lncRNA-AC079061.1/VIPR1 axis may suppress the development of hepatocellular carcinoma: a bioinformatics analysis and experimental validation

Lin, Xiahui; Zhang, Danying; Liu, Zhiyong; Tang, Wenyi; Chen, Rongxin; Li, Dongping; Weng, Shuqiang; Dong, Ling

doi:10.1186/s12967-022-03573-7

Cited by 7 publications

(6 citation statements)

References 47 publications

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“…The 12 characteristic genes are primarily enriched in pathway associated with metabolic process. Among the characteristic gene generated from the model, loss of VIPR1 expression in HCC facilitated CAD phosphorylation and tumor progression, suggesting that the restoration of VIPR1 and treatment with the VIPR1 agonist may represent a promising approach for HCC treatment ( 32 , 36 ). Our research suggested that VIPR1 may play a role in the classification of early stage HCC and advanced HCC, but further research is needed to determine its specific function.…”

Section: Discussionmentioning

confidence: 99%

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Jiang,

Zhou,

Jiang

et al. 2024

Front. Oncol.

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Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

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Section: Discussionmentioning

confidence: 99%

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Jiang,

Zhou,

Jiang

et al. 2024

Front. Oncol.

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“…Within the ceRNA network, we discovered that Lnc-PCBP1-AS1 plays a pivotal role, which may influence variable targets through 16 lncRNA-associated miRNAs. Interestingly, a number of miRNAs, including miR-363 [40] , miR-125a-5p [41] , miR-193a [42] , miR-4465 [43] , miR-4319 [44] , miR-125b-5p [45] , miR-22-3p [46] , miR-107 [47] , miR-17-5p [48] , miR-20b-5p [49] , and miR-24-3p [50] , have all been associated with the initiation and progression of hepatocellular carcinoma. Furthermore, miR-20b-5p can be sponged by the lncRNA WWOX-AS1, which upregulates WWOX and thereby inhibits the progression of hepatocellular carcinoma [51] .…”

Section: Discussionmentioning

confidence: 99%

Integrated analysis of high-throughput sequencing-based lncRNA-mediated ceRNA network in Hepatic Alveolar Echinococcosis

Liu,

Shang,

Wang

et al. 2024

Preprint

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BackgroundNumerous studies have indicated that long non-coding RNAs (lncRNAs) can modulate the expression of target gene mRNAs by adsorbing microRNAs (miRNAs). The lncRNA–miRNA–mRNA ceRNA network has been theorized to play an indispensable role in many types of tumors, and has been garnering increasing attention. However, the role of the lncRNA-associated ceRNA regulatory network in Hepatic Alveolar Echinococcosis (HAE) remains unclear and requires further exploration.MethodsIn this study, high-throughput sequencing was performed on lesion tissues and adjacent tissues from three patients with Hepatic Alveolar Echinococcosis (HAE) to identify differentially expressed RNAs. We utilized Cytoscape (version 3.10.1) to construct the lncRNA-miRNA-mRNA ceRNA network based on the interactions from the miRcode, miRTarBase, miRDB, and TargetScan databases, and identified hub lncRNAs from within the ceRNA network. Through the use of the “clusterProfiler” package in R, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations for the DEGs (Differentially Expressed Genes) within the ceRNA network. Concurrently, we utilized these DEGs to construct a protein-protein interaction network (PPI). Finally, an analysis was conducted on the PCBP1-AS1-miR-20b-5p/CAPRIN2 axis within the ceRNA network.ResultsIn HAE, a total of 979 differentially expressed lncRNAs (DELncRNAs) and 870 differentially expressed mRNAs (DEmRNAs) were identified. An HAE-specific ceRNA network comprising 11 lncRNAs, 21 miRNAs, and 56 mRNAs was established, and analysis of this network led to the construction of a sub-network associated with hub lncRNAs. GO and KEGG pathway analyses indicated that the HAE-specific ceRNA network is related to molecular functions and pathways associated with cancer. Subsequent experiments using qPCR and dual-luciferase assays validated the interactions between PCBP1-AS1 and miR-20b-5p, as well as between miR-20b-5p and CAPRIN2. Analysis of the target gene in relation to clinical characteristics of HAE patients suggested that the PCBP1-AS1-miR-20b-5p/CAPRIN2 axis may influence the development of HAE.ConclusionIn this study, we described the gene regulation within the lncRNA-miRNA-mRNA ceRNA network during the development of Hepatic Alveolar Echinococcosis (HAE), which contributes to a deeper exploration of the molecular mechanisms underlying HAE. Additionally, we discovered that PCBP1-AS1 may regulate the expression of CAPRIN2 by adsorbing miR-20b-5p, affecting the onset and progression of HAE. PCBP1-AS1 could potentially serve as a useful target for the diagnosis and treatment of HAE.

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“…Furthermore, the pseudogene DUXAP8 encouraged HCC cell differentiation by modifying kinase function through DUXAP8/miR-490–5p/BUB1 ( Zhang et al, 2020a ). However, when certain lncRNAs target miRNAs of oncogenes, they often inhibit HCC proliferation, as observed with lncRNA MIR31HG ( Yan et al, 2018 ) and lncRNA-AC079061.1 ( Lin et al, 2022 ).…”

Section: The Role Of Cerna Network In the Development Of Hccmentioning

confidence: 99%

The role of competing endogenous RNA network in the development of hepatocellular carcinoma: potential therapeutic targets

Tang,

Li,

Zheng

et al. 2024

Front. Cell Dev. Biol.

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The current situation of hepatocellular carcinoma (HCC) management is challenging due to its high incidence, mortality, recurrence and metastasis. Recent advances in gene genetic and expression regulation have unveiled the significant role of non-coding RNA (ncRNA) in various cancers. This led to the formulation of the competing endogenous RNA (ceRNA) hypothesis, which posits that both coding RNA and ncRNA, containing miRNA response elements (MRE), can share the same miRNA sequence. This results in a competitive network between ncRNAs, such as lncRNA and mRNA, allowing them to regulate each other. Extensive research has highlighted the crucial role of the ceRNA network in HCC development, impacting various cellular processes including proliferation, metastasis, cell death, angiogenesis, tumor microenvironment, organismal immunity, and chemotherapy resistance. Additionally, the ceRNA network, mediated by lncRNA or circRNA, offers potential in early diagnosis and prevention of HCC. Consequently, ceRNAs are emerging as therapeutic targets for HCC. The complexity of these gene networks aligns with the multi-target approach of traditional Chinese medicine (TCM), presenting a novel perspective for TCM in combating HCC. Research is beginning to show that TCM compounds and prescriptions can affect HCC progression through the ceRNA network, inhibiting proliferation and metastasis, and inducing apoptosis. Currently, the lncRNAs TUG1, NEAT1, and CCAT1, along with their associated ceRNA networks, are among the most promising ncRNAs for HCC research. However, this field is still in its infancy, necessitating advanced technology and extensive basic research to fully understand the ceRNA network mechanisms of TCM in HCC treatment.

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lncRNA-AC079061.1/VIPR1 axis may suppress the development of hepatocellular carcinoma: a bioinformatics analysis and experimental validation

Cited by 7 publications

References 47 publications

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Integrated analysis of high-throughput sequencing-based lncRNA-mediated ceRNA network in Hepatic Alveolar Echinococcosis

The role of competing endogenous RNA network in the development of hepatocellular carcinoma: potential therapeutic targets

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