&lt;p&gt;Construction and Comprehensive Analysis of a ceRNA Network to Reveal Potential Novel Biomarkers for Triple-Negative Breast Cancer&lt;/p&gt;

Ma, Lifei; Song, Guiqin; Li, Meiyu; Hao, Xiu-Qing; Huang, Yong; Lan, J Y; Yang, Siqian; Zhang, Zetian; Zhang, Guohui; Mu, Jiao

doi:10.2147/cmar.s260150

Cited by 6 publications

(4 citation statements)

References 61 publications

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“…To date, the four pyroptosis-related lncRNAs in our model have not yet been studied in bladder cancer. However, survival analysis performed by Ma et al indicated that lncRNA AC124312.3 was notably correlated with the OS of triple-negative breast cancer patients and may be a promising therapeutic target [ 24 ]. Moreover, the upregulation of lncRNA AC007128.1 is associated with the poor prognosis of esophageal squamous cell carcinoma patients [ 25 , 26 ].…”

Section: Discussionmentioning

confidence: 99%

Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Bladder Cancer

Tang

et al. 2022

Disease Markers

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Objective. Bladder cancer (BC) is the most common malignancy in the urinary system and is prone to recurrence and metastasis. Pyroptosis is a kind of cell necrosis that is triggered by the gasdermin protein family. lncRNAs are noncoding RNAs that are more than 200 nucleotides long. Both pyroptosis and lncRNAs are associated with tumor development and progression. This study is aimed at exploring and establishing a prognostic signature of BC based on pyroptosis-related lncRNAs. Methods. In this study, The Cancer Genome Atlas (TCGA) database provided us with the RNA sequencing transcriptome data of bladder cancer patients, and we identified differentially expressed pyroptosis-related lncRNAs in bladder cancer. Then, the prognostic significance of these lncRNAs was assessed using univariate Cox regression analysis and LASSO regression analysis. Subsequently, 4 pyroptosis-related lncRNAs, namely, AL121652.1, AL161729.4, AC007128.1, and AC124312.3, were identified by multivariate Cox regression analysis, thus constructing the prognostic risk model. Then, we compared the levels of immune infiltration, differences in cell function, immune checkpoints, and m6A-related gene expression levels between the high- and low-risk groups. Result. Patients were divided into low-risk or high-risk groups based on the median risk score. Kaplan–Meier survival analysis indicated that the overall survival of bladder cancer patients in the low-risk group was substantially superior to that in the high-risk group ( p < 0.001 ). The receiver operating characteristic (ROC) curve further confirmed the credibility of our model. Moreover, gene set enrichment analysis (GSEA) indicated that these were different signal pathways significantly enriched between the two groups. Immune infiltration, immune checkpoint, and N6-methyladenosine-related gene analysis also reflected that there were notable differences between the two groups. Conclusion. Therefore, this prognostic risk model is based on the level of pyroptotic lncRNAs, which is conducive to individualized assessment of the risk of patients and provides a reference for clinical treatment. This will also help provide insights into the prognosis and treatment of bladder cancer.

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Section: Discussionmentioning

confidence: 99%

Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Bladder Cancer

Tang

et al. 2022

Disease Markers

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“…However, this is the first study to provide a risk sampling model for osteosarcoma based on three long RNAs with prognostic relevance linked to pyroptosis. Bioinformatics study has previously shown RPARP-AS1 and AC124312.3 in breast cancer [ 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value of a Pyroptosis-Related Long Noncoding RNA Signature Associated with Osteosarcoma Microenvironment

