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A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer.

ABSTRACT: Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment. Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR. Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132-3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues. Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

SUBMITTER: Ge M

PROVIDER: S-EPMC8076739 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Ferroptosis, a regulated cell death which is driven by the iron-dependent peroxidation of lipids, plays an important role in cancer. However, studies about ferroptosis-related Long non-coding RNAs (lncRNAs) in breast cancer (BC) are limited. Besides, the prognostic role of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer remain unclear. This study aimed to explore the potential prognostic value of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer. Methods: RNA-sequencing data of female breast cancer patients were downloaded from TCGA database. 937 patients were randomly separated into training or validation cohort in 2:1 ratio. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 239 reported ferroptosis-related genes. A ferroptosis-related lncRNAs signature was constructed with univariate and multivariate Cox regression analyses in the training cohort, and its prognostic value was further tested in the validation cohort. Results: An 8-ferroptosis-related-lncRNAs signature was developed by multivariate Cox regression analysis to divide patients into two risk groups. Patients in the high-risk group had worse prognosis than patients in the low-risk group. Multivariate Cox regression analysis showed the risk score was an independent prognostic indicator. Receiver operating characteristic curve (ROC) analysis proved the predictive accuracy of the signature. The area under time-dependent ROC curve (AUC) reached 0.853 at 1 year, 0.802 at 2 years, 0.740 at 5 years in the training cohort and 0.791 at 1 year, 0.778 at 2 years, 0.722 at 5 years in the validation cohort. Further analysis demonstrated that immune-related pathways were significantly enriched in the high-risk group. Analysis of the immune cell infiltration landscape showed that breast cancer in the high-risk group tended be immunologically "cold". Conclusion: We identified a novel ferroptosis-related lncRNA signature which could precisely predict the prognosis of breast cancer patients. Ferroptosis-related lncRNAs may have a potential role in the process of anti-tumor immunity and serve as therapeutic targets for breast cancer.

Project description:BACKGROUND:Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. METHODS:A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. RESULTS:LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature's genes with the influence of ferroptosis induced by sorafenib. CONCLUSIONS:We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients' prognoses and targeting ferroptosis may be an alternative for PTC's therapy.

Project description:Osteosarcoma has been the most common malignant bone tumor in children and adolescents, while the 5-y survival of osteosarcoma patients gained no significant improvement over the past decades. This study aimed to explore the role of ferroptosis-related genes (FRGs) in the development and prognosis of osteosarcoma. The datasets of osteosarcoma patients including RNA sequencing data and clinical information were acquired from the TRGET and Gene Expression Omnibus (GEO) databases. The identification of molecular subgroups with different FRG expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. The prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis. The ESTIMATE algorithm was applied for determining the stromal score, immune score, ESTIMA score, and tumor purity of osteosarcoma patients. Functional analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were conducted to explore the underlying mechanisms in the development and prognosis of osteosarcoma. Two molecular subgroups with different FRGs expression patterns were identified. The molecular subgroups with higher immune score and more active immune status showed better prognostic survival. On the basis of FRGs, a prognostic model and a nomogram integrating clinical characteristics were constructed and their prediction efficiency for osteosarcoma prognosis were well validated. Gene functional enrichment analysis showed that these differentially expressed FRGs were mainly enriched in immunity-related signaling pathways, indicating that FRGs may affect the development and prognosis of osteosarcoma by regulating the immune microenvironment. The expression profiles of FRGs were closely related to the immunity status and prognostic survival of osteosarcoma patients. The interaction between ferroptosis and immunity in the development of osteosarcoma could provide a new insight into the exploration of molecular mechanisms and targeted therapies of osteosarcoma patients.

