Network‐based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

Lin, Jie; Wu, Yanjun; Liang, Xing; Ji, Meng; Ying, Huimin; Wang, Xinyu; Sun, Xia; Shao, Chenghao; Zhan, Lixing; Zhang, Yan

doi:10.1002/mc.22920

Cited by 18 publications

(15 citation statements)

References 83 publications

(150 reference statements)

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“…However, its expression is correlated with the cellular malignancy, cancer invasion, and metastases (Lum et al, 2010;Sahay et al, 2016;Sadras et al, 2017). Strong correlation of GPR110 gene with PaCa was identified in a study where its overexpression is observed in all PaCa patients (Lin et al, 2019). The solute carrier family 2 member 1 (SLC2A1) gene code for glucose transporter 1 (or GLUT1) protein facilitates the transport of glucose across plasma membranes.…”

Section: Discussionmentioning

confidence: 99%

“…They used TopHat2 tool for sequence alignment, and DESeq2 for differential gene expression analysis. For determining miRNA and mRNA relationship, Human miRNA Disease Database (HMDD) and miR2Disease database were used (Lin et al, 2019 ). In our analysis, only mRNA sample files are processed for DEGs identification, sequence files are aligned using HISAT2 tool, and DESeq2 is used for differential gene expression analysis.…”

Section: Discussionmentioning

confidence: 99%

“…The Dataset E-GEOD-18670 comprised of 24 samples including six circulating tumor cells (metastases), six haematological, six PDAC tumor tissues, and six adjacent normal tissue. Out of these samples, six PDAC tumor and 6 adjacent normal samples were Lin et al (2019) analyzed in this study according to our sample inclusion criteria. The main objective of our study is to identify biomarkers or targets present on the surface of tumor cells.…”

Section: Datasets Inclusion Criteriamentioning

confidence: 99%

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Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

et al. 2021

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Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFβ1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM–receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Datasets Inclusion Criteriamentioning

confidence: 99%

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Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

et al. 2021

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“…Three miRNA microarray datasets (GSE119794, 14 GSE32678, 15 and GSE24279 16 ), which consisted of tumor tissues and normal tissues of the pancreas, were downloaded from the GEO database to identify differentially expressed miRNAs. The raw data of these datasets were extracted and normalized according to the methods provided by the data contributors.…”

Section: Bioinformatic Analysis and Survival Analysismentioning

confidence: 99%

<p>The miR-1224-5p/ELF3 Axis Regulates Malignant Behaviors of Pancreatic Cancer via PI3K/AKT/Notch Signaling Pathways</p>

Kong¹,

Liu²,

Zheng³

et al. 2020

OTT

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Purpose: Aberrant expression of microRNAs contributes to the progression of pancreatic cancer by targeting downstream genes. A novel regulatory axis, miR-1224-5p/ELF3, was identified by bioinformatic analysis and experimental verification. Studies of the underlying molecular mechanisms behind this axis lead to a better understanding of the development of pancreatic cancer. Materials and Methods: The differential expression of miR-1224-5p and ELF3 was verified based on Gene Expression Omnibus (GEO) datasets and clinical samples. The relationship between miR-1224-5p and ELF3 was demonstrated by luciferase assay and Western blot. The related signaling pathways of the miR-1224-5p/ELF3 axis in pancreatic cancer were investigated by gene set enrichment analysis (GSEA) and verified by Western blot. An analysis between ELF3 expression and immune infiltration was performed. Cellular and animal experiments were utilized to explore the effects of miR-1224-5p and ELF3 in pancreatic cancer. Results: Suppressed expression of miR-1224-5p in pancreatic tumor tissues and cancer cells was identified first. Furthermore, miR-1224-5p is correlated with clinicopathological features, and decreased expression of miR-1224-5p indicates poor prognosis. miR-1224-5p serves as a tumor suppressor and inhibits malignant behaviors of pancreatic cancer based on in vivo and in vitro assays. The putative target gene ELF3 was predicted by bioinformatic analysis and confirmed by dual-luciferase reporter assay. Overexpression of ELF3 can improve the malignant behaviors of pancreatic cancer and demonstrates poor prognosis and advanced clinical stage. The inhibitory role of miR-1224-5p in pancreatic cancer is manifested by its direct targeting of ELF3. A negative correlation between ELF3 expression and immune cell infiltration was identified, suggesting an immunosuppressive state resulting from ELF3 overexpression. The PI3K/AKT/Notch signaling pathways and epithelial-tomesenchymal transition (EMT) are important underlying mechanisms. Conclusion: The miR-1224-5p/ELF3 axis may serve as a new diagnostic, therapeutic, and prognostic biomarker in pancreatic cancer. The related PI3K/AKT/Notch/EMT signaling pathways greatly promote the elucidation of the progression of pancreatic cancer.

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“…It has been reported that miRNAs exerted their effects on cancer progression through targeting specific genes 17‐19 . Therefore, we speculated that miR‐497‐5p also boosted PCa progression in a similar way.…”

Section: Resultsmentioning

confidence: 85%

DLX6‐AS1 accelerates cell proliferation through regulating miR‐497‐5p/SNCG pathway in prostate cancer

Zhu

Zhao

et al. 2020

Environmental Toxicology

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Prostate cancer (PCa) has become the second leading cause of cancer‐related mortality in males worldwide. Although the long noncoding RNA DLX6‐AS1 has been recognized to be an oncogene in multiple cancers, the biological function and regulatory mechanism of DLX6‐AS1 in prostate cancer are still obscure. In the present study, we observed that DLX6‐AS1 was significantly upregulated in PCa tissues and cells. Knockdown of DLX6‐AS1 inhibited PCa progression by suppressing cell proliferation and accelerating cell apoptosis. Molecular mechanism exploration indicated that DLX6‐AS1 acted as a sponge for miR‐497‐5p and synuclein gamma (SNCG) was a downstream target gene of miR‐497‐5p. In addition, there was a negative correlation between DLX6‐AS1 and miR‐497‐5p in PCa tissues. Rescue assays showed that SNCG overexpression could partially recover DLX6‐AS1 knockdown‐mediated inhibition of progression in PCa. Furthermore, xenograft tumor model was established to determine the role of DLX6‐AS1 in PCa tumor growth and the results suggested that DLX6‐AS1 could facilitate tumor growth by regulating SNCG in vivo. In conclusion, our study investigated the biological function and underlying mechanism of DLX6‐AS1 in PCa and validated that DLX6‐AS1 functioned as an oncogene through miR‐497‐5p/SNCG axis.

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Network‐based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

Cited by 18 publications

References 83 publications

Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

<p>The miR-1224-5p/ELF3 Axis Regulates Malignant Behaviors of Pancreatic Cancer via PI3K/AKT/Notch Signaling Pathways</p>

DLX6‐AS1 accelerates cell proliferation through regulating miR‐497‐5p/SNCG pathway in prostate cancer

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