Cuproptosis-related lncRNA signatures: Predicting prognosis and evaluating the tumor immune microenvironment in lung adenocarcinoma

Zhang, Pengpeng; Pei, Shengbin; Liu, Jian-Lan; Zhang, Xiao; Feng, Yanlong; Gong, Zeitian; Zeng, Tianyu; Li, Jun; Wang, Wei

doi:10.3389/fonc.2022.1088931

Cited by 22 publications

(18 citation statements)

References 56 publications

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“…After investigating the landscape of immune and stromal cell infiltrations in both low- and high-risk groups, we found that Figure 8A illustrates how patients in the low-risk group have higher proportions of immune and stromal cell infiltrations compared to those in the high-risk group. Moreover, using the CIBERSORT algorithm ( 42 , 43 ), we calculated the immune cell proportions between the high- and low-risk groups ( Figure 8B ) and found that patients in the high-risk group significantly shared higher proportions of CD8 T cells, activated memory CD4 T cells, activated NK cells, Macrophages (M0), and Macrophages (M1). On the other hand, B cells, resting memory CD4 T cells, Monocytes, resting dendritic cells, and Activated mast cells were significantly enriched in the low-risk group.…”

Section: Resultsmentioning

confidence: 99%

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

et al. 2023

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BackgroundExtensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.MethodsWe obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD.ResultsWe identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments.ConclusionsThe risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.

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Section: Resultsmentioning

confidence: 99%

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

et al. 2023

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“…In this investigation, single‐sample gene set enrichment analysis (ssGSEA) 15,16 was employed to calculate the enrichment percentage of specific gene sets within each sample, facilitating the assessment of Treg enrichment values in individual specimens from The Cancer Genome Atlas‐Ovarian Cancer (TCGA‐OC) data set. The WGCNA R package served as the methodological foundation for constructing gene co‐expression networks 17 .…”

Section: Methodsmentioning

confidence: 99%

Prognostic and immunotherapeutic potential of regulatory T cell‐associated signature in ovarian cancer

Liu,

Shan,

Sun

et al. 2024

J Cellular Molecular Medi

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Tumour‐induced immunosuppressive microenvironments facilitate oncogenesis, with regulatory T cells (Tregs) serving as a crucial component. The significance of Treg‐associated genes within the context of ovarian cancer (OC) remains elucidated insufficiently. Utilizing single‐cell RNA sequencing (scRNA‐Seq) for the identification of Treg‐specific biomarkers, this investigation employed single‐sample gene set enrichment analysis (ssGSEA) for the derivation of a Treg signature score. Weighted gene co‐expression network analysis (WGCNA) facilitated the identification of Treg‐correlated genes. Machine learning algorithms were employed to determine an optimal prognostic model, subsequently exploring disparities across risk strata in terms of survival outcomes, immunological infiltration, pathway activation and responsiveness to immunotherapy. Through WGCNA, a cohort of 365 Treg‐associated genes was discerned, with 70 implicated in the prognostication of OC. A Tregs‐associated signature (TAS), synthesized from random survival forest (RSF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, exhibited robust predictive validity across both internal and external cohorts. Low TAS OC patients demonstrated superior survival outcomes, augmented by increased immunological cell infiltration, upregulated immune checkpoint expression, distinct pathway enrichment and differential response to immunotherapeutic interventions. The devised TAS proficiently prognosticates patient outcomes and delineates the immunological milieu within OC, offering a strategic instrument for the clinical stratification and selection of patients.

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“…We screened out single cells with any gene expressed in fewer than three cells or those expressing fewer than 250 genes. The percentage of rRNA and mitochondria was then calculated with the PercentageFeatureSet function in the Seurat R package ( 22 ). Consequently, 12118 cells were totally obtained for subsequent analysis.…”

Section: Methodsmentioning

confidence: 99%

“…To further probe into the heterogeneity of ESCC, all ESCC patients were separated into different clusters according to the expression of CAF-related genes with the R package ‘ConsensusClusterPlus’ ( 22 ). Differences in survival, TIME, and immune checkpoints were evaluated among subgroups using the same methodology as previously employed.…”

Section: Methodsmentioning

confidence: 99%

A fibroblast-associated signature predicts prognosis and immunotherapy in esophageal squamous cell cancer

Ren

Zhang

et al. 2023

Front. Immunol.

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BackgroundCurrent paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma’s functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients.MethodsThe GEO database provided the single-cell RNA sequencing (scRNA-seq) data. The GEO and TCGA databases were used to obtain bulk RNA-seq data and microarray data of ESCC, respectively. CAF clusters were identified from the scRNA-seq data using the Seurat R package. CAF-related prognostic genes were subsequently identified using univariate Cox regression analysis. A risk signature based on CAF-related prognostic genes was constructed using Lasso regression. Then, a nomogram model based on clinicopathological characteristics and the risk signature was developed. Consensus clustering was conducted to explore the heterogeneity of ESCC. Finally, PCR was utilized to validate the functions that hub genes play on ESCC.ResultsSix CAF clusters were identified in ESCC based on scRNA-seq data, three of which had prognostic associations. A total of 642 genes were found to be significantly correlated with CAF clusters from a pool of 17080 DEGs, and 9 genes were selected to generate a risk signature, which were mainly involved in 10 pathways such as NRF1, MYC, and TGF-Beta. The risk signature was significantly correlated with stromal and immune scores, as well as some immune cells. Multivariate analysis demonstrated that the risk signature was an independent prognostic factor for ESCC, and its potential in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the CAF-based risk signature and clinical stage was developed, which exhibited favorable predictability and reliability for ESCC prognosis prediction. The consensus clustering analysis further confirmed the heterogeneity of ESCC.ConclusionThe prognosis of ESCC can be effectively predicted by CAF-based risk signatures, and a comprehensive characterization of the CAF signature of ESCC may aid in interpreting the response of ESCC to immunotherapy and offer new strategies for cancer treatment.

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Cuproptosis-related lncRNA signatures: Predicting prognosis and evaluating the tumor immune microenvironment in lung adenocarcinoma

Cited by 22 publications

References 56 publications

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

Prognostic and immunotherapeutic potential of regulatory T cell‐associated signature in ovarian cancer

A fibroblast-associated signature predicts prognosis and immunotherapy in esophageal squamous cell cancer

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