Integrated profiling uncovers prognostic, immunological, and pharmacogenomic features of ferroptosis in triple-negative breast cancer.

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.

Comprehensive Analysis of Ferroptosis-Related LncRNAs in Breast Cancer Patients Reveals Prognostic Value and Relationship With Tumor Immune Microenvironment.

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.

lncRNA FGD5­AS1 promotes breast cancer progression by regulating the hsa­miR­195­5p/NUAK2 axis.

Breast cancer is the second most prevalent cancer in women worldwide. Long non­coding RNAs (lncRNAs) have been identified as important regulators of tumorigenesis and tumor metastasis. lncRNA FGD5­AS1 has been previously reported as a carcinogenic gene, however its role in breast cancer has yet to be investigated. The present study aimed to understand the function of lncRNA FGD5­AS1 in breast cancer and examine the underlying molecular mechanisms. Sample tissues for downstream gene expression profiling were collected from patients with breast cancer (n=23). The effect of FGD5­AS1 overexpression on cell viability, invasion and migration has been studied in breast cancer cells (MDA­MB­231). Changes in glycolysis were monitored by comparing glucose consumption, lactate production and ATP levels. Using StarBase and TargetScan databases a putative interaction between FGD5­AS1, miR­195­5p and SNF1­like kinase 2 (NUAK2) was predicted in silico. Expression levels of FGD5­AS1, has­miR­195­5p and NUAK2 were validated by reverse transcription­quantitative PCR and interactions were validated using dual­luciferase reporter assays and RNA pull­down. High expression of lncRNA FGD5­AS1 was detected in breast cancer tissue samples and disease model cell lines. Silencing of FGD5­AS1 led to decreased cell proliferation, migration and invasion. It was identified that at a molecular level FGD5­AS1 serves as a sponge of miR­195­5p and alters the expression of its downstream target gene NUAK2. In breast cancer lncRNA FGD5­AS1 serve a key role in glycolysis and tumor progression via the miR­195­5p/NUAK2 axis. The findings of the present study indicated FGD5­AS1 as a candidate target for intervention in patients with breast cancer.

A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer.

Objective: Tumor suppressor genes (TSGs) play critical roles in the cell cycle checkpoints and in modulating genomic stability. Here, we aimed to develop a TSG-based prognostic classifier for breast cancer. Methods: Gene expression profiles and clinical information of breast cancer were curated from TCGA (discovery set) and Gene Expression Omnibus (GEO) repository (GSE12093 and GSE17705 datasets as testing sets). Univariate cox regression analysis and random forest machine learning method were presented for screening characteristic TSGs. After multivariate cox regression analyses, a TSG-based prognostic classifier was constructed. The predictive efficacy was verified by C-index and receiver operating characteristic (ROC) curves. Meanwhile, the predictive independency was assessed through uni- and multivariate cox regression analyses and stratified analyses. Tumor immune infiltration was estimated via ESTIMATE and CIBERSORT algorithms. Small molecule agents were predicted through CMap method. Molecular subtypes were clustered based on the top 100 TSGs with the most variance. Results: A prognostic classifier including nine TSGs was established. High-risk patients were predictive of undesirable prognosis. C-index and ROC curves demonstrated its excellent predictive performance in prognosis. Also, this prognostic classifier was independent of conventional clinicopathological parameters. Low-risk patients exhibited increased infiltration levels of immune cells like T cells CD8. Totally, 48 small molecule compounds were predicted to potentially treat breast cancer. Five TSG-based molecular subtypes were finally constructed, with distinct prognosis and clinicopathological features. Conclusion: Collectively, this study provided a TSG-based prognostic classifier with the potential to predict clinical outcomes and immune infiltration in breast cancer and identified potential small molecule agents against breast cancer.