Background: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contributes to the complexity of PTC. Understanding these complexities is essential for precise risk stratification and therapeutic decisions. Methods: This study involved a comprehensive analysis of 521 patients with PTC from our hospital and 499 patients from The Cancer Genome Atlas (TCGA). The real-world cohort 1 comprised 256 patients with stage I–III PTC. Tissues from 252 patients were analyzed by DNA-based next-generation sequencing, and tissues from four patients were analyzed by single-cell RNA sequencing (scRNA-seq). Additionally, 586 PTC pathological sections were collected from TCGA, and 275 PTC pathological sections were collected from the real-world cohort 2. A deep-learning multi-modal model was developed using matched histopathology images, genomic, transcriptomic, and immune cell data to predict LNM and disease-free survival (DFS). Results: This study included a total of 1011 PTC patients, comprising 256 patients from cohort 1, 275 patients from cohort 2, and 499 patients from TCGA. In cohort 1, the authors categorized PTC into four molecular subtypes based on BRAF, RAS, RET, and other mutations. BRAF mutations were significantly associated with LNM and impacted DFS. ScRNA-seq identified distinct T-cell subtypes and reduced B-cell diversity in BRAF-mutated PTC with LNM. The study also explored cancer-associated fibroblasts and macrophages, highlighting their associations with LNM. The deep-learning model was trained using 405 pathology slides and RNA sequences from 328 PTC patients and validated with 181 slides and RNA sequences from 140 PTC patients in the TCGA cohort. It achieved high accuracy, with an area under the curve (AUC) of 0.86 in the training cohort, 0.84 in the validation cohort, and 0.83 in the real-world cohort 2. High-risk patients in the training cohort had significantly lower DFS rates ( P <0.001). Model AUCs were 0.91 at 1 year, 0.93 at 3 years, and 0.87 at 5 years. In the validation cohort, high-risk patients also had lower DFS ( P <0.001); the AUCs were 0.89, 0.87, and 0.80 at 1, 3, and 5 years. The authors utilized the GradCAM algorithm to generate heatmaps from pathology-based deep-learning models, which visually highlighted high-risk tumor areas in PTC patients. This enhanced clinicians’ understanding of the model’s predictions and improved diagnostic accuracy, especially in cases with lymph node metastasis. Conclusion: The artificial intelligence (AI)-based analysis uncovered vital insights into PTC molecular heterogeneity, emphasizing BRAF mutations’ impact. The integrated deep-learning model shows promise in predicting metastasis, offering valuable contributions to improved diagnostic and therapeutic strategies.

Background: The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS). Methods: We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95). Result: Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016–0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041–0.334, P < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017–0.218, P < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively. Conclusion: This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.

The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C‐X‐C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single‐cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune‐inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune‐related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune‐related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G‐related genes was developed using multi‐level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune‐active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

Non-small cell lung cancer (NSCLC) remains one of the leading causes of cancer-related deaths worldwide. Different treatment approaches are typically employed based on the stage of NSCLC. Common clinical treatment methods include surgical resection, drug therapy, and radiation therapy. However, with the introduction and utilization of immune checkpoint inhibitors, cancer treatment has entered a new era, completely revolutionizing the treatment landscape for various cancers and significantly improving overall patient survival. Concurrently, treatment resistance often poses a critical challenge, with many patients experiencing disease progression following an initial response due to treatment resistance. Increasing evidence suggests that the tumor microenvironment (TME) plays a pivotal role in treatment resistance. Tumor-associated macrophages (TAMs) within the TME can promote treatment resistance in NSCLC by secreting various cytokines activating signaling pathways, and interacting with other immune cells. Therefore, this article will focus on elucidating the key mechanisms of TAMs in treatment resistance and analyze how targeting TAMs can reduce the levels of treatment resistance in NSCLC, providing a comprehensive understanding of the principles and approaches to overcome treatment resistance in NSCLC.

Breast cancer is the leading cancer among women, with a significant number experiencing recurrence and metastasis, thereby reducing survival rates. This study focuses on the role of long noncoding RNAs (lncRNAs) in breast cancer immunotherapy response. We conducted an analysis involving 1027 patients from Sun Yat‐sen Memorial Hospital, Sun Yat‐sen University, and The Cancer Genome Atlas, utilizing RNA sequencing and pathology whole‐slide images. We employed unsupervised clustering to identify distinct lncRNA expression patterns and developed an AI‐based pathology model using convolutional neural networks to predict immune–metabolic subtypes. Additionally, we created a multimodal model integrating lncRNA data, immune‐cell scores, clinical information, and pathology images for prognostic prediction. Our findings revealed four unique immune–metabolic subtypes, and the AI model demonstrated high predictive accuracy, highlighting the significant impact of lncRNAs on antitumor immunity and metabolic states within the tumor microenvironment. The AI‐based pathology model, DeepClinMed‐IM, exhibited high accuracy in predicting these subtypes. Additionally, the multimodal model, DeepClinMed‐PGM, integrating pathology images, lncRNA data, immune‐cell scores, and clinical information, showed superior prognostic performance. In conclusion, these AI models provide a robust foundation for precise prognostication and the identification of potential candidates for immunotherapy, advancing breast cancer research and treatment strategies.

Metabolic reprogramming in cancer significantly impacts immune responses within the tumor microenvironment, but its influence on cancer immunotherapy effectiveness remains uncertain. This study aims to elucidate the prognostic significance of metabolic genes in cancer immunotherapy through a comprehensive analytical approach. Utilizing data from the IMvigor210 trial (n = 348) and validated by retrospective datasets, we performed patient clustering using non‐negative matrix factorization based on metabolism‐related genes. A metabiotic score was developed using a “DeepSurv” neural network to assess correlations with overall survival (OS), progression‐free survival, and immunotherapy response. Validation of the metabolic score and key genes was achieved via comparative gene expression analysis using qPCR. Our analysis identified four distinct metabolic classes with significant variations in OS. Notably, the metabolism‐inactive and hypoxia‐low class demonstrated the most pronounced benefit in terms of OS. The metabolic score predicted immunotherapeutic benefits with high accuracy (AUC: 0.93 at 12 months). SETD3 emerged as a crucial gene, showing strong correlations with improved OS outcomes. This study underscores the importance of metabolic profiling in predicting cancer immunotherapy success. Specifically, patients classified as metabolism‐inactive and hypoxia‐low appear to derive substantial benefits. SETD3 is established as a promising prognostic marker, linking metabolic activity with patient outcomes, advocating for the integration of metabolic profiling into immunotherapy strategies to enhance treatment precision and efficacy.