Identification of lncRNA-mRNA Regulatory Module to Explore the Pathogenesis and Prognosis of Melanoma

Zhang, Jiaqi; Liu, Hui; Zhang, Wenhao; Li, Yinfang; Fan, Zhipeng; Jiang, Hua; Luo, Judong

doi:10.3389/fcell.2020.615671

Cited by 20 publications

(21 citation statements)

References 66 publications

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“…Based on the PPI network, the MCODE algorithm was used to cluster the network into important modules and then the cytoHubba tool was used to calculate the degree value of each node. In line with the previous research, only the nodes with a degree value > 20 were considered as hub mRNAs (21).…”

Section: Additional Ltering Of Mrnas and Lncrnasmentioning

confidence: 69%

“…The LUAD cell line A549 and all compounds in the database were chosen for this analysis, and a list of compounds with connectivity scores ranging from -100 to 100 was obtained. In brief, a positive score indicates the similarity between a compound's signature and the signature of the query, while a negative score indicates that the two signatures are opposing (21,34). The magnitude of the score corresponds to the magnitude of similarity or dissimilarity.…”

Section: Connectivity Map-based Drug Screening and Molecular Dockingmentioning

confidence: 99%

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Identification of a lncRNA-mRNA Regulatory Module for Prognosis, Classification and Potential Treatment of Lung Adenocarcinoma

Zhao

Liu

et al. 2021

Preprint

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Background: Lung cancer remains the leading cause of cancer death worldwide, with lung adenocarcinoma (LUAD) being the most prevalent subtype of lung cancer. This study aimed to identify a lncRNA-mRNA regulatory module related to the prognosis, classification, and potential treatment of LUAD.Methods: Publicly available gene expression data of three cohorts were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Differential expression analysis between LUAD and normal samples, as well as the survival analysis, was performed. Protein-protein interaction (PPI) network and co-expression analyses were conducted to further identify key genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was developed to predict overall survival. Five machine learning models, including logistic regression, K-nearest neighbor (KNN), support vector machine (SVM), random forest, and extremely gradient boosting (XGBOOST), were trained to distinguish early-stage or epidermal growth factor receptor (EGFR)-mutation LUAD from others. Furthermore, connectivity map (CMap) and molecular docking analyses were performed to identify compounds with the ability to reverse the expression profiles of the key genes.Results: A cohort comprised of 535 LUAD and 59 normal samples in TCGA was used as the training set, while the GSE31210 and GSE30219 datasets were used as validation sets. 189 mRNAs and 11 lncRNAs were differentially expressed and associated with the overall survival. 43 hub mRNAs were further identified from the PPI network, and 3 lncRNAs were significantly correlated to the expression of hub mRNAs. Six genes with nonzero coefficients were selected by using the LASSO COX regression analysis, and the corresponding risk score was derived. The time-dependent ROC and Kaplan Meier analysis demonstrated that the risk score accurately discriminates the patients with a high or low risk. KNN and XGBOOST were the best models to recognize early-stage and EGFR-mutation LUAD, respectively. Purvalanol-a and Etoposide obtained a score of -99.98 in CMap and were successfully docked with three key genes.Conclusions: A lncRNA-mRNA regulatory module, including 4 mRNAs and 2 lncRNAs, was identified, and this module can facilitate the exploration of pathogenesis of LUAD and speed up the development of new treatments for LUAD patients.

