Ferroptosis biomarkers predict tumor mutation burden's impact on prognosis in HER2-positive breast cancer.

BACKGROUND: Ferroptosis has recently been associated with multiple degenerative diseases. Ferroptosis induction in cancer cells is a feasible method for treating neoplastic diseases. However, the association of iron proliferation-related genes with prognosis in HER2+ breast cancer (BC) patients is unclear. AIM: To identify and evaluate fresh ferroptosis-related biomarkers for HER2+ BC. METHODS: First, we obtained the mRNA expression profiles and clinical information of HER2+ BC patients from the TCGA and METABRIC public databases. A four-gene prediction model comprising PROM2, SLC7A11, FANCD2, and FH was subsequently developed in the TCGA cohort and confirmed in the METABRIC cohort. Patients were stratified into high-risk and low-risk groups based on their median risk score, an independent predictor of overall survival (OS). Based on these findings, immune infiltration, mutations, and medication sensitivity were analyzed in various risk groupings. Additionally, we assessed patient prognosis by combining the tumor mutation burden (TMB) with risk score. Finally, we evaluated the expression of critical genes by analyzing single-cell RNA sequencing (scRNA-seq) data from malignant vs normal epithelial cells. RESULTS: We found that the higher the risk score was, the worse the prognosis was (P < 0.05). We also found that the immune cell infiltration, mutation, and drug sensitivity were different between the different risk groups. The high-risk subgroup was associated with lower immune scores and high TMB. Moreover, we found that the combination of the TMB and risk score could stratify patients into three groups with distinct prognoses. HRisk-HTMB patients had the worst prognosis, whereas LRisk-LTMB patients had the best prognosis (P < 0.0001). Analysis of the scRNA-seq data showed that PROM2, SLC7A11, and FANCD2 were significantly differentially expressed, whereas FH was not, suggesting that these genes are expressed mainly in cancer epithelial cells (P < 0.01). CONCLUSION: Our model helps guide the prognosis of HER2+ breast cancer patients, and its combination with the TMB can aid in more accurate assessment of patient prognosis and provide new ideas for further diagnosis and treatment.

Causal Association Between mTOR-Dependent Protein Levels and Alzheimer's Disease: A Mendelian Randomization Study.

BACKGROUND: Most previous studies supported that the mammalian target of rapamycin (mTOR) is over-activated in Alzheimer's disease (AD) and exacerbates the development of AD. It is unclear whether the causal associations between the mTOR signaling-related protein and the risk for AD exist. OBJECTIVE: This study aims to investigate the causal effects of the mTOR signaling targets on AD. METHODS: We explored whether the risk of AD varied with genetically predicted AKT, RP-S6K, EIF4E-BP, eIF4E, eIF4A, and eIF4G circulating levels using a two-sample Mendelian randomization analysis. The summary data for targets of the mTOR signaling were acquired from published genome-wide association studies for the INTERVAL study. Genetic associations with AD were retrieved from the International Genomics of Alzheimer's Project. We utilized the inverse variance weighted as the primary approach to calculate the effect estimates. RESULTS: The elevated levels of AKT (OR = 0.910, 95% CI=0.840-0.986, p = 0.02) and RP-S6K (OR = 0.910, 95% CI=0.840-0.986, p = 0.02) may decrease the AD risk. In contrast, the elevated eIF4E levels (OR = 1.805, 95% CI=1.002-1.174, p = 0.045) may genetically increase the AD risk. No statistical significance was identified for levels of EIF4-BP, eIF4A, and eIF4G with AD risk (p > 0.05). CONCLUSION: There was a causal relationship between the mTOR signaling and the risk for AD. Activating AKT and RP-S6K, or inhibiting eIF4E may be potentially beneficial to the prevention and treatment of AD.