Project description:BackgroundOsteosarcoma is a malignant tumor that usually occurs in adolescents aged 10-20 years and is associated with poor prognosis. Ferroptosis is an iron-dependent cell death mechanism that plays a vital role in cancer.MethodsOsteosarcoma transcriptome data were downloaded from the public database TARGET and from previous studies. A prognostic risk score signature was constructed using bioinformatics analysis, and its efficacy was determined by analyzing typical clinical features. The prognostic signature was then validated with external data. Differences in immune cell infiltration between high- and low-risk groups were analyzed. The potential of the prognostic risk signature as a predictor of immunotherapy response was evaluated using the GSE35640 (melanoma) dataset. Five key genes expression were measured by real-time PCR and western blot in human normal osteoblasts and osteosarcoma cells. Moreover, malignant biological behaviors of osteosarcoma cells were tested by modulating gene expression level.ResultsWe obtained 268 ferroptosis-related genes from the online database FerrDb and published articles. Transcriptome data and clinical information of 88 samples in the TARGET database were used to classify genes into two categories using clustering analysis, and significant differences in survival status were identified. Differential ferroptosis-related genes were screened, and functional enrichment showed that they were associated with HIF-1, T cells, IL17, and other inflammatory signaling pathways. Prognostic factors were identified by univariate Cox regression and LASSO analysis, and a 5-factor prognostic risk score signature was constructed, which was also applicable for external data validation. Experimental validation indicated that the mRNA and protein expression level of MAP3K5, LURAP1L, HMOX1 and BNIP3 decreased significantly, though meanwhile MUC1 increased in MG-63 and SAOS-2 cells compared with hFOB1.19 cells. Cell proliferation and migration ability of SAOS-2 were affected based on alterations of signature genes.ConclusionsSignificant differences in immune cell infiltration between high- and low-risk groups indicated that the five ferroptosis-related prognostic signature was constructed and could be used to predict the response to immunotherapy in osteosarcoma.

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:BackgroundThe prevalence of patients newly diagnosed with early-stage lung adenocarcinoma (LUAD) is growing alongside significant advances in screening approaches. This study aimed to construct ferroptosis-related gene score (FRGscore) for predicting recurrence, explore immune-molecular characteristics, and determine the benefit of immunotherapy in distinct ferroptosis-based patterns and FRGscore-defined subgroups.MethodsA total of 1,085 early-stage LUAD patients from four independent cohorts were included. Consensus clustering analysis was performed using 217 co-expressed FRGs to explore different ferroptosis-mediated patterns. An FRG scoring system was established to predict relapse, quantify ferroptosis-mediated patterns, and evaluate the response to immunotherapy in individual patients based on Lasso-penalized and stepwise Cox regression analyses. Immune landscape involving multiple parameters was further evaluated, stratified by cluster subtypes and FRGscore subgroups.ResultsTwo ferroptosis-mediated patterns were identified and verified, which were characterized by significantly distinct prognosis and immune profiles. Analyses of immune characteristics showed that identified ferroptosis patterns were characterized as immune-inflamed phenotype and immune-exhausted phenotype. The FRG scoring model based on 11 FRG-derived signatures panel classified patients into the FRGscore-high and FRGscore-low subgroups. Significantly longer recurrence-free survival (RFS) and overall survival (OS) were observed in the FRGscore-low subgroup. FRGscore-low patients were characterized by higher tumor mutational burden (TMB), immunoscore, immunophenoscore, and PD-L1 expression level and were associated with lower Tumor Immune Dysfunction and Exclusion (TIDE) score, whereas the opposite was observed in FRGscore-high patients. Immune-active pathways were remarkably enriched in the FRGscore-low subgroup. This scoring model remained highly predictive of prognosis across different clinical, molecular, and immune subgroups. Further analysis indicated that FRGscore-low patients exhibited higher response to anti-PD-1/PD-L1 immunotherapy and better clinical benefits based on two independent immunotherapy cohorts.ConclusionThe proposed FRGscore could highly distinguish the recurrence patterns and molecular and immune characteristics and could predict immunotherapy prognosis, potentially representing a powerful prognostic tool for further optimization of individuated treatment and management strategies in early-stage LUAD.

Project description:BackgroundFerroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. This study aims to investigate the potential correlation between ferroptosis and the prognosis of lung adenocarcinoma (LUAD).MethodsRNA-seq data were collected from the LUAD dataset of The Cancer Genome Atlas (TCGA) database. Based on ferroptosis-related genes, differentially expressed genes (DEGs) between LUAD and paracancerous specimens were identified. The univariate Cox regression analysis was performed to screen key genes associated with the prognosis of LUAD. LUAD patients were divided into the training set and validation set. Then, we screened out key genes and built a prognostic prediction model involving 5 genes using the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation and the multivariate Cox regression analysis. After dividing LUAD patients based on the median level of risk score as cut-off value, the generated prognostic prediction model was validated in the validation set. Moreover, we analyzed the somatic mutations, and estimated the scores of immune infiltration in the high-risk and low-risk groups. Functional enrichment analysis of DEGs was performed as well.ResultsHigh-risk scores indicated the worse prognosis of LUAD. The maximum area under curve (AUC) of the training set and the validation set in this study was 0.7 and 0.69, respectively. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of LUAD cases with the survival time of 1, 3 and 5 years was 0.698, 0.71 and 0.73, respectively. In addition, the mutation frequency of LUAD patients in the high-risk group was significantly higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results.ConclusionsThis study constructs a novel LUAD prognosis prediction model involving 5 ferroptosis-related genes, which can be used as a promising tool for decision-making of clinical therapeutic strategies of LUAD.

