&lt;p&gt;Long Non-Coding RNA 691 Regulated PTEN/PI3K/AKT Signaling Pathway in Osteosarcoma Through miRNA-9-5p&lt;/p&gt;

Yao, Pengju; Ni, Yangming; Liu, Changlu

doi:10.2147/ott.s249827

Cited by 16 publications

(8 citation statements)

References 39 publications

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“…Since the aberrant DNA methylation was proven to be associated with the OS tumorigenesis, it may serve as a predictive biomarker for OS prognosis ( 18 , 30 ). The DNA methylation genes, including FGD1, METTL3, ALKBH5, and PTEN, are associated with OS tumorigenesis reported in many previous studies ( 23 , 31 - 33 ) list of them keeps growing. It has been reported that methylation of frizzled-related proteins (SFRPs) may promote the Wnt signaling pathway, thereby enhancing OS cell invasion ( 34 ).…”

Section: Discussionmentioning

confidence: 94%

Identifying four DNA methylation gene sites signature for predicting prognosis of osteosarcoma

Zhang¹,

Zheng²,

Li³

et al. 2020

Transl Cancer Res TCR

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Background: Osteosarcoma (OS) is a common malignant bone tumor in children and adolescents.DNA methylation plays a crucial role in the prognosis prediction of cancer. Identification of novel DNA methylation sites biomarkers could be beneficial for the prognosis of OS patients. In this study, we aim to find an efficient methylated site model for predicting survival in OS.Methods: DNA methylation data were downloaded from the Cancer Genome Atlas database (TCGA) and the GEO database. Cox proportional hazard regression and random survival forest algorithm (RSFVH) were applied to identify DNA methylated site signature in the samples randomly assigned to the training subset and the other samples as the test subset. By randomizing 71 clinical samples into two individual groups and a series of statistical analyses between the two groups, a DNA methylation signature is verified.Results: This signature comprises four methylation sites (cg04533248, cg12401425, cg13997435, and cg15075357) associated with the patient training group from the univariate Cox proportional hazards regression analysis, RSFVH, and multivariate Cox regression analysis. Kaplan-Meier survival curves showed the OS patients in the high-risk group have a poor 5-year overall survival compared with the low-risk group, and this finding was identified in the test data set. A ROC analysis was performed in the current research.The results revealed that this signature was an independent predictor of patient survival by investigating the AUC of the four methylation sites signature in the training data set (AUC =0.861) and test data set, respectively (AUC =0.920). The nomogram described in the current study placed a great guiding value for predicting 1-, 2-, 3-year survival of the OS by combining age, gender, grade, and TNM stage as covariates with the RS of patients' methylation related signatures.Conclusions: Our study proved that this signature might be a powerful prognostic tool for survival rate evaluation and guide tailored therapy for OS patients.

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Section: Discussionmentioning

confidence: 94%

Identifying four DNA methylation gene sites signature for predicting prognosis of osteosarcoma

Zhang¹,

Zheng²,

Li³

et al. 2020

Transl Cancer Res TCR

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“…e study showed that CDKN1A upregulation induced by miR-93-5p CDKN1A could block the TGF-β and thus increase the therapeutic effects of chemotherapy on small cell lung cancer cells [29]. miRNA-9-5p has been found to upregulate and activate PI3K/AKT pathways in osteosarcoma [30].…”

Section: Discussionmentioning

confidence: 98%

“…The study showed that CDKN1A upregulation induced by miR-93-5p CDKN1A could block the TGF- β and thus increase the therapeutic effects of chemotherapy on small cell lung cancer cells [ 29 ]. miRNA-9-5p has been found to upregulate and activate PI3K/AKT pathways in osteosarcoma [ 30 ]. Besides, miR-485-3p has been also proved to be involved in the cancer development via regulating TGF- β pathways, and RUNX2 has been widely confirmed as the downstream factors of TGF- β .…”

Section: Discussionmentioning

confidence: 99%

Screening and Analysis of Biomarkers in the miRNA-mRNA Regulatory Network of Osteosarcoma

