer aggressiveness and metastasis when expressed on the cell surface. This study aimed to develop a LAMP1-targeted antibody-based PET tracer and assess its efficacy in mouse models of human breast and colon adenocarcinoma. Methods: To determine the source of LAMP1 expression, we utilized human single-cell RNA sequencing and spatial transcriptomics, complemented by in-house flow cytometry on xenografted mouse models. Tissue microarrays of multiple epithelial cancers and normal tissue were stained for LAMP-1, and staining was quantified. An anti-LAMP1 monoclonal antibody was conjugated with desferrioxamine (DFO) and labeled with zirconium-89 (⁸⁹Zr). Human triple-negative breast cancer (MDA-MB-231) and colon cancer (Caco-2) cell lines were implanted in nude mice. PET/CT imaging was conducted at 24, 72, and 168 h post-intravenous injection of ⁸⁹Zr-DFO-anti-LAMP1 and ⁸⁹Zr-DFO-IgG (negative control), followed by organ-specific biodistribution analyses at the final imaging time point. Results: Integrated single-cell and spatial RNA sequencing demonstrated that LAMP1 expression was localized to myeloid-derived suppressor cells (MDSCs) and cancer-associated fibroblasts (CAFs) in addition to the cancer cells. Tissue microarray showed significantly higher staining for LAMP-1 in tumor tissue compared to normal tissue (3986 ± 2635 vs. 1299 ± 1291, p < 0.001). Additionally, xenograft models showed a significantly higher contribution of cancer cells than the immune cells to cell surface LAMP1 expression. In vivo, PET imaging with ⁸⁹Zr-DFO-anti-LAMP1 PET/CT revealed detectable tumor uptake as early as 24 h post-injection. The ⁸⁹Zr-DFO-anti-LAMP1 tracer demonstrated significantly higher uptake than the control ⁸⁹Zr-DFO-IgG in both models across all time points (MDA-MB-231 SUV_max at 168 h: 12.9 ± 5.7 vs. 4.4 ± 2.4, p = 0.003; Caco-2 SUV_max at 168 h: 8.53 ± 3.03 vs. 3.38 ± 1.25, p < 0.01). Conclusions: Imaging of cell surface LAMP-1 in breast and colon adenocarcinoma is feasible by immuno-PET. LAMP-1 imaging can be expanded to adenocarcinomas of other origins, such as prostate and pancreas. Full article

Cancer remains a leading global health burden, profoundly affecting patient survival and quality of life. Current treatments—including chemotherapy, radiotherapy, immunotherapy, and surgery—are often limited by toxicity or insufficient specificity. Conventional chemotherapy, for instance, indiscriminately attacks rapidly dividing cells, causing severe side effects. In contrast, peptide-based therapeutics offer a paradigm shift, combining high tumour-targeting precision with minimal off-target effects. Their low immunogenicity, multi-pathway modulation capabilities, and adaptability for diagnostics and therapy make them ideal candidates for advancing oncology care. Innovative peptide platforms now enable three transformative applications: (1) precision molecular diagnostics (e.g., ¹⁸F-PSMA-1007 for prostate cancer detection), (2) targeted therapies (e.g., BT5528 and SAR408701 targeting tumour-specific antigens), and (3) theranostic systems (e.g., RAYZ-8009 and ¹⁷⁷Lu-FAP-2286 integrating imaging and radiotherapy). Despite their promise, peptides face challenges like metabolic instability and short half-lives. Recent advances in structural engineering (e.g., cyclization and D-amino acid incorporation) and delivery systems (e.g., nanoparticles and PEGylation) have significantly enhanced their clinical potential. This review highlights peptide-based agents in development, showcasing their ability to improve early cancer detection, reduce metastasis, and enhance therapeutic efficacy with fewer adverse effects. Examples like CLP002 underscore their role in personalised medicine. By overcoming current limitations, peptide drugs are poised to redefine cancer management, offering safer, more effective alternatives to conventional therapies. Their integration into clinical practice could mark a critical milestone in achieving precision oncology. Full article

