Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer

Liu, Xinyu; Zhou, Lei; Gao, Minghui; Dong, Shuhong; Hu, Yanan; Hu, Chunjie

doi:10.3389/fgene.2022.989646

Cited by 13 publications

(7 citation statements)

References 71 publications

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“…Numerous studies have highlighted the contributions of cuproptosis-related genes to uncontrolled tumor growth. Dihydrolipoamide Branched Chain Transacylase E2 (DBT), a key enzyme in transforming α-keto acid into acyl-CoA, served as a protective factor in cervical cancer [ 53 ]. FOXD2-AS1 and LINC02154 are the risk factors for KIRC patients [ 48 , 54 , 55 ].…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis and experimental validation of m6A and cuproptosis-related lncRNA NFE4 in clear cell renal cell carcinoma

Feng,

Li,

Meng

et al. 2024

Discov Onc

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Purpose This study aimed to construct an m6A and cuproptosis-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using the information acquired from The Cancer Genome Atlas (TCGA) database. Methods First, the co-expression analysis was performed to identify lncRNAs linked with N6-methyladenosine (m6A) and cuproptosis in ccRCC. Then, a model encompassing four candidate lncRNAs was constructed via univariate, least absolute shrinkage together with selection operator (LASSO), and multivariate regression analyses. Furthermore, Kaplan–Meier, principal component, functional enrichment annotation, and nomogram analyses were performed to develop a risk model that could effectively assess medical outcomes for ccRCC cases. Moreover, the cellular function of NFE4 in Caki-1/OS-RC-2 cultures was elucidated through CCK-8/EdU assessments and Transwell experiments. Dataset outcomes indicated that NFE4 can have possible implications in m6A and cuproptosis, and may promote ccRCC progression. Results We constructed a panel of m6A and cuproptosis-related lncRNAs to construct a prognostic prediction model. The Kaplan–Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. Furthermore, the m6A and cuproptosis-related lncRNA model indicated higher diagnostic efficiency than other clinical features. Moreover, the NFE4 function analysis indicated a gene associated with m6A and cuproptosis-related lncRNAs in ccRCC. It was also revealed that the proliferation and migration of Caki-1 /OS-RC-2 cells were inhibited in the NFE4 knockdown group. Conclusion Overall, this study indicated that NFE4 and our constructed risk signature could predict outcomes and have potential clinical value.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis and experimental validation of m6A and cuproptosis-related lncRNA NFE4 in clear cell renal cell carcinoma

Feng,

Li,

Meng

et al. 2024

Discov Onc

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“…Then, 18 cuproptosis-related lncRNAs associated with CC prognosis were identified by univariate Cox regression, and after LASSO and multivariate Cox regression, a risk prediction model containing AL441992.1, SOX21-AS1, AC011468.3, AC012306.2, FZD4-DT, AP001922.5, RUSC1-AS1, and AP001453.2 was constructed. There have been several previous reports on the establishment of cuproptosis-related lncRNA models for predicting the prognosis of cervical cancer 37 – 39 (Table 2 ). Compared to a study by Liu, Wang et al using only the TCGA database, we added the GEO database (GSE44001) as an external validation to make the obtained results more persuasive.…”

Section: Discussionmentioning

confidence: 99%

Identification of cuproptosis-related lncRNA for predicting prognosis and immunotherapeutic response in cervical cancer

