Development and Validation of a Ferroptosis-Related Gene Signature and Nomogram for Predicting the Prognosis of Esophageal Squamous Cell Carcinoma
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ABSTRACT:
SUBMITTER: Ye J
PROVIDER: S-EPMC8576261 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Esophageal squamous cell carcinoma (ESCC) is the most prevalent form of esophageal cancer in China and is closely associated with malignant biological characteristics and poor survival. Ferroptosis is a newly discovered iron-dependent mode of cell death that plays an important role in the biological behavior of ESCC cells. The clinical significance of ferroptosis-related long noncoding RNAs (FRLs) in ESCC remains unknown and warrants further research. The current study obtained RNA sequencing profiles and corresponding clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and FRLs were obtained through coexpression analysis. Consensus clustering was employed to divide the subjects into clusters, and immune-associated pathways were identified by functional analysis. The current study observed significant differences in the enrichment scores of immune cells among different clusters. Patients from TCGA-ESCC database were designated as the training cohort. A ten-FRL prediction signature was established using the least absolute shrinkage and selection operator Cox regression model and validated using the GEO cohort and our own independent validation database. Real-time quantitative polymerase chain reaction was used to verify the expression of the ten FRLs, and the ssGSEA analysis was employed to evaluate their function. In addition, the IMvigor database was used to assess the predictive value of the signature in terms of immunotherapeutic responses. Multivariate Cox and stratification analyses revealed that the ten-FRL signature was an independent predictor of the overall survival (OS). Patients with ESCC in the high-risk group displayed worse survival, a characteristic tumor immune microenvironment, and low immunotherapeutic benefits compared to those in the low-risk group. Collectively, the risk model established in this study could serve as a promising predictor of prognosis and immunotherapeutic response in patients with ESCC.
| S-EPMC9315452 | biostudies-literature
Project description:ObjectiveThis study aimed to develop a novel ferroptosis-related gene-based prognostic signature for esophageal carcinoma (ESCA).MethodsThe TCGA-ESCA gene expression profiles and corresponding clinical data were downloaded from the TCGA database. Ferroptosis-related genes were identified from the literature and public databases, which were intersected with the differentially expressed genes between ESCA and normal samples. After univariate Cox regression and random forest analyses, several ferroptosis-related feature genes were identified and used to construct a prognostic signature. Then, the prognostic value of the complex value and the correlation of the complex value with immune cell infiltration were analyzed. Moreover, function analysis, mutation analysis, and molecular docking on the ferroptosis-related feature genes were performed.ResultsBased on the TCGA dataset and ferroptosis pathway genes, 1929 ferroptosis-related genes were preliminarily selected. Following univariate Cox regression analysis and survival analysis, 14 genes were obtained. Then, random forest analysis identified 10 ferroptosis key genes. These 10 genes were used to construct a prognostic complex value. It was found that low complex value indicated better prognosis compared with high complex value. In different ESCA datasets, there were similar differences in the proportion of immune cell distribution between the high and low complex value groups. Furthermore, TNKS1BP1, AC019100.7, KRI1, BCAP31, and RP11-408E5.5 were significantly correlated with ESCA tumor location, lymph node metastasis, and age of patients. KRI1 had the highest mutation frequency. BCAP31 had the strongest binding ability with small molecules DB12830, DB05812, and DB07307.ConclusionWe constructed a novel ferroptosis-related gene signature, which has the potential to predict patient survival and tumor-infiltrating immune cells of ESCA.
| S-EPMC9270146 | biostudies-literature
Project description:BackgroundAutophagy, a highly conserved self-digesting process, has been deeply involved in the development and progression of oral squamous cell carcinoma (OSCC). However, the prognostic value of autophagy-related genes (ARGs) for OSCC still remains unclear. Our study set out to develop a multigene expression signature based on ARGs for individualized prognosis assessment in OSCC patients.MethodsBased on The Cancer Genome Atlas (TCGA) database, we identified prognosis-related ARGs through univariate COX regression analysis. Then we performed the least absolute shrinkage and selection operator (LASSO) regression analysis to identify an optimal autophagy-related multigene signature with the subsequent validation in testing set, GSE41613 and GSE42743 datasets.ResultsWe identified 36 prognosis-related ARGs for OSCC. Subsequently, the multigene signature based on 13 prognostic ARGs was constructed and successfully divided OSCC patients into low and high-risk groups with significantly different overall survival in TCGA training set (p < 0.0001). The autophagy signature remained as an independent prognostic factor for OSCC in univariate and multivariate Cox regression analyses. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves for 1, 3, and 5-year survival were 0.758, 0.810, 0.798, respectively. Then the gene signature was validated in TCGA testing set, GSE41613 and GSE42743 datasets. Moreover, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single-sample gene set enrichment analysis (ssGSEA) revealed the underlying biological characteristics and signaling pathways associated with this signature in OSCC. Finally, we constructed a nomogram by combining the gene signature with multiple clinical parameters (age, gender, TNM-stage, tobacco, and alcohol history). The concordance index (C-index) and calibration plots demonstrated favorable predictive performance of our nomogram.ConclusionIn summary, we identified and verified a 13-ARGs prognostic signature and nomogram, which provide individualized prognosis evaluation and show insight for potential therapeutic targets for OSCC.
