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Identification and verification of a prognostic ferroptosis-related lncRNAs signature for patients with lung adenocarcinoma.

ABSTRACT: Lung cancer has been identified as one of the deadliest malignant tumors worldwide. Mounting evidence suggests that ferroptosis is a well-known non-apoptotic cell death process that participates in pathological mechanisms and is a new cancer treatment strategy. Aberrantly expressed long non-coding RNAs (lncRNAs) that drive lung cancer progression have attracted increasing attention. Herein, we explored the prognostic significance of ferroptosis-related lncRNAs in lung cancer patients. LUAD gene expression patterns and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) database. Based on LASSO-Cox regression, A 14 ferroptosis-related differentially expressed lncRNAs (FRDELs) signature was constructed. Subsequently, a nomogram model for predicting the prognosis of LUAD patients was constructed based on clinicopathological data and the 14 - FRDELs signature. The signature was shown to be correlated with tumor mutational burden (TMB) and immune cell infiltration within the tumor microenvironment. Furthermore, Gene Set Enrichment Analysis (GSEA) confirmed that the signature was correlated with LUAD-related biological functions such as the P53 signaling pathway, DNA replication, and cell cycle. The roles and mechanisms of PACERR in the signature were explored by si-lncRNA-mediated knockdown and transfection-mediated overexpression via in vitro experiments in A549 and H1299 cells. PACERR was significantly upregulated in A549 and H1299 cells, and higher expression promoted LUAD cell proliferation, migration, and invasion via in vitro experiments, while knockdown of PACERR presented the opposite effects. In conclusion, our study provided information regarding ferroptosis-related lncRNA expression and established a prognostic nomogram based on 14 FRDELs to predict overall survival in LUAD accurately. Additionally, our results in vitro revealed that PACERR played an oncogenic role in LUAD proliferation and metastasis, which provides mechanistic insights into the roles of ferroptosis-related lncRNA in LUAD progression and that it may be a potential biomarker for LUAD treatment.

SUBMITTER: Qi K

PROVIDER: S-EPMC9274545 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Lung adenocarcinoma (LUAD) is an aggressive disease of heterogeneous characteristics with poor prognosis and high mortality. Pyroptosis, a newly uncovered type of programmed cell death with an inflammatory nature, has been determined to hold substantial importance in the progression of tumors. Despite this, the knowledge about pyroptosis-related genes (PRGs) in LUAD is limited. This study aimed to develop and validate a prognostic signature for LUAD based on PRGs. Methods: In this research, gene expression information from The Cancer Genome Atlas (TCGA) served as the training cohort and data from Gene Expression Omnibus (GEO) was utilized as the validation cohort. PRGs list was taken from the Molecular Signatures Database (MSigDB) and previous studies. Univariate Cox regression and Lasso analysis were then conducted to identify prognostic PRGs and develop a LUAD prognostic signature. The Kaplan-Meier method, univariate and multivariate Cox regression models were employed to assess the independent prognostic value and forecasting accuracy of the pyroptosis-related prognostic signature. The correlation between prognostic signature and immune infiltrating was analyzed to examine the role in tumor diagnosis and immunotherapy. Further, RNA-seq as well as quantitative real-time polymerase chain reaction (qRT-PCR) analysis in separate data sets was applied in order to validate the potential biomarkers for LUAD. Results: A novel prognostic signature based on 8 PRGs (BAK1, CHMP2A, CYCS, IL1A, CASP9, NLRC4, NLRP1, and NOD1) was established to predict the survival of LUAD. The prognostic signature proved to be an independent prognostic factor of LUAD with satisfactory sensitivity and specificity in the training and validation sets. High-risk scores subgroups in the prognostic signature were significantly associated with advanced tumor stage, poor prognosis, less immune cell infiltration, and immune function deficiency. RNA sequencing and qRT-PCR analysis confirmed that the expression of CHMP2A and NLRC4 could be used as biomarkers for LUAD. Conclusion: We have successfully developed a prognostic signature consisting of eight PRGs that providing a novel perspective on predicting prognosis, assessing infiltration levels of tumor immune cells, and determining the outcomes of immunotherapy for LUAD.

