LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution

Wang, Peng; Guo, Qiuyan; Hao, Yangyang; Liu, Qian; Gao, Yue; Zhi, Hui; Li, Xin; Shang, Shuai; Guo, Shuang; Zhang, Yunpeng; Ning, Shangwei

doi:10.1093/nar/gkaa1017

Cited by 53 publications

(35 citation statements)

References 33 publications

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“…According to a previous study ( 21 ), NEAT1 and ZFX competitively bind to miR-138-5p . Next, we performed multivariate Cox regression analysis for the NEAT1 – ZFX – miR-138-5p regulation axis using the “ceRNA survival” tool of LnCeCell ( 7 ). We divided all breast cancer patients from TCGA into two groups, based on the median expression value of NEAT1 – ZFX – miR-138-5p .…”

Section: Resultsmentioning

confidence: 99%

“…A recently published paper ( 7 ) has provided a web tool, “ceRNA survival”, to perform survival analysis for ceRNA composed of lncRNA–miRNA–mRNA, based on The Cancer Genome Atlas (TCGA) datasets. The web tool was used to perform multivariate Cox regression analysis based on miRNA, mRNA, and lncRNA expression, without co-factors.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, it is adequate for the characterization of single cells from the viewpoint of the ceRNA network. Wang et al applied the cell-specific network method developed by Dai et al and integrated public ceRNA regulations to build a database named LnCeCell, which comprised the predicted lncRNA-associated ceRNA networks at single-cell resolution (7). In this study, we aimed to investigate cell heterogeneity upon estrogen stimulation, from the perspective of the ceRNA network.…”

Section: Introductionmentioning

confidence: 99%

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Inferring Cell Subtypes and LncRNA Function by a Cell-Specific CeRNA Network in Breast Cancer

Chen

Zeng

et al. 2021

Front. Oncol.

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Single-cell RNA sequencing is a powerful tool to explore the heterogeneity of breast cancer. The identification of the cell subtype that responds to estrogen has profound significance in breast cancer research and treatment. The transcriptional regulation of estrogen is an intricate network involving crosstalk between protein-coding and non-coding RNAs, which is still largely unknown, particularly at the single cell level. Therefore, we proposed a novel strategy to specify cell subtypes based on a cell-specific ceRNA network (CCN). The CCN was constructed by integrating a cell-specific RNA-RNA co-expression network (RCN) with an existing ceRNA network. The cell-specific RCN was built based on single cell expression profiles with predefined reference cells. Heterogeneous cell subtypes were inferred by enriching RNAs in CCN to the estrogen response hallmark. Edge biomarkers were identified in the early estrogen response subtype. Topological analysis revealed that NEAT1 was a hub lncRNA for the early response subtype, and its ceRNAs could predict patient survival. Another hub lncRNA, DLEU2, could potentially be involved in GPCR signaling, based on CCN. The CCN method that we proposed here facilitates the inference of cell subtypes from a network perspective and explores the function of hub lncRNAs, which are promising targets for RNA-based therapeutics.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inferring Cell Subtypes and LncRNA Function by a Cell-Specific CeRNA Network in Breast Cancer

Chen

Zeng

et al. 2021

Front. Oncol.

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“…where βi is the Cox regression coefficient for each RNA (denoted by C i ), n is the number of RNAs in the gene set, and Exp(C i ) is the expression value of RNA C i in the corresponding sample. Patients were classified into high expression and low expression groups according to the different classifications ( 16 ).…”

Section: Methodsmentioning

confidence: 99%

TTSurv: Exploring the Multi-Gene Prognosis in Thousands of Tumors

Xin

Zhang

et al. 2021

Front. Oncol.

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Thoracic malignancies are a common type of cancer and area major global health problem. These complex diseases, including lung cancer, esophageal cancer, and breast cancer, etc. have attracted considerable attention from researchers. Potential gene-cancer associations can be explored by demonstrating the association between clinical data and gene expression data. Emerging evidence suggests that the transcriptome plays a particularly critical role as a diagnostic biomarker in pathology and histology studies. Thus, there is an urgent need to develop a platform that allows users to perform a comprehensive prognostic analysis of thoracic cancers. Here, we developed TTSurv, which aims to correlate coding and noncoding genes with cancers by combining high-throughput data with clinical prognosis. TTSurv focuses on the application of high-throughput data to detect ncRNAs, such as lncRNAs and microRNAs, as novel diagnostic and prognostic biomarkers. For a more comprehensive analysis, a large amount of public expression profile data with clinical follow-up information have been integrated into TTSurv. TTSurv also provides flexible methods such as a minimum p-value algorithm and unsupervised clustering methods that can classify thoracic cancer samples into different risk groups. TTSurv will expand our understanding of ncRNAs in thoracic malignancies and provide new insights into their application as potential prognostic/diagnostic biomarkers.

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“…Work from the past decade has given us a whole new perspective on non-coding RNAs. For example, Long non-coding RNA (lncRNA) have been demonstrated to play an important role in chromatin reprogramming, transcription, post-transcriptional modifications and signal transduction ( Anastasiadou et al, 2018 ; Wang et al, 2021 ). LncRNA could act as a miRNA sponge to participate in competitive endogenous RNA (ceRNA) regulation determined by microRNA (miRNA) response elements (MREs) ( Salmena et al, 2011 ), which is an important way for it to regulate gene expression post-transcriptionally.…”

Section: Introductionmentioning

confidence: 99%

Integrating Genetic and Transcriptomic Data to Reveal Pathogenesis and Prognostic Markers of Pancreatic Adenocarcinoma

Bai

Zhao

et al. 2021

Front. Genet.

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Pancreatic adenocarcinoma (PAAD) is one of the deadliest malignancies and mortality for PAAD have remained increasing under the conditions of substantial improvements in mortality for other major cancers. Although multiple of studies exists on PAAD, few studies have dissected the oncogenic mechanisms of PAAD based on genomic variation. In this study, we integrated somatic mutation data and gene expression profiles obtained by high-throughput sequencing to characterize the pathogenesis of PAAD. The mutation profile containing 182 samples with 25,470 somatic mutations was obtained from The Cancer Genome Atlas (TCGA). The mutation landscape was generated and somatic mutations in PAAD were found to have preference for mutation location. The combination of mutation matrix and gene expression profiles identified 31 driver genes that were closely associated with tumor cell invasion and apoptosis. Co-expression networks were constructed based on 461 genes significantly associated with driver genes and the hub gene FAM133A in the network was identified to be associated with tumor metastasis. Further, the cascade relationship of somatic mutation-Long non-coding RNA (lncRNA)-microRNA (miRNA) was constructed to reveal a new mechanism for the involvement of mutations in post-transcriptional regulation. We have also identified prognostic markers that are significantly associated with overall survival (OS) of PAAD patients and constructed a risk score model to identify patients’ survival risk. In summary, our study revealed the pathogenic mechanisms and prognostic markers of PAAD providing theoretical support for the development of precision medicine.

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LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution

Cited by 53 publications

References 33 publications

Inferring Cell Subtypes and LncRNA Function by a Cell-Specific CeRNA Network in Breast Cancer

Inferring Cell Subtypes and LncRNA Function by a Cell-Specific CeRNA Network in Breast Cancer

TTSurv: Exploring the Multi-Gene Prognosis in Thousands of Tumors

Integrating Genetic and Transcriptomic Data to Reveal Pathogenesis and Prognostic Markers of Pancreatic Adenocarcinoma

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