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Section: Additional Ltering Of Mrnas and Lncrnasmentioning

confidence: 69%

Section: Connectivity Map-based Drug Screening and Molecular Dockingmentioning

confidence: 99%

Identification of a lncRNA-mRNA Regulatory Module for Prognosis, Classification and Potential Treatment of Lung Adenocarcinoma

Zhao

Liu

et al. 2021

Preprint

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“…As precision medicine becomes increasingly relevant in healthcare, the field of pharmacogenomic also continues to gain prominence in the clinical setting [ 50 ]. Meanwhile, multiple studies have demonstrated that small molecule drugs can modify lncRNA expression, which suggests a promising therapy for human diseases [ 51 , 52 ]. Thus, based on the hypo-EMTRN and the information in D-lnc, we inferred that some potential drugs could be used for the treatment of GBM patients by constructing the drug-target association network targeting lncRNAs (Fig.…”

Section: Resultsmentioning

confidence: 99%

The regulatory pattern of target gene expression by aberrant enhancer methylation in glioblastoma

Zhao

Shi-jia

et al. 2021

BMC Bioinformatics

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Background Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor with grim prognosis. Aberrant DNA methylation is an epigenetic mechanism that promotes GBM carcinogenesis, while the function of DNA methylation at enhancer regions in GBM remains poorly described. Results We integrated multi-omics data to identify differential methylation enhancer region (DMER)-genes and revealed global enhancer hypomethylation in GBM. In addition, a DMER-mediated target genes regulatory network and functional enrichment analysis of target genes that might be regulated by hypomethylation enhancer regions showed that aberrant enhancer regions could contribute to tumorigenesis and progression in GBM. Further, we identified 22 modules in which lncRNAs and mRNAs synergistically competed with each other. Finally, through the construction of drug-target association networks, our study identified potential small-molecule drugs for GBM treatment. Conclusions Our study provides novel insights for understanding the regulation of aberrant enhancer region methylation and developing methylation-based biomarkers for the diagnosis and treatment of GBM.

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“…Details of the three datasets were listed in Table 1. The differential expression analysis by integration of TCGA and GTEx datasets, and identi cation of the differentially expressed genes (DEGs) between melanoma and normal samples was conducted as described in our previous study [19].…”

Section: Gene Expressions and Differential Expression Analysismentioning

confidence: 99%

Identification of TUBB4A as a Prognostic Biomarker of Melanoma by Transcriptomic Data and in vitro Experiments

Zhang¹,

Z²,

H³

et al. 2021

Preprint

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Background Melanoma is one of the most malignant skin carcinomas with high metastatic potential. Increasing evidences have demonstrated that β-tubulin 4A (TUBB4A) play key role in development and progression of several types of human cancer. However, the potential function of TUBB4A in cutaneous melanoma remains to be determined. Methods We first performed differential expression analysis between skin melanoma tissues and normal tissues from GEO and TCGA datasets, and then ran survival analysis to identify prognostic-related key genes. We went further to conduct the verification of in vitro biochemical experiments to explore the functional roles of key gene TUBB4A. Two small molecule inhibitors of TUBB4A, Dihydroartemisinin (DHA) and Nocodazole, were used to examined the effect on apoptosis and cell cycle progression of melanoma cells. Results We found that TUBB4A is markedly correlated to the overall survival of cutaneous melanoma patients. The co-expressed genes with TUBB4A are enriched in the melanoma-related pathways and function. Then, the experimental results showed that knockdown of TUBB4A inhibit the proliferation and migration of A375 and B16-F10 melanoma cells. Moreover, two small molecular agents targeting TUBB4A, Dihydroartemisinin and Nonocodazole, dpromote the apoptosis of melanoma cells and made the tumor cells significantly blocked in G2/M stage. Conclusion We identified and validated in vitro that TUBB4A may be a prognostic biomarker and therapeutic target for melanoma.

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Identification of lncRNA-mRNA Regulatory Module to Explore the Pathogenesis and Prognosis of Melanoma

Cited by 20 publications

References 66 publications

Identification of a lncRNA-mRNA Regulatory Module for Prognosis, Classification and Potential Treatment of Lung Adenocarcinoma

Identification of a lncRNA-mRNA Regulatory Module for Prognosis, Classification and Potential Treatment of Lung Adenocarcinoma

The regulatory pattern of target gene expression by aberrant enhancer methylation in glioblastoma

Identification of TUBB4A as a Prognostic Biomarker of Melanoma by Transcriptomic Data and in vitro Experiments

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