Liu

Ding

et al. 2021

Journal of Oncology

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Background. Osteosarcoma is the most prevalent bone cancer that affects young adults and adolescents. It is the most frequent malignancy of the bone. In spite of the fact that complete surgical resection and chemotherapy have increased the overall survival of osteosarcoma patients considerably, the prognosis remains dismal in patients with recurring and/or metastasized osteosarcoma. Thus, finding predictive biomarkers representing osteosarcoma's biological variability may result in more effective treatment for osteosarcoma patients. Methods. In this research, RNA data and clinical information were obtained from TARGET database. The risk score was calculated using a technique that incorporated both univariate and multivariate Cox regression. A variety of statistical methods were employed to assess the risk score's accuracy. These included ROC curves, nomograms, and Kaplan-Meier curves. Following that, bioinformatics studies were carried out in order to investigate the possible biological processes that influence the prognosis of osteosarcoma patients. GSEA was used to investigate the variations in pathway enrichment among the different groups of genes. To examine the disparities in the immune microenvironment, the analytical methods CIBERSORT and ssGSEA were employed. Results. We discovered three differentially expressed lncRNAs (RPARP-AS1, AC009159.3, and AC124312.3) that are linked to osteosarcoma prognosis. Kaplan-Meier analysis showed the presence of a signature of high-risk lncRNAs linked with a poor prognosis for osteosarcoma. Furthermore, the AUC of the lncRNAs signature was 0.773, indicating that they are useful in predicting osteosarcoma prognosis in certain cases. In predicting osteosarcoma prognosis, our risk assessment approach outperformed conventional clinicopathological characteristics. In the high-risk group of people, GSEA showed the presence of tumor-related pathways as well as immune-related pathways. Furthermore, TARGET revealed that immune-related functions such as checkpoint, T-cell coinhibition, and costimulation were significantly different between the high-risk and low-risk groups. LAIR1, LAG3, CD44, and CD22, as well as other immune checkpoints, were shown to be expressed differentially across the two risk groups. Conclusion. This study established that pyroptosis-derived lncRNAs had a significant predictive value for osteosarcoma patients' survival, indicating that they may be a viable target for future therapy.

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“…[ 12 ] LncRNAs act as ceRNA that play an important role in cellular biological processes and tumorigenesis. [ 15 ] The ceRNA network has been constructed in different types of cancers including gastric cancer, [ 16 ] tongue squamous cell carcinoma, [ 17 ] hepatocellular carcinoma, [ 18 ] breast cancer, [ 19 ] and so on. In 2018, Zhang et al constructed a lncRNA-associated ceRNA network in colon cancer using the TCGA database to identify biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Construction of competing endogenous RNA network and identification of novel molecular biomarkers in colon cancer

Liu

et al. 2021

Medicine

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Colon cancer patients suffer from high incidence and mortality rates worldwide. More novel molecular biomarkers should be used for the diagnosis and treatment of colon cancer. Long noncoding RNAs (lncRNAs) are found to be involved in colon cancer tumorigenesis and metastasis. This study aimed to identify novel lncRNAs in colon cancer. Two independent datasets (GSE70880 and GSE110715) were downloaded from the Gene Expression Omnibus database and merged with the sva package. R software was used to distinguish differentially expressed lncRNAs and mRNAs in the merged dataset. The competing endogenous RNA (ceRNA) network was constructed using differentially expressed lncRNAs and mRNAs with Cytoscape. Differentially expressed RNAs in the ceRNA network were further verified using the Cancer Genome Atlas database. Gene oncology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment and survival analysis were also performed to identify hub genes. A total of 99 differentially expressed lncRNAs and 95 differentially expressed mRNAs were identified in the merged database. Ten lncRNAs, 8 miRNAs, and 6 mRNAs were involved in the ceRNA network, in which LINC00114 and UCA1 were highly expressed in colon cancer. They were both associated with early tumor stages and might be used for the early diagnosis of colon cancer. High expression of LINC00114 can lead to poor overall survival of colon cancer patients. Furthermore, new pathways such as LINC00114/miR-107/PCKS5, UCA1/miR-107/PCKS5, and UCA1/miR-129-5p/SEMA6A were identified. Two novel lncRNAs (LINC00114 and UCA1) in colon cancer were identified by bioinformatics analysis. They might contribute to the occurrence and development of colon cancer. In addition, LINC00114 may be involved in the overall survival of colon cancer patients.

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<p>Construction and Comprehensive Analysis of a ceRNA Network to Reveal Potential Novel Biomarkers for Triple-Negative Breast Cancer</p>

Cited by 6 publications

References 61 publications

Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Bladder Cancer

Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Bladder Cancer

Prognostic Value of a Pyroptosis-Related Long Noncoding RNA Signature Associated with Osteosarcoma Microenvironment

Construction of competing endogenous RNA network and identification of novel molecular biomarkers in colon cancer

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