Novel CircRNAs in Hub ceRNA Axis Regulate Gastric Cancer Prognosis and Microenvironment

Li, Xianghui; Li, Zhiyan; Liu, Ping; Ai, Shichao; Sun, Feng; Hu, Qiongyuan; Dong, Yulan; Xia, Xuefeng; Guan, Wenxian; Liu, Song

doi:10.3389/fmed.2021.771206

Cited by 5 publications

(3 citation statements)

References 60 publications

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“…On the other hand, the machine learning method used in this study was demonstrated three significant down-regulated miRNAs has-miR-6510, has-miR-6507, has-miR-6512 in gastric cancer which recently gained attention in gastric cancer. Li et.al showed that miR-6512 downregulated in gastric cancer patients and seems to have a correlation with skin manifestations and fibrosis 46 . Moreover, a study by Ding demonstrated that miR-6507 was among top five down expressed miRNAs in GC and may serve as a good predictive biomarker 47 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

Azari

Nazari

Mohit

et al. 2023

Sci Rep

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Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein–protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.

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Section: Discussionmentioning

confidence: 99%

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

Azari

Nazari

Mohit

et al. 2023

Sci Rep

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“…In recent years, increasing evidence has demonstrated that circRNAs acting as ceRNAs participate in GC progression by sponging miRNAs to regulate target genes of miRNAs. [31][32][33] Zhang et al reported that circ_HN1, miR-628-5p and Ecto-5'-nucleotidase could construct a ceRNA network to regulate GC development. 33 The circ-ITCH as a ceRNA regulated the miR-199a-5p/Klotho axis, Fig.…”

Section: Discussionmentioning

confidence: 99%

hsa_circ_0017842 acts as a competing endogenous RNA to enhance the malignancy of gastric cancer

2023

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“…The competing endogenous RNA (ceRNA) hypothesis was first proposed by Salmena et al ( 13 ), who suggested that any gene containing miRNA response elements can be combined with miRNA through the miRNA response elements, thereby inhibiting the effect of miRNAs and indirectly regulating the expression of protein-coding genes. ceRNA is a cross-regulatory network that mediates malignant tumor cell phenotypes, including proliferation and inhibition, autophagy, infinite growth, induction of angiogenesis and angiogenic mimicry, and immune escape ( 14 , 15 ). Dong et al found that the lncRNA GAS5 significantly reduced ovarian cancer cell proliferation and invasion by acting as a ceRNA and suppressing miR-96-5p expression, thereby providing a therapeutic strategy for ovarian cancer treatment ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

Prognostic biomarkers of pancreatic cancer identified based on a competing endogenous RNA regulatory network

Qu¹,

Lu²,

Mei³

et al. 2022

Transl Cancer Res TCR

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Background: Pancreatic cancer is an insidious and heterogeneous malignancy with poor prognosis that is often locally unresectable. Therefore, determining the underlying mechanisms and effective prognostic indicators of pancreatic cancer may help optimize clinical management. This study was conducted to develop a prognostic model for pancreatic cancer based on a competing endogenous RNA (ceRNA) network.Methods: We obtained transcriptomic data and corresponding clinicopathological information of pancreatic cancer samples from The Cancer Genome Atlas (TCGA) database (training set). Based on the ceRNA interaction network, we screened candidate genes to build prediction models. Univariate Cox regression analysis was performed to screen for genes associated with prognosis, and least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive model. A receiver operating characteristic (ROC) curve was drawn, and the C-index was calculated to evaluate the accuracy of the prediction model. Furthermore, we downloaded transcriptomic data and related clinical information of pancreatic cancer samples from the Gene Expression Omnibus database (validation set) to evaluate the robustness of our prediction model.Results: Eight genes (ANLN, FHDC1, LY6D, SMAD6, ACKR4, RAB27B, AUNIP, and GPRIN3) were used to construct the prediction model, which was confirmed as an independent predictor for evaluating the prognosis of patients with pancreatic cancer through univariate and multivariate Cox regression analysis. By plotting the decision curve, we found that the risk score model is an independent predictor has the greatest impact on survival compared to pathological stage and targeted molecular therapy.Conclusions: An eight-gene prediction model was constructed for effectively and independently predicting the prognosis of patients with pancreatic cancer. These eight genes identified show potential as diagnostic and therapeutic targets.

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Novel CircRNAs in Hub ceRNA Axis Regulate Gastric Cancer Prognosis and Microenvironment

Cited by 5 publications

References 60 publications

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

hsa_circ_0017842 acts as a competing endogenous RNA to enhance the malignancy of gastric cancer

Prognostic biomarkers of pancreatic cancer identified based on a competing endogenous RNA regulatory network

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