A Novel Ferroptosis-Related Gene Risk Signature for Predicting Prognosis and Immunotherapy Response in Gastric Cancer.
Ontology highlight
ABSTRACT: Background
Gastric cancer (GC) is the third leading cause of cancer death worldwide with complicated molecular and cellular heterogeneity. Iron metabolism and ferroptosis play crucial roles in the pathogenesis of GC. However, the prognostic role and immunotherapy biomarker potential of ferroptosis-related genes (FRGs) in GC still remains to be clarified.Methods
We comprehensively analyzed the prognosis of different expression FRGs, based on gastric carcinoma patients in the TCGA cohort. The functional enrichment and immune microenvironment associated with these genes in gastric cancer were investigated. The prognostic model was constructed to clarify the relation between FRGs and the prognosis of GC. Meanwhile, the ceRNA network of FRGs in the prognostic model was performed to explore the regulatory mechanisms.Results
Gastric carcinoma patients were classified into the A, B, and C FRGClusters with different features based on 19 prognostic ferroptosis-related differentially expressed genes in the TCGA database. To quantify the FRG characteristics of individual patients, FRGScore was constructed. And the research shows the GC patients with higher FRGScore had worse survival outcome. Moreover, thirteen prognostic ferroptosis-related differentially expressed genes (DEGs) were selected to construct a prognostic model for GC survival outcome with a superior accuracy in this research. And we also found that FRG RiskScore can be an independent biomarker for the prognosis of GC patients. Interestingly, GC patients with lower RiskScore had less immune dysfunction and were more likely to respond to immunotherapy according to TIDE value analysis. Finally, a ceRNA network based on FRGs in the prognostic model was analyzed to show the concrete regulation mechanisms.Conclusions
The ferroptosis-related gene risk signature has a superior potent in predicting GC prognosis and acts as the biomarkers for immunotherapy, which may provide a reference in clinic.
SUBMITTER: Liu SJ
PROVIDER: S-EPMC8642032 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The majority of gastric cancer (GC) patients are in a progressive stage at the initial stage of treatment, and the overall response rate to immunotherapy remains unsatisfactory largely due to the lack of effective prognostic biomarkers. Immunogenic cell death (ICD) was identified as a new form of regulated cell death that can activate adaptive immune responses and further promote immunotherapy efficacy. Therefore, we attempted to characterize the ICD-associated signature to stratify patients who could benefit from immunotherapy. In our study, two subgroups of patients were identified based on the data of 34 ICD-related genes extracted from The Cancer Genome Atlas database via consensus clustering. The estimated scores, stromal scores, immune scores, tumor purity, and survival rate showed significant differences between the low and high ICD groups. Then, we constructed an ICD-related risk signature, including IFNB1, IL6, LY96, and NT5E, using least absolute shrinkage and selection operator Cox regression analysis; then, high- and low-risk groups could be clearly distinguished. Notably, the risk score is a reliable predictor of the prognosis and immunotherapy outcome in GC, which was further validated in an immunohistochemistry assay. These results suggest that ICD is closely associated with the prognosis and tumor immune microenvironment in GC. Taken together, this study first constructed and validated a prognostic ICD-related signature to predict the survival and effect of immunotherapy in GC, which provided new insight for potent individualized immunotherapy strategies.
| S-EPMC10196197 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC.MethodsFerroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model.ResultsWe constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410-2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR.ConclusionsThis risk model was shown to be a novel method for predicting prognosis for GC patients.
| S-EPMC8590758 | biostudies-literature
Project description: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.
| S-EPMC8674955 | biostudies-literature
Project description:Ferroptosis is an iron-dependent, regulated form of cell death, and the process is complex, consisting of a variety of metabolites and biological molecules. Ovarian cancer (OC) is a highly malignant gynecologic tumor with a poor survival rate. However, the predictive role of ferroptosis-related genes in ovarian cancer prognosis remains unknown. In this study, we demonstrated that the 57 ferroptosis-related genes were expressed differently between ovarian cancer and normal ovarian tissue, and based on these genes, all OC cases can be well divided into 2 subgroups by applying consensus clustering. We utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to develop a multigene risk signature from the TCGA cohort and then validated it in an OC cohort from the GEO database. A 5-gene signature was built and reveals a favorable predictive efficacy in both TCGA and GEO cohort (P < 0.001 and P = 0.03). The GO and KEGG analysis revealed that the differentially expressed genes (DEGs) between the low- and high-risk subgroup divided by our risk model were associated with tumor immunity, and lower immune status in the high-risk group was discovered. In conclusion, ferroptosis-related genes are vital factors predicting the prognosis of OC and could be a novel potential treatment target.