Background: Increased IL-1β levels may promote carcinogenesis and metastasis by affecting tumor biology and the tumor microenvironment (TME). In this context, extracellular vesicles (EVs) play a key role in cell-to-cell communication, thus modulating the TME and immune response. Here, we aimed to test whether tumor-derived small EVs (TEVs) isolated from sensitive and osimertinib-resistant (OR) non-small-cell lung cancer (NSCLC) cells may promote EMT via fibronectin binding to α5β1 integrin as well as suppress the immune system and if these effects may be favored by IL-1β. Methods: TEVs were isolated from control, OR, and IL-1β-stimulated NSCLC cells. Expressions of fibronectin and PD-L1 were screened in TEVs and the mRNA levels of vimentin and SMAD3 were also assessed in cancer cells after TEV co-culturing. Furthermore, to detect the effect on immune cells, we co-cultured TEVs with lung cancer patients’ peripheral blood mononuclear cells (PBMCs). Results: TEVs were positive for fibronectin and the highest protein levels were found in TEVs obtained from the OR and IL-1β-stimulated cells. TEV-mediated activation of α5β1 signaling led to the upregulation of vimentin and SMAD3 mRNA in NSCLC cells and stimulated cell migration. EVs also increased PD-1, CTLA-4, FOXP3, TNF-α, IL-12, and INF-γ mRNA in lung cancer patients’ immune cells. Conclusions: Our findings indicate that TEVs promote EMT in NSCLC cells by the activation of the fibronectin–α5β1 axis. Finally, IL-1β stimulation induces TEV release with biological properties similar to OR TEVs, thus leading to cancer invasion and immune suppression and suggesting that inflammation can promote tumor spreading. Full article

Background: The mechanisms underlying the metastasis of non-small-cell lung cancer (NSCLC) have long been a focal point of medical research. The anti-tumor effects of chelerythrine (CHE) have been confirmed; however, its ability to inhibit tumor metastasis and the underlying mechanisms remain unknown. The aim of this study was to investigate the inhibitory effects and molecular mechanisms of CHE on transforming growth factor-beta (TGF-β)-induced epithelial–mesenchymal transition (EMT). Methods: Wound healing and Transwell assays were employed to evaluate TGF-β-induced migration in A549 cells and the inhibitory effects of CHE. Ribonucleotide reductase subunit M2 (RRM2) expression levels were detected via Western blot and immunofluorescence staining. Western blot and RT-qPCR were used to examine the expression levels of EMT-related markers. Animal experiments were conducted to analyze the role of RRM2 in the CHE inhibition of TGF-β-induced lung cancer metastasis. Results: This study found that TGF-β treatment enhanced the metastasis of A549 cells, while CHE inhibited the expression of TGF-β-induced EMT-related transcription factors by RRM2, thereby suppressing tumor cell migration (p < 0.05). Furthermore, the oral administration of CHE inhibited the metastasis of A549 cells to the lungs from the tail vein in mice, consistent with in vitro findings. Despite the high doses of CHE used, there was no evidence of toxicity. Conclusions: Our data reveal the mechanism of the anti-metastatic effects of CHE on TGF-β-induced EMT and indicate that CHE can be used as an effective anti-tumor treatment. Full article