Project description:ObjectiveTo establish a prognostic signature for lung adenocarcinoma (LUAD) based on cuproptosis-related long non-coding RNAs (lncRNAs), and to study the immune-related functions of LUAD.MethodsFirst, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate COX analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate COX analysis were performed to analyze the cuproptosis-related lncRNAs, and a prognostic signature was established. Second, univariate COX analysis and multivariate COX analysis were performed for independent prognostic analyses. Receiver operating characteristic (ROC) curves, C index, survival curve, nomogram, and principal component analysis (PCA) were performed to evaluate the results of the independent prognostic analyses. Finally, gene enrichment analyses and immune-related function analyses were also carried out.Results(1) A total of 1,297 cuproptosis-related lncRNAs were screened. (2) A LUAD prognostic signature containing 13 cuproptosis-related lncRNAs was constructed (NIFK-AS1, AC026355.2, SEPSECS-AS1, AL360270.1, AC010999.2, ABCA9-AS1, AC032011.1, AL162632.3, LINC02518, LINC0059, AL031600.2, AP000346.1, AC012409.4). (3) The area under the multi-indicator ROC curves at 1, 3, and 5 years were AUC1 = 0.742, AUC2 = 0.708, and AUC3 = 0.762, respectively. The risk score of the prognostic signature could be used as an independent prognostic factor that was independent of other clinical indicators. (4) The results of gene enrichment analyses showed that 13 biomarkers were primarily related to amoebiasis, the wnt signaling pathway, hematopoietic cell lineage. The ssGSEA volcano map showed significant differences between high- and low-risk groups in immune-related functions, such as human leukocyte antigen (HLA), Type_II_IFN_Reponse, MHC_class_I, and Parainflammation (P < 0.001).ConclusionsThirteen cuproptosis-related lncRNAs may be clinical molecular biomarkers for the prognosis of LUAD.

Project description:BackgroundIdentifying populations that benefit from immune checkpoint blockade (ICB) therapy remains a major challenge in the treatment of lung adenocarcinoma (LUAD). Existing programmed cell death (PCD) related prognostic models only consider a single mechanism, such as ferroptosis, necroptosis, and pyroptosis, and do not reflect the interaction of multiple mechanisms. This study aims to explore lncRNAs associated with multiple modes of PCD and reveal a risk signature to assess prognosis and treatment outcomes in LUAD patients.MethodsBased on expression data in The Cancer Genome Atlas (TCGA) database, ferroptosis, necroptosis, and pyroptosis-related lncRNAs (FNPRlncRNAs) were obtained by taking the intersection of ferroptosis-related lncRNAs (FRlncRNAs), necroptosis-related lncRNAs (NRlncRNAs), and pyroptosis-related lncRNAs (PRlncRNAs) differentially expressed in LUAD and normal tissues. Patients with complete survival information and expression data from TCGA database were randomly assigned to training and testing sets (1:1). Univariate, LASSO, and multivariate Cox regression analyses were performed on the training set, and a risk signature was established. Kaplan-Meier survival curves were used to verify the prognostic ability of risk signature, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy. We then analyzed molecular and immune profile differences between high and low-risk subgroups. T-cell dysfunction and Exclusion (TIDE) scores were used to assess the response to immunotherapy in each risk subgroup. Finally, three LUAD clusters (C1, C2, and C3) were identified according to the risk signature.ResultsPatients in the low-risk subgroup had higher overall survival (OS) than that in the high-risk subgroup in the K-M survival curve. The area under ROC curves (AUC) of 1-, 3-, and 5-year ROC were 0.742, 0.762, and 0.749 in the training set, and 0.672, 0.642, and 0.563 in the testing set, respectively. Compared with the high-risk subgroup, patients in the low-risk subgroup have beneficial tumor immune microenvironment and molecular characteristics, but are less likely to benefit from immunotherapy. Finally, the three LUAD clusters (C1, C2, C3) identified by risk signature had different responses to drug treatment.ConclusionsThe prognosis risk signature constructed using FNPRlncRNAs is helpful to predict the prognosis of LUAD and may contribute to its individualized treatment.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Ferroptosis is a newly recognized process of cell death, which is different from other forms of cell death in morphology, biochemistry, and genetics, and has played a vital role in cancer biology. This study aimed to identify a ferroptosis-related gene signature associated with LUAD prognosis.MethodsDataset TCGA-LUAD which came from the TCGA portal was taken as the training cohort. GSE72094 and GSE68465 from the GEO database were treated as validation cohorts. Two hundred fifty-nine ferroptosis-related genes were retrieved from the FerrDb database. In the training cohort, Kaplan-Meier and univariate Cox analyses were conducted for preliminary screening of ferroptosis-related genes with potential prognostic capacity. These genes then entered into the LASSO Cox regression model, constructing a gene signature. The latter was then evaluated in the training and validation cohorts via Kaplan-Meier, Cox, and ROC analyses. In addition, the correlations between risk score and autophagy were examined by Pearson correlation coefficient. The analyses of GSEA and immune infiltrating were performed for better studying the function annotation of the gene signature and the character of each kind of immune cells played in the tumor microenvironment.ResultsA 15-gene signature was found from the training cohort and validated by Kaplan-Meier and Cox regression analyses, revealing its independent prognosis value in LUAD. Moreover, the ROC analysis was conducted, confirming a strong predictive ability that this signature owned for LUAD prognosis. One hundred fifty-one of 222 (68.01%) autophagy-related genes were discovered significantly correlated with risk scores. Analyses of GSEA and immune infiltration exhibited in detail the specific pathways that associate with the 15-gene signature and identified the crucial roles of resting mast cells and resting dendritic cells owned in the prognosis of the 15-gene signature.ConclusionIn this present study, a novel ferroptosis-related 15-gene signature (RELA, ACSL3, YWHAE, EIF2S1, CISD1, DDIT4, RRM2, PANX1, TLR4, ARNTL, LPIN1, HERPUD1, NCOA4, PEBP1, and GLS2) was built. It could accurately predict the prognosis of LUAD and was related to resting mast cells and resting dendritic cells, which provide potential for the personalized outcome prediction and the development of new therapies in LUAD population.