| S-EPMC8047312 | biostudies-literature
Project description:AimsGlioma is a highly invasive brain tumor, which makes prognosis challenging and renders patients resistant to various treatments. Induction of cell death is promising in cancer therapy. Ferroptosis, a recently discovered regulated cell death, can be induced for killing glioma cells. However, the prognostic prediction of ferroptosis-related genes (FRGs) in glioma remains elusive.MethodsThe mRNA expression profiles and gene variation and corresponding clinical data of glioma patients and NON-TUMOR control were downloaded from public databases. Risk score based on a FRGs signature was constructed in REMBRANDT cohort and validated in other datasets including CGGA-693, CGGA-325, and TCGA.ResultsOur results demonstrated that the majority of FRGs was differentially expressed among GBM, LGG, and NON-TUMOR groups (96.6%). Furthermore, the glioma patients with low-risk score exhibited a more satisfactory clinical outcome. The better prognosis was also validated in the glioma patients with low-risk score no matter to which grade they were affiliated. Functional analysis revealed that the high-risk score group was positively correlated with the enrichment scores for immune checkpoint blockade-related positive signatures, indicating the critical role of glioma immunotherapy via risk score.ConclusionA novel FRGs-related risk score can predict prognosis and immunotherapy in glioma patients.
| S-EPMC8265949 | biostudies-literature
Project description:Ubiquitination is crucial for the growth of cancer. However, the role of ubiquitination-related genes (URGs) in stomach adenocarcinoma (STAD) remains unclear. Differentially expressed URGs (DE-URGs) were examined in the whole TCGA-STAD dataset, and the prognosis-related genes were discovered from the The Cancer Genome Atlas (TCGA) training set. Prognostic genes were discovered using selection operator regression analysis and absolute least shrinkage (LASSO). A multivariate Cox analysis was further employed, and a polygene-based risk assessment system was established. Signatures were verified using the Gene Expression Omnibus (GEO) database record GSE84433 and the TCGA test set. Using the MEXPRESS dataset, a detailed analysis of gene expression and methylation was carried out. Using the DAVID database, DE-URG function and pathway enrichment was examined. The identified 163 DE-URGs were significantly associated with pathways related to protein ubiquitination, cell cycle, and cancer. A prognostic signature based on 13 DE-URGs was constructed, classifying patients into two risk groups. Compared to low-risk patients, people at high risk had considerably shorter survival times. Cox regression analyses considered prognostic parameters independent of age and risk score and were used to generate nomograms. Calibration curves show good agreement between nomogram predictions and observations. Furthermore, the results of the MEXPRESS analysis indicated that 13 prognostic DE-URGs had an intricate methylation profile. The enhanced Random Forest-based model showed greater efficacy in predicting prognosis, mutation, and immune infiltration. The in vitro validation, including CCK8, EdU, Transwell, and co-culture Transwell, proved that RNF144A was a potent oncogene in STAD and could facilitate the migration of M2 macrophages. In this research, we have created a genetic model based on URGs that can appropriately gauge a patient's prognosis and immunotherapy response, providing clinicians with a reliable tool for prognostic assessment and supporting clinical treatment decisions.
| S-EPMC10838090 | biostudies-literature
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.
| S-EPMC8595480 | biostudies-literature
Project description:Ferroptosis is a newly discovered, iron-dependent, nonapoptotic form of programmed cell death that plays an important role in the development of lung adenocarcinoma (LUAD). In this study, ferroptosis-related genes (FRGs) were identified from the FerrDb dataset, and the mRNA expression profiles and corresponding clinical data of LUAD patients were downloaded from the University of California, Santa Cruz (UCSC) databases. Data from LUAD patients from the Gene Expression Omnibus (GEO) dataset were used as the verification set. Cox and Lasso regression analyses were used to screen the FRGs with prognostic value, and six prognostic-related FRGs were selected to construct prognostic risk score signatures. Immunohistochemistry was utilized to manifest the differential expression of six FRGs in tumor and normal tissues at the protein level. Functional enrichment analysis indicated that FRGs were mainly enriched in ferroptosis-related pathways. Patients were divided into high- and low-risk groups based on the median risk score. The Kaplan-Meier survival curves confirmed that patients with a high score had significantly worse overall survival. Receiver operating characteristic (ROC) curves proved that the prognostic signature has good sensitivity and specificity for predicting the prognosis of LUAD patients. Nomogram analysis showed that the prognostic signature has potential independent prognostic value. Moreover, the prognostic signature has been shown to be significantly associated with some clinical features (T stage, N stage, tumor stage, and survival status) as well as many immune-activity-related genes and immune-checkpoint-related genes. In conclusion, we constructed a prognostic signature consisting of six FGRs, which can provide a reference for predicting the prognosis of LUAD patients.