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.

Project description:BackgroundLung cancer has the highest prevalence and mortality of all cancers, and lung adenocarcinoma (LUAD) occupies the largest proportion of lung cancers. Herein, this study is aimed at constructing a ferroptosis-related prognostic signature for LUAD and conducting functional analysis based on the signature, highlighting the importance of ferroptosis in LUAD.MethodsWe employed RNA-sequencing (RNA-seq) and clinical data from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were conducted to build the ferroptosis-related genes (FRGs) prognostic signature. The efficacy of this FRG signature was further analyzed with Kaplan-Meier (KM) plot, multivariate Cox regression, and the receiver operating characteristic (ROC) curves. Enrichment analysis was used to evaluate key pathways. The expression of immunomodulators, immune infiltration status, and drug sensitivity correlation were explored to predict the response to various therapies. The expression of FRGs was validated in LUAD samples with western blot (WB) and immunohistochemistry (IHC) staining. Cell viability assay and lipid peroxidation detection were measured after small interfering RNA (siRNA) knockdown of two FRGs in lung cancer cell lines.ResultsA seven-gene signature was constructed and used to divide LUAD patients into high- and low-risk groups. High-risk patients were notably related to shorter overall survival (OS), and multivariate Cox regression demonstrated that our signature was an independent predictor of OS. ROC curve analysis presented a maximum area under the curve (AUC) value of 0.740 for the experimental cohort and 0.705 for the validation cohort. The low-risk group showed higher levels of plasma cell infiltration and higher expression of programmed cell death protein 1 (PDCD1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4). Ferroptosis inducers such as talazoparib or cisplatin had lower IC50 values in the high-risk group, while navitoclax (BCL-2 gene family inhibition and apoptosis inducer) had higher IC50 values in the high-risk group. Additionally, peroxiredoxin-6 (PRDX6) and acyl-CoA synthetase long chain family member 3 (ACSL3) were upregulated in LUAD tissues. Lipid peroxide assay showed that silencing PRDX6 or ACSL3 promoted lipid peroxidation and ferroptosis in lung cancer cells.ConclusionsOur novel ferroptosis-related signature shows potential clinical and functional importance in LUAD patients, and further research on ferroptosis as a therapeutic target in LUAD is warranted.

Project description:Background: Necroptosis is closely related to the tumorigenesis and development of cancer. An increasing number of studies have demonstrated that targeting necroptosis could be a novel treatment strategy for cancer. However, the predictive potential of necroptosis-related long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) still needs to be clarified. This study aimed to construct a prognostic signature based on necroptosis-related lncRNAs to predict the prognosis of LUAD. Methods: We downloaded RNA sequencing data from The Cancer Genome Atlas database. Co-expression network analysis, univariate Cox regression, and least absolute shrinkage and selection operator were adopted to identify necroptosis-related prognostic lncRNAs. We constructed the predictive signature by multivariate Cox regression. Kaplan-Meier analysis, time-dependent receiver operating characteristics, nomogram, and calibration curves were used to validate and evaluate the signature. Subsequently, we used gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between the predictive signature and tumor immune microenvironment of risk groups. Finally, the correlation between the predictive signature and immune checkpoint expression of LUAD patients was also analyzed. Results: We constructed a signature composed of 7 necroptosis-related lncRNAs (AC026355.2, AC099850.3, AF131215.5, UST-AS2, ARHGAP26-AS1, FAM83A-AS1, and AC010999.2). The signature could serve as an independent predictor for LUAD patients. Compared with clinicopathological variables, the necroptosis-related lncRNA signature has a higher diagnostic efficiency, with the area under the receiver operating characteristic curve being 0.723. Meanwhile, when patients were stratified according to different clinicopathological variables, the overall survival of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the low-risk group. ssGSEA further confirmed that the predictive signature was significantly related to the immune status of LUAD patients. The immune checkpoint analysis displayed that low-risk patients had a higher immune checkpoint expression, such as CTLA-4, HAVCR2, PD-1, and TIGIT. This suggested that immunological function is more active in the low-risk group LUAD patients who might benefit from checkpoint blockade immunotherapies. Conclusion: The predictive signature can independently predict the prognosis of LUAD, helps elucidate the mechanism of necroptosis-related lncRNAs in LUAD, and provides immunotherapy guidance for patients with LUAD.

Dataset Information

Clinical and functional significance of a novel ferroptosis-related prognosis signature in lung adenocarcinoma.

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Clinical and functional significance of a novel ferroptosis-related prognosis signature in lung adenocarcinoma.

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