Identification of a prognostic ferroptosis-related lncRNA signature in the tumor microenvironment of lung adenocarcinoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Guo Y
PROVIDER: S-EPMC8313561 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Ferroptosis is a more relatively recently identified type of programmed cell death, which is associated with tumor progression. However, the mechanism underlying the effect of ferroptosis-related long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains elusive. Therefore, the current study aimed to investigate the role of ferroptosis-related lncRNAs in LUAD and to develop a prognostic model. The clinicopathological characteristics of patients and the gene sequencing data were obtained from The Cancer Genome Atlas, while the ferroptosis-associated mRNAs were downloaded from the FerrDb database. A ferroptosis-related lncRNA signature was established with Least Absolute Shrinkage and Selection Operator Cox regression analysis. Furthermore, the risk scores of ferroptosis-related lncRNAs were calculated and LUAD patients were then assigned to high- and low-risk groups based on the median risk score. The prognostic model was established by K-M plotters and nomograms. Gene set enrichment analysis (GSEA) was performed to evaluate the association between immune responses and ferroptosis-related lncRNAs. A total of 10 ferroptosis-related lncRNAs were identified as independent predictors of LUAD outcome, namely RP11-386M24.3, LINC00592, FENDRR, AC104699.1, AC091132.1, LANCL1-AS1, LINC-PINT, IFNG-AS1, LINC00968 and AC006129.2. The area under the curve verified that the established signatures could determine LUAD prognosis. The nomogram model was used to assess the predictive accuracy of the established signatures. Additionally, GSEA revealed that the 10 ferroptosis-related lncRNAs could be involved in immune responses in LUAD. Overall, the results of the current study may provide novel insights into the development of novel therapies or diagnostic strategies for LUAD.
| S-EPMC9395983 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common type of lung cancer and is a severe threat to human health. Although many therapies have been applied to LUAD, the long-term survival rate of patients remains unsatisfactory. We aim to find reliable immune microenvironment-related lncRNA biomarkers to improve LUAD prognosis.MethodsESTIMATE analysis was performed to evaluate the degree of immune infiltration of each patient in TAGA LUAD cohort. Correlation analysis was used to identify the immune microenvironment-related lncRNAs. Univariate cox regression analysis, LASSO analysis, and Kaplan Meier analysis were used to construct and validate the prognostic model based on microenvironment-related lncRNAs.ResultsWe obtained 1,178 immune microenvironment-related lncRNAs after correlation analysis. One hundred and eighty of them are independent prognostic lncRNAs. Sixteen key lncRNAs were selected by LASSO method. This lncRNA-based model successfully predicted patients' prognosis in validation cohort, and the risk score was related to pathological stage. Besides, we also found that TP53 had the highest frequency mutation in LUAD, and the mutation of TP53 in the high-risk group, which was identified by our survival model, has a poor prognosis. lncRNA-mRNA co-expression network further suggested that these lncRNAs play a vital role in the prognosis of LUAD.ConclusionHere, we filtered 16 key lncRNAs, which could predict the survival of LUAD and may be potential biomarkers and therapeutic targets.
| S-EPMC8366027 | biostudies-literature
Project description:This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan-Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.
| S-EPMC8301374 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.
| S-EPMC8260838 | biostudies-literature
Project description:Purpose: The purpose of this study was to construct a novel risk scoring model with prognostic value that could elucidate tumor immune microenvironment of hepatocellular carcinoma (HCC). Samples and methods: Data were obtained through The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis were carried out to screen for glycolysis-related long noncoding RNAs (lncRNAs) that could provide prognostic value. Finally, we established a risk score model to describe the characteristics of the model and verify its prediction accuracy. The receiver operating characteristic (ROC) curves of 1, 3, and 5 years of overall survival (OS) were depicted with risk score and some clinical features. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and CIBERSORT analysis were employed to reveal the characteristics of tumor immune microenvironment in HCC. The nomogram was drawn by screening indicators with high prognostic accuracy. The correlation of risk signature with immune infiltration and immune checkpoint blockade (ICB) therapy was analyzed. After enrichment of related genes, active behaviors and pathways in high-risk groups were identified and lncRNAs related to poor prognosis were validated in vitro. Finally, the impact of MIR4435-2HG upon ICB treatment was uncovered. Results: After screening through multiple steps, four glycolysis-related lncRNAs were obtained. The risk score constructed with the four lncRNAs was found to significantly correlate with prognosis of samples. From the ROC curve of samples with 1, 3, and 5 years of OS, two indicators were identified with high prognostic accuracy and were used to draw a nomogram. Besides, the risk score significantly correlated with immune score, immune-related signature, infiltrating immune cells (i.e. B cells, etc.), and ICB key molecules (i.e. CTLA4,etc.). Gene enrichment analysis indicated that multiple biological behaviors and pathways were active in the high-risk group. In vitro validation results showed that MIR4435-2HG was highly expressed in the two cell lines, which had a significant impact on the OS of samples. Finally, we corroborated that MIR4435-2HG had intimate relationship with ICB therapy in hepatocellular carcinoma. Conclusion: We elucidated the crucial role of risk signature in immune cell infiltration and immunotherapy, which might contribute to clinical strategies and clinical outcome prediction of HCC.
