Dataset Information

Identification of endoplasmic reticulum stress-related lncRNAs in lung adenocarcinoma by bioinformatics and experimental validation.

ABSTRACT:

Background

Endoplasmic reticulum stress (ERs) is an important cellular self-defence mechanism, which is closely related to tumorigenesis and development. However, the role of endoplasmic reticulum stress state in the development of lung adenocarcinoma (LUAD) has not been clarified.

Methods

The lncRNAs associated with endoplasmic reticulum stress were identified by co-expression analysis in public databases, and by the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression modelling, we constructed a prognostic model based on endoplasmic reticulum stress-related lncRNAs (ERs-related lncRNAs), performed immune analysis, TME, TMB and clinical drug prediction for model-related risk scores, and performed correlation validation in public databases and at the human tissue level.

Results

Five ERs-related lncRNAs were used to construct an ERs-related lncRNA signature (ERs-related LncSig), which can predict the prognosis of LUAD. Patients in the high-risk group had worse survival, and differences existed in immune cell infiltration, immune function, immune checkpoint analysis, tumour microenvironment (TME), tumour mutational burden (TMB), immunotherapy efficacy, and sensitivity to some commonly used chemotherapeutic agents between high and low risk groups.

Conclusion

Our study demonstrated that ERs-related lncRNA signature can be used for the prognostic evaluation of LUAD patients and may provide new insights into clinical decision-making and personalised medicine for LUAD.

SUBMITTER: Xin T

PROVIDER: S-EPMC10467521 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Identification of endoplasmic reticulum stress-related lncRNAs in lung adenocarcinoma by bioinformatics and experimental validation.

Xin Tong T Sun Yue Y Meng Hongxue H Zhang Ningzhi N Peng Bo B Yang Xinxin X Hu Jing J Cao Mengru M

Annals of medicine 20230101 2

<h4>Background</h4>Endoplasmic reticulum stress (ERs) is an important cellular self-defence mechanism, which is closely related to tumorigenesis and development. However, the role of endoplasmic reticulum stress state in the development of lung adenocarcinoma (LUAD) has not been clarified.<h4>Methods</h4>The lncRNAs associated with endoplasmic reticulum stress were identified by co-expression analysis in public databases, and by the least absolute shrinkage and selection operator (LASSO) regress ...[more]

PMID: 37643369

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Project description:BackgroundLung Adenocarcinoma (LUAD) is a major component of lung cancer. Endoplasmic reticulum stress (ERS) has emerged as a new target for some tumor treatments.MethodsThe expression and clinical data of LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database, followed by acquiring ERS-related genes (ERSGs) from the GeneCards database. Differentially expressed endoplasmic reticulum stress-related genes (DE-ERSGs) were screened and used to construct a risk model by Cox regression analysis. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were plotted to determine the risk validity of the model. Moreover, enrichment analysis of differentially expressed genes (DEGs) between the high- and low- risk groups was conducted to investigate the functions related to the risk model. Furthermore, the differences in ERS status, vascular-related genes, tumor mutation burden (TMB), immunotherapy response, chemotherapy drug sensitivity and other indicators between the high- and low- risk groups were studied. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the mRNA expression levels of prognostic model genes.ResultsA total of 81 DE-ERSGs were identified in the TCGA-LUAD dataset, and a risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was constructed by Cox regression analysis. K-M and ROC analyses showed that the high-risk group had a low survival, and the Area Under Curve (AUC) of ROC curves of 1-, 3- and 5-years overall survival was all greater than 0.6. In addition, functional enrichment analysis suggested that the risk model was related to collagen and extracellular matrix. Furthermore, differential analysis showed vascular-related genes FLT1, TMB, neoantigen, PD-L1 protein (CD274), Tumor Immune Dysfunction and Exclusion (TIDE), and T cell exclusion score were significantly different between the high- and low-risk groups. Finally, qRT-PCR results showed that the mRNA expression levels of 6 prognostic genes were consistent with the analysis.ConclusionA novel ERS-related risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was developed and validated, which provided a theoretical basis and reference value for ERS-related fields in the study and treatment of LUAD.