Deciphering the spatiotemporal distribution of bacteria during breast cancer progression may provide critical insights for developing bacterial-based therapeutic strategies. Using a murine breast cancer model, we longitudinally profiled the microbiota in breast tumor tissue, mammary gland, spleen, and cecal contents at 3-, 5-, and 7- weeks post-tumor implantation through 16S rRNA gene sequencing. Breast tumor progression was associated with lung metastasis and splenomegaly, accompanied by distinct tissue-specific microbial dynamics. While alpha diversity remained stable in tumors, mammary tissue, and cecal contents, it significantly increased in the spleen (p < 0.05). Longitudinal analysis revealed a progressive rise in Firmicutes and a decline in Proteobacteria abundance within tumors, mammary tissue, and cecum, whereas the spleen microbiota displayed unique phylum-level compositional shifts. Tissue- and time-dependent microbial signatures were identified at phylum, genus, and species levels during breast tumor progression. Strikingly, the spleen microbiota integrated nearly all genera enriched in other sites, suggesting its potential role as a microbial reservoir. Gut-associated genera (Lactobacillus, Desulfovibrio, Helicobacter) colonized both cecal contents and the spleen, with Lactobacillus consistently detected across all tissues, suggesting microbial translocation. The spleen exhibited uniquely elevated diversity and compositional shifts, potentially driving splenomegaly. These results delineated the trajectory of microbiota translocation and colonization, and demonstrated tissue-specific microbial redistribution during breast tumorigenesis, offering valuable implications for advancing microbiome-targeted cancer therapies. Full article

Introduction: Ovarian cancer (OC) is one of the most common gynecologic malignancies and has the highest mortality rate among them. OC has a multifactorial pathogenesis and is characterized by silent onset, progression, and late-stage detection. Therefore, accurate and early detection is of great importance in order to improve survival rates. Emerging evidence reveals that tumor markers are valuable diagnostic and monitoring tools. In this study, we evaluated the aforementioned potential of three markers CA-125, CA 15-3, and serum Calprotectin. CA-125 is a protein that is found elevated in cases of ovarian, breast, and lung cancer. Cancer Antigen 15-3 (CA 15-3) is a protein detected in high levels in women with breast cancer and ovarian cancer and it is significantly elevated in patients with metastasis and recurrence of OC. Calprotectin is a protein released from activated neutrophils, related to inflammatory conditions and can be a potential immune-mediated marker in OC. Purpose: The purpose of this study was to explore the significance of serum calprotectin, CA-125, and CA 15-3 in women diagnosed with serous OC. Methodology: Thirty-eight (38) women with diagnosed OC were included in this research as the study group and twenty-seven (27) healthy women with no history or current diagnosis of OC were included in the control group. Women in both groups shared similar past histories to avoid any other parameters interfering with the study. Our study group was further subdivided into early stage and advanced stage patients. Blood samples were collected from all women of both groups and were examined using ELISA kits to evaluate the levels of the above markers. Results: When comparing patients versus control patients, those with OC exhibited higher levels of Calprotectin compared to healthy individuals. Additionally, Calprotectin showed a statistically significant elevation between the control group and advanced patients. CA-125 remains the current standard of care biomarker exhibiting 90% sensitivity, whereas sensitivities in Calprotectin and CA 15-3 were 60% and 50%, respectively. Conclusions: Serum CA-125 remains the single most valuable biomarker for ovarian cancer, having the highest statistical significance, correlation with disease stage, detecting both early or advanced patients, and sensitivity of 90%. It appears to be a promising inflammatory biomarker in the early diagnosis of ovarian cancer, showing an elevation in patients, while CA 15-3 provides moderate complementary information and exhibits inferior sensitivity when compared to both CA-125 and Calprotectin. The latter appears to be a promising marker and further studies could show if its addition to established protocols could improve early detection, disease progression, or risk stratification. Calprotectin enhances the detection range for ovarian cancer when used alongside CA-125, while this combined approach detected a greater proportion of patients than CA-125 alone, indicating improved diagnostic potential. Full article

