Development and Validation of a Robust Ferroptosis-Related Gene Panel for Breast Cancer Disease-Specific Survival

Natural killer cell-related prognostic risk model predicts prognosis and treatment outcomes in triple-negative breast cancer

Background

Natural killer (NK) cells are crucial to the emergence, identification, and prognosis of cancers. The roles of NK cell-related genes in the tumor immune microenvironment (TIME) and immunotherapy treatment are unclear. Triple-negative breast cancer (TNBC) is a highly aggressive malignant tumor. Hence, this study was conducted to develop a reliable risk model related to NK cells and provide a novel system for predicting the prognosis of TNBC.

Methods

NK cell-related genes were collected from previous studies. Based on TCGA and GEO database, univariate and LASSO cox regression analysis were used to establish the NK cell-related gene signature. The patients with TNBC were separated to high-risk and low-risk groups. After that, survival analysis was conducted and the responses to immunotherapies were evaluated on the basis of the signature. Moreover, the drug sensitivity of some traditional chemotherapeutic drugs was assessed by using the "oncoPredict" R package. In addition, the expression levels of the genes involved in the signature were validated by using qRT-PCR in TNBC cell lines.

Results

The patients with TNBC were divided into high- and low-risk groups according to the median risk score of the 5-NK cell-related gene signature. The low-risk group was associated with a better clinical outcome. Besides, the differentially expressed genes between the different risk groups were enriched in the biological activities associated with immunity. The tumor immune cells were found to be highly infiltrated in the low-risk groups. In accordance with the TIDE score and immune checkpoint-related gene expression analysis, TNBC patients in the low-risk groups were suggested to have better responses to immunotherapies. Eventually, some classical anti-tumor drugs were shown to be less effective in high-risk groups than in low-risk groups.

Conclusion

The 5-NK cell-related gene signature exhibit outstanding predictive performance and provide fresh viewpoints for evaluating the success of immunotherapy. It will provide new insights to achieve precision and integrated treatment for TNBC in the future.

Life span-associated ferroptosis-related genes identification and validation for hepatocellular carcinoma patients as hepatitis B virus carriers

BACKGROUND

Hepatitis B virus (HBV)-infected population accounts for approximately 50% of all hepatocellular carcinoma (HCC) cases and has a relatively poor prognosis. Although the significant role of ferroptosis in the development and therapeutic response of various cancers has been validated, the key ferroptosis-related genes (FRGs) on the stratification of HBV-associated HCC are still unclear.

METHODS

Through the random forest, GSVA and Cox regression analyses, we established a comprehensive prognostic system covering multiple FRGs to elevate the predictive accuracy for the survival rate of HBV-related HCC using information obtained from public databases. The association between key FRGs and the immune microenvironment was evaluated, and the molecular mechanism was identified by GSEA and SNV analyses. Finally, the differential expression of key FRGs was validated by immunohistochemistry staining of patient tissue microarrays.

RESULTS

Within the top 10 key FRGs, EPAS1 and GABARAPL1 were taken as protective factors, and SQLE, RAD51AP1, RPL8, CAPG, RRM2, SLC1A5, SLC38A1, and SRC were the other eight dangerous markers. Cox regression analysis combined with clinicopathological features indicated the independent prognostic efficacy of GSVA complex score based on these FRGs. In addition, key FRGs were related to immune and metabolic-related functions. Especially, the immunohistochemical analysis of SQLE in 50 clinical samples showed significantly higher expression in HBV+ HCC tissues.

CONCLUSIONS

These results indicate that 10 FRGs may be potential biomarkers and therapeutic targets for long-term survival in HBV-related HCC.