Project description:Hepatitis C virus (HCV)-related cirrhosis leads to a heavy global burden of disease. Clinical risk stratification in HCV-related compensated cirrhosis remains a major challenge. Here, we aim to develop a signature comprised of immune-related genes to identify patients at high risk of progression and systematically analyze immune infiltration in HCV-related early-stage cirrhosis patients. Bioinformatics analysis was applied to identify immune-related genes and construct a prognostic signature in microarray data set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted with the "clusterProfiler" R package. Besides, the single sample gene set enrichment analysis (ssGSEA) was used to quantify immune-related risk term abundance. The nomogram and calibrate were set up via the integration of the risk score and clinicopathological characteristics to assess the effectiveness of the prognostic signature. Finally, three genes were identified and were adopted to build an immune-related prognostic signature for HCV-related cirrhosis patients. The signature was proved to be an independent risk element for HCV-related cirrhosis patients. In addition, according to the time-dependent receiver operating characteristic (ROC) curves, nomogram, and calibration plot, the prognostic model could precisely forecast the survival rate at the first, fifth, and tenth year. Notably, functional enrichment analyses indicated that cytokine activity, chemokine activity, leukocyte migration and chemotaxis, chemokine signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved in HCV-related cirrhosis progression. Moreover, ssGSEA analyses revealed fierce immune-inflammatory response mechanisms in HCV progress. Generally, our work developed a robust prognostic signature that can accurately predict the overall survival, Child-Pugh class progression, hepatic decompensation, and hepatocellular carcinoma (HCC) for HCV-related early-stage cirrhosis patients. Functional enrichment and further immune infiltration analyses systematically elucidated potential immune response mechanisms.

Project description:Breast cancer (BC) is the most diagnosed cancer in women. Cuproptosis is new regulated cell death, distinct from known death mechanisms and dependent on copper and mitochondrial respiration. However, the comprehensive relationship between cuproptosis and BC is still blank until now. In the present study, we acquired 13 cuproptosis-related regulators (CRRs) from the previous research and downloaded the RNA sequencing data of TCGA-BRCA from the UCSC XENA database. The 13 CRRs were all differently expressed between BC and normal samples. Using consensus clustering based on the five prognostic CRRs, BC patients were classified into two cuproptosis-clusters (C1 and C2). C2 had a significant survival advantage and higher immune infiltration levels than C1. According to the Cox and LASSO regression analyses, a novel cuproptosis-related prognostic signature was developed to predict the prognosis of BC effectively. The high- and low-risk groups were divided based on the risk scores. Kaplan-Meier survival analysis indicated that the high-risk group had shorter overall survival (OS) than the low-risk group in the training, test and entire cohorts. GSEA indicated that the immune-related pathways were significantly enriched in the low-risk group. According to the CIBERSORT and ESTIMATE analyses, patients in the high-risk group had higher infiltrating levels of antitumor lymphocyte cell subpopulations and higher immune score than the low-risk group. The typical immune checkpoints were all elevated in the high-risk group. Furthermore, the high-risk group showed a better immunotherapy response than the low-risk group based on the Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS). In conclusion, we identified two cuproptosis-clusters with different prognoses using consensus clustering in BC. We also developed a cuproptosis-related prognostic signature and nomogram, which could indicate the outcome, the tumor immune microenvironment, as well as the response to immunotherapy.

Project description:Background: Osteosarcoma is a highly malignant bone tumor commonly occurring in adolescents with a poor 5-year survival rate. The unfolded protein response (UPR) can alleviate the accumulation of misfolded proteins to maintain homeostasis under endoplasmic reticulum stress. The UPR is linked to the occurrence, progression, and drug resistance of tumors. However, the function of UPR-related genes (UPRRGs) in disease progression and prognosis of osteosarcoma remains unclear. Methods: The mRNA expression profiling and corresponding clinical features of osteosarcoma were acquired from TARGET and GEO databases. Consensus clustering was conducted to confirm different UPRRG subtypes. Subsequently, we evaluated the prognosis and immune status of the different subtypes. Functional analysis of GO, GSEA, and GSVA was used to reveal the molecular mechanism between the subtypes. Finally, four genes (STC2, PREB, TSPYL2, and ATP6V0D1) were screened to construct and validate a risk signature to predict the prognosis of patients with osteosarcoma. Result: We identified two subtypes according to the UPRRG expression patterns. The subgroup with higher immune scores, lower tumor purity, and active immune status was linked to a better prognosis. Meanwhile, functional enrichment revealed that immune-related signaling pathways varied markedly in the two subtypes, suggesting that the UPR might influence the prognosis of osteosarcoma via influencing the immune microenvironment. Moreover, prognostic signature and nomogram models were developed based on UPRRGs, and the results showed that our model has an excellent performance in predicting the prognosis of osteosarcoma. qPCR analysis was also conducted to verify the expression levels of the four genes. Conclusion: We revealed the crucial contribution of UPRRGs in the immune microenvironment and prognostic prediction of osteosarcoma patients and provided new insights for targeted therapy and prognostic assessment of the disease.

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A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer.

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