In the era of digital healthcare, electronic health records generate vast amounts of data, much of which is unstructured, and therefore, not in a usable format for conventional machine learning and artificial intelligence applications. This study investigates how to extract meaningful insights from unstructured radiology reports written in Finnish, a minority language, using machine learning techniques for text analysis. With this approach, unstructured information could be transformed into a structured format. The results of this research show that relevant information can be effectively extracted from Finnish medical reports using classification algorithms with default parameter values. For the detection of breast tumour mentions from medical texts, classifiers achieved high accuracy, almost 90%. Detection of metastasis mentions, however, proved more challenging, with the best-performing models Support Vector Machine (SVM) and logistic regression achieving an F1-score of 81%. The lower performance in metastasis detection is likely due to the more complex problem, ambiguous labeling, and the smaller dataset size. The results of classical classifiers were also compared with FinBERT, a domain-adapted Finnish BERT model. However, classical classifiers outperformed FinBERT. This highlights the challenge of medical language processing when working with minority languages. Moreover, it was noted that parameter tuning based on translated English reports did not significantly improve the detection rates, likely due to linguistic differences between the datasets. This larger translated dataset used for tuning comes from a different clinical domain and employs noticeably simpler, less nuanced language than the Finnish breast cancer reports, which are written by native Finnish-speaking medical experts. This underscores the need for localised datasets and models, particularly for minority languages with unique grammatical structures. Full article

Background: Duodenal adenocarcinoma (DA) is often insidious due to the low rate of early diagnosis and because the mechanisms that underlie its malignant progression are poorly understood. The tumor microenvironment (TME) plays a crucial regulatory role in promoting tumor malignancy. Hence, this study aimed to identify novel biomarkers for early diagnosis and potential therapeutic targets for DA. Methods: Surgical resection samples and normal tissues from DA patients were collected for RNA sequencing (RNA-seq). The characteristics of TME in DA patients were analyzed, and the differentially expressed long non-coding RNAs (lncRNA) were screened. Functional experiments were performed to verify the relationship between Linc01559, G-rich sequence binding factor 1 (GRSF1), and tumor malignant phenotype. Results: The present study revealed that DA exhibits a significantly upregulated expression of acidic environment markers and a high degree of macrophage infiltration. Further investigation revealed that macrophages upregulate the expression of the long noncoding RNA, Linc01559, in DA through the STAT3/c-MYC signaling pathway, thereby promoting malignant phenotypes such as invasion, metastasis, tumor stemness, and apoptosis. The interaction between GRSF1 and Linc01559 was subsequently confirmed using RNA pulldown-mass spectrometry. It was further revealed that Linc01559 promotes the malignant phenotype of duodenal cancer cells through its interaction with GRSF1. Conclusions: These findings demonstrate that the acidic microenvironment influences the phenotype of DA by regulating the Linc01559–GRSF1 axis. Therefore, these findings provide potential targets for the early detection and treatment of DA. Full article

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types due to its late diagnosis, low survival rates, and high frequency of metastasis. Considering the molecular mechanism of PDAC development has not been fully elucidated, this study aimed to shed more light on the molecular regulatory signatures of circular RNAs (circRNAs) in PDAC progression and provide a different perspective to identify potential biomarkers as well as discover candidate repositioned drug molecules for the prevention or treatment of PDAC with network-based integrative analysis. The mRNA, miRNA, and circRNA expression profiles of PDAC were obtained from nine microarray datasets. Differentially expressed genes (DEGs), microRNAs (DEmiRNAs), and circular RNAs (DEcircRNAs) were identified. The competing endogenous RNA (ceRNA; DEG–DEmiRNA–DEcircRNA) regulatory network was constructed, which included 12 DEcircRNAs, 64 DEGs, and 6 miRNAs specific to PDAC. The ADAM12, MET, QKI, SEC23A, and ZEB2 were identified as hub genes and demonstrated significant survival probability for PDAC. In addition to providing novel biomarkers for diagnosis that can be detected non-invasively, the secretion levels of hub genes-associated proteins were found in plasma, serum, and oral epithelium. The drug repositioning analysis revealed vorinostat, meclocycline sulfosalicylate, and trichostatin A, which exhibited significant binding affinities to the hub genes compared to their inhibitors via molecular docking analysis. Full article

