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.

Background: The role of cuproptosis, 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 cuproptosis-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 personalize 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.

Study on the Relationship Between PTPRO Methylation in Plasma and Efficacy Neoadjuvant Chemotherapy in Patients with Early Breast Cancer.

Objective: This study aimed to explore the correlation between PTPRO methylation in plasma and the efficacy of neoadjuvant chemotherapy (NAC) for early breast cancer (BC). Methods: Eighty-two patients with early BC undergoing NAC were included. PTPRO methylation status in plasma before and after NAC was detected using methylation-specific PCR and the relationship between PTPRO methylation and NAC efficacy was analyzed. Results: The rate of pathologic complete response (pCR) was only 25.0% (12/48) in patients with positive PTPRO methylation result before NAC, but 61 0.8% (21/34) in pre-NAC methylation-negative patients (OR = 0.24, 95% CI: 0.09-0.65, P = 0.005). In addition, the pCR rate was 12.1% (4/33) in patients with positive PTPRO methylation results both before and after NAC, but 53.3% (8/15) in patients with pre-NAC positive methylation and post-NAC negative methylation results (OR = 0.12, 95% CI: 0.03-0.52, P = 0.004). Conclusion: Plasma PTPRO methylation is a potential biomarker for predicting the efficacy of NAC in early BC.

Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a complex disease with a poor outlook for patients in advanced stages. Immune cells play an important role in the progression of HCC. The metabolism of sphingolipids functions in both tumor growth and immune infiltration. However, little research has focused on using sphingolipid factors to predict HCC prognosis. This study aimed to identify the key sphingolipids genes (SPGs) in HCC and develop a reliable prognostic model based on these genes. Methods: The TCGA, GEO, and ICGC datasets were grouped using SPGs obtained from the InnateDB portal. A prognostic gene signature was created by applying LASSO-Cox analysis and evaluating it with Cox regression. The validity of the signature was verified using ICGC and GEO datasets. The tumor microenvironment (TME) was examined using ESTIMATE and CIBERSORT, and potential therapeutic targets were identified through machine learning. Single-cell sequencing was used to examine the distribution of signature genes in cells within the TME. Cell viability and migration were tested to confirm the role of the key SPGs. Results: We identified 28 SPGs that have an impact on survival. Using clinicopathological features and 6 genes, we developed a nomogram for HCC. The high- and low-risk groups were found to have distinct immune characteristics and response to drugs. Unlike CD8 T cells, M0 and M2 macrophages were found to be highly infiltrated in the TME of the high-risk subgroup. High levels of SPGs were found to be a good indicator of response to immunotherapy. In cell function experiments, SMPD2 and CSTA were found to enhance survival and migration of Huh7 cells, while silencing these genes increased the sensitivity of Huh7 cells to lapatinib. Conclusion: The study presents a six-gene signature and a nomogram that can aid clinicians in choosing personalized treatments for HCC patients. Furthermore, it uncovers the connection between sphingolipid-related genes and the immune microenvironment, offering a novel approach for immunotherapy. By focusing on crucial sphingolipid genes like SMPD2 and CSTA, the efficacy of anti-tumor therapy can be increased in HCC cells.

Correction: Memory-effect-induced electrochemical oscillation of an Al-doped Li4Ti5O12 composite in Li-ion batteries.

Correction for 'Memory-effect-induced electrochemical oscillation of an Al-doped Li4Ti5O12 composite in Li-ion batteries' by Liao Zhang et al., Chem. Commun., 2019, 55, 1279-1282.

Memory-effect-induced electrochemical oscillation of an Al-doped Li4Ti5O12 composite in Li-ion batteries.

Memory effects and electrochemical oscillation have recently been found in modified Li4Ti5O12, but the correlation between these two phenomena has not been reported yet. Here, we found that these two phenomena could simultaneously occur in an Al-doped Li4Ti5O12 composite, and the electrochemical oscillation can be controlled by regulating the memory effect.