Using weighted gene co-expression network analysis to identify key modules and hub genes in tongue squamous cell carcinoma

Yin, Ke; Zhang, Ying; Zhang, Suxin; Bao, Yong-Rui; Guo, Jie; Zhang, Hang; Li, Tianke

doi:10.1097/md.0000000000017100

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…Numerous studies have reported hub genes as playing key roles in cancer ( Chen et al, 2017 ; Das et al, 2017 ; Yin et al, 2019 ). To identify the hub lncRNAs and miRNAs in our synergistic network, we computed a degree for each node and sorted them in a descending order.…”

Section: Resultsmentioning

confidence: 99%

Construction and Characterization of a Synergistic lncRNA–miRNA Network Reveals a Crucial and Prognostic Role of lncRNAs in Colon Cancer

Zhao

et al. 2020

Front. Genet.

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Non-coding RNAs such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been found to be indispensable factors in carcinogenesis and cancer development. Numerous studies have explored the regulatory functions of these molecules and identified the synergistic interactions among lncRNAs or miRNAs, while those between lncRNAs and miRNAs remain to be investigated. In this study, we constructed and characterized an lncRNA–miRNA synergistic network following a four-step approach by integrating the regulatory pairs and expression profiles. The synergistic interactions with more shared regulatory mRNAs were found to have higher interactional intensity. Through the analysis of nodes in the network, we found that lncRNAs played roles that are more central and had similar synergistic interactions with their neighbors when compared with miRNAs. In addition, known colon adenocarcinoma (COAD)-related RNAs were found to be enriched in this synergistic network, with higher degrees, betweenness, and closeness. Finally, we proposed a risk score model to predict the clinical outcome for COAD patients based on two prognostic hub lncRNAs, MEG3 and ZEB1-AS1. Moreover, the hierarchical networks of these two lncRNAs could contribute to the understanding of the biological mechanism of tumorigenesis. For each lncRNA–miRNA interaction in the hub-related subnetwork and two hierarchical networks, we performed RNAup method to evaluate their binding energy. Our results identified two important lncRNAs with prognostic roles in colon cancer and dissected their regulatory mechanism involving synergistic interaction with miRNAs.

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Section: Resultsmentioning

confidence: 99%

Construction and Characterization of a Synergistic lncRNA–miRNA Network Reveals a Crucial and Prognostic Role of lncRNAs in Colon Cancer

Zhao

et al. 2020

Front. Genet.

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“… 41 , 43 Moreover, AIMP2-DX2, one of variant splicing isoform of AIMP, was significantly associated with the tumorigenesis, cancer cell proliferation, invasion, and migration in many cancers, including lung cancer, ovarian cancer, and nasopharyngeal carcinoma 44 - 47 In the study of Yin K et al, elevating the expression of AIMP2 splicing variation could shorten the survival of patients with tongue squamous cell carcinoma, which was also in high accordance with our study. 48 Taken together, AIMP2 may also serve as a novel therapeutic target of SKCM.…”

Section: Discussionmentioning

confidence: 99%

The Identification of Prognostic and Metastatic Alternative Splicing in Skin Cutaneous Melanoma

Huang

Zeng

et al. 2022

Cancer Control

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Skin cutaneous melanoma (SKCM) is a type of highly invasive cancer originated from melanocytes. It is reported that aberrant alternative splicing (AS) plays an important role in the neoplasia and metastasis of many types of cancer. Therefore, we investigated whether ASEs of pre-RNA have such an influence on the prognosis of SKCM and the related mechanism of ASEs in SKCM. The RNA-seq data and ASEs data for SKCM patients were obtained from the TCGA and TCGASpliceSeq database. The univariate Cox regression revealed 1265 overall survival-related splicing events (OS-SEs). Screened by Lasso regression, 4 OS-SEs were identified and used to construct an effective prediction model (AUC: .904), whose risk score was proved to be an independent prognostic factor. Furthermore, Kruskal–Wallis test and Mann–Whitney–Wilcoxon test showed that an aberrant splicing type of aminoacyl tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2) regulated by CDC-like kinase 1 (CLK1) was associated with the metastasis and stage of SKCM. Besides, the overlapped signal pathway for AIMP2 was galactose metabolism identified by the co-expression analysis. External database validation also confirmed that AIMP2, CLK1, and the galactose metabolism were associated with the metastasis and stage of SKCM patients. ChIP-seq and ATAC-seq methods further confirmed the transcription regulation of CLK1, AIMP2, and other key genes, whose cellular expression was detected by Single Cell Sequencing. In conclusion, we proposed that CLK1-regulated AIMP2-78704-ES might play a critical role in the tumorigenesis and metastasis of SKCM via galactose metabolism. Besides, we established an effective model with MTMR14-63114-ES, URI1-48867-ES, BATF2-16724-AP, and MED22-88025-AP to predict the metastasis and prognosis of SKCM patients.

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“…First, the correlation of all gene pairs was calculated to construct a similarity matrix. Second, the soft threshold β was a weighted parameter of the adjacent function, which the optimal value was obtained by the pickSoftThreshold function in the R package WGCNA [ 15 ]. Third, the TOM similarity function was used to convert the adjacency value into a TOM matrix.…”

Section: Methodsmentioning

confidence: 99%

Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis

Chyr

Jia

et al. 2020

Med Sci Monit

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Using weighted gene co-expression network analysis to identify key modules and hub genes in tongue squamous cell carcinoma

Abstract: Supplemental Digital Content is available in the text

Cited by 12 publications

References 28 publications

Construction and Characterization of a Synergistic lncRNA–miRNA Network Reveals a Crucial and Prognostic Role of lncRNAs in Colon Cancer

Construction and Characterization of a Synergistic lncRNA–miRNA Network Reveals a Crucial and Prognostic Role of lncRNAs in Colon Cancer

The Identification of Prognostic and Metastatic Alternative Splicing in Skin Cutaneous Melanoma

Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis

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