Project description:Lung adenocarcinoma (LUAD) is one of the most universal types of cancer all over the world and its morbidity continues to rise year by year. Growing evidence has demonstrated that endoplasmic reticulum stress is highly activated in cancer cells and plays a key role in regulating the fate of cancer cells. However, the role and mechanism of endoplasmic reticulum stress in lung adenocarcinoma genesis and development remains unclear. In this research, we developed a prognostic model to predict the overall survival of patients with LUAD utilizing endoplasmic reticulum stress-related genes and screened out potential small molecular compounds, which could assist the clinician in making accurate decisions and better treat LUAD patients. Firstly, we downloaded 419 endoplasmic reticulum stress-related genes (ERSRGs) from Molecular Signatures Database (MSigDB). Secondly, we obtained information about the transcriptome profiling and corresponding clinical data of 59 normal samples and 535 lung adenocarcinoma samples from The Cancer Genome Atlas (TCGA) database. Next, we used the DESeq2 package to identify differentially expressed genes related to endoplasmic reticulum stress. We performed univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis to establish a prognostic model for LUAD patients based on ERSRGs. Then, we carried out univariate and multivariate independent prognostic analysis of endoplasmic reticulum stress-related gene (ERSRG) score and some clinical traits of lung adenocarcinoma. Additionally, we developed a clinically applicable nomogram for predicting survival for LUAD patients over one, three, and five years. Moreover, we carried out a drug sensitivity analysis to identify novel small molecule compounds for LUAD treatment. Finally, we examined the tumor microenvironment (TME) and immune cell infiltrating analysis to explore the interactions between immune and cancer cells. 142 differentially expressed ERSRGs were identified by using the DESeq2 package. A prognostic model was built based on 7 differentially expressed ERSRGs after performing univariate Cox regression, LASSO regression, and multivariate Cox regression analysis. According to the results of univariate and multivariate independent prognostic analysis, we found ERSRG score can be used as an independent prognostic maker. Using the Kaplan-Meier curves, we found low-risk patients had higher survival probability than high-risk patients in both training set and test set. A nomogram was drawn to predict 1-, 3-, and 5-year survival probability. The calibration curves explained good performance of the model for the prediction of survival. Phenformin, OSU-03012, GSK-650394 and KIN001-135 were identified as the drugs most likely to provide important information to clinicians about the treatment of LUAD patients. A prognostic prediction model was established based on 7 differentially expressed ERSRGs (PDX1, IGFBP1, DDIT4, PPP1R3G, CFTR, DERL3 and NUPR1), which could effectively predict the prognosis of LUAD patients and give a reference for clinical doctors to help LUAD patients to make better treatment tactics. Based on the 4 small molecule compounds (Phenformin, OSU-03012, GSK-650394 and KIN001-135) we discovered, targeting endoplasmic reticulum stress-related genes may also be a therapeutic approach for LUAD patients.

Project description:BackgroundThe high case-fatality rate of patients with lung adenocarcinoma (LUAD) emphasizes the importance of identifying a robust and reliable prognostic signature for LUAD patients. Endoplasmic reticulum (ER) stress results from protein misfolding imbalance and has been shown to participate in the development of cancer. We aimed to develop and validation a reliable and robust ER stress-related prognostic signature to accurately predict prognosis for patients with LUAD.MethodsThe mRNA expressions data and the clinical information were downloaded from The Cancer Genome Atlas (TCGA) as training set. The data of external validation sets were downloaded from GEO database with the accession number GSE 30219, GSE 31210, GSE 50081 and GSE 37745. Univariate Cox regression analyses was performed to identify mRNAs associated with overall survival (OS) in LUAD. ER-associated genes were retrieved using GeneCards database. Next, we construct the best risk score model by the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation. Subsequently, predictive models and risk scores were developed in the TCGA training dataset. Cox proportional hazards regression models were used for univariate and multivariate analysis of risk score and clinicopathologic characteristics. As a validation set GSE30219, GSE31210 and (GSE50081+GSE37745) were used to validate the predictive performance of the model in TCGA. Finally, functional enrichment analysis, including the gene ontology (GO) enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene set enrichment analysis (GSEA) were performed to further explore function and mechanisms.ResultsA prognostic prediction model based on eight genes was developed in the TCGA training dataset. As expected, in validation sets, patients with higher risk scores were found to have worse prognosis. Time-dependent ROC curve analyses demonstrated that the risk score model was reliable. The nomograms showed excellent predictive ability. Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for LUAD. Additionally, functional enrichment analysis showed that the relevant biomarkers were enriched in cell cycle and glycolysis related signaling pathways.ConclusionsThe 8-gene signature may enable improved the prediction of clinical events and decisions about management of LUAD.

Project description:Lung adenocarcinoma (LUAD) represents one of the most common subtypes of lung cancer with high rates of incidence and mortality, which contributes to substantial health and economic demand across the globe. Treatment today mainly consists of surgery, radiotherapy, and chemotherapy, but their efficacy in advanced stages is often suboptimal and emphasizes the clear need for new biomarkers and therapeutic targets. Using comprehensive bioinformatics analyses consisting of the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Human Protein Atlas (HPA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), immune infiltration analysis and functional enrichment analysis, and single-cell analysis, we examined the potential of keratin 18 (KRT18) as a candidate biomarker in advanced LUAD. KRT18 was significantly elevated in LUAD tissue relative to normal adjacent tissue (p < 0.05), and its expression was correlated with poor clinical-pathological features and inferior prognostic outcome. Furthermore, KRT18 expression was associated with several populations of immune cells, suggesting KRT18 may contribute to the local tumor microenvironment and potentially pathways of immune evasion. Survival analysis indicated that elevated KRT18 expression correlated with poor overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI), reinforcing its legitimacy as a prognostic tool (AUC = 0.846). Importantly, gene enrichment analysis found KRT18-associated genes enriched for pathways associated with lymphocyte differentiation and immune response pathways, which provides mechanistic insight into biological effects attributed to KRT18. Notably, NU.1025 has demonstrated the capability of reversing KRT18-modulated oncogenic features, and targeted therapeutic strategies can be developed moving forward. In conclusion, our data demonstrate that KRT18 has utility as a potential biomarker but may also serve as a therapeutic target in LUAD and merit further investigation into underlying mechanistic functions and potential therapeutic roles in the clinic.