| S-EPMC8564102 | biostudies-literature
Project description:Background: Gastric cancer (GC) is a multifactorial progressive disease with high mortality and heterogeneous prognosis. Effective prognostic biomarkers for GC were critically needed. Hippo signaling pathway is one of the critical mechanisms regulating the occurrence and development of GC, and has potential clinical application value for the prognosis and treatment of GC patients. However, there is no effective signature based on Hippo signaling pathway-related genes (HSPRGs) to predict the prognosis and treatment response of GC patients. Our study aimed to build a HSPRGs signature and explore its performance in improving prognostic assessment and drug therapeutic response in GC. Methods: Based on gene expression profiles obtained from The Cancer Genome Atlas (TCGA) database, we identified differentially expressed HSPRGs and conducted univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a multigene risk signature. Subsequently, the Kaplan-Meier curve and receiver operating characteristic (ROC) were performed to evaluate the predictive value of the risk signature in both training and validation cohort. Furthermore, we carried out univariate and multivariate Cox regression analysis to investigate the independent prognostic factors and establish a predictive nomogram. The enriched signaling pathways in risk signature were analyzed by gene set enrichment analysis (GSEA). Tumor immune dysfunction and exclusion (TIDE) and drug sensitivity analysis were performed to depict therapeutic response in GC. Results: In total, 38 differentially expressed HSPRGs were identified, and final four genes (DLG3, TGFB3, TGFBR1, FZD6) were incorporated to build the signature. The ROC curve with average 1-, 3-, and 5-year areas under the curve (AUC) equal to .609, .634, and .639. Clinical ROC curve revealed that risk signature was superior to other clinicopathological factors in predicting prognosis. Calibration curves and C-index (.655) of nomogram showed excellent consistency. Besides, in the immunotherapy analysis, exclusion (p < 2.22 × 10-16) and microsatellite instability (p = .0058) performed significantly differences. Finally, our results suggested that patients in the high-risk group were more sensitive to specific chemotherapeutic agents. Conclusion: Results support the hypothesis that Hippo-related signature is a novel prognostic biomarker and predictor, which could help optimize GC prognostic stratification and inform clinical medication decisions.
| S-EPMC9873967 | biostudies-literature
Project description:Background: Cuproptosis is a newly discovered non-apoptotic form of cell death that may be related to the development of tumors. Nonetheless, the potential role of cuproptosis-related lncRNAs in tumor immunity formation and patient-tailored treatment optimization of lung adenocarcinoma (LUAD) is still unclear. Methods: RNA sequencing and survival data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database for model training. The patients with LUAD in GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081 were used for validation. The proofed cuproptosis-related genes were extracted from the previous studies. The Pearson correlation was applied to select cuproptosis-related lncRNAs. We chose differentially expressed cuproptosis-related lncRNAs in the tumor and normal tissues and allowed them to go to a Cox regression and a LASSO regression for a lncRNA signature that predicts the LUAD prognosis. Kaplan-Meier estimator, Cox model, ROC, tAUC, PCA, nomogram predictor, decision curve analysis, and real-time PCR were further deployed to confirm the model's accuracy. We examined this model's link to other regulated cell death forms. Applying TMB, immune-related signatures, and TIDE demonstrated the immunotherapeutic capabilities of signatures. We evaluated the relationship of our signature to anticancer drug sensitivity. GSEA, immune infiltration analysis, and function experiments further investigated the functional mechanisms of the signature and the role of immune cells in the prognostic power of the signature. Results: An eight-lncRNA signature (TSPOAP1-AS1, AC107464.3, AC006449.7, LINC00324, COLCA1, HAGLR, MIR4435-2HG, and NKILA) was built and demonstrated owning prognostic power by applied to the validation cohort. Each signature gene was confirmed differentially expressed in the real world by real-time PCR. The eight-lncRNA signature correlated with 2321/3681 (63.05%) apoptosis-related genes, 11/20 (55.00%) necroptosis-related genes, 34/50 (68.00%) pyroptosis-related genes, and 222/380 (58.42%) ferroptosis-related genes. Immunotherapy analysis suggested that our signature may have utility in predicting immunotherapy efficacy in patients with LUAD. Mast cells were identified as key players that support the predicting capacity of the eight-lncRNA signature through the immune infiltrating analysis. Conclusion: In this study, an eight-lncRNA signature linked to cuproptosis was identified, which may improve LUAD management strategies. This signature may possess the ability to predict the effect of LUAD immunotherapy. In addition, infiltrating mast cells may affect the signature's prognostic power.
| S-EPMC10475834 | biostudies-literature