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Updated: Jan 1, 2026

miRNA Expression Analyses in Prostate Cancer Clinical Tissues
Published on: September 8, 2015
Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer
Ping Wang1,2, Weidong Li1,3, Bo Zhai1,3
1The Hepatosplenic Surgery Center, Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Abstract:
Pancreatic cancer is a malignancy of the digestive system characterized by poor prognosis. A number of prognostic messenger RNA (mRNA) signatures have been identified by using the high-throughput expression profiles. MicroRNAs (miRNA) play a critical role in regulating multiple cellular functions. However, no such integrated analysis of miRNAs and mRNAs for studying the prognostic mechanisms of pancreatic cancer has been reported. In this study, we first identified prognostic mRNAs and miRNAs based on The Cancer Genome Atlas datasets, and then performed an enrichment analysis to explore the underlying biological mechanisms involved in pancreatic cancer prognosis at the mRNA level. Furthermore, we performed an integrated analysis of mRNAs and miRNAs to identify prognostic subpathways, which were closely associated with pancreatic cancer genes and tumor hallmarks and involved in hypoxia, oxidative phosphyorylation and xenobiotic metabolisms. Meanwhile, we performed a random walk algorithm based on global network, prognostic mRNAs and miRNAs, and identified top risk mRNAs as the prognostic signature. Finally, an independent testing set was used to confirm the predictive power of the top mRNA signature, and most of these genes involved were known oncogenes. In conclusion, we performed a series of integrated analyses by comprehensively exploring pancreatic cancer prognosis and systematically optimized the prognostic signature for clinical use.
Insights
This study identifies key messenger RNAs (mRNAs) and microRNAs (miRNAs) for predicting pancreatic cancer outcomes. An optimized mRNA signature shows strong predictive power for clinical use.
Area of Science:
- Oncology
- Genomics
- Molecular Biology
Background:
- Pancreatic cancer has a poor prognosis, necessitating improved prognostic tools.
- Messenger RNA (mRNA) and microRNA (miRNA) signatures are emerging biomarkers.
- Integrated analysis of mRNA and miRNA for pancreatic cancer prognosis is lacking.
Purpose of the Study:
- To identify prognostic mRNA and miRNA signatures in pancreatic cancer.
- To explore underlying biological mechanisms using enrichment analysis.
- To develop an optimized prognostic signature for clinical application.
Main Methods:
- Utilized The Cancer Genome Atlas (TCGA) datasets for mRNA and miRNA identification.
- Performed enrichment analysis and integrated mRNA-miRNA analysis for subpathways.
- Employed a random walk algorithm on a global network to identify a prognostic mRNA signature.
- Validated the signature using an independent testing set.
Main Results:
- Identified prognostic mRNAs and miRNAs associated with pancreatic cancer.
- Discovered prognostic subpathways linked to tumor hallmarks, hypoxia, and metabolism.
- Developed a top mRNA signature with significant predictive power.
- Confirmed that most genes in the signature are known oncogenes.
Conclusions:
- Integrated analysis of mRNA and miRNA provides novel insights into pancreatic cancer prognosis.
- The identified mRNA signature is a promising tool for clinical prognostication.
- Further optimization enhances the signature's potential for clinical use.
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