Exploration of prognostic genes and risk signature in breast cancer patients based on RNA binding proteins associated with ferroptosis

Background: Breast cancer (BRCA) is a life-threatening malignancy in women with an unsatisfactory prognosis. The purpose of this study was to explore the prognostic biomarkers and a risk signature based on ferroptosis-related RNA-binding proteins (FR-RBPs). Methods: FR-RBPs were identified using Spearman correlation analysis. Differentially expressed genes (DEGs) were identified by the "limma" R package. The univariate Cox and multivariate Cox analyses were executed to determine the prognostic genes. The risk signature was constructed and verified with the training set, testing set, and validation set. Mutation analysis, immune checkpoint expression analysis in high- and low-risk groups, and correlation between risk signature and chemotherapeutic agents were conducted using the "maftools" package, "ggplot2" package, and the CellMiner database respectively. The Human Protein Atlas (HPA) database was employed to confirm protein expression trends of prognostic genes in BRCA and normal tissues. The expression of prognostic genes in cell lines was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-meier (KM) plotter database analysis was applied to predict the correlation between the expression levels of signature genes and survival statuses. Results: Five prognostic genes (GSPT2, RNASE1, TIPARP, TSEN54, and SAMD4A) to construct an FR-RBPs-related risk signature were identified and the risk signature was validated by the International Cancer Genome Consortium (ICGC) cohort. Univariate and multivariate Cox regression analysis demonstrated the risk score was a robust independent prognostic factor in overall survival prediction. The Tumor Mutational Burden (TMB) analysis implied that the high- and low-risk groups responded differently to immunotherapy. Drug sensitivity analysis suggested that the risk signature may serve as a chemosensitivity predictor. The results of GSEA suggested that five prognostic genes might be related to DNA replication and the immune-related pathways. RT-qPCR results demonstrated that the expression trends of prognostic genes in cell lines were consistent with the results from public databases. KM plotter database analysis suggested that high expression levels of GSPT2, RNASE1, and SAMD4A contributed to poor prognoses. Conclusion: In conclusion, this study identified the FR-RBPs-related prognostic genes and developed an FR-RBPs-related risk signature for the prognosis of BRCA, which will be of great significance in developing new therapeutic targets and prognostic molecular biomarkers for BRCA.

Targeting ferroptosis, the achilles' heel of breast cancer: A review

Ferroptosis is referred as a novel type of cell death discovered in recent years with the feature of the accumulation of iron-dependent lipid reactive oxygen species. Breast cancer is one of the most common malignant cancers in women. There is increasing evidence that ferroptosis can inhibit breast cancer cell growth, improve the sensitivity of chemotherapy and radiotherapy and inhibit distant metastases. Therefore, ferroptosis can be regarded a new target for tumor suppression and may expand the landscape of clinical treatment of breast cancer. This review highlights the ferroptosis mechanism and its potential role in breast cancer treatment to explore new therapeutic strategies of breast cancer.

Identification of a novel necroptosis-related classifier to predict prognosis and guide immunotherapy in breast invasive carcinoma

Background

Necroptosis plays a crucial function in the progression of breast invasive carcinoma (BRCA). It may be triggered in cancer therapy to enhance anti-tumor immunity. However, the functions of necroptosis in tumors and its relationship with the tumor microenvironment (TME) remain largely unclear.

Methods

Necroptosis-related genes (NRGs) were collated from high-quality literature reviews. A robust risk model was constructed to systematically evaluate the clinical value, functional status, effects exerted by the risk model on the TME, and the genomic variations based on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) meta-cohorts.

Results

A risk model was constructed which comprised of six NRGs, including TNF receptor-associated factor 5 (TRAF5), Toll-like receptor 3 (TLR3), a riboflavin kinase (RFK), Fas ligand (FASLG), Fas-associated protein with death domain (FADD), and baculoviral IAP repeat-containing 3 (BIRC3). The stability and accuracy of the risk model were demonstrated for both the training and validation cohorts and its utility as an independent prognostic model for BRCA was verified. Patients in the low-risk group exhibited “hot” tumors having active immune and cell killing functions, while those in the high-risk group showed “cold” tumors having active tumor proliferation and immunosuppression. Moreover, patients in the high-risk group had a greater number of CNV events in their genome, while the somatic mutations were fewer. Furthermore, patients in the low-risk group showed high sensitivity toward immunotherapy and chemotherapy.

Conclusion

A reliable risk model based on NRGs to assess patient prognoses and guide clinical decision-making was constructed and validated. Our findings may contribute to the understanding of necroptosis and aid clinical management, along with precision treatment in BRCA.