Background: Accumulating literature demonstrates that long noncoding RNAs (lncRNAs) are involved in ferroptosis and gastric cancer progression. However, the predictive value of ferroptosis-related lncRNAs for prognosis and therapeutic response is yet to be elucidated in gastric cancer (GC).Method: The transcriptomic data and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. The association between ferroptosis-related lncRNAs and ferroptosis regulators was analyzed by Spearman correlation analysis. Then, we established a risk predictive model based on the ferroptosis-related lncRNAs using multivariate Cox regression analysis. Furthermore, we performed correlation analysis for the risk score and characteristics of biological processes, immune landscape, stromal activity, genomic integrity, drug response, and immunotherapy efficacy.Results: We constructed a 17-ferroptosis-related-lncRNA signature via multivariate Cox analysis to divide patients into two groups: low- and high-risk groups. The low-risk group was linked to prolonged overall survival and relapse-free survival. The risk score had good predictive ability to predict the prognosis of GC patients compared with other clinical biomarkers. We found that the high-risk group was associated with activation of carcinogenetic signaling pathways, including stromal activation, epithelial-mesenchymal-transition (EMT) activation, and immune escape through integrated bioinformatics analysis. In contrast, the low-risk group was associated with DNA replication, immune-flamed state, and genomic instability. Additionally, through Spearman correlation analysis, we found that patients in the high-risk group may respond well to drugs targeting cytoskeleton, WNT signaling, and PI3K/mTOR signaling, and drugs targeting chromatin histone acetylation, cell cycle, and apoptosis regulation could bring more benefits for the low-risk group. The high-risk group was associated with poor immunotherapy efficacy.Conclusion: Our study systematically evaluated the role of ferroptosis-related lncRNAs in t tumor microenvironment, therapeutic response, and prognosis of GC. Risk score–based stratification could reflect the characteristic of biological processes, immune landscape, stromal activity, genomic stability, and pharmaceutical profile in GC patients. The ferroptosis-related lncRNA signature could serve as a reliable biomarker to predict prognosis and therapeutic response of patients with GC.

Background: Ferroptosis is a form of regulated cell death that occurs as a consequence of lethal lipid peroxidation. A wealth of studies has demonstrated that ferroptosis profoundly modulated numerous biological behaviors of tumor. However, its natural functions in gastric cancer (GC) remain to be explored.Methods: Firstly, a total of over 1,000 GC patients from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database were included in our study. Secondly, 32 ferroptosis-related genes were extracted from the ferrDb website. Then, unsupervised clustering was performed to classify patients into three distinct ferroptosis-related clusters. Subsequently, we systematically and comprehensively explored the biological characteristics of each cluster. Finally, we constructed a scoring system, named ferroptosis score, to quantify each cluster and also investigated the predictive therapeutic value of the ferroptosis score for chemotherapy and immunotherapy.Results: Based on the expressions of 32 ferroptosis-related genes, three distinct ferroptosis-related subtypes with various biological characteristics were determined. Integrated analysis showed that cluster 1 is a microsatellite instability (MSI)-like subtype, cluster 2 is an epithelial–mesenchymal transition (EMT)-like subtype, while cluster 3 tends to be a metabolic-like subtype. Prognostic analysis revealed that patients in cluster 2 had a worse overall survival and relapse-free survival. The distribution of the ferroptosis score was significantly different in clusters and gene clusters. The ferroptosis score could predict the biological characteristics of each cluster, the stromal activity, and progression of tumor. The low ferroptosis score group was characterized by the activation of antigen processing and presentation, DNA damage repair pathways, and metabolic pathways, while the high ferroptosis score group was characterized by stromal activation. In response to anticancer drugs, the ferroptosis score was highly negatively associated with drugs targeting MAPK signaling and PI3K/mTOR signaling, while it was positively correlated with drugs targeting the cell cycle, mitosis, and metabolism. Finally, we also proved that the ferroptosis score could serve as a reliable biomarker to predict response to immunotherapy.Conclusion: This work revealed that tumor cells and their surrounding microenvironment could be shaped by varying the activation degrees of ferroptosis. Establishing ferroptosis-related subtypes would guide in predicting the biological features of individual tumors and selecting appropriate treatment protocols for patients.

BackgroundThe role of ferroptosis in tumor progression and immune microenvironment is extensively investigated. However, the potential value of ferroptosis regulators in predicting prognosis and therapeutic strategies for osteosarcoma (OS) patients remains to be elucidated.MethodsHere, we extracted transcriptomic and survival data from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) to investigate the expression and prognostic value of ferroptosis regulators in OS patients. After comprehensive analyses, including Gene set variation analysis (GSVA), single-sample gene-set enrichment analysis (ssGSEA), Estimated Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE), single-cell RNA sequencing, and biological experiments, our constructed 8-ferroptosis-regulators prognostic signature effectively predicted the immune landscape, prognosis, and chemoradiotherapy strategies for OS patients.ResultsWe constructed an 8-ferroptosis-regulators signature that could predict the survival outcome of OS. The signature algorithm scored samples, and high-scoring patients were more prone to worse prognoses. The tumor immune landscape suggested the positive relevance between risk score and immunosuppression. Interfering HILPDA and MUC1 expression would inhibit tumor cell proliferation and migration, and MUC1 might improve the ferroptosis resistance of OS cells. Moreover, we predicted chemoradiotherapy strategies of cancer patients following ferroptosis-risk-score groups.ConclusionDysregulated ferroptosis gene expression can affect OS progression by affecting the tumor immune landscape and ferroptosis resistance. Our risk model can predict OS survival outcomes, and we propose that HILPDA and MUC1 are potential targets for cancer therapy.