Development and validation of an endoplasmic reticulum stress long non-coding RNA signature for the prognosis and immune landscape prediction of patients with lung adenocarcinoma

Zeng, Jie; Wu, Zhenyu; Luo, Meifeng; Xu, X. George; Bai, Wenjie; Xie, Guijing; Chen, Quhai; Liang, Dengfeng; Xu, Zixun; Chen, Mindong; Xie, Jianjiang

doi:10.3389/fgene.2023.1024444

Cited by 7 publications

(6 citation statements)

References 34 publications

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“…Therefore, to predict the prognosis of LUAD patients, further molecular indicators must be investigated. Increasing bioinformatic articles get published recent years and achieved excellent efficacy (6,(16)(17)(18)(19). The scRNA-seq has good application potential in disease research since it can obtain gene expression maps at the level of a single cell and identify heterogeneous tissue samples in groups (20)(21)(22).…”

Section: Discussionmentioning

confidence: 99%

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

Yan

et al. 2023

Front. Oncol.

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BackgroundLung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. The research strived to establish a prognostic model based on malignancy-related risk score (MRRS) in LUAD.MethodsWe applied the single-cell RNA sequencing (scRNA-seq) data from Tumor Immune Single Cell Hub database to recognize malignancy-related geneset. Meanwhile, we extracted RNA-seq data from The Cancer Genome Atlas database. The GSE68465 and GSE72094 datasets from the Gene Expression Omnibus database were downloaded to validate the prognostic signature. Random survival forest analysis screened MRRS with prognostic significance. Multivariate Cox analysis was leveraged to establish the MRRS. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the malignancy-related signature. In addition, we used qRT-PCR to explore the expression profile of MRRS-constructed genes in LUAD cells.ResultsThe scRNA-seq analysis revealed the markers genes of malignant celltype. The MRRS composed of 7 malignancy-related genes was constructed for each patient, which was shown to be an independent prognostic factor. The results of the GSE68465 and GSE72094 datasets validated MRRS’s prognostic value. Further analysis demonstrated that MRRS was involved in oncogenic pathways, genetic mutations, and immune functions. Moreover, the results of qRT-PCR were consistent with bioinformatics analysis.ConclusionOur research recognized a novel malignancy-related signature for predicting the prognosis of LUAD patients and highlighted a promising prognostic and treatment marker for LUAD patients.

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Section: Discussionmentioning

confidence: 99%

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

Yan

et al. 2023

Front. Oncol.

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“…Differences in survival rates between these categories were assessed using Kaplan-Meier survival plots and log-rank tests, facilitated by the “survival” and “survivalminer” software packages. Additionally, the model’s predictive accuracy was evaluated over 1, 3, and 5 years, using receiver operating characteristic (ROC) analysis ( 34 , 35 ), implemented via an R package.…”

Section: Methodsmentioning

confidence: 99%

Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma

Li,

Fang,

Liu

et al. 2024

Front. Immunol.

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BackgroundThe current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells.MethodsThis study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis.ResultsIn this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells.ConclusionThe impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.

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“…A previous study stratified patients with LUAD into higher- and lower-TMB subgroups, screened nine immune genes, and used a prognostic signature based on these nine immune genes to predict patient prognoses ( Zhao et al, 2021 ). Other studies have used similar strategies to screen biomarkers and construct LUAD prognostic nomograms based on either a group of specific genes, a gene family, or biological/physiological factors, such as lncRNA ( Zeng et al, 2023 ), pyroptosis ( Song et al, 2021 ), T-cell marker genes ( Peng et al, 2024 ), tumor microenvironment-related genes ( Li et al, 2023 ), integrin genes ( Wang Y. et al, 2021 ; Zhang S. et al, 2023 ) and oxidative stress ( Qian et al, 2023 ). Although most studies used the TCGA and/or GEO databases and incorporated the genomic profiles and clinical information to construct prediction models, some used databases such as SEER without incorporating genomic information ( Zuo et al, 2021 ) whereas a few others were based on cohorts from local hospitals ( Sun et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Comprehensive landscape of junctional genes and their association with overall survival of patients with lung adenocarcinoma

Xie,

Wu,

Hong

et al. 2024

Front. Mol. Biosci.

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ObjectivesJunctional proteins are involved in tumorigenesis. Therefore, this study aimed to investigate the association between junctional genes and the prognosis of patients with lung adenocarcinoma (LUAD).MethodsTranscriptome, mutation, and clinical data were retrieved from The Cancer Genome Atlas (TCGA). “Limma” was used to screen differentially expressed genes. Moreover, Kaplan–Meier survival analysis was used to identify junctional genes associated with LUAD prognosis. The junctional gene-related risk score (JGRS) was generated based on multivariate Cox regression analysis. An overall survival (OS) prediction model combining the JGRS and clinicopathological properties was proposed using a nomogram and further validated in the Gene Expression Omnibus (GEO) LUAD cohort.ResultsTo our knowledge, this study is the first to demonstrate the correlation between the mRNA levels of 14 junctional genes (CDH15, CDH17, CDH24, CLDN6, CLDN12, CLDN18, CTNND2, DSG2, ITGA2, ITGA8, ITGA11, ITGAL, ITGB4, and PKP3) and clinical outcomes of patients with LUAD. The JGRS was generated based on these 14 genes, and a higher JGRS was associated with older age, higher stage levels, and lower immune scores. Thus, a prognostic prediction nomogram was proposed based on the JGRS. Internal and external validation showed the good performance of the prediction model. Mechanistically, JGRS was associated with cell proliferation and immune regulatory pathways. Mutational analysis revealed that more somatic mutations occurred in the high-JGRS group than in the low-JGRS group.ConclusionThe association between junctional genes and OS in patients with LUAD demonstrated by our “TCGA filtrating and GEO validating” model revealed a new function of junctional genes.

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Development and validation of an endoplasmic reticulum stress long non-coding RNA signature for the prognosis and immune landscape prediction of patients with lung adenocarcinoma

Cited by 7 publications

References 34 publications

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma

Comprehensive landscape of junctional genes and their association with overall survival of patients with lung adenocarcinoma

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