A high level of the long non-coding RNA <i>MCF2L-AS1</i> is associated with poor prognosis in breast cancer and <i>MCF2L-AS1</i> activates YAP transcriptional activity to enhance breast cancer proliferation and metastasis

She, Qing-Bai; Chen, Yuanyuan; Liu, Hong; Tan, Jichao; Li, Youhuai

doi:10.1080/21655979.2022.2074108

Cited by 3 publications

(2 citation statements)

References 56 publications

(48 reference statements)

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“…SORBS1 acts as a potential suppressor in tumorigenesis and low levels of the gene enhances the invasive ability of BRCA cells through activation of c-Jun N-terminal kinase (JNK) (Song et al, 2017). ADAMTSL5 loci is targeted by estrogen receptor alpha (ERα) ( Zhang et al, 2021 ), while MCF2L Antisense RNA 1 promotes the transcriptional activity of yes-associated protein (YAP) to enhance the proliferation and metastasis in Breast cancer ( She et al, 2022 ). LRP5 activity associated with the β-catenin signaling pathway in breast cancer is reported in the literature ( Björklund et al, 2009 ).…”

Section: Resultsmentioning

confidence: 99%

Invention of 3Mint for feature grouping and scoring in multi-omics

et al. 2023

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Advanced genomic and molecular profiling technologies accelerated the enlightenment of the regulatory mechanisms behind cancer development and progression, and the targeted therapies in patients. Along this line, intense studies with immense amounts of biological information have boosted the discovery of molecular biomarkers. Cancer is one of the leading causes of death around the world in recent years. Elucidation of genomic and epigenetic factors in Breast Cancer (BRCA) can provide a roadmap to uncover the disease mechanisms. Accordingly, unraveling the possible systematic connections between-omics data types and their contribution to BRCA tumor progression is crucial. In this study, we have developed a novel machine learning (ML) based integrative approach for multi-omics data analysis. This integrative approach combines information from gene expression (mRNA), microRNA (miRNA) and methylation data. Due to the complexity of cancer, this integrated data is expected to improve the prediction, diagnosis and treatment of disease through patterns only available from the 3-way interactions between these 3-omics datasets. In addition, the proposed method bridges the interpretation gap between the disease mechanisms that drive onset and progression. Our fundamental contribution is the 3 Multi-omics integrative tool (3Mint). This tool aims to perform grouping and scoring of groups using biological knowledge. Another major goal is improved gene selection via detection of novel groups of cross-omics biomarkers. Performance of 3Mint is assessed using different metrics. Our computational performance evaluations showed that the 3Mint classifies the BRCA molecular subtypes with lower number of genes when compared to the miRcorrNet tool which uses miRNA and mRNA gene expression profiles in terms of similar performance metrics (95% Accuracy). The incorporation of methylation data in 3Mint yields a much more focused analysis. The 3Mint tool and all other supplementary files are available at https://github.com/malikyousef/3Mint/.

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Section: Resultsmentioning

confidence: 99%

Invention of 3Mint for feature grouping and scoring in multi-omics

et al. 2023

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“…A series of metastasis-related long non-coding RNA (MRLNRs) have been identified to regulate the metastatic processes of various tumors. The high expression of lncR-MCF2L-AS1A in breast tumor cells promotes metastasis by increasing YAP transcription [ 14 ]. In addition, Dong et al found a positively modulating relationship between lncR-TRIM28-14 and COL4A1 in the peritoneal metastatic tissues of gastric cancer, indicating the potential role of lncR-TRIM28-14 in metastatic evaluation and strategy [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Metastasis-related long non-coding RNAs AL359220.1, SH3BP5-AS1 and ZF-AS1 are significant for prognostic assessment of lung adenocarcinoma

Tan

Mao

Long

et al. 2023

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Background: Metastasis of lung adenocarcinoma (LUAD) severely worsens prognosis. Genetic alteration in the tumor microenvironment (TME) is closely associated with metastasis and other malignant biological properties of LUAD. In this study, we establish a metastasis-related risk model to accurately predict LUAD prognosis. Methods: RNA-sequencing profiles and clinical data of LUAD patients including 503 tumor tissues and 54 adjacent normal tissues were collected in TCGA database. Additionally, the paired specimens from 156 LUAD patients were obtained in a single center. The metastatic relevance and clinical significance of metastasis-related long non-coding RNA (MRLNRs) was validated by series of in vitro experiments including western blotting, qPCR and transwell assays. Results: Six MRLNRs were significantly correlated to prognoses of LUAD patients, of which AL359220.1, SH3BP5-AS1 and ZF-AS1 were further used to establish a metastasis-related risk scoring model (MRRS) due to the close associations with overall survival of LUAD patients. According to the MRRS, patients with higher scores in the high-risk group obtained poorer prognoses and survival outcomes. ZFAS1 expressed highly in tumor tissues and showed the inverse results compared to SH3BP5-AS1 and AL359220.1. In addition, the high expression of ZFAS1 was prominently correlated to the more advanced T-stage and distant metastasis. The reduction of ZFAS1 induced by siRNAs dramatically diminished the migration and invasion abilities of LUAD cells. Conclusions: In the present research, we elucidate the metastatic relevance and clinical significance of AL359220.1, SH3BP5-AS1 and ZF-AS1 in LUAD. Moreover, MRRS provide a promising assessing model for clinical decision making and prognosis of LUAD.

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