Kong

Xiong

Meng

et al. 2023

Sci Rep

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Patients diagnosed with advanced cervical cancer (CC) have poor prognosis after primary treatment, and there is a lack of biomarkers for predicting patients with an increased risk of recurrence of CC. Cuproptosis is reported to play a role in tumorigenesis and progression. However, the clinical impacts of cuproptosis-related lncRNAs (CRLs) in CC remain largely unclear. Our study attempted to identify new potential biomarkers to predict prognosis and response to immunotherapy with the aim of improving this situation. The transcriptome data, MAF files, and clinical information for CC cases were obtained from the cancer genome atlas, and Pearson correlation analysis was utilized to identify CRLs. In total, 304 eligible patients with CC were randomly assigned to training and test groups. LASSO regression and multivariate Cox regression were performed to construct a cervical cancer prognostic signature based on cuproptosis-related lncRNAs. Afterwards, we generated Kaplan–Meier curves, receiver operating characteristic curves and nomograms to verify the ability to predict prognosis of patients with CC. Genes for assessing differential expression among risk subgroups were also evaluated by functional enrichment analysis. Immune cell infiltration and the tumour mutation burden were analysed to explore the underlying mechanisms of the signature. Furthermore, the potential value of the prognostic signature to predict response to immunotherapy and sensitivity to chemotherapy drugs was examined. In our study, a risk signature containing eight cuproptosis-related lncRNAs (AL441992.1, SOX21-AS1, AC011468.3, AC012306.2, FZD4-DT, AP001922.5, RUSC1-AS1, AP001453.2) to predict the survival outcome of CC patients was developed, and the reliability of the risk signature was appraised. Cox regression analyses indicated that the comprehensive risk score is an independent prognostic factor. Moreover, significant differences were found in progression-free survival, immune cell infiltration, therapeutic response to immune checkpoint inhibitors, and IC50 for chemotherapeutic agents between risk subgroups, suggesting that our model can be well employed to assess the clinical efficacy of immunotherapy and chemotherapy. Based on our 8-CRLs risk signature, we were able to independently assess the outcome and response to immunotherapy of CC patients, and this signature might benefit clinical decision-making for individualized treatment.

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“…26 Despite the progress made in the treatment of cervical cancer, due to the heterogeneity of tumors, there are significant differences in prognosis even among patients with similar clinical characteristics, which indicates that the existing classification and clinicopathological characteristics are insufficient to predict the prognosis of patients. 27 As cervical cancer is a highly preventable tumor, there is a great need for more effective biomarkers to more accurately determine which treatments patients will benefit most from. 28 Lactate has long been considered a metabolic waste product, but recent studies have found that lactate tightly links two fundamental hallmarks of cancer: immune evasion and metabolic reprogramming.…”

Section: Discussionmentioning

confidence: 99%

Construction and Assessment of a Prognostic Risk Model for Cervical Cancer Based on Lactate Metabolism-Related lncRNAs

Gao¹,

Li²,

Wan³

et al. 2023

IJGM

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Purpose Cervical cancer (CC) has the fourth highest incidence and mortality rate among female cancers. Lactate is a key regulator promoting tumor progression. Long non-coding RNAs (lncRNAs) are closely associated with cervical cancer (CC). The study was aimed to develop a prognostic risk model for cervical cancer based on lactate metabolism-associated lncRNAs and to determine their clinical prognostic value. Patients and Methods In this study, CESC transcriptome data were obtained from the TCGA database. 262 lactate metabolism-associated genes were extracted from MsigDB (Molecular Characterization Database). Then, correlation analysis was used to identify LRLs. Univariate Cox regression analysis was performed afterwards, followed by least absolute shrinkage and selection operator (LASSO) regression analysis and multiple Cox regression analysis. 10 lncRNAs were finally identified to construct a risk score model. They were divided into two groups of high risk and low risk according to the median of risk scores. The predictive performance of the models was assessed by Kaplan-Meier (K-M) analysis, subject work characteristics (ROC) analysis, and univariate and multivariate Cox analyses. To assess the clinical utility of the prognostic model, we performed functional enrichment analysis, immune microenvironment analysis, mutation analysis, and column line graph generation. Results We constructed a prognostic model consisting of 10 LRLs at CC. We observed that high-risk populations were strongly associated with poor survival outcomes. Risk score was an independent risk factor for CC prognosis and was strongly associated with immune microenvironment analysis and tumor mutational load. Conclusion We developed a risk model of lncRNAs associated with lactate metabolism and used it to predict prognosis of CC, which could guide and facilitate the progress of new treatment strategies and disease monitoring in CC patients.

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Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer

Cited by 13 publications

References 71 publications

Bioinformatics analysis and experimental validation of m6A and cuproptosis-related lncRNA NFE4 in clear cell renal cell carcinoma

Bioinformatics analysis and experimental validation of m6A and cuproptosis-related lncRNA NFE4 in clear cell renal cell carcinoma

Identification of cuproptosis-related lncRNA for predicting prognosis and immunotherapeutic response in cervical cancer

Construction and Assessment of a Prognostic Risk Model for Cervical Cancer Based on Lactate Metabolism-Related lncRNAs

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