| S-EPMC7596585 | biostudies-literature
Project description:Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide and has a high mortality. Ferroptosis, an iron-dependent form of programmed cell death, plays a crucial role in tumor suppression and chemotherapy resistance in cancer. However, the prognostic and clinical values of ferroptosis-related genes (FRGs) in HNSCC remain to be further explored. In the current study, we constructed a ferroptosis-related prognostic model based on the Cancer Genome Atlas database and then explored its prognostic and clinical values in HNSCC via a series of bioinformatics analyses. As a result, we built a four-gene prognostic signature, including FTH1, BNIP3, TRIB3, and SLC2A3. Survival analysis showed that the high-risk group presented significantly poorer overall survival than the low-risk group. Moreover, the ferroptosis-related signature was found to be an independent prognostic predictor with high accuracy in survival prediction for HNSCC. According to immunity analyses, we found that the low-risk group had higher anti-tumor immune infiltration cells and higher expression of immune checkpoint molecules and meanwhile corelated more closely with some anti-tumor immune functions. Meanwhile, all the above results were validated in the independent HSNCC cohort GSE65858. Besides, the signature was found to be remarkably correlated with sensitivity of common chemotherapy drugs for HNSCC patients and the expression levels of signature genes were also significantly associated with drug sensitivity to cancer cells. Overall, we built an effective ferroptosis-related prognostic signature, which could predict the prognosis and help clinicians to perform individualized treatment strategy for HNSCC patients.
| S-EPMC8566369 | biostudies-literature
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.
| S-EPMC8290602 | biostudies-literature
Project description:Tobacco smoking is an established risk factor for squamous cell carcinoma (SCC). We obtained smoking-related SCC, including cervical SCC (CSCC), esophageal SCC (ESCC), head and neck SCC (HNSC), and lung SCC (LUSC), from The Cancer Genome Atlas (TCGA) database to investigate the association between smoking status (reformed and current smoking) and prognosis. We found that reformed smokers had a better prognosis than current smokers in CSCC (p = 0.003), HNSC (p = 0.019), and LUSC (p < 0.01) cohorts. Then, we selected LUSC cohorts as the training cohort and other SCC cohorts as the test cohorts. Function analysis revealed that homologous recombination (HR) was the most significant pathway involved in smoking-induced LUSC. Moreover, the effect of cross-talk between the smoking status and HR deficiency (HRD) on the prognosis was further evaluated, revealing that quitting smoking with high HRD scores could significantly improve patients' prognosis (p < 0.01). To improve prognosis prediction and more effectively screen suitable populations for platinum drugs and poly-ADP-ribose polymerase (PARP) inhibitors, we constructed a risk score model using smoking- and HRD-related genes in LUSC. The risk score model had high power for predicting 2-, 3-, and 5-year survival (p < 0.01, AUC = 0.67, 0.66, and 0.66). In addition, the risk scores were an independent risk factor for LUSC (HR = 2.34, 95%CI = 1.70-3.23). The practical nomogram was also built using the risk score, smoking status, and other clinical information with a good c-index (0.72, 95%CI = 0.70-0.74). Finally, we used other TCGA SCC cohorts to confirm the reliability and validity of the risk score model (p < 0.01 and AUC > 0.6 at 2, 3, and 5 years in CSCC and HNSC cohorts). In conclusion, the present study suggested that smoking cessation should be a part of smoking-related SCC treatment, and also provided a risk score model to predict prognosis and improve the effectiveness of screening the platinum/PARP population.
| S-EPMC8100348 | biostudies-literature
Project description:Esophageal squamous cell carcinoma (ESCC) accounts for the main esophageal cancer (ESCA) type, which is also associated with the greatest malignant grade and low survival rates worldwide. Ferroptosis is recently discovered as a kind of programmed cell death, which is indicated in various reports to be involved in the regulation of tumor biological behaviors. This work focused on the comprehensive evaluation of the association between ferroptosis-related gene (FRG) expression profiles and prognosis in ESCC patients based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). ALOX12, ALOX12B, ANGPTL7, DRD4, MAPK9, SLC38A1, and ZNF419 were selected to develop a novel ferroptosis-related gene signature for GEO and TCGA cohorts. The prognostic risk model exactly classified patients who had diverse survival outcomes. In addition, this study identified the ferroptosis-related signature as a factor to independently predict the risk of ESCC. Thereafter, we also constructed the prognosis nomogram by incorporating clinical factors and risk score, and the calibration plots illustrated good prognostic performance. Moreover, the association of the risk score with immune checkpoints was observed. Collectively, the proposed ferroptosis-related gene signature in our study is effective and has a potential clinical application to predict the prognosis of ESCC.
