Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Although immune checkpoint inhibitors (ICIs) have brought new treatment options for advanced patients, a considerable proportion still shows limited response. Mitochondrial dysfunction plays a crucial role in tumor development and immune evasion, but its regulatory mechanisms in LUAD immune microenvironment remain unclear. Methods: We integrated 149 mitochondria-related pathways (1,136 coding proteins) to develop and validate the Mitochondrial Pathway Signature (MitoPS) using machine learning approaches across seven independent LUAD cohorts (n=1,231). The system was systematically compared with 129 published LUAD prognostic signatures and validated in seven immunotherapy cohorts (n=451). Multiomics analysis, immunofluorescence staining, and experimental validation were performed to investigate its molecular mechanism. Results: MitoPS demonstrated consistent predictive performance across validation cohorts, with high scores indicating poor prognosis, outperforming 129 existing prognostic models. In immunotherapy cohorts, MitoPS reliably predicted treatment response and prognosis. Immune microenvironment analysis revealed that low MitoPS scores correlated with higher immune cell infiltration and active immune function. Mechanistic studies identified mitochondria-related gene NDUFB10 as a core gene of MitoPS (r=0.38, p<0.05), where its high expression was significantly associated with immune desert phenotype and worse prognosis. Functional experiments confirmed that NDUFB10 knockdown significantly enhanced ICIs therapy and increased GZMB+CD8+T cell infiltration, indicating NDUFB10’s crucial role in regulating tumor immune microenvironment and immunotherapy response. Conclusion: The MitoPS scoring system reliably predicts prognosis and immunotherapy response in patients with LUAD, providing a novel reference for clinical decision-making. Furthermore, its core gene NDUFB10 regulates tumor immune microenvironment, offering a potential therapeutic target for improving immunotherapy outcomes.

A comprehensive understanding of tumor heterogeneity, tumor microenvironment and the mechanisms of drug resistance is fundamental to advancing breast cancer research. While single-cell RNA sequencing has resolved the issue of “temporal dynamic expression” of genes at the single-cell level, the lack of spatial information still prevents us from gaining a comprehensive understanding of breast cancer. The introduction and application of spatial transcriptomics addresses this limitation. As the annual technical method of 2020, spatial transcriptomics preserves the spatial location of tissues and resolves RNA-seq data to help localize and differentiate the active expression of functional genes within a specific tissue region, enabling the study of spatial location attributes of gene locations and cellular tissue environments. In the context of breast cancer, spatial transcriptomics can assist in the identification of novel breast cancer subtypes and spatially discriminative features that show promise for individualized precise treatment. This article summarized the key technical approaches, recent advances in spatial transcriptomics and its applications in breast cancer, and discusses the limitations of current spatial transcriptomics methods and the prospects for future development, with a view to advancing the application of this technology in clinical practice.

Tumor immunotherapy has significantly transformed the field of oncology over the past decade. An optimal tumor immunotherapy would ideally elicit robust innate and adaptive immune responses within tumor immune microenvironment (TIME). Unfortunately, immune system experiences functional decline with chronological age, a process termed “immunosenescence,” which contributes to impaired immune responses against pathogens, suboptimal vaccination outcomes, and heightened vulnerability to various diseases, including cancer. In this context, we will elucidate hallmarks and molecular mechanisms underlying immunosenescence, detailing alterations in immunosenescence at molecular, cellular, organ, and disease levels. The role of immunosenescence in tumorigenesis and senescence‐related extracellular matrix (ECM) has also been addressed. Recognizing that immunosenescence is a dynamic process influenced by various factors, we will evaluate treatment strategies targeting hallmarks and molecular mechanisms, as well as methods for immune cell, organ restoration, and present emerging approaches in immunosenescence for tumor immunotherapy. The overarching goal of immunosenescence research is to prevent tumor development, recurrence, and metastasis, ultimately improving patient prognosis. Our review aims to reveal latest advancements and prospective directions in the field of immunosenescence research, offering a theoretical basis for development of practical anti‐immunosenescence and anti‐tumor strategies.

PURPOSE: Robotic-assisted proctectomy (RAP) has emerged as the predominant surgical approach for patients with rectal cancer in recent years; although good postoperative patient recovery with accurate prediction is a guarantee of adaptive surveillance management, there is still a lack of easy-to-use prognostic tools and risk scores designed specifically for those patients undergoing RAP. METHODS: This study used the electronic health records of 506 RAP participants, including a National Specialist Center for da Vinci Robotic Colorectal Surgery (NSCVRCS) meta cohort, and an independent external validation Sun Yat-sen Memorial Hospital cohort. In the NSCVRCS meta cohort, patients were divided into a discovery cohort (70%, n = 268), where the best-fit model was applied to model our prediction system, RAP-AIscore. Subsequently, an internal validation process for RAP-AIscore was conducted using a replication cohort (30%, n = 116). The study designed and implemented a large-scale artificial intelligence (AI) hybrid framework to identify the best strategy for building a survival assessment system, the RAP-AIscore, from 132 potential modeling scenarios through a combination of iterative cross-validation, Monte Carlo cross-validation, and bootstrap resampling. The 10 variables most relevant to clinical interpretability were identified on the basis of the AI hybrid optimal model values, which helps provide reliable prognostic survival guidance for new patients. RESULTS: The consistent evaluation of discrimination, calibration, generalization, and prognostic value across cohorts reaffirmed the accuracy and robust extrapolation capability of this system. The 10 feature variables most associated with clinical interpretability on the basis of Shapley values were identified, facilitating reliable prognostic survival guidance for new patients. CONCLUSION: This study introduces a promising and informative tool, the RAP-AIscore, which can be explained through nomograms for interpreting clinical outcomes. It facilitates postoperative risk stratification management and enhances clinical management of prognosis for RAP patients.