Project description:BackgroundSepsis-induced apoptosis of immune cells leads to widespread depletion of key immune effector cells. Endoplasmic reticulum (ER) stress has been implicated in the apoptotic pathway, although little is known regarding its role in sepsis-related immune cell apoptosis. The aim of this study was to develop an ER stress-related prognostic and diagnostic signature for sepsis through bioinformatics and machine learning algorithms on the basis of the differentially expressed genes (DEGs) between healthy controls and sepsis patients.MethodsThe transcriptomic datasets that include gene expression profiles of sepsis patients and healthy controls were downloaded from the GEO database. The immune-related endoplasmic reticulum stress hub genes associated with sepsis patients were identified using the new comprehensive machine learning algorithm and bioinformatics analysis which includes functional enrichment analyses, consensus clustering, weighted gene coexpression network analysis (WGCNA), and protein-protein interaction (PPI) network construction. Next, the diagnostic model was established by logistic regression and the molecular subtypes of sepsis were obtained based on the significant DEGs. Finally, the potential diagnostic markers of sepsis were screened among the significant DEGs, and validated in multiple datasets.ResultsSignificant differences in the type and abundance of infiltrating immune cell populations were observed between the healthy control and sepsis patients. The immune-related ER stress genes achieved strong stability and high accuracy in predicting sepsis patients. 10 genes were screened as potential diagnostic markers for sepsis among the significant DEGs, and were further validated in multiple datasets. In addition, higher expression levels of SCAMP5 mRNA and protein were observed in PBMCs isolated from sepsis patients than healthy donors (n = 5).ConclusionsWe established a stable and accurate signature to evaluate the diagnosis of sepsis based on the machine learning algorithms and bioinformatics. SCAMP5 was preliminarily identified as a diagnostic marker of sepsis that may affect its progression by regulating ER stress.

Project description:BackgroundEndoplasmic reticulum (ER) stress had a crucial impact on cell survival, proliferation, and metastasis in various cancers. However, the role of ER stress in lung adenocarcinoma remains unclear.MethodGene expression and clinical data of lung adenocarcinoma (LUAD) samples were extracted from The Cancer Genome Atlas (TCGA) and three Gene Expression Omnibus (GEO) datasets. ER stress score (ERSS) was constructed based on hub genes selected from 799 ER stress-related genes by least absolute shrinkage and selection operator (LASSO) regression. A Cox regression model, integrating ERSS and the TNM stage, was developed to predict overall survival (OS) in TCGA cohort and was validated in GEO cohorts. Gene set enrichment analysis (GSEA), single-sample GSEA (ssGSEA), and gene mutation analyses were performed to further understand the molecular features of ERSS. The tumor immune infiltration was evaluated by ESTIMATE, CIBERSORT, and xCell algorithms. The receiver operating characteristic (ROC) curves were used to evaluate the predictive value of the risk model. p< 0.05 was considered statistically significant.ResultsOne hundred fifty-seven differentially expressed genes (DEGs) were identified between tumor and para-carcinoma tissues, and 45 of them significantly correlated with OS. Next, we identified 18 hub genes and constructed ERSS by LASSO regression. Multivariate analysis demonstrated that higher ERSS (p< 0.0001, hazard ratio (HR) = 3.8, 95%CI: 2.8-5.2) and TNM stage (p< 0.0001, HR = 1.55, 95%CI: 1.34-1.8) were independent predictors for worse OS. The prediction model integrating ERSS and TNM stage performed well in TCGA cohort (area under the curve (AUC) at five years = 0.748) and three GEO cohorts (AUC at 5 years = 0.658, 0.717, and 0.739). Pathway enrichment analysis showed that ERSS significantly correlated with unfolded protein response. Meanwhile, pathways associated with the cell cycle, growth, and metabolism were significantly enriched in the high ERSS group. Patients with SMARCA4, TP53, and EGFR mutations showed significantly higher ERSS (p = 4e-04, 0.0027, and 0.035, respectively). Tissues with high ERSS exhibited significantly higher infiltration of M1 macrophages, activated dendritic cells, and lower infiltration of CD8+ T cells and B cells, which indicate an activated tumor antigen-presenting but suppressive immune response status.ConclusionWe developed and validated an ER stress-related risk model that exhibited great predictive value for OS in patients with LUAD. Our work also expanded the understanding of the role of ER stress in LUAD.

Dataset Information

Identification of endoplasmic reticulum stress-related lncRNAs in lung adenocarcinoma by bioinformatics and experimental validation.

Background

Methods

Results

Conclusion

Publications

Identification of endoplasmic reticulum stress-related lncRNAs in lung adenocarcinoma by bioinformatics and experimental validation.

Similar Datasets

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