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Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma.

ABSTRACT: 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.

SUBMITTER: Shi J

PROVIDER: S-EPMC8670268 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe prognosis of oral squamous cell carcinoma (OSCC) patients is difficult to predict or describe due to its high-level heterogeneity and complex aetiologic factors. Ferroptosis is a novel form of iron-dependent cell death that is closely related to tumour growth and progression. This study aims to clarify the predictive value of ferroptosis-related genes (FRGs) on the overall survival(OS) of OSCC patients.MethodsThe mRNA expression profile of FRGs and clinical information of patients with OSCC were collected from the TCGA database. Candidate differentially expressed ferroptosis-related genes (DE-FRGs) were identified by analysing differences between OSCC and adjacent normal tissues. A gene signature of prognosis-related DE-FRGs was established by univariate Cox analysis and LASSO analysis in the training set. Patients were then divided into high- and low-risk groups according to the cut-off value of risk scores, A nomogram was constructed to quantify the contributions of gene signature and clinical parameters to OS. Then several bioinformatics analyses were used to verify the reliability and accuracy of the model in the validation set. Finally, single-sample gene set enrichment analysis (ssGSEA) was also performed to reveal the underlying differences in immune status between different risk groups.ResultsA prognostic model was constructed based on 10 ferroptosis-related genes. Patients in high-risk group had a significantly worse OS (p < 0.001). The gene signature was verified as an independent predictor for the OS of OSCC patients (HR > 1, p < 0.001). The receiver operating characteristic curve displayed the favour predictive performance of the risk model. The prediction nomogram successfully quantified each indicator's contribution to survival and the concordance index and calibration plots showed its superior predictive capacity. Finally, ssGSEA preliminarily indicated that the poor prognosis in the high-risk group might result from the dysregulation of immune status.ConclusionThis study established a 10-ferroptosis-releated gene signature and nomogram that can be used to predict the prognosis of OSCC patients, which provides new insight for future anticancer therapies based on potential FRG targets.

Project description:BackgroundGenomic instability (GI) is among the top ten characteristics of malignancy. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in GI. So far, the clinical value of GI-related lncRNAs (GIlncs) in papillary thyroid cancer (PTC) has not been clarified.MethodsIntegrative analysis of lncRNA expression and somatic mutation profiles was performed to identify GIlncs. Analysis of differentially expressed lncRNAs in the group with high- and low- cumulative number of somatic mutations revealed significant GIlncs in PTC. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify hub-GIlncs.ResultsA computational model based on four lncRNAs (FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1) was identified as a quantitative index using an in-silicon discovery cohort. GILS score was significantly associated with poor prognosis, as validated in the TCGA dataset and further tested in our local RNA-Seq cohort. Moreover, a combination of clinical characteristics and the composite GILS-clinical prognostic nomogram demonstrates satisfactory discrimination and calibration. Furthermore, the GILS score and FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 were also associated with driver mutations and multiple clinical-pathological variables, respectively. Moreover, RNA-Seq confirmed the expression patterns of FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 in PTC and normal thyroid tissues. Biological experiments demonstrated that downregulated or overexpressed LINC01614 affect PTC cell proliferation, migration, and invasion in vitro. Activation of the stromal and immune cell infiltration was also observed in the high LINC01614 group in the PTC microenvironment.ConclusionIn summary, we identified a signature for clinical outcome prediction in PTC comprising four lncRNAs associated with GI. A better understanding of the GI providing an alternative evaluation of the progression risk of PTC. Our study also demonstrated LINC01614 as a novel oncogenic lncRNA and verified its phenotype in PTC.

