Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis

Chen, Xuan; Wang, Jingyao; Peng, Xiqi; Liu, Kaihao; Zhang, Chunduo; Zeng, X J; Lai, Yongqing

doi:10.1097/md.0000000000019628

Cited by 12 publications

(7 citation statements)

References 39 publications

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“…This is consistent with our results, and we also found that MYH11 expression was lower in the high-risk group. Abnormal expression of ACTG2 has been found in many cancers, such as ACTG2 involved in cell migration and distant metastasis in liver cancer [19]. Our results found that the expression of ACTG2 was lower in the high-risk group of patients.…”

Section: Discussionsupporting

confidence: 49%

Multi-omics data integration analysis of prostate cancer based on sparse least partial squares regression algorithm based on hypergraph regularization

Huang

et al. 2023

Preprint

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Prostate cancer (PCa) is a malignant tumor of the male reproductive system, and its incidence has increased significantly in recent years. This study aimed to further identify candidate biomarkers with prognostic and diagnostic significance by integrating gene expression and DNA methylation data from PCa patients through association analysis. To this end, this paper proposes a sparse partial least squares regression algorithm based on hypergraph regularization (HR-SPLS) by integrating and clustering two kinds of data. Next, module 2, with the most significant weight, was selected for further analysis according to the weight of each module related to DNA methylation and mRNAs. Based on the DNA methylation sites in module 2, this paper uses multiple machine learning methods to construct a PCa diagnosis-related model of 10-DNA methylation sites. The results of ROC analysis showed that the DNA methylation-related diagnostic model we constructed could diagnose PCa patients with high accuracy. Subsequently, based on the mRNAs in module 2, we constructed a prognostic model for 7-mRNAs (MYH11, ACTG2, DDR2, CDC42EP3, MARCKSL1, LMOD1, and MYLK) using multivariate Cox regression analysis. The prognostic model could predict the disease free survival of PCa patients with moderate to high accuracy (AUC=0.761). In addition, GSEA and immune analysis indicated that the prognosis of patients in the risk group might be related to immune cell infiltration. Our findings may provide new methods and insights for identifying disease-related biomarkers by integrating DNA methylation and gene expression data.

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Section: Discussionsupporting

confidence: 49%

Multi-omics data integration analysis of prostate cancer based on sparse least partial squares regression algorithm based on hypergraph regularization

Huang

et al. 2023

Preprint

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“…We finally established the PPI network (Fig. 3 a) through the STRING online tool and Cytoscape [ 21 ]. The network provides a clear and direct expression of complex interactions between gene node, and many domain analyses are based on network analysis.…”

Section: Resultsmentioning

confidence: 99%

Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids

et al. 2021

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Background In the novel coronavirus pandemic, the high infection rate and high mortality have seriously affected people’s health and social order. To better explore the infection mechanism and treatment, the three-dimensional structure of human bronchus has been employed in a better in-depth study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods We downloaded a separate microarray from the Integrated Gene Expression System (GEO) on a human bronchial organoids sample to identify differentially expressed genes (DEGS) and analyzed it with R software. After processing with R software, Gene Ontology (GO) and Kyoto PBMCs of Genes and Genomes (KEGG) were analyzed, while a protein–protein interaction (PPI) network was constructed to show the interactions and influence relationships between these differential genes. Finally, the selected highly connected genes, which are called hub genes, were verified in CytoHubba plug-in. Results In this study, a total of 966 differentially expressed genes, including 490 upregulated genes and 476 downregulated genes were used. Analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways related to immune response and cytokines. We construct protein-protein interaction network and identify 10 hub genes, including IL6, MMP9, IL1B, CXCL8, ICAM1, FGF2, EGF, CXCL10, CCL2, CCL5, CXCL1, and FN1. Finally, with the help of GSE150728, we verified that CXCl1, CXCL8, CXCL10, CCL5, EGF differently expressed before and after SARS-CoV-2 infection in clinical patients. Conclusions In this study, we used mRNA expression data from GSE150819 to preliminarily confirm the feasibility of hBO as an in vitro model to further study the pathogenesis and potential treatment of COVID-19. Moreover, based on the mRNA differentiated expression of this model, we found that CXCL8, CXCL10, and EGF are hub genes in the process of SARS-COV-2 infection, and we emphasized their key roles in SARS-CoV-2 infection. And we also suggested that further study of these hub genes may be beneficial to treatment, prognostic prediction of COVID-19.

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“…This technology allows the use of function-specific mRNAs panels for simultaneous evaluations of cellular pathways and processes. On this light, we have used the PanCancer Progression Panel to perform multiplex gene expression analysis with 770 genes that are involved in each step of cancer progression, including angiogenesis, extracellular matrix remodeling (ECM), epithelial-to-mesenchymal transition (EMT) and metastasis [ 33 ]. From the analysis of the results a reduction of the metastatic response of the cells transduced with mimic miR-423-5p is recorded (Fig.…”

Section: Resultsmentioning

confidence: 99%

MiR-423-5p prevents MALAT1-mediated proliferation and metastasis in prostate cancer

Ferri

Biase

Bocchetti

et al. 2022

J Exp Clin Cancer Res

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Background The long non-coding RNA (lncRNA), MALAT1, plays a key role in the development of different cancers, and its expression is associated with worse prognosis in patients. However, its mechanism of action and its regulation are not well known in prostate cancer (PCa). A general mechanism of action of lncRNAs is their interaction with other epigenetic regulators including microRNAs (miRNAs). Methods Using lentiviral stable miRNA transfection together with cell biology functional assays and gene expression/target analysis, we investigated the interaction between MALAT1 and miR-423-5p, defined as a target with in silico prediction analysis, in PCa. Results Through bioinformatic analysis of data available from TCGA, we have found that MALAT1 expression correlates with high Gleason grade, metastasis occurrence, and reduced survival in PCa patients. These findings were validated on a TMA of PCa showing a significant correlation between MALAT1 expression with both stage and grading. We report that, in PCa cells, MALAT1 expression and activity is regulated by miR-423-5p that binds MALAT1, downregulates its expression and inhibits its activity in promoting proliferation, migration, and invasion. Using NanoString analysis, we unraveled downstream cell pathways that were affected by miR-423-5p expression and MALAT1 downregulation and identified several alterations in genes that are involved in metastatic response and angiogenic pathways. In addition, we showed that the overexpression of miR-423-5p increases survival and decreases metastases formation in a xenograft mouse model. Conclusions We provide evidence on the role of MALAT1 in PCa tumorigenesis and progression. Also, we identify a direct interaction between miR-423-5p and MALAT1, which results in the suppression of MALAT1 action in PCa.

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Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis

Abstract: Supplemental Digital Content is available in the text

Cited by 12 publications

References 39 publications

Multi-omics data integration analysis of prostate cancer based on sparse least partial squares regression algorithm based on hypergraph regularization

Multi-omics data integration analysis of prostate cancer based on sparse least partial squares regression algorithm based on hypergraph regularization

Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids

MiR-423-5p prevents MALAT1-mediated proliferation and metastasis in prostate cancer

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