Recent advances in cancer research have highlighted the pivotal role of tertiary lymphoid structures (TLSs) in modulating immune responses, particularly in breast cancer (BRCA). Here, we performed an integrated analysis of bulk transcriptome data from over 6000 BRCA samples using biological network-based computational strategies and machine learning (ML) methods, and identified LGALS2 as a key marker within TLSs. Single-cell sequencing and spatial transcriptomics uncover the role of LGALS2 in TLS-associated dendritic cells (DCs) stimulation and reveal the complexity of the tumor microenvironment (TME) at both the macro and micro levels. Elevated LGALS2 expression correlates with prolonged survival, which is associated with a robust immune response marked by diverse immune cell infiltration and active anti-tumor pathways leading to a ‘hot’ tumor microenvironment. The colocalization of LGALS2 with TLS-associated DCs and its role in immune activation in BRCA were confirmed by hematoxylin-eosin (HE), immunohistochemistry (IHC), and in vivo validation analyses. The identification of LGALS2 as a key factor in BRCA not only highlights its therapeutic potential in novel TLS-directed immunotherapy but also opens new avenues in patient stratification and treatment selection, ultimately improving clinical management.

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins and play a critical role as pharmacological targets. An improved understanding of GPCRs’ involvement in tumor microenvironment may provide new perspectives for cancer therapy. This study used machine learning to classify head and neck squamous cell carcinoma (HNSCC) patients into two GPCR-based subtypes. Notably, these subtypes showed significant differences in prognosis, gene expression, and immune microenvironment, particularly CD8⁺ T cell infiltration. S1PR4 emerged as a key regulator distinguishing the subtypes, positively correlated with CD8⁺ T cell proportion and cytotoxicity in HNSCC. It was predominantly expressed in CX3CR1⁺CD8⁺ T cells among T cells. Upregulation of S1PR4 enhanced T cell function during CAR-T cell therapy, suggesting its potential in cancer immunotherapy. These findings highlight S1PR4 as an immune modulator for favorable prognosis in HNSCC, and offer a potential GPCR-targeted therapeutic option for HNSCC treatment.

Millions of new cases of cancer are diagnosed worldwide each year, making it a serious public health concern. Developments in customized therapy and early detection have significantly enhanced treatment for and results from cancer. Therefore, it is important to investigate new molecular biomarkers. In this study, we created an efficient transient receptor potential melastatin (TRPM) family members-related TRPM-Score for 17 solid tumors. CCNE1, produced from TRPM-Score, was found to be an exceptional biomarker through several sophisticated machine learning and deep learning computational techniques. TRPM-Score and CCNE1 immunotherapeutic prediction, immunological characteristics, and predictive value were thoroughly assessed. In most cancer types, CCNE1 was a substantially dangerous marker. Additional in vitro tests validated CCNE1’s immunomodulatory properties, demonstrating that silencing impeded macrophage movement and decreased PD-L1 expression. Additionally, CCNE1 may accurately predict responses to cancer immunotherapy. These findings indicate that the TRPM family—particularly CCNE1, which is associated with TRPM—is a significant player in the pan-cancer domain and can be utilized as a therapeutic target and prognostic biomarkers, especially in immuno-oncology. The thorough characterization of the TRPM family and the discovery of CCNE1 as a crucial downstream effector mark important developments in our comprehension of pan-cancer biology.

Integrating complementary surface and cross sectional cues is central to preoperative assessment of colorectal cancer, but technically challenging because endoscopic images and pelvic CT encode anatomy at different scales. Here we present HydraMamba, a multimodal selective state space framework that fuses endoscopy and CT for joint lesion segmentation, lesion detection, and survival prediction. The model couples a shared state space backbone with two lightweight modules. Across the endoscopic dataset and the CT dataset, HydraMamba achieved state-of-the-art lesion analysis (endoscopy: Dice 0.856, F1 0.918; CT: Dice 0.812, F1 0.888) and delivered calibrated survival modeling on the CT dataset (Harrell’s C index 0.832, Uno’s C@1y 0.853, integrated Brier score 0.161, calibration slope ≈1.01). By unifying endoscopic and CT information in a single coherent architecture, HydraMamba provides an accurate and well-calibrated foundation for lesion analysis and prognostication in colorectal cancer.

Weakly supervised segmentation of cancerous regions in whole-slide images (WSIs) is a crucial task in computational pathology, but it is severely hampered by the need for expensive pixel-level annotations. Existing Multiple Instance Learning (MIL) frameworks, while popular, typically fail to produce accurate segmentation masks because they treat WSIs as an unordered ’bag-of-patches’, ignoring the critical tissue topology and architectural patterns that define malignancy. In this paper, we address this fundamental limitation by proposing Geometric Multi-Instance Learning (Geo-MIL), a novel graph-based framework that explicitly models the spatial relationships between tissue patches. At the core of our method is a new topological attention mechanism that operates on the WSI graph, learning to identify and prioritize entire diagnostically relevant tissue structures over isolated patch features. Through extensive experiments on three public gastric cancer datasets, we demonstrate that Geo-MIL significantly outperforms a wide array of state-of-the-art baselines, achieving a new benchmark in both segmentation accuracy and classification performance. Our work represents a significant step towards bridging the gap between weak slide-level labels and precise, pixel-level predictions, paving the way for scalable and accurate quantitative analysis in digital pathology.