This study aimed to develop and evaluate an AI-driven platform, the Adaptive RAG Assistant MRI Platform (ARAMP), for assisting in the diagnosis and reporting of brain metastases using post-contrast axial T1-weighted (AX_T1+C) MRI. In this retrospective study, 2447 cancer patients who underwent MRI between 2010 and 2022 were screened. A subset of 100 randomized patients with confirmed brain metastases and 100 matched non-cancer controls were selected for evaluation. ARAMP integrates quantitative radiomic feature extraction with an adaptive Retrieval-Augmented Generation (RAG) framework based on a large language model (LLM, GPT-4o), incorporating five authoritative medical references. Three board-certified neuroradiologists and an independent LLM (Gemini 2.0 Pro) assessed ARAMP performance. Metrics of the assessment included Pre-/Post-Trained Inference Difference, Inter-Inference Agreement, and Sensitivity. Post-training, ARAMP achieved a mean Inference Similarity score of 67.45%. Inter-Inference Agreement among radiologists averaged 30.20% (p = 0.01). Sensitivity for brain metastasis detection improved from 0.84 (pre-training) to 0.98 (post-training). ARAMP also showed improved reliability in identifying brain metastases as the primary diagnosis post-RAG integration. This adaptive RAG-based framework may improve diagnostic efficiency and standardization in radiological workflows. Full article

Introduction: Early-onset colorectal cancer (EOCRC) is rising rapidly, particularly among the Hispanic/Latino (H/L) populations, who face disproportionately poor outcomes. The transforming growth factor-beta (TGF-β) signaling pathway plays a critical role in colorectal cancer (CRC) progression by mediating epithelial-to-mesenchymal transition (EMT), immune evasion, and metastasis. However, integrative analyses linking TGF-β alterations to clinical features remain limited—particularly for diverse populations—hindering translational research and the development of precision therapies. To address this gap, we developed AI-HOPE-TGFbeta (Artificial Intelligence agent for High-Optimization and Precision Medicine focused on TGF-β), the first conversational artificial intelligence (AI) agent designed to explore TGF-β dysregulation in CRC by integrating harmonized clinical and genomic data via natural language queries. Methods: AI-HOPE-TGFbeta utilizes a large language model (LLM), Large Language Model Meta AI 3 (LLaMA 3), a natural language-to-code interpreter, and a bioinformatics backend to automate statistical workflows. Tailored for TGF-β pathway analysis, the platform enables real-time cohort stratification and hypothesis testing using harmonized datasets from the cBio Cancer Genomics Portal (cBioPortal). It supports mutation frequency comparisons, odds ratio testing, Kaplan–Meier survival analysis, and subgroup evaluations across race/ethnicity, microsatellite instability (MSI) status, tumor stage, treatment exposure, and age. The platform was validated by replicating findings on the SMAD4, TGFBR2, and BMPR1A mutations in EOCRC. Exploratory queries were conducted to examine novel associations with clinical outcomes in H/L populations. Results: AI-HOPE-TGFbeta successfully recapitulated established associations, including worse survival in SMAD4-mutant EOCRC patients treated with FOLFOX (fluorouracil, leucovorin and oxaliplatin) (p = 0.0001) and better outcomes in early-stage TGFBR2-mutated CRC patients (p = 0.00001). It revealed potential population-specific enrichment of BMPR1A mutations in H/L patients (OR = 2.63; p = 0.052) and uncovered MSI-specific survival benefits among SMAD4-mutated patients (p = 0.00001). Exploratory analysis showed better outcomes in SMAD2-mutant primary tumors vs. metastatic cases (p = 0.0010) and confirmed the feasibility of disaggregated ethnicity-based queries for TGFBR1 mutations, despite small sample sizes. These findings underscore the platform’s capacity to detect both known and emerging clinical–genomic patterns in CRC. Conclusions: AI-HOPE-TGFbeta introduces a new paradigm in cancer bioinformatics by enabling natural language-driven, real-time integration of genomic and clinical data specific to TGF-β pathway alterations in CRC. The platform democratizes complex analyses, supports disparity-focused investigation, and reveals clinically actionable insights in underserved populations, such as H/L EOCRC patients. As a first-of-its-kind system studying TGF-β, AI-HOPE-TGFbeta holds strong promise for advancing equitable precision oncology and accelerating translational discovery in the CRC TGF-β pathway. Full article