| S-EPMC8100457 | biostudies-literature
Project description:BackgroundN-6 methylation (m6A) pushes forward an immense influence on the occurrence and development of lung adenocarcinoma (LUAD). However, the methylation on non-coding RNA in LUAD, especially long non-coding RNA (lncRNA), has not been received sufficient attention.MethodsSpearman correlation analysis was used to screen lncRNA correlated with m6A regulators expression from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, respectively. Then, the least absolute shrinkage and selection operator (LASSO) was applied to build a risk signature consisting m6A-related lncRNA. Univariate and multivariate independent prognostic analysis were applied to evaluate the performance of signature in predicting patients' survival. Next, we applied Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) to conduct pathway enrichment analysis of 3344 different expression genes (DEGs). Finally, we set up a competing endogenous RNAs (ceRNA) network to this lncRNA.ResultsA total of 85 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by LASSO regression contained 11 lncRNAs: ARHGEF26-AS1, COLCA1, CRNDE, DLGAP1-AS2, FENDRR, LINC00968, TMPO-AS1, TRG-AS1, MGC32805, RPARP-AS1, and TBX5-AS1. M6A-related lncRNA risk score could predict the prognostic of LUAD and was significantly associated with clinical pathological. And in the evaluation of lung adenocarcinoma tumor microenvironment (TME) by using ESTIMATE algorithm, we found a statistically significant correlation between risk score and stromal/immune cells.ConclusionM6A-related lncRNA was a potential prognostic and therapy target for lung adenocarcinoma.
| S-EPMC8605119 | biostudies-literature
Project description: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.
| S-EPMC9274545 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis.MethodThe information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters.ResultA total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year.ConclusionA series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.
| S-EPMC7811787 | biostudies-literature
Project description:BackgroundLong non-coding RNAs (lncRNAs) are increasingly recognized as the crucial mediators in the regulation of ferroptosis and iron metabolism. A systematic understanding of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in lung adenocarcinoma (LUAD) is essential for new diagnostic and therapeutic strategies.MethodsFIRLs were obtained through Pearson correlation analysis between ferroptosis and iron-metabolism related genes and all lncRNAs. Univariate and multivariate Cox regression analysis were used to identify optimal prognostic lncRNAs. Next, a novel signature was constructed and risk score of each patient was calculated. Survival analysis and ROC analysis were performed to evaluate the predictive performance using The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus (GEO) datasets, respectively. Furthermore, multivariate Cox and stratification analysis were used to assess prognostic value of this signature in whole cohort and various subgroups. The correlation of risk signature with immune infiltration and gene mutation was also discussed. The expression of lncRNAs was verified by quantitative real-time PCR (qRT-PCR).ResultsA 7-FIRLs signature including ARHGEF26-AS1, LINC01137, C20orf197, MGC32805, TMPO-AS1, LINC00324, and LINC01116 was established in the present study to assess the overall survival (OS) of LUAD. The survival analysis and ROC curve indicated good predictive performance of the signature in both the TCGA training set and the GEO validation set. Multivariate Cox and stratification analysis indicated that the 7-FIRLs signature was an independent prognostic factor for OS. Nomogram exhibited robust validity in prognostic prediction. Differences in immune cells, immune functions and gene mutation were also found between high-risk and low-risk groups.ConclusionsThis risk signature based on the FIRLs may be promising for the clinical prediction of prognosis and immunotherapeutic responses in LUAD patients.
| S-EPMC8254945 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is a highly prevalent malignancy worldwide, and its clinical prognosis assessment and treatment is a major research direction. Both ferroptosis and cuproptosis are novel forms of cell death and are considered to be important factors involved in cancer progression. To further understand the correlation between the cuproptosis-related ferroptosis genes (CRFGs) and the prognosis of LUAD, we explore the molecular mechanisms related to the development of the disease. We constructed a prognostic signature containing 13 CRFGs, which, after grouping based on risk score, revealed that the LUAD high-risk group exhibited poor prognosis. Nomogram confirmed that it could be an independent risk factor for LUAD, and ROC curves and DCA validated the validity of the model. Further analysis showed that the three prognostic biomarkers (LIFR, CAV1, TFAP2A) were significantly correlated with immunization. Meanwhile, we found that a LINC00324/miR-200c-3p/TFAP2A regulatory axis could be involved in the progression of LUAD. In conclusion, our report reveals that CRFGs are well correlated with LUAD and provide new ideas for the construction of clinical prognostic tools, immunotherapy, and targeted therapy for LUAD.
| S-EPMC10042693 | biostudies-literature