Project description:Background: Long non-coding RNAs (lncRNAs) are drawing increasing attention as promising predictors of prognosis for lung adenocarcinoma (LUAD) patients. Necroptosis, a novel regulated mechanism of necrotic cell death, plays an important role in the biological process of cancer. The aim of this study was to identify the necroptosis-related lncRNAs (NRLRs) in a LUAD cohort and establish a necroptosis-related lncRNA signature (NRLSig) to stratify LUAD patients. Methods: NRLRs were identified in LUAD patients from The Cancer Genome Atlas (TCGA) database using Pearson correlation analysis between necroptosis-related genes and lncRNAs. Then the NRLSig was identified using univariate Cox regression analysis and LASSO regression analysis. Assessments of the signature were performed based on survival analysis, receiver operating characteristic (ROC) curve analysis and clustering analysis. Next, a nomogram containing the NRLSig and clinical information was developed through univariate and multivariate Cox regression analysis. Further, functional enrichment analysis of the selected lncRNAs in NRLSig and the association between NRLSig and the immune infiltration were also evaluated. Results: A 4-lncRNA signature, incorporating LINC00941, AP001453.2, AC026368.1, and AC236972.3, was identified to predict overall survival (OS) and stratify LUAD patients into different groups. Survival analysis, ROC curve analysis and clustering analysis showed good performance in the prognostic prediction of the lncRNA signature. Then, a nomogram containing the NRLSig was developed and showed satisfactory predictive accuracy, calibration and clinical usefulness. The co-expressed genes of selected NRLRs were enriched in several biological functions and signaling pathways. Finally, differences in the abundance of immune cells were investigated among the high-risk group and low-risk group divided by the NRLSig. Conclusion: The proposed NRLSig may provide promising therapeutic targets or prognostic predictors for LUAD patients.

Project description:BackgroundFerroptosis is a recently recognized non-apoptotic cell death that is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in esophageal adenocarcinoma (EAC) remains unclear. This study aims to explore the ferroptosis-related genes (FRG) expression profiles and their prognostic values in EAC.MethodsThe FRG data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regressions were used to identify the prognostic FRG, and the predictive ROC model was established using the independent risk factors. GO and 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 TIMER database. Finally, SDG were verified in clinical EAC specimens and normal esophageal mucosal tissues.ResultsTwenty-eight significantly different FRG were screened from 78 EAC and 9 normal tissues. Enrichment analyses showed these SDG were mainly related to the iron-related pathways and metabolisms of ferroptosis. Gene network demonstrated the TP53, G6PD, NFE2L2 and PTGS2 were the hub genes in the biology of ferroptosis. Cox regression analyses demonstrated four FRG (CARS1, GCLM, GLS2 and EMC2) had prognostic values for overall survival (OS) (all P < 0.05). ROC curve showed better predictive ability using the risk score (AUC = 0.744). Immune cell enrichment analysis demonstrated that the types of immune cells and their expression levels in the high-risk group were significant different with those in the low-risk group (all P < 0.05). The experimental results confirmed the ALOX5, NOX1 were upregulated and the MT1G was downregulated in the EAC tissues compared with the normal esophageal mucosal tissues (all P < 0.05).ConclusionsWe identified differently expressed ferroptosis-related genes that may involve in EAC. These genes have significant values in predicting the patients' OS and targeting ferroptosis may be an alternative for therapy. Further studies are necessary to verify these results of our study.

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Identification and verification of a prognostic ferroptosis-related lncRNAs signature for patients with lung adenocarcinoma.

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