Background: Prostate-specific membrane antigen (PSMA)-PET imaging has significantly improved prostate cancer (PCa) staging, yet its interpretation remains challenging, even for experienced specialists. No prior study has assessed urologists’ ability to interpret PSMA-PET. Methods: We conducted a multicenter prospective study involving 63 urologists from eight Italian institutions. Participants evaluated 20 PSMA-PET scans of high-risk PCa cases, with no clinical information provided. Proficiency was defined as correctly identifying at least two of three staging components (T, N, M) in ≥75% of cases. Associations between performance and factors such as hierarchy (resident vs. consultant), institution type, surgical volume, and multidisciplinary team (MDT) presence were analyzed using univariable and multivariable logistic regression. Results: Only one participant achieved full staging proficiency, while 44% reached the ≥75% threshold for partial (almost correct) staging. Urologists from centers with ≥300 PCa diagnoses per year demonstrated better T and M stage identification. Institutions with ≥150 robot-assisted radical prostatectomies (RARPs) per year and those with MDTs showed higher accuracy in M staging. No significant predictors of proficiency emerged in the multivariable analysis, although hierarchy and surgical volume approached significance for nodal metastasis detection. Conclusion: PSMA-PET interpretation is complex for urologists, with particular challenges in T and M staging. High institutional case volumes and MDT involvement may enhance interpretation skills. Structured training programs and increased exposure to multidisciplinary imaging discussions are essential to optimize urologists’ diagnostic proficiency and ultimately improve patient care. Full article

Cancer research faces significant biological, technological, and systemic limitations that hinder the development of effective therapies and improved patient outcomes. Traditional preclinical models, such as 2D and 3D cell cultures, murine xenografts, and organoids, often fail to reflect the complexity of human tumor architecture, microenvironment, and immune interactions. This discrepancy results in promising laboratory findings not always translating effectively into clinical success. A core obstacle is tumor heterogeneity, characterized by diverse genetic, epigenetic, and phenotypic variations within tumors, which complicates treatment strategies and contributes to drug resistance. Hereditary malignancies and cancer stem cells contribute strongly to generating this complex panorama. Current early detection technologies lack sufficient sensitivity and specificity, impeding timely diagnosis. The tumor microenvironment, with its intricate interactions and resistance-promoting factors, further promotes treatment failure. Additionally, we only partially understand the biological processes driving metastasis, limiting therapeutic advances. Overcoming these barriers involves not only the use of new methodological approaches and advanced technologies, but also requires a cultural effort by researchers. Many cancer studies are still essentially observational. While acknowledging their significance, it is crucial to recognize the shift from deterministic to indeterministic paradigms in biomedicine over the past two to three decades, a transition facilitated by systems biology. It has opened the doors of deep metabolism where the functional processes that control and regulate cancer progression operate. Beyond biological barriers, systemic challenges include limited funding, regulatory complexities, and disparities in cancer care access across different populations. These socio-economic factors exacerbate research stagnation and hinder the translation of scientific innovations into clinical practice. Overcoming these obstacles requires multidisciplinary collaborations, advanced modeling techniques that better emulate human cancer, and innovative technologies for early detection and targeted therapy. Strategic policy initiatives must address systemic barriers, promoting health equity and sustainable research funding. While the complexity of cancer biology and systemic challenges are formidable, ongoing scientific progress and collaborative efforts inspire hope for breakthroughs that can transform cancer diagnosis, treatment, and survival outcomes worldwide. Full article