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      Construction of a competing endogenous RNA network to analyse glucose-6-phosphate dehydrogenase dysregulation in hepatocellular carcinoma

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          Abstract

          Hepatocellular carcinoma (HCC) is a common malignant tumour with high rates of morbidity and mortality worldwide. Therefore, it is of great significance to find new molecular markers for HCC diagnosis and treatment. G6PD is known to be dysregulated in a variety of tumours. In addition, the ceRNA network plays a crucial role in the occurrence and development of HCC. However, the mechanism by which the ceRNA network regulates G6PD in HCC remains unclear. We used TCGA-LIHC data to analyse the possibility of using G6PD as an independent prognostic marker. Univariate Cox proportional hazards regression, multivariate Cox proportional hazards regression, and receiver operating characteristic curve analysis were used to analyse the influence of G6PD overexpression on the prognosis of HCC patients. We also analysed the biological function of G6PD, its effect on the immune microenvironment, and drug sensitivity. Finally, we constructed a ceRNA network of lncRNAs/miR-122-5p/ G6PD to explore the regulatory mechanism of G6PD. G6PD was highly expressed in HCC, was related to pathological stage and poor prognosis, and could be used as an independent prognostic indicator of HCC. The expression of G6PD was closely related to the immune microenvironment of HCC. In addition, the expression of G6PD in HCC could be regulated by the ceRNA network. Therefore, G6PD can be used as an immunotherapy target to improve the survival and prognosis of HCC patients, and the ceRNA regulatory network of G6PD has potential diagnostic and therapeutic value for HCC.

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: Writing—original draft
                Role: Data curationRole: Visualization
                Role: Data curationRole: Visualization
                Role: SoftwareRole: Visualization
                Role: ResourcesRole: Writing—review & editing
                Role: Writing—review & editing
                Journal
                Biosci Rep
                Biosci Rep
                bsr
                Bioscience Reports
                Portland Press Ltd.
                0144-8463
                1573-4935
                30 June 2022
                27 June 2022
                : 42
                : 6
                : BSR20220674
                Affiliations
                [1 ]School of Clinical Medicine, Dali University, Dali, Yunnan Province, China
                [2 ]Center of Genetic Testing, The First Affiliated Hospital of Dali University, Dali, Yunnan Province, China
                Author notes
                Correspondence: Guangming Wang ( wgm1991@ 123456dali.edu.cn ) or Yuanyuan Zhang ( zhyy@ 123456dali.edu.cn )
                Author information
                https://orcid.org/0000-0002-6336-7673
                https://orcid.org/0000-0002-6203-3313
                https://orcid.org/0000-0002-0220-1493
                Article
                BSR20220674
                10.1042/BSR20220674
                9245079
                35712981
                f68642c9-1d11-4cae-a6f8-1001c3b1f1c7
                © 2022 The Author(s).

                This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

                History
                : 29 March 2022
                : 29 May 2022
                : 16 June 2022
                : 17 June 2022
                Page count
                Pages: 17
                Categories
                Bioinformatics
                Cancer
                Diagnostics & Biomarkers
                Research Articles

                Life sciences
                biomarkers,cerna,g6pd,hcc,immunomodulation,mir-122-5p
                Life sciences
                biomarkers, cerna, g6pd, hcc, immunomodulation, mir-122-5p

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