Project description:Objective: Hepatocellular carcinoma (HCC) is a genetically and phenotypically heterogeneous tumor, and the prediction of its prognosis remains a challenge. In the past decade, studies elucidating the mechanisms that induce tumor cell pyroptosis has rapidly increased. The elucidation of their mechanisms is essential for the clinical development optimal application of anti-hepatocellular carcinoma therapeutics. Methods: Based on the different expression profiles of pyroptosis-related genes in HCC, we constructed a LASSO Cox regression pyroptosis-related genes signature that could more accurately predict the prognosis of HCC patients. Results: We identified seven pyroptosis-related genes signature (BAK1, CHMP4B, GSDMC, NLRP6, NOD2, PLCG1, SCAF11) in predicting the prognosis of HCC patients. Kaplan Meier survival analysis showed that the pyroptosis-related high-risk gene signature was associated with poor prognosis HCC patients. Moreover, the pyroptosis-related genes signature performed well in the survival analysis and ICGC validation group. The hybrid nomogram and calibration curve further demonstrated their feasibility and accuracy for predicting the prognosis of HCC patients. Meanwhile, the evaluation revealed that our novel signature predicted the prognosis of HCC patients more accurately than traditional clinicopathological features. GSEA analysis further revealed the novel signature associated mechanisms of immunity response in high-risk groups. Moreover, analysis of immune cell subsets with relevant functions revealed significant differences in aDCs, APC co-stimulation, CCR, check-point, iDCs, Macrophages, MHC class-I, Treg, and type II INF response between high- and low-risk groups. Finally, the expression of Immune checkpoints was enhanced in high-risk group, and m6A-related modifications were expressed differently between low- and high-risk groups. Conclusion: The novel pyroptosis-related genes signature can predict the prognosis of patients with HCC and insight into new cell death targeted therapies.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is still highly aggressive and lethal even with various therapeutic approaches. As the kidney is an iron metabolism-related organ, exploring and assessing the clinical value of ferroptosis, an iron-dependent regulated cell death, is practical and important.MethodsPrognostic ferroptosis-related differentially expressed genes (DEGs) were identified from the KIRC cohort in the cancer genome atlas (TCGA) database, from which a prognostic signature was established using Lasso-penalized Cox regression analysis. Each patient in the KIRC cohort and the E-MTAB-1980 cohort (from the ArrayExpress database) was assigned a calculated signature-correlated risk score and categorized to be either in the high- or low-risk group divided by the median risk score in the KIRC cohort. Then, the independent prognostic value of the signature was further assessed by Kaplan-Meier (K-M) survival, time-dependent receiver operating characteristic (ROC) and Cox regression analyses based on overall survival (OS) in both cohorts. Finally, risk-related DEGs were identified in both cohorts and subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration.ResultsAmong 60 ferroptosis-related genes, 32 prognostic DEGs were identified, from which we constructed a prognostic 12-gene signature with CARS1, HMGCR, CHAC1, GOT1, CD44, STEAP3, AKR1C1, CBS, DPP4, FANCD2, SLC1A5 and NCOA4. Patients in both cohorts were divided into high- and low-risk groups, which were visually distributed in two sets and had positive-risk-related mortality. The K-M survival and the ROC curves validated that the signature has prognostic value with P < 0.05 and area under the curve > 0.7 in both cohorts, respectively. Multivariate Cox regression further confirmed the risk score as an independent prognostic predictor for OS. Commonly enriched terms in GO and KEGG not only showed a high iron correlation but also, interestingly, immune relevance of 3 immune cells (macrophages, mast cells and regulatory T cells) and 1 immune-related function (antigen processing cell co-stimulation).ConclusionWe established a novel 12 ferroptosis-related-gene signature that was proven to be an independent prognostic predictor for OS and inferred to be related to tumour immunity in ccRCC; however, the underlying mechanism is still poorly characterized and needs further exploration.

Project description:Ferroptosis is a non-apoptotic regulated cell death process, and much research has indicated that ferroptosis can induce the non-apoptotic death of tumor cells. Ferroptosis-related genes are expected to become a biological target for cancer treatment. However, the regulation of ferroptosis-related genes in skin cutaneous melanoma (SKCM) has not been well studied. In the present study, we conducted a systematic analysis of SKCM based on RNA sequencing data and clinical data obtained from The Cancer Genome Atlas (TCGA) database and the FerrD database. SKCM patients from the GSE78220 and MSKCC cohorts were used for external validation. Applying consensus clustering on RNA sequencing data from TCGA the generated ferroptosis subclasses of SKCM, which were analyzed based on the set of differentially expressed ferroptosis-related genes. Then, a least absolute shrinkage and selection operator (LASSO)-Cox regression was used to construct an eight gene survival-related linear signature. The median cut-off risk score was used to divide patients into high- and low-risk groups. The time-dependent receiver operating characteristic curve was used to examine the predictive power of the model. The areas under the curve of the signature at 1, 3, and 5 years were 0.673, 0.716, and 0.746, respectively. Kaplan-Meier survival analysis showed that the prognosis of high-risk patients was worse than that of low-risk patients. Univariate and multivariate Cox regression analyses showed that the risk signature was a robust independent prognostic indicator. By incorporating risk scores with tumor staging, a nomogram was constructed to predict prognostic outcomes for SKCM patients. In addition, the immunological analysis showed different immune cell infiltration patterns. Programmed-death-1 (PD-1) immunotherapy showed more significant benefits in the low-risk group than in the high-risk group. In summary, a model based on ferroptosis-related genes can predict the prognosis of SKCM and could have a potential role in guiding targeted therapy of SKCM.

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Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma.

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