Cancer Diagnosis Through IsomiR Expression with Machine Learning Method
Abstract
Background: IsomiR is an isoform of microRNA (miRNA), and its sequences vary from those of a reference miRNA, which arose with the advencements of deep sequencing, high miRNA variability has been detected from the same miRNA precursor. IsomiR exists in four main types formed through the following processes: 5' or 3' trimming, Nucleotide addition, Nucleotide removal, and posttranscriptional RNA editing. Objective: For cancer diagnosis, it needs to explore differential expression profiles which can be used to distinguish cancer and normal cell lines, especially in the isomiR-mRNA regulatory networks, because aberrant isomiR expression profiles may contribute to tumorigenesis. Method: We extracted five features of the isomiR read counts from RNA-SEQ data in TCGA, with a random forest classification algorithm, these features were applied to diagnose six cancers: breast invasive carcinoma, lung adenocarcinoma, squamous-cell carcinoma of the lung, stomach adenocarcinoma, thyroid carcinoma, and uterine corpus endometrial carcinoma. Results: Compare with the classifier libD3C, our method can be utilized to distinguish cancers from their normal counterparts by performance based on sn, sp, ACC and MCC measures. Conclusion: IsomiR can be successfully and effectively used to diagnose cancer through machine learning method from high-throughput data. Keywords: MicroRNA, isomiR, cancer, machine learning, high-throughput data, RNA-SEQ data.
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Several researches proved that coding genes, noncoding RNAs, and proteins are related to diseases, including AD.
“Several researches proved that coding genes/noncoding RNAs/proteins were related to diseases, including AD [29,30,31,32,33,34,35,36].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [33]
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Machine Learning Methods in Prediction of Protein Palmitoylation Sites: A Brief Review
Li, Y., Pu, F., Wang, J., Zhou, Z., Zhang, C., He, F., Ma, Z., & Zhang, J. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666201112142826 · Cited by 7 · see citing works
Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method
Wu, D., Fang, X., Luan, K., Xu, Q., Lin, S., Sun, S., Yang, J., Dong, B., Manavalan, B., & Liao, Z. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107065 · Cited by 7 · see citing works
Self-citationHirway, S. U., & Weinberg, S. H. (2023) · Computational and Systems Oncology · doi:10.1002/cso2.1044 · Cited by 5 · see citing works
Prediction of Thermophilic Proteins Using Voting Algorithm
Li, J., Zhu, P., & Zou, Q. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17938-0_18 · Cited by 7 · see citing works
Self-citationSequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction
Min, X., Lu, F., & Li, C. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666201124112710 · Cited by 6 · see citing works
Yu, L., Ju, B., & Ren, S. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113155 · Cited by 6 · see citing works
Identification of SNARE Proteins Through a Novel Hybrid Model
Li, G. (2020) · IEEE Access · doi:10.1109/access.2020.3004446 · Cited by 6 · see citing works
Simple Linear Cancer Risk Prediction Models With Novel Features Outperform Complex Approaches
Kulm, S., Kofman, L., Mezey, J., et al. (2022) · JCO Clinical Cancer Informatics · doi:10.1200/cci.21.00166 · Cited by 5 · see citing works
Linc00261 Inhibited High-Grade Serous Ovarian Cancer Progression through miR-552-ATG10-EMT Axis
Wang, L., Wang, H., & Chen, J. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/9450353 · Cited by 5 · see citing works
A Machine Learning Method to Trace Cancer Primary Lesion Using Microarray-Based Gene Expression Data
Lu, Q., Chen, F., Li, Q., Chen, L., Tong, L., Tian, G., & Zhou, X. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.832567 · Cited by 4 · see citing works
“Finally, immunohistochemical and diagnostic methods combined with machine learning or deep learning algorithms were also widely used to detect the primary site of CUP (16, 18, 21, 22).”
MethodA Cancer Gene Module Mining Method Based on Bio-Network of Multi-Omics Gene Groups
Wang, C., Zhao, N., Sun, K., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01159 · Cited by 3 · see citing works
- Authors
- Liao, Z., Li, D., Wang, X., Li, L., & Zou, Q.
- Affiliations
- Fujian Medical University (CