Dataset Information

Construction and analysis of a novel ferroptosis-related gene signature predicting prognosis in lung adenocarcinoma.

ABSTRACT: 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.

SUBMITTER: Zhou J

PROVIDER: S-EPMC8564102 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundMultiple myeloma (MM) is a highly malignant hematological tumor with a poor overall survival (OS). Due to the high heterogeneity of MM, it is necessary to explore novel markers for the prognosis prediction for MM patients. Ferroptosis is a form of regulated cell death, playing a critical role in tumorigenesis and cancer progression. However, the predictive role of ferroptosis-related genes (FRGs) in MM prognosis remains unknown.MethodsThis study collected 107 FRGs previously reported and utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to construct a multi-genes risk signature model upon FRGs. The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were carried out to evaluate immune infiltration level. Drug sensitivity was assessed based on the Genomics of Drug Sensitivity in Cancer database (GDSC). Then the synergy effect was determined with Cell counting kit-8 (CCK-8) assay and SynergyFinder software.ResultsA 6-gene prognostic risk signature model was constructed, and MM patients were divided into high and low risk groups. Kaplan-Meier survival curves showed that patients in the high risk group had significantly reduced OS compared with patients in the low risk group. Besides, the risk score was an independent predictor for OS. Receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the risk signature. Combination of risk score and ISS stage had better prediction performance. Enrichment analysis revealed immune response, MYC, mTOR, proteasome and oxidative phosphorylation were enriched in high risk MM patients. We found high risk MM patients had lower immune scores and immune infiltration levels. Moreover, further analysis found that MM patients in high risk group were sensitive to bortezomib and lenalidomide. At last, the results of the in vitro experiment showed that ferroptosis inducers (RSL3 and ML162) may synergistically enhance the cytotoxicity of bortezomib and lenalidomide against MM cell line RPMI-8226.ConclusionThis study provides novel insights into roles of ferroptosis in MM prognosis prediction, immune levels and drug sensitivity, which complements and improves current grading systems.

Project description:ObjectiveLung cancer is the most common malignancy worldwide and exhibits both high morbidity and mortality. In recent years, scientists have made substantial breakthroughs in the early diagnosis and treatment of lung adenocarcinoma (LUAD), however, patient prognosis still shows vast individual differences. In this study, bioinformatics methods were used to identify and analyze ferroptosis-related genes to establish an effective signature for predicting prognosis in LUAD patients.MethodsThe gene expression profiles of LUAD patients with complete clinical and follow-up information were downloaded from two public databases, univariate Cox regression and multivariate Cox regression analysis were used to obtain ferroptosis-related genes for constructing the prognos tic risk model, AUC and calibration plot were used to evaluate the predictive accuracy of the FRGS and nomogram.ResultsA total of 74 ferroptosis-related differentially expressed genes (DEGs) were identi fied between LUAD and normal tissues from The Cancer Genome Atlas (TCGA) database. A five-gene panel for prediction of LUAD prognosis was established by multivariate regression and was verified using the GSE68465 cohort from the Gene Expression Omnibus (GEO) database. Patients were divided into two different risk groups according to the median risk score of the five genes. Based on Kaplan-Meier (KM) analysi, the OS rate of the high-risk group was markedly worse than that of the low-risk group. We also found that risk score was an independent prognostic indicator. The receiver operating characteristic ROC curve showed that the proposed model had good prediction ability. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses indicated that risk score was prominently enriched in ferroptosis processes. Moreover, at the score of immune-associated gene sets, significant differences were found between the two risk groups.ConclusionsThis study demonstrated that ferroptosis-related gene signatures can be used as a potential predictor for the prognosis of LUAD, thus providing a novel strategy for individualized treatment in LUAD patients.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.MethodsFirst, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.ResultsA nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.ConclusionsThis study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.

Dataset Information

Construction and analysis of a novel ferroptosis-related gene signature predicting prognosis in lung adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets