Comparative Analysis of Oncogenes Identified by Microarray and RNA-Sequencing as Biomarkers for Clinical Prognosis

Liu, Yuan; Jing, Runyu; Xu, Junmei; Liu, Keqin; Xue, Jiwei; Wen, Zhining; Li, Menglong

doi:10.2217/bmm.15.97

Cited by 14 publications

(11 citation statements)

References 24 publications

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“…With the development of human gene sequencing technology, high-throughput gene expression profiles have been widely used to detect the biomarkers of human diseases, which provides a chance to explore effective indicators for guiding the diagnosis, treatment and evaluating the prognosis of ovarian cancer [15,16]. In recent years, database-based bioinformatic analysis of expression profile has been widely applied to screen out target biomarkers of diagnostic and prognostic value [17].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer

et al. 2020

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Background Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV. Methods Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored. Results A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P < 0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8). Conclusion The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

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Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer

et al. 2020

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“…Therefore, there is a need to explore new melanoma markers to guide the clinical treatment and improve melanoma prognosis. Gene-based biomarkers have become more popular with the advances in human gene sequencing [11,12].…”

Section: Introductionmentioning

confidence: 99%

A novel immune-related genes prognosis biomarker for melanoma: associated with tumor microenvironment

Huang

Mao

et al. 2020

Aging

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Background: Melanoma is a cancer of the skin with potential to spread to other organs and is responsible for most deaths due to skin cancer. It is imperative to identify immune biomarkers for early melanoma diagnosis and treatment. Results: 63 immune-related genes of the total 1039 unique IRGs retrieved were associated with overall survival of melanoma. A multi-IRGs classifier constructed using eight IRGs showed a powerful predictive ability. The classifier had better predictive power compared with the current clinical data. GSEA analysis showed multiple signaling differences between high and low risk score group. Furthermore, biomarker was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusions: The immune-related genes prognosis biomarker is an effective potential prognostic classifier in the immunotherapies and surveillance of melanoma. Methods: Melanoma samples of genes were retrieved from TCGA and GEO databases while the immunerelated genes (IRGs) were retrieved from the ImmPort database. WGCNA, Cox regression analysis and LASSO analysis were used to classify melanoma prognosis. ESTIMATE and CIBERSORT algorithms were used to explore the relationship between risk score and tumor immune microenvironment. GSEA analysis was performed to explore the biological signaling pathway.

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“…Recent studies have shown a tightly association between miRNAs and the pathogenesis of pterygium [6, 15–19]. Nowadays, high-throughput platform has become a mainstream method to identify general RNA (including noncoding RNA) alteration [20, 21]. Because of bioinformatics' information integration ability, it is conducive to get new direction effectively.…”

Section: Introductionmentioning

confidence: 99%

Identification and Interaction Analysis of Significant Genes and MicroRNAs in Pterygium

Sun

Huang

et al. 2019

BioMed Research International

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Purpose. MiRNAs have been widely analyzed in the occurrence and development of many diseases, including pterygium. This study aimed to identify the key genes and miRNAs in pterygium and to explore the underlying molecular mechanisms. Methods. MiRNA expression was initially extracted and pooled by published literature. Microarray data about differentially expressed genes was downloaded from Gene Expression Omnibus (GEO) database and analyzed with the R programming language. Functional and pathway enrichment analyses were performed using the database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction network was constructed with the STRING database. The associations between chemicals, differentially expressed miRNAs, and differentially expressed genes were predicted using the online resource. All the networks were constructed using Cytoscape. Results. We found that 35 miRNAs and 301 genes were significantly differentially expressed. Functional enrichment analysis showed that upregulated genes were significantly enriched in extracellular matrix (ECM) organization, while downregulated genes were mainly involved in cell death and apoptotic process. Finally, we concluded the chemical-gene affected network, miRNA-mRNA interacted networks, and significant pathway network. Conclusion. We identified lists of differentially expressed miRNAs and genes and their possible interaction in pterygium. The networks indicated that ECM breakdown and EMT might be two major pathophysiological mechanisms and showed the potential significance of PI3K-Akt signalling pathway. MiR-29b-3p and collagen family (COL4A1 and COL3A1) might be new treatment target in pterygium.

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Comparative Analysis of Oncogenes Identified by Microarray and RNA-Sequencing as Biomarkers for Clinical Prognosis

Abstract: The highly consistency of DEGs in their regulation directionality and molecular functions indicated the good reproducibility between microarray and RNA-sequencing in identifying potential oncogenes for clinical prognosis.

Cited by 14 publications

References 24 publications

Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer

Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer

A novel immune-related genes prognosis biomarker for melanoma: associated with tumor microenvironment

Identification and Interaction Analysis of Significant Genes and MicroRNAs in Pterygium

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