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mirMachine: A One-Stop Shop for Plant miRNA Annotation
Published on: May 1, 2021
Predicting miRNA targets for head and neck squamous cell carcinoma using an ensemble method
Hong Gao1, Hui Jin1, Guijun Li2
11 Department of Otorhinolaryngology, Head and Neck Surgery, Jilin Cancer Hospital, Changchun, Jilin - PR China.
Background:
This study aimed to uncover potential microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method which combined 3 different methods: Pearson's correlation coefficient (PCC), Lasso and a causal inference method (i.e., intervention calculus when the directed acyclic graph (DAG) is absent [IDA]), based on Borda count election.
Methods:
The Borda count election method was used to integrate the top 100 predicted targets of each miRNA generated by individual methods. Afterwards, to validate the performance ability of our method, we checked the TarBase v6.0, miRecords v2013, miRWalk v2.0 and miRTarBase v4.5 databases to validate predictions for miRNAs. Pathway enrichment analysis of target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions was conducted to focus on significant KEGG pathways. Finally, we extracted target genes based on occurrence frequency ≥3.
Results:
Based on an absolute value of PCC >0.7, we found 33 miRNAs and 288 mRNAs for further analysis. We extracted 10 target genes with predicted frequencies not less than 3. The target gene MYO5C possessed the highest frequency, which was predicted by 7 different miRNAs. Significantly, a total of 8 pathways were identified; the pathways of cytokine-cytokine receptor interaction and chemokine signaling pathway were the most significant.
Conclusions:
We successfully predicted target genes and pathways for HNSCC relying on miRNA expression data, mRNA expression profile, an ensemble method and pathway information. Our results may offer new information for the diagnosis and estimation of the prognosis of HNSCC.
Insights
Researchers identified key microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method. The study highlights MYO5C as a significant target and identifies crucial signaling pathways for HNSCC diagnosis and prognosis.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Head and neck squamous cell carcinoma (HNSCC) is a significant health concern.
- Identifying specific microRNA (miRNA) targets is crucial for understanding HNSCC pathogenesis.
- Existing methods for miRNA target prediction have limitations.
Purpose of the Study:
- To develop and validate an ensemble method for predicting miRNA targets in HNSCC.
- To identify novel miRNA-mRNA interactions and associated pathways in HNSCC.
- To provide potential biomarkers for HNSCC diagnosis and prognosis.
Main Methods:
- An ensemble approach combining Pearson's correlation coefficient (PCC), Lasso, and intervention calculus without a directed acyclic graph (IDA) was employed.
- The Borda count election method integrated predictions from individual methods.
- Target gene validation utilized multiple databases (TarBase, miRecords, miRWalk, miRTarBase) and pathway enrichment analysis (KEGG).
Main Results:
- The study identified 33 miRNAs and 288 mRNAs with high correlation (absolute PCC >0.7).
- Ten target genes were extracted with a frequency of occurrence ≥3, with MYO5C showing the highest prediction frequency (7 miRNAs).
- Eight significant KEGG pathways were identified, notably cytokine-cytokine receptor interaction and chemokine signaling pathways.
Conclusions:
- The developed ensemble method effectively predicts miRNA targets and pathways in HNSCC.
- The findings offer valuable insights into HNSCC biology and potential diagnostic/prognostic markers.
- MYO5C and identified pathways represent promising areas for further HNSCC research.
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