Dataset Information

Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis.

ABSTRACT: Background/Aims:Leiomyosarcoma (LMS) is a tumor derived from malignant mesenchymal tissue associated with poor prognosis. Determining potential prognostic markers for LMS can provide clues for early diagnosis, recurrence, and treatment. Methods:RNA sequence data and clinical features of 103 LMS were obtained from the Cancer Genome Atlas (TCGA) database. Application Weighted Gene Co-Expression Network Analysis (WGCNA) was used to construct a free-scale gene co-expression network, to study the interrelationship between its potential modules and clinical features, and to identify hub genes in the module. The hub gene function was verified by an external database. Results:Twenty-four co-expression modules were constructed using WGCNA. A dark red co-expression module was found to be significantly associated with disease recurrence. Functional enrichment analysis and GEPIA and ONCOMINE database analyses demonstrated that hub genes CDK4, CCT2, and MGAT1 may play an important role in LMS recurrence. Conclusion:Our study constructed an LMS co-expressing gene module and identified prognostic markers for LMS recurrence detection and treatment.

SUBMITTER: Yang J

PROVIDER: S-EPMC7010600 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis.

Yang Jun J Li Cuili C Zhou Jiaying J Liu Xiaoquan X Wang Shaohua S

Frontiers in genetics 20200204

<h4>Background/aims</h4>Leiomyosarcoma (LMS) is a tumor derived from malignant mesenchymal tissue associated with poor prognosis. Determining potential prognostic markers for LMS can provide clues for early diagnosis, recurrence, and treatment.<h4>Methods</h4>RNA sequence data and clinical features of 103 LMS were obtained from the Cancer Genome Atlas (TCGA) database. Application Weighted Gene Co-Expression Network Analysis (WGCNA) was used to construct a free-scale gene co-expression network, t ...[more]

PMID: 32117430

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Project description:BackgroundWhile large-scale genomic analyses symbolize a precious attempt to decipher the molecular foundation of uterine leiomyosarcoma (ULMS), bioinformatics results associated with the occurrence of ULMS based totally on WGCNA and CIBERSORT have not yet been reported. This study aimed to screen the hub genes and the immune cell infiltration pattern in ULMS by bioinformatics methods.MethodsFirstly, the GSE67463 dataset, including 25 ULMS tissues and 29 normal myometrium (NL) tissues, was downloaded from the public database. The differentially expressed genes (DEGs) were screened by the 'limma' package and hub modules were identified by weighted gene co-expression network analysis (WGCNA). Subsequently, gene function annotations were performed to investigate the biological role of the genes from the intersection of two groups (hub module and DEGs). The above genes were calculated in the protein-protein interaction (PPI) network to select the hub genes further. The hub genes were validated using external data (GSE764 and GSE68295). In addition, the differential immune cell infiltration between UL and ULMS tissues was investigated using the CIBERSORT algorithm. Finally, we used western blot to preliminarily detect the hub genes in cell lines.ResultsWGCNA analysis revealed a green-yellow module possessed the highest correlation with ULMS, including 1063 genes. A total of 172 DEGs were selected by thresholds set in the 'limma' package. The above two groups of genes were intersected to obtain 72 genes for functional annotation analysis. Interestingly, it indicated that 72 genes were mainly involved in immune processes and the Neddylation pathway. We found a higher infiltration of five types of cells (memory B cells, M0-type macrophages, mast cells activated, M1-type macrophages, and T cells follicular helper) in ULMS tissues than NL tissues, while the infiltration of two types of cells (NK cells activated and mast cells resting) was lower than in NL tissues. In addition, a total of five genes (KDR, CCL21, SELP, DPT, and DCN) were identified as the hub genes. Internal and external validation demonstrated that the five genes were over-expressed in NL tissues compared with USML tissues. Finally, the correlation analysis results indicate that NK cells activated and mast cells activated positively correlated with the hub genes. However, M1-type macrophages had a negative correlation with the hub genes. Moreover, only the DCN may be associated with the Neddylation pathway.ConclusionA series of evidence confirm that the five hub genes and the infiltration of seven types of immune cells are related to USML occurrence. These hub genes may affect the occurrence of USML through immune-related and Neddylation pathways, providing molecular evidence for the treatment of USML in the future.

Dataset Information

Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis.

Publications

Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis.

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