Background: Breast cancer (BC) is a heterogeneous malignant tumor, leading to the second major cause of female mortality. This study aimed to establish an in-depth relationship between ferroptosis-related LncRNA (FRlncRNA) and the prognosis as well as immune microenvironment of the patients with BC.Methods: We downloaded and integrated the gene expression data and the clinical information of the patients with BC from The Cancer Genome Atlas (TCGA) database. The co-expression network analysis and univariate Cox regression analysis were performed to screen out the FRlncRNAs related to prognosis. A cluster analysis was adopted to explore the difference of immune microenvironment between the clusters. Furthermore, we determined the optimal survival-related FRLncRNAs for final signature by LASSO Cox regression analysis. Afterward, we constructed and validated the prediction models, which were further tested in different subgroups.Results: A total of 31 FRLncRNAs were filtrated as prognostic biomarkers. Two clusters were determined, and C1 showed better prognosis and higher infiltration level of immune cells, such as B cells naive, plasma cells, T cells CD8, and T cells CD4 memory activated. However, there were no significantly different clinical characters between the clusters. Gene Set Enrichment Analysis (GSEA) revealed that some metabolism-related pathways and immune-associated pathways were exposed. In addition, 12 FRLncRNAs were determined by LASSO analysis and used to construct a prognostic signature. In both the training and testing sets, patients in the high-risk group had a worse survival than the low-risk patients. The area under the curves (AUCs) of receiver operator characteristic (ROC) curves were about 0.700, showing positive prognostic capacity. More notably, through the comprehensive analysis of heatmap, we regarded LINC01871, LINC02384, LIPE-AS1, and HSD11B1-AS1 as protective LncRNAs, while LINC00393, AC121247.2, AC010655.2, LINC01419, PTPRD-AS1, AC099329.2, OTUD6B-AS1, and LINC02266 were classified as risk LncRNAs. At the same time, the patients in the low-risk groups were more likely to be assigned to C1 and had a higher immune score, which were consistent with a better prognosis.Conclusion: Our research indicated that the ferroptosis-related prognostic signature could be used as novel biomarkers for predicting the prognosis of BC. The differences in the immune microenvironment exhibited by BC patients with different risks and clusters suggested that there may be a complementary synergistic effect between ferroptosis and immunotherapy.

Bovine serum albumin-Fe₃O₄ nanoparticles with undoubted biosafety and robust dual-modal T₁ and T₂ MR imaging ability were fabricated using a biomineralization approach in a facile way under mild conditions for in vivo MR imaging.

Objective: Ferroptosis is an iron-dependent type of regulated cell death triggered by the toxic buildup of lipid peroxides on cell membranes. Nonetheless, the implication of ferroptosis in triple‐negative breast cancer (TNBC), which is the most aggressive subtype of breast carcinoma, remains unexplored.Methods: Three TNBC cohorts—TCGA-TNBC, GSE58812, and METABRIC—were adopted. Consensus molecular subtyping on prognostic ferroptosis-related genes was implemented across TNBC. Ferroptosis classification-relevant genes were selected through weighted co-expression network analysis (WGCNA), and a ferroptosis-relevant scoring system was proposed through the LASSO approach. Prognostic and immunological traits, transcriptional and post-transcriptional modulation, therapeutic response, and prediction of potential small-molecule agents were conducted.Results: Three disparate ferroptosis patterns were identified across TNBC, with prognostic and immunological traits in each pattern. The ferroptosis-relevant scoring system was proposed, with poorer overall survival in high-risk patients. This risk score was strongly linked to transcriptional and post-transcriptional mechanisms. The high-risk group had a higher response to anti-PD-1 blockade or sunitinib, and the low-risk group had higher sensitivity to cisplatin. High relationships of risk score with immunological features were observed across pan-cancer. Two Cancer Therapeutics Response Portal (CTRP)-derived agents (SNX-2112 and brefeldin A) and PRISM-derived agents (MEK162, PD-0325901, PD-318088, Ro-4987655, and SAR131675) were predicted, which were intended for high-risk patients.Conclusion: Altogether, our findings unveil prognostic, immunological, and pharmacogenomic features of ferroptosis in TNBC, highlighting the potential clinical utility of ferroptosis in TNBC therapy.

