A novel cuproptosis-related lncRNA signature predicts prognosis and therapeutic response in bladder cancer

Cai, Jinming; Xie, Haoran; Yan, Yi‐Lin; Huang, Zhengnan; Tang, Pengyu; Cao, Xiangqian; Wang, Zeyi; Yang, Chenkai; Wen, Jiling; Tan, Mingyue; Zhang, Fang; Shen, Bing

doi:10.3389/fgene.2022.1082691

Cited by 5 publications

(4 citation statements)

References 63 publications

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“…[43] SMARCA5-AS1, LINC00513, and AL590428.1 have been utilized to construct predictive models for cuproptosis-related lncRNAs. [44,45] Additionally, MIR4713HG has been shown to exacerbate the malignant behavior of oral squamous cell carcinoma by interacting with microRNA let-7c-5p. [46] However, the molecular functions of AL359762.3 and AL122035.1 in various cancers remain to be elucidated.…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of a prognostic model for bladder cancer based on disulfidptosis-related lncRNAs

Yang,

Zhang,

Liu

et al. 2024

Medicine

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Background: Bladder cancer (BLCA) is a prevalent and aggressive cancer associated with high mortality and poor prognosis. Currently, studies on the role of disulfidptosis-related long non-coding RNAs (DRLs) in BLCA are limited. This study aims to construct a prognostic model based on DRLs to improve the accuracy of survival predictions for patients and identify novel targets for therapeutic intervention in BLCA management. Methods: Transcriptomic and clinical datasets for patients with BLCA were obtained from The Cancer Genome Atlas. Using multivariate Cox regression and least absolute shrinkage and selection operator techniques, a risk prognostic signature defined by DRLs was developed. The model’s accuracy and prognostic relevance were assessed through Kaplan–Meier survival plots, receiver operating characteristic curves, concordance index, and principal component analysis. Functional and pathway enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, were conducted to elucidate the underlying biological processes. Immune cell infiltration was quantified using the CIBERSORT algorithm. Differences and functions of immune cells in different risk groups were evaluated through single-sample Gene Set Enrichment Analysis. The Tumor Immune Dysfunction and Exclusion predictor and tumor mutational burden (TMB) assessments were utilized to gauge the likelihood of response to immunotherapy. Drug sensitivity predictions were made using the Genomics of Drug Sensitivity in Cancer database. Results: A robust 8-DRL risk prognostic model, comprising LINC00513, SMARCA5-AS1, MIR4435-2HG, MIR4713HG, AL122035.1, AL359762.3, AC006160.1, and AL590428.1, was identified as an independent prognostic indicator. This model demonstrated strong predictive power for overall survival in patients with BLCA, revealing significant disparities between high- and low-risk groups regarding tumor microenvironment, immune infiltration, immune functions, TMB, Tumor Immune Dysfunction and Exclusion scores, and drug susceptibility. Conclusion: This study introduces an innovative prognostic signature of 8 DRLs, offering a valuable prognostic tool and potential therapeutic targets for bladder carcinoma. The findings have significant implications for TMB, the immune landscape, and patient responsiveness to immunotherapy and targeted treatments.

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Section: Discussionmentioning

confidence: 99%

Construction and validation of a prognostic model for bladder cancer based on disulfidptosis-related lncRNAs

Yang,

Zhang,

Liu

et al. 2024

Medicine

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“…In accordance with our data for EOC, high expression levels of GUSBP11 are predicted to bind miR-22-3p to avoid CCN2A mRNA degradation, correlating to poor overall survival in hepatocellular carcinoma patients [ 81 ]. Conversely, GUSBP11 expression correlates with better prognosis in head and neck squamous cell carcinoma [ 82 ], bladder cancer [ 83 ], papillary renal cell carcinoma [ 84 ], and pancreatic adenocarcinoma [ 85 ]. Regarding MIR924HG , there is no information about it in the literature.…”

Section: Discussionmentioning

confidence: 99%

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Salamini-Montemurri

Lamas-Maceiras

Lorenzo-Catoira

et al. 2023

IJMS

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Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

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“…Mao et al developed a prognostic signature of gastric adenocarcinoma (GA) containing three lncRNAs, including AC103702.2 and three mRNAs, with better accuracy than the conventional TNM pathological staging system 40 . AL138756.1 has been reported as autophagy or cuproptosis-associated lncRNA predicting prognosis in colorectal or bladder cancer 41,42 .…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a novel redox- related differentially expressed lncRNA prognostic signature for predicting clinical immunotherapy response in gastric cancer

Peng

Shan

et al. 2023

Preprint

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Redox responses modulated by intracellular long noncoding RNA (lncRNA) can be involved in tumorigenesis and progression. However, the role of redox-related lncRNAs (RRlncRNAs) in gastric cancer (GC) development remains mostly unknown. Our research aims to establish and validate novel prognostic and immune infiltration markers for GC by constructing a prognostic model of RRlncRNAs. We downloaded the transcriptomic and mutational data for 407 GC pa-tients from The Cancer Genome Atlas (TCGA) database and randomized them 1:1 into a training and validation set to show that redox-related lncRNAs affect GC patients' prognosis. Subse-quently, the prognostic model was constructed for the screened RRlncRNAs using the Least Absolute Shrinkage and Selection Operator (LASSO) and the multivariate COX regression algo-rithm. Then, Survival analyses were performed on the train and test sets. The overall survival rate of GC patients was significantly correlated with the signatures of eight RRlncRNAs, including AC103702.2, AL138756.1, AL356417.2, CFAP61-AS1, RHPN1-AS1, CDK6-AS1, LINC02864, and AL355574.1. Meanwhile, we validated the model's accuracy through nomograms, Decision Curve Analysis (DCA), and comparisons using models from other studies. The results demonstrated that our model is more effective and outperforms the signature of Jiang et al. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of gene enrichment in high-risk patients shows significant enrichment in immune-related pathways. Waterfall plots of gene mutations, tumor mutation burden (TMB), and tumor immune dysfunction and exclusion (TIDE) showed significant differences in immune function between high- and low-risk groups. Then, we divided the 407 GC patients into two clusters using a consensus clustering algorithm and found significant differences in their immune microenvironment through immune cell difference anal-ysis, ESTIMATEScore, and gene set enrichment analysis (GSEA). Taken together, we conclude that the prognostic model constructed by RRlncRNAs can significantly affect the prognosis of GC patients and may alter their tumor progression by modulating the immune microenvironment in vivo. Our study found eight RRlncRNA-associated signatures, representing promising new markers for immunotherapy and diagnosis in GC patients.

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A novel cuproptosis-related lncRNA signature predicts prognosis and therapeutic response in bladder cancer

Cited by 5 publications

References 63 publications

Construction and validation of a prognostic model for bladder cancer based on disulfidptosis-related lncRNAs

Construction and validation of a prognostic model for bladder cancer based on disulfidptosis-related lncRNAs

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Identification and validation of a novel redox- related differentially expressed lncRNA prognostic signature for predicting clinical immunotherapy response in gastric cancer

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