Research advances in tumor budding in malignant tumors

Tumor budding (TB), defined as the presence of discrete single tumor cells or small clusters of up to four cells at the invasive front, is a key histopathological hallmark of aggressive tumor behavior and a robust, independent prognostic factor across numerous cancer types. This review synthesizes current knowledge on TB, with a focus on its assessment methodologies, biological underpinnings, and clinical relevance. Standardized evaluation is crucial for its application. The International Tumor Budding Consensus Conference (ITBCC) criteria provide a foundational three-tier grading system, and now are widely adopted in colorectal cancer. However, its application to other malignancies such as gastric or lung carcinoma requires adaptation due to histological heterogeneity. While hematoxylin and eosin (HE) staining is routinely used, immunohistochemistry (IHC) enhances diagnostic accuracy by facilitating the precise identification of epithelial tumor buds. Biologically, TB is closely associated with the epithelial-mesenchymal transition (EMT), a process driven by tumor microenvironment (TME) signals including hypoxia and crosstalk with cancer-associated fibroblasts. This triggers key pathways such as TGF-β and Wnt/β-catenin, resulting in the loss of epithelial markers and acquisition of mesenchymal markers, thereby promoting cellular invasion. Furthermore, TB is linked to an immunosuppressive TME, characterized by a reduction in cytotoxic lymphocytes and an increase in regulatory immune cells and programmed death-ligand 1(PD-L1) expression, suggesting its role in facilitating immune evasion. Clinically, high-grade TB consistently correlates with adverse clinic-pathological features including lymph node metastasis and advanced stage, and predicts worse survival outcomes in cancers such as colorectal, gastric, pancreatic, and breast carcinoma. Despite its prognostic strength, the integration of TB into routine clinical practice faces several challenges: a lack of uniform standards beyond colorectal cancer, significant inter-observer variability, an urgent need for prospective validation, and the integration of artificial intelligence (AI) technologies into diagnostic workflows. AI offers promising solutions by enabling automated, quantitative, and reproducible TB assessment. Future priorities should include establishing cancer-specific guidelines through multicenter collaborations, validating the predictive value of TB for therapies such as immunotherapy, and developing AI-driven tools for practical clinical integration. Ultimately, TB holds significant potential to evolve into a precise biomarker for risk stratification and personalized treatment strategies in oncology. <alternatives id="alt1"> </alternatives>