Analysis of lncRNA–mRNA networks after MEK1/2 inhibition based on WGCNA in pancreatic ductal adenocarcinoma

Qian, Ji; Yang, Jun; Liu, Xianchen; Chen, Zhiming; Yan, Xiaohui; Xue, Qiang; Xingqin, Zhou; Gai, Ling; Lu, Pingli; Shi, Yu; Yao, Ninghua

doi:10.1002/jcp.29255

Cited by 7 publications

(8 citation statements)

References 30 publications

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“…In previous studies, WGCNA was used to analyze biomarkers and targets of various diseases like schizophrenia, Alzheimer's disease, sickle cell disease, and cancers [29][30][31][32]. As a systematical biological method, WGCNA has been used in various cancer studies including non-small-cell lung cancer (NSCLC), bladder cancer, clear cell renal cell carcinoma (ccRCC), acute myeloid leukemia (AML), and pancreatic ductal adenocarcinoma (PDAC) [28,[33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA)

Kui

Yang

et al. 2021

BioMed Research International

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Lung cancer is known as the leading cause which presents the highest fatality rate worldwide; non-small-cell lung cancer (NSCLC) is the most prevalent type of lung carcinoma with high severity and affects 80% of patients with lung malignancies. Up to now, the general treatment for NSCLC includes surgery, chemotherapy, and radiotherapy; however, some therapeutic drugs and approaches could cause side effects and weaken the immune system. The combination of conventional therapies and traditional Chinese medicine (TCM) significantly improves treatment efficacy in lung cancer. Therefore, it is necessary to investigate the chemical composition and underlying antitumor mechanisms of TCM, so as to get a better understanding of the potential natural ingredient for lung cancer treatment. In this study, we selected 78 TCM to treat NSCLC cell line (A549) and obtained 92 transcriptome data; differential expression and WGCNA were applied to screen the potential natural ingredient and target genes. The sample which was treated with A. pierreana generated the most significant DEG set, including 6130 DEGs, 2479 upregulated, and 3651 downregulated. KEGG pathway analyses found that four pathways (MAPK, NF-kappa B, p53, and TGF-beta signaling pathway) were significantly enriched; 16 genes were significantly regulated in these four pathways. Interestingly, some of them such as EGFR, DUSP4, IL1R1, IL1B, MDM2, CDKNIA, and IDs have been used as the target biomarkers for cancer diagnosis and therapy. In addition, classified samples into 14 groups based on their pharmaceutical effects, WGCNA was used to identify 27 modules. Among them, green and darkgrey were the most relevant modules. Eight genes in the green module and four in darkgrey were identified as hub genes. In conclusion, we screened out three new TCM (B. fruticose, A. pierreana, and S. scandens) that have the potential to develop natural anticancer drugs and obtained the therapeutic targets for NSCLC therapy. Our study provides unique insights to screen the natural components for NSCLC therapy using high-throughput transcriptome analysis.

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Section: Introductionmentioning

confidence: 99%

High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA)

Kui

Yang

et al. 2021

BioMed Research International

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“…Recent studies showed that long noncoding RNAs (lncRNAs) regulates the initiation, progression, metastasis and chemo/radio-resistance of PDAC [14][15][16]. LncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) has been reported to be dysregulated in many human cancers and contribute to cancer development [17][18][19][20][21].…”

Section: Ivyspringmentioning

confidence: 99%

NEAT1/miR-101-dependent Up-regulation of DNA-PKcs Enhances Malignant Behaviors of Pancreatic Ductal Adenocarcinoma Cells

Chen

Zhang

et al. 2021

J. Cancer

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Background: Although we previously revealed that DNA-dependent protein kinase catalytic subunit (DNA-PKcs) is overexpressed in pancreatic ductal adenocarcinoma (PDAC) and important for gemcitabine resistance, the role of DNA-PKcs in the progression and metastasis of PDAC remain unclear. To date, the upstream signaling events stimulating DNA-PKcs overexpression in PDAC are still not well characterized. Methods: Expression of DNA-PKcs was measured by western blot. The levels of miRNA-101 and lncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) were detected by real-time PCR. Cell viability was determined by CCK-8. Cell migration and cell invasion were measured by transwell assay. The regulatory relationship between NEAT1 and miR-101 was determined by a luciferase assay. Results: DNA-PKcs expression was significantly elevated in human PDAC tissues and cells. DNA-PKcs overexpression was correlated with TNM stage and lymph node metastasis. Higher expression of DNA-PKcs was closely correlated with patients of worse overall survival (OS). DNA-PKcs knockdown suppresses malignant behaviors of PDAC cells. Further study showed that miRNA-101 level was decreased in PDAC tissues and cells, which could be responsible for DNA-PKcs overexpression and DNA-PKcs mediated oncogenic actions in PDAC cells. Moreover, NEAT1 functions as an oncogene influencing cell proliferation, migration and invasion in part by serving as a competing endogenous RNA (ceRNAs) modulating miR-101 expression, leading to up-regulation of DNA-PKcs. Conclusion: These findings suggest that NEAT1/miR-101-dependent up-regulation of DNA-PKcs promotes the malignant behaviors of PDAC cells. The NEAT1/miR-101/DNA-PKcs axis may serve as a viable prognostic marker and therapeutic target for PDAC.

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“…WGCNA is an algorithm based on high-throughput gene expression pro les [24,25]. It is most suitable for gene co-expression network analysis and is used for cancer identi cation, revealing correlation between genes and nding signi cantly related gene modules.…”

Section: Co-expression Network Constructionmentioning

confidence: 99%

Co-expression Network Identification and Clinical Prognostic Evaluation of Hub Genes in Head and Neck Squamous Cell Carcinoma

Feng¹,

Han²,

Yu³

et al. 2020

Preprint

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Background: According to statistics, even with active treatment, the recurrence rate of HNSCC is at 40%-50%. Head and neck cancer remains a challenge for otolaryngologists. Therefore, the identification of new biomarkers is an urgent need for the diagnosis, treatment, and prognosis of malignant tumors of the head and neck. Methods: In this study, transcriptome data from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules and hub genes related to head and neck squamous cell carcinoma (HNSCC). Protei-Protei interaction(PPI) network and Cytoscape software were used to analyze the protein interaction network. HNSCC clinical data from the TCGA and Gene Expression Profile Interactive Analysis 2 databases were used to analyze the survival rate of hub genes, and the correlation between hub genes and tumor stage was further analyzed.Results: A total of 2836 and 570 DEGs were identified from the TCGA expression data and GEO gene chip datasets, respectively. We found that the green module had the highest correlation with HNSCC. A total of 15 hub genes were also identified. In the Human Protein Atlas database, we found that thioredoxin reductase 1 (TXNRD1) was overexpressed in HNSCC tumors compared with normal tissues at the transcriptional level. Survival analysis also suggested that TXNRD1 was a poor prognostic factor for HNSCC.Conclusion: Our results indicate that TXNRD1 is very likely to be identified as a potential biomarker and target for HNSCC. However, further research is required to fully reveal its role in HNSCC pathogenesis as well as its value as a prognostic biomarker.

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Analysis of lncRNA–mRNA networks after MEK1/2 inhibition based on WGCNA in pancreatic ductal adenocarcinoma

Cited by 7 publications

References 30 publications

High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA)

High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA)

NEAT1/miR-101-dependent Up-regulation of DNA-PKcs Enhances Malignant Behaviors of Pancreatic Ductal Adenocarcinoma Cells

Co-expression Network Identification and Clinical Prognostic Evaluation of Hub Genes in Head and Neck Squamous Cell Carcinoma

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