Han

Guo

et al. 2022

Journal of Healthcare Engineering

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Osteosarcoma is a malignant disease, and few effective strategies can completely overcome the prognosis of these patients. This study attempted to reveal the key factors and related molecular mechanisms of osteosarcoma via excavating public microarray datasets. The data were obtained from the Gene Expression Omnibus (GEO) database; the differentially expressed miRNAs and differentially expressed genes were obtained in GSE69470 and GSE12685l, respectively; the target of miRNAs were predicted with the miRDIP database; the functions of the factors were analyzed and visualized by the David database and R language, respectively. Moreover, the protein-protein interaction network and miRNA-mRNA network were performed with the STRING database and Cytoscape software to identify the hub nodes in GSE69470 and GSE12685. The results showed that 834 DEGs were found in GSE12685 and 37 miRNAs were found in GSE69470. Moreover, the target of 37 miRNAs were enriched in PI3K/AKT, P53, Wnt/β-catenin, and TGF-β pathways and related with skeletal system development and cell growth. Besides, the miRNAs including miR-22-3p, miR-154-5p, miR-34a-5p, miR-485-3p, miR-93-5p, and miR-9-5p and the genes including LEF1, RUNX2, CSF1R, CDKN1A, and FBN1 were identified as the hub nodes via network analysis. In conclusion, this study suggested that the miRNAs including miR-22-3p, miR-154-5p, miR-34a-5p, miR-485-3p, miR-93-5p, and miR-9-5p and the genes including LEF1, RUNX2, CSF1R, CDKN1A, and FBN1 act as key factors in the progression of osteosarcoma.

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“…Aberrant regulation of lncRNAs is associated with tumor development and carcinogenesis, and lncRNAs may be applied as diagnostic, prognostic or therapeutic biomarkers for several types of cancer, including gastric cancer ( 29 ), osteosarcoma ( 30 ) and ovarian cancer ( 31 – 33 ). For example, Zhao et al ( 34 ) reported that the lncRNA LINC00092 induces glycolysis in fibroblasts associated with cancer, and influences the progression of ovarian cancer.…”

Section: Discussionmentioning

confidence: 99%

Long non‑coding RNA SNHG9 inhibits ovarian cancer progression by sponging microRNA‑214‑5p

et al. 2020

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Ovarian cancer ranks 7th among the most common cancer types affecting women worldwide. A number of studies have confirmed that multiple long non-coding RNAs participate in the occurrence and progression of ovarian cancer. Small nucleolar RNA host gene 9 (SNHG9) serves a role in the progression of glioblastoma and pancreatic cancer. However, the specific biological function of SNHG9 in ovarian cancer has not yet been fully investigated. The present study aimed to determine the biological role and potential molecular mechanism underlying the influence of SNHG9 in ovarian cancer. SNHG9 expression in ovarian cancer cell lines and tissues were measured via reverse transcription-quantitative PCR analysis, and cell proliferation was detected via Cell Counting Kit-8 and colony formation assays. Flow cytometry was performed to assess cell cycle progression, and Transwell and wound healing assays were performed to assess cell invasion and migration abilities. Bioinformatics software was utilized to determine the target genes of SNHG9, which were subsequently verified via dual-luciferase reporter and RNA immunoprecipitation assays. The results demonstrated that SNHG9 expression was remarkably lower in ovarian cancer cell lines and tissues compared with the negative controls. Cell function assays demonstrated that decreased SNHG9 expression notably induced the migration, colony formation, proliferation and invasiveness of ovarian cancer cells. Furthermore, the inhibitory effect of SNHG9 on the migration, colony formation, proliferation and invasion of ovarian cancer cells was partially reversed by miR-214-5p upregulation. Thus, taken together, the current results suggest that SNHG9 may serve as a tumor suppressor gene in ovarian cancer by regulating the miR-214-5p/cryptochrome circadian regulator 2 axis.

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<p>Long Non-Coding RNA 691 Regulated PTEN/PI3K/AKT Signaling Pathway in Osteosarcoma Through miRNA-9-5p</p>

Cited by 16 publications

References 39 publications

Identifying four DNA methylation gene sites signature for predicting prognosis of osteosarcoma

Identifying four DNA methylation gene sites signature for predicting prognosis of osteosarcoma

Screening and Analysis of Biomarkers in the miRNA-mRNA Regulatory Network of Osteosarcoma

Long non‑coding RNA SNHG9 inhibits ovarian cancer progression by sponging microRNA‑214‑5p

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