Dataset Information

Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis.

ABSTRACT: Several microRNAs (miRNAs) have been suggested as novel biomarkers for diagnosing gastric cancer (GC) at an early stage, but the single-marker strategy may ignore the co-regulatory relationships and lead to low diagnostic specificity. Thus, multi-target modular diagnostic biomarkers are urgently needed. In this study, a Zsummary and NetSVM-based method was used to identify GC-related hub miRNAs and activated modules from clinical miRNA co-expression networks. The NetSVM-based sub-network consisting of the top 20 hub miRNAs reached a high sensitivity and specificity of 0.94 and 0.82. The Zsummary algorithm identified an activated module (miR-486, miR-451, miR-185, and miR-600) which might serve as diagnostic biomarker of GC. Three members of this module were previously suggested as biomarkers of GC and its 24 target genes were significantly enriched in pathways directly related to cancer. The weighted diagnostic ROC AUC of this module was 0.838, and an optimized module unit (miR-451 and miR-185) obtained a higher value of 0.904, both of which were higher than that of individual miRNAs. These hub miRNAs and module have the potential to become robust biomarkers for early diagnosis of GC with further validations. Moreover, such modular analysis may offer valuable insights into multi-target approaches to cancer diagnosis and treatment.

SUBMITTER: Zhang F

PROVIDER: S-EPMC5991748 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis.

Zhang Fengbin F Xu Wenjuan W Liu Jun J Liu Xiaoyan X Huo Bingjie B Li Bing B Wang Zhong Z

PloS one 20180607 6

Several microRNAs (miRNAs) have been suggested as novel biomarkers for diagnosing gastric cancer (GC) at an early stage, but the single-marker strategy may ignore the co-regulatory relationships and lead to low diagnostic specificity. Thus, multi-target modular diagnostic biomarkers are urgently needed. In this study, a Zsummary and NetSVM-based method was used to identify GC-related hub miRNAs and activated modules from clinical miRNA co-expression networks. The NetSVM-based sub-network consist ...[more]

PMID: 29879180

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Project description:BackgroundEarly detection and diagnosis of malignant tumors is critical for improving the survival rate and treatment outcomes of oral cancer. Thus, the current prospective investigation was designed to verify the role, sensitivity, and specificity of salivary LINC00657 and miRNA-106a as diagnostic markers in oral squamous cell carcinoma patients as compared to oral lichen planus (as an example of oral potentially malignant disorders) and normal individuals, and to show LINC00657 relation to miR-106a.MethodsA total of 36 participants were included, subdivided into 3 groups: Group I: 12 patients diagnosed with oral squamous cell carcinoma (OSCC). Group II: 12 patients diagnosed with oral lichen planus (OLP). Group III: 12 systemically free individuals with no oral mucosal lesions. Unstimulated salivary samples were collected from all participants to evaluate level of LINC00657 and miR-106a in different groups using quantitative real-time PCR.ResultsOSCC showed the highest LINC00657 and lowest miR-106a fold change among included groups. Receiver Operating Characteristic (ROC) curve analysis of the two biomarkers for detecting OSCC revealed that LINC00657 had higher diagnostic accuracy (DA) (83.3%) compared to miR-106a (80.4%). As for detecting OLP, ROC analysis showed that miR-106a had higher (DA) (61%) compared to LINC00657 (52.5%). To discriminate OSCC from OLP, the diagnostic accuracy of both markers is the same (75%). Moreover, differentiating OSCC grades II and III, ROC analysis showed that miR-106a had lower (DA) (60%) compared to LINC00657 (DA) (83.3%).ConclusionsSalivary LINC00657 and miR-106a could be promising diagnostic markers for oral squamous cell carcinoma. Salivary LINC00657 may differentiate oral squamous cell carcinoma from oral potentially malignant disorders with considerable diagnostic accuracy. Moreover, low levels of salivary miR-106a could have the potential to indicate malignancy.Trial registrationThe study was retrospectively registered on clinicaltrial.gov with NCT05821179 (first trial registration in 26/3/2023), date of registration: 19/4/2023.

Project description:Gastric cancer (GC) is one of the leading causes of malignancy‑associated mortality worldwide. However, the underlying molecular mechanisms of GC are unclear and the prognosis of GC is poor. Therefore, it is important and urgent to explore the underlying mechanisms and screen for novel diagnostic and prognostic biomarkers, as well as therapeutic targets. In the current study, scale‑free gene co‑expression networks were constructed using weighted gene co‑expression network analysis, the potential associations between gene sets and clinical features were investigated, and the hub genes were identified. The gene expression profiles of GSE38749 were downloaded from the Gene Expression Omnibus database. RNA‑seq and clinical data for GC from The Cancer Genome Atlas were utilized for verification. Furthermore, the expression of candidate biomarkers in gastric tissues was investigated. Survival analysis was performed using Kaplan‑Meier and log‑rank test. The predictive role of candidate biomarkers in GC was evaluated using a receiver operator characteristic (ROC) curve. Gene Ontology, gene set enrichment analysis and gene set variation analysis methods were used to interpret the function of candidate biomarkers in GC. A total of 29 modules were identified via the average linkage hierarchical clustering. A significant module consisting of 48 genes associated with clinical traits was found; three genes with high connectivity in the clinical significant module were identified as hub genes. Among them, SLC5A6 and microfibril‑associated protein 2 (MFAP2) were negatively associated with the overall survival, and their expression was elevated in GC compared with non‑tumor tissues. Additionally, ROC curves indicated that SLC5A6 and MFAP2 showed a good diagnostic power in discriminating cancerous from normal tissues. SLC5A6 and MFAP2 were identified as novel diagnostic and prognostic biomarkers in GC patients; both of these genes were first reported here in connection with GC and deserved further research.

Dataset Information

Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis.

Publications

Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis.

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