Objective: N⁶-methyladenosine (m⁶A) modification may modulate various biological processes. Nonetheless, clinical implications of m⁶A modification in pancreatic cancer are undefined. Herein, this study comprehensively characterized the m⁶A modification patterns in pancreatic cancer based on m⁶A regulators.Methods: Genetic mutation and expression pattern of 21 m⁶A regulators and their correlations were assessed in pancreatic cancer from TCGA dataset. m⁶A modification patterns were clustered using unsupervised clustering analysis in TCGA and ICGC datasets. Differences in survival, biological functions and immune cell infiltrations were assessed between modification patterns. A m⁶A scoring system was developed by principal component analysis. Genetic mutations and TIDE scores were compared between high and low m⁶A score groups.Results: ZC3H13 (11%), RBM15B (9%), YTHDF1 (8%), and YTHDC1 (6%) frequently occurred mutations among m⁶A regulators. Also, most of regulators were distinctly dysregulated in pancreatic cancer. There were tight crosslinks between regulators. Two m⁶A modification patterns were constructed, with distinct prognoses, immune cell infiltration and biological functions. Furthermore, we quantified m⁶A score in each sample. High m⁶A scores indicated undesirable clinical outcomes. There were more frequent mutations in high m⁶A score samples. Lower TIDE score was found in high m⁶A score group, with AUC = 0.61, indicating that m⁶A scores might be used for predicting the response to immunotherapy.Conclusion: Collectively, these data demonstrated that m⁶A modification participates pancreatic cancer progress and ornaments immune microenvironment, providing an insight into pancreatic cancer pathogenesis and facilitating precision medicine development.

Borderline resectable pancreatic cancer (BRPC) is a complex clinical entity with specific biological features. Criteria for resectability need to be assessed in combination with tumor anatomy and oncology. Neoadjuvant therapy (NAT) for BRPC patients is associated with additional survival benefits. Research is currently focused on exploring the optimal NAT regimen and more reliable ways of assessing response to NAT. More attention to management standards during NAT, including biliary drainage and nutritional support, is needed. Surgery remains the cornerstone of BRPC treatment and multidisciplinary teams can help to evaluate whether patients are suitable for surgery and provide individualized management during the perioperative period, including NAT responsiveness and the selection of surgical timing.

Purpose: Mucinous carcinoma of the breast (MCB) is a rare malignant tumour. Therefore, it is urgent to establish a survival prediction model for MCB patients. Methods: Clinicopathological and follow-up data of MCB patients diagnosed between 2010 and 2015 were retrieved from the Surveillance, Epidemiology, and End Result (SEER) database. The significant factors were screened out and generated Kaplan–Meier (K-M) curves for each prognostic factor. Additionally, these factors were then utilized to build a nomogram for predicting 3-, 4-, and 5-year overall survival (OS) of MCB patients. The nomogram was evaluated using calibration curves, receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results: Moreover, a total of 4326 MCB patients were retrieved. Age, American Joint Committee on Cancer (AJCC) stage, surgery, radiotherapy and bone metastasis were identified as independently prognosis factors for OS. The corresponding areas under the ROC curves (AUCs) of the nomogram at 3, 4 and 5 years in the training and validation set were 0.770, 0.788, 0.805, 0.778, 0.797, and 0.802, respectively. The calibration curves and DCA revealed that the prediction model had an excellent performance. Finally, the risk stratification system confirmed that the powerful role of the nomogram in distinguishing results and risk stratification. Conclusion: Briefly, the nomogram incorporating various clinicopathological indicators was established for MCB patients and may facilitate clinical decision-making.