| S-EPMC8287967 | biostudies-literature
Project description:Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in China, and its prognosis remains poor. Autophagy is an evolutionarily conserved catabolic process involved in the occurrence and development of ESCC. In this study, we described the expression profile of autophagy-related genes (ARGs) in ESCC and developed a prognostic prediction model for ESCC patients based on the expression pattern of ARGs. We used four ESCC cohorts, GSE53624 (119 samples) set as the discovery cohort, The Cancer Genome Atlas (TCGA) ESCC set (95 samples) as the validation cohort, 155 ESCC cohort, and Oncomine cohort were used to screen and verify differentially expressed ARGs. We identified 34 differentially expressed genes out of 222 ARGs. In the discovery cohort, we divided ESCC patients into three groups that showed significant differences in prognosis. Then, we analyzed the prognosis of 34 differentially expressed ARGs. Three genes [poly (ADP-ribose) polymerase 1 (PARP1), integrin alpha-6 (ITGA6), and Fas-associated death domain (FADD)] were ultimately obtained through random forest feature selection and were constructed as an ARG-related prognostic model. This model was further validated in TCGA ESCC set. Cox regression analysis confirmed that the three-gene signature was an independent prognostic factor for ESCC patients. This signature effectively stratified patients in both discovery and validation cohorts by overall survival (P = 5.162E-8 and P = 0.052, respectively). We also constructed a clinical nomogram with a concordance index of 0.713 to predict the survival possibility of ESCC patients by integrating clinical characteristics and the ARG signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. In conclusion, we constructed a new ARG-related prognostic model, which shows the potential to improve the ability of individualized prognosis prediction in ESCC.
| S-EPMC8321089 | biostudies-literature
Project description:Background Ferroptosis, a novel form of regulating cell death, is related to various cancers. However, the role of ferroptosis-related genes (FRGs) on the occurrence and development of colon cancer (CC) needs to be further elucidated. Method CC transcriptomic and clinical data were downloaded from TCGA and GEO databases. The FRGs were obtained from the FerrDb database. The consensus clustering was performed to identify the best clusters. Then, the entire cohort was randomly divided into the training and testing cohorts. Univariate Cox, LASSO regression and multivariate Cox analyses were used to construct a novel risk model in training cohort. The testing and the merged cohorts were performed to validate the model. Moreover, CIBERSORT algorithm analyze TIME between high- and low- risk groups. The immunotherapy effect was evaluated by analyzing the TIDE score and IPS between high- and low- risk groups. Lastly, RT-qPCR were performed to analyze the expression of the three prognostic genes, and the 2-years OS and DFS between the high- and low- risk groups of 43 clinical CC samples to further validate the value of the risk model. Results SLC2A3, CDKN2A, and FABP4 were identified to construct a prognostic signature. Kaplan–Meier survival curves showed that OS between the high- and low-risk groups were statistically significant (pmerged<0.001, ptraining<0.001, ptesting<0.001). TIDE score and IPS were higher in the high-risk group (pTIDE<0.005, pDysfunction<0.005, pExclusion<0.001, pmAb-CTLA-4 = 3e-08, pmAb-PD-1 = 4.1e-10). The clinical samples were divided into high- and low- risk groups according to the risk score. There was a statistical difference in DFS (p=0.0108). Conclusion This study established a novel prognostic signature and provided more insight into the immunotherapy effect of CC.
| S-EPMC10243598 | biostudies-literature
Project description:The lymphovascular invasion (LVI) status facilitates the determination of the optimal therapeutic strategy for superficial esophageal squamous cell carcinoma (SESCC), but in clinical practice, LVI must be confirmed by postoperative pathology. However, studies of the risk factors for LVI in SESCC are limited. Consequently, this study aimed to identify the risk factors for LVI and use these factors to establish a prediction model. The data of 516 patients who underwent radical esophagectomy between January 2007 and September 2019 were retrospectively collected (training set, n=361, January 2007 to May 2015; validation set, n=155, June 2015 to September 2019). In the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate analyses were utilized to identify predictive factors for LVI in patients with SESCC. A nomogram was then developed using these predictors. The area under the curve (AUC), calibration curve, and decision curve were used to evaluate the efficiency, accuracy, and clinical utility of the model. LASSO regression indicated that the tumor size, depth of invasion, tumor differentiation, lymph node metastasis (LNM), sex, circumferential extension, the presence of multiple lesions, and the resection margin were correlated with LVI. However, multivariate analysis revealed that only the tumor size, depth of invasion, tumor differentiation, and LNM were independent risk factors for LVI. Incorporating these four variables, model 1 achieved an AUC of 0.817 in predicting LVI. Adding circumferential extension to model 1 did not appreciably change the AUC and integrated discrimination improvement, but led to a significant increase in the net reclassification improvement (p=0.011). A final nomogram was constructed by incorporating tumor size, depth of invasion, tumor differentiation, LNM, and circumferential extension and showed good discrimination (training set, AUC=0.833; validation set, AUC=0.819) and good calibration in the training and validation sets. Decision curve analysis demonstrated that the nomogram was clinically useful in both sets. Thus, it is possible to predict the status of LVI using this nomogram scoring system, which can aid the selection of an appropriate treatment plan.
| S-EPMC8141657 | biostudies-literature