LncSEA: a platform for long non-coding RNA related sets and enrichment analysis

Chen, Jiaxin; Zhang, Jian; Gao, Yu; Li, Yanyu; Feng, Chenchen; Song, Chao; Ning, Ziyu; Zhou, Xinyuan; Zhao, Jian; Feng, Mingfang; Zhang, Yuexin; Wei, Ling; Pan, Qi; Jiang, Yong; Qian, Fengcui; Han, Junwei; Yang, Yongsan; Wang, Qiuyu; Li, Chunquan

doi:10.1093/nar/gkaa806

Cited by 96 publications

(80 citation statements)

References 76 publications

(56 reference statements)

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“…We implemented LncSEA [ 24 ], an online tool developed for lncRNA enrichment analysis, to primarily assess the related functions ( Figure 3(f) and Supplementary Table S4 ). In the risk model, 36 frlncRNAs were mostly enriched in BCa-related functions, indicating that the signature had a degree of BCa specificity.…”

Section: Resultsmentioning

confidence: 99%

“…Functional Enrichment of the 22-frlncRNA Pair. We implemented LncSEA [24], an online tool developed for lncRNA enrichment analysis, to primarily assess the related functions (Figure 3(f) and Supplementary Table S4). In the risk model, 36 frlncRNAs were mostly enriched in BCa- Prognostic values of ImmuneScore (g, p < 0:05), StromalScore (h, p < 0:05), and ESTIMATEScore (i, p > 0:05).…”

Section: Construction Of Frlncrna Pairs and Prognostic Signaturementioning

confidence: 99%

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Development of a Ferroptosis-Related lncRNA Signature to Predict the Prognosis and Immune Landscape of Bladder Cancer

et al. 2021

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The tight relationship between ferroptotic cell death and immune response demonstrated by recent studies enlightened us to detect the underlying roles of ferroptosis-related long noncoding RNAs (frlncRNAs) in the tumor microenvironment of bladder cancer (BCa). We collected 121 ferroptosis regulators from previous studies. Based on their expression values, 408 cases with BCa were clustered. The patients in different clusters showed diverse immune infiltration, immunotherapy response, and chemotherapy effectiveness, revalidating the tight correlation with ferroptosis and tumor immunity. Through differential, coexpression, Kaplan-Meier, Lasso, and Cox analysis, we developed a 22-lncRNA-pair signature to predict the prognosis of BCa based on gene-pair strategy, where there is no need for definite expression values. The areas under the curves are all over 0.8. The risk model also helped to predict immune infiltration, immunotherapeutic outcomes, and chemotherapy sensitivity. Totally, the prognostic assessment model indicated a promising predictive value, also providing clues for the interaction between ferroptosis and BCa immunity.

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Section: Resultsmentioning

confidence: 99%

Section: Construction Of Frlncrna Pairs and Prognostic Signaturementioning

confidence: 99%

Development of a Ferroptosis-Related lncRNA Signature to Predict the Prognosis and Immune Landscape of Bladder Cancer

et al. 2021

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“…The stable set of 128 lncRNAs derived from mrCAE were validated using existing literature (25). Of 128 lncRNAs, 103 are known lncRNAs associated with different cancer types, Figure 8(a).…”

Section: Validationsmentioning

confidence: 99%

Multi-run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers

Mamun

Tanvir

Sobhan

et al. 2021

Preprint

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Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that leads to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. To discover the critical lncRNAs that can identify the origin of different cancers, we proposed to use the state-of-the-art deep learning algorithm Concreate Autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We proposed a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, a total of 4,768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to the final list of key lncRNAs, which are capable of identifying 12 different cancers. Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study discovered a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability in differentiating high- and low-risk groups of patients in different cancers. The proposed mrCAE outperformed the standard autoencoder, which selects the latent features and is thought to be the upper limit in dimension reduction. Since the proposed mrCAE selects actual features and outperformed AE, it has the potential to provide information that can be used for precision medicine, such as identifying prognostic lncRNAs (this work) and mRNAs, miRNAs, and DNA methylated genes (future work) for different cancers.

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“…LncSEA is a comprehensive database of annotation and enrichment analysis of lncRNA published by Harbin Medical University in China in 2020 ( 74 ). The developers integrated lncRNA datasets from more than 20 databases, such as the genome region of the sample from Cistrome ( 75 ) and NCBI, which collected lncRNA related to cancer survival from The Cancer Genome Atlas (TCGA) ( 76 ) and information on diseases and drugs from databases such as Lnc2Cancer2.0 ( 77 ) and LncRNADisease2.0 ( 46 ).…”

Section: General Platformsmentioning

confidence: 99%

OUP accepted manuscript

Zhang

Qin

et al. 2021

Database

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Accumulated evidence suggests that the widely expressed long-non-coding RNAs (lncRNAs) are involved in biogenesis. Some aberrant lncRNAs are closely related to pathological changes, for instance, in cancer. Both in tumorigenesis and cancer progression, depending on the interplay with cellular molecules, lncRNAs can modulate transcriptional interference, chromatin remodeling, post-translational regulation and protein modification, and further interfere with signaling pathways. Aiming to the diagnosis/ prognosis markers or potential therapeutical targets, it is important to figure out the specific mechanism and the tissue-specific expressing patterns of lncRNAs. Generally, the bioinformatics analysis is the first step. More and more in silico databases are increasing. But the existing integrative online platforms’ functions are not only having their unique features but also share some common features, which may lead to a waste of time for researchers. Here, we reviewed these web tools according to the functions. For each database, we clarified the data source, analysis method and the evidence that the analysis result is derived from. This review also illustrated examples in practical use for a specific lncRNA by these web tools. It will provide convenience for researchers to quickly choose the appropriate bioinformatics web tools in oncology studies.

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LncSEA: a platform for long non-coding RNA related sets and enrichment analysis

Cited by 96 publications

References 76 publications

Development of a Ferroptosis-Related lncRNA Signature to Predict the Prognosis and Immune Landscape of Bladder Cancer

Development of a Ferroptosis-Related lncRNA Signature to Predict the Prognosis and Immune Landscape of Bladder Cancer

Multi-run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers

OUP accepted manuscript

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