Dataset Information

Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis.

ABSTRACT: Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co-expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co-expression of lncRNAs and protein-coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA-PCG differential co-expression network (DCN). DCN was characterized as a scale-free, small-world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA-PCG subnetworks were identified from the DCN by integrating both differential expression and differential co-expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.

SUBMITTER: Liu W

PROVIDER: S-EPMC7176870 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis.

Liu Wei W Gan Cai-Yan CY Wang Wei W Liao Lian-Di LD Li Chun-Quan CQ Xu Li-Yan LY Li En-Min EM

Journal of cellular and molecular medicine 20200312 8

Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co-expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differenti ...[more]

PMID: 32164040

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Project description:The poor outcome of patients with esophageal squamous cell carcinoma (ESCC) highlights the importance of the identification of novel effective prognostic biomarkers. Long non‑coding RNAs (lncRNAs) serve regulatory roles in various types of cancer. The aim of the present study was to investigate the lncRNA expression profile in ESCC and to identify lncRNAs associated with the prognosis of ESCC by performing comprehensive bioinformatics analyses. The RNA‑sequencing (Seq) expression dataset GSE53625 generated from ESCC samples was used as a training dataset. Additional RNA‑Seq datasets relative to ESCC samples were downloaded from The Cancer Genome Atlas and used as a validation dataset. Data were screened using the limma package, and differentially expressed lncRNAs between early‑ and late‑stage ESCC were identified. A random forest algorithm was used to select the optimal lncRNA biomarkers, which were then analyzed using the support vector machine (SVM) algorithm with R software. The identified lncRNA biomarkers were examined in the validation dataset by bidirectional hierarchical clustering and using an SVM classifier. Subsequently, univariate and multivariate Cox regression analyses were performed to analyze the potential ability lncRNAs to predict the survival rate of patients with ESCC. By examining the training group, 259 deregulated lncRNAs between early‑ and advanced‑stage ESCC were identified. Further bioinformatics analyses identified a nine‑lncRNA signature, including AC098973, AL133493, RP11‑51M24, RP11‑317N8, RP11‑834C11, RP11‑69C17, LINC00471, LINC01193 and RP1‑124C. This nine‑lncRNA signature was used to predict the tumor stage and patient survival rate with high reliability and accuracy in the training and validation datasets. Furthermore, these nine lncRNA biomarkers were primarily involved in regulating the cell cycle and DNA replication, and these processes were previously identified to be associated with the progression of ESCC. The identified nine‑lncRNA signature was identified to be associated with the tumor stage, and could be used as predictor of the survival rate of patients with ESCC.

Dataset Information

Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis.

Publications

Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis.

Similar Datasets

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