Prognostic Significance and Diagnostic Value of Overexpressed lncRNA PVT1 in Colorectal Cancer

Pan, Xuefeng; Cheng, Rui; Zhu, Xiaoshan; Cai, Fengbo; Zheng, Guobao; Li, Juntang; Gao, Chunfang

doi:10.7754/clin.lab.2019.190412

Cited by 9 publications

(8 citation statements)

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“…23 LncRNAs can be used to identify cancers, provide prognostic value for cancers, and inform therapeutic options for cancer patients. [23][24][25][26][27][28][29][30][31][32][33] Some abnormally expressed lncRNAs in CRC are closely related to cancer progression.…”

Section: Discussionmentioning

confidence: 99%

“…36 Some lncRNAs, such as lncRNA HOTAIR, lncRNA PVT1, circulating lncRNA DANCER, and others, can act as prognostic factors in CRC. 25,37,38 Although there have been many studies on the associations between lncRNAs and CRC, the roles and mechanisms of lncRNAs in CRC and their clinical applications still need extensive exploration. Recently, studies have reported different genetic models for predicting human cancer.…”

Section: Discussionmentioning

confidence: 99%

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Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Shi

Guo

Yang

et al. 2022

Cancer Biotherapy and Radiopharmaceuticals

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Background: Knowledge about the prognostic role of long noncoding RNA (lncRNA) in colorectal cancer (CRC) is limited. Therefore, we constructed a lncRNA-related prognostic model based on data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Materials and Methods: CRC transcriptome and clinical data were downloaded from the GSE20916 dataset and the TCGA database, respectively. R software was used for data processing and analysis. The differential lncRNA expression within the two datasets was first screened, and then intersections were measured. Cox regression and the Kaplan-Meier method were used to evaluate the effects of various factors on prognosis. The area under the curve (AUC) of the receiver operating characteristic curve and a nomogram based on multivariate Cox analysis were used to estimate the prognostic value of the lncRNA-related model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to elucidate the significantly involved biological functions and pathways. Results: A total of 11 lncRNAs were crossed. The univariate Cox analysis screened out two lncRNAs, which were analyzed in the multivariate Cox analysis. A nomogram based on the two lncRNAs and other clinicopathological risk factors was constructed. The AUC of the nomogram was 0.56 at 3 years and 0.71 at 5 years. The 3-year nomogram model was compared with the ideal model, which showed that some indices of the 3-year model were consistent with the ideal model, suggesting that our model was highly accurate. The GO and KEGG enrichment analyses showed that positive regulation of secretion by cells, positive regulation of secretion, positive regulation of exocytosis, endocytosis, and the calcium signaling pathway were differentially enriched in the two-lncRNA-associated phenotype. Conclusions: A two-lncRNA prognostic model of CRC was constructed by bioinformatics analysis. The model had moderate prediction accuracy, LncRNA BBOX1-AS1 and lncRNA FOXP4-AS1 were identified as prognostic biomarkers.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Shi

Guo

Yang

et al. 2022

Cancer Biotherapy and Radiopharmaceuticals

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“…In addition, compared with healthy controls, plasma levels of lncRNA TINCR were significantly elevated in CRC patients, which suggests its potential for the detecting early CRC (154). A correlation analysis by Pan et al (223) showed that in patients with early CRC, plasma levels of lncRNA PVT1 are significantly higher than those of CEA, suggesting that PVT1 has great potential as a marker for the diagnosis of early CRC. A decrease in lncRNA-ATB expression significantly affects the progression of colon cancer by altering the expression of epithelial markers such as E-cad.…”

Section: Clinical Significance Of Lncrnas In Crc Metastasismentioning

confidence: 99%

The Emerging Landscape of Long Non-Coding RNAs in Colorectal Cancer Metastasis

et al. 2021

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Colorectal cancer (CRC) is one of the most common gastrointestinal cancers, with extremely high rates of morbidity and mortality. The main cause of death in CRC is distant metastasis; it affects patient prognosis and survival and is one of the key challenges in the treatment of CRC. Long non-coding RNAs (lncRNAs) are a group of non-coding RNA molecules with more than 200 nucleotides. Abnormal lncRNA expression is closely related to the occurrence and progression of several diseases, including cancer. Recent studies have shown that numerous lncRNAs play pivotal roles in the CRC metastasis, and reversing the expression of these lncRNAs through artificial means can reduce the malignant phenotype of metastatic CRC to some extent. This review summarizes the major mechanisms of lncRNAs in CRC metastasis and proposes lncRNAs as potential therapeutic targets for CRC and molecular markers for early diagnosis.

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“…In addition to establishing the functional role of lncRNAs in diagnosis, some lncRNAs such as AL117190.1 , COL4A2-AS1 , LINC00184 , MEG3 and MIR22HG could function as crucial prognostic markers for patients ( Yao et al, 2019 ). Besides, as diagnostic and prognostic markers, lncRNAs such as H19 ( Yörüker et al, 2018 ), MEG3 ( Wan and Zhao, 2020 ), PVT1 ( Pan et al, 2019 ), FAM83H antisense RNA 1 ( FAM83H-AS1 ) ( El-Ashmawy et al, 2020 ), SNHG1 ( Xiao et al, 2018 ), and LUCAT1 ( Xing et al, 2021 ) are involved in the process of various cancer progression. Thus, we speculate that dysregulated lncRNAs may be used as biomarkers to provide diagnosis and prognostic of lipid-related diseases but also are useful in therapeutic applications.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

LncRNAs as Therapeutic Targets and Potential Biomarkers for Lipid-Related Diseases

et al. 2021

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Lipid metabolism is an essential biological process involved in nutrient adjustment, hormone regulation, and lipid homeostasis. An irregular lifestyle and long-term nutrient overload can cause lipid-related diseases, including atherosclerosis, myocardial infarction (MI), obesity, and fatty liver diseases. Thus, novel tools for efficient diagnosis and treatment of dysfunctional lipid metabolism are urgently required. Furthermore, it is known that lncRNAs based regulation like sponging microRNAs (miRNAs) or serving as a reservoir for microRNAs play an essential role in the progression of lipid-related diseases. Accordingly, a better understanding of the regulatory roles of lncRNAs in lipid-related diseases would provide the basis for identifying potential biomarkers and therapeutic targets for lipid-related diseases. This review highlighted the latest advances on the potential biomarkers of lncRNAs in lipid-related diseases and summarised current knowledge on dysregulated lncRNAs and their potential molecular mechanisms. We have also provided novel insights into the underlying mechanisms of lncRNAs which might serve as potential biomarkers and therapeutic targets for lipid-related diseases. The information presented here may be useful for designing future studies and advancing investigations of lncRNAs as biomarkers for diagnosis, prognosis, and therapy of lipid-related diseases.

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Prognostic Significance and Diagnostic Value of Overexpressed lncRNA PVT1 in Colorectal Cancer

Cited by 9 publications

References 0 publications

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

The Emerging Landscape of Long Non-Coding RNAs in Colorectal Cancer Metastasis

LncRNAs as Therapeutic Targets and Potential Biomarkers for Lipid-Related Diseases

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