Comprehending the cuproptosis and cancer-immunity cycle network: delving into the immune landscape and its predictive role in breast cancer immunotherapy responses and clinical endpoints - Liu, Xiangwei; Xu, Feng; Zhao, Kunkun; Liu, Yunfei; Ye, Guolin; Zhang, Xin; Qu, Yanyu (Frontiers in Immunology, 2024)
Background: The role of cuprotosis, a phenomenon associated with tumor metabolism and immunological identification, remains underexplored, particularly in relation to the cancer-immunity cycle (CIC) network. This study aims to rigorously examine the impact of the cuprotosis-CIC nexus on immune reactions and prognostic outcomes in patients with breast cancer (BC), striving to establish a comprehensive prognostic model. Methods: In the study, we segregated data obtained from TCGA, GEO, and ICGC using CICs retrieved from the TIP database. We constructed a genetic prognostic framework using the LASSO-Cox model, followed by its validation through Cox proportional hazards regression. This framework's validity was further confirmed with data from ICGC and GEO. Explorations of the tumor microenvironment were carried out through the application of ESTIMATE and CIBERSORT algorithms, as well as machine learning techniques, to identify potential treatment strategies. Single-cell sequencing methods were utilized to delineate the spatial distribution of key genes within the various cell types in the tumor milieu. To explore the critical role of the identified CICs, experiments were conducted focusing on cell survival and migration abilities. Results: In our research, we identified a set of 4 crucial cuproptosis-CICs that have a profound impact on patient longevity and their response to immunotherapy. By leveraging these identified CICs, we constructed a predictive model that efficiently estimates patient prognoses. Detailed analyses at the single-cell level showed that the significance of CICs. Experimental approaches, including CCK-8, Transwell, and wound healing assays, revealed that the protein HSPA9 restricts the growth and movement of breast cancer cells. Furthermore, our studies using immunofluorescence techniques demonstrated that suppressing HSPA9 leads to a notable increase in ceramide levels. Conclusion: This research outlines a network of cuproptosis-CICs and constructs a predictive nomogram. Our model holds great promise for healthcare professionals to personalise treatment approaches for individuals with breast cancer. The work provides insights into the complex relationship between the cuproptosis-CIC network and the cancer immune microenvironment, setting the stage for novel approaches to cancer immunotherapy. By focusing on the essential gene HSPA9 within the cancer-immunity cycle, this strategy has the potential to significantly improve the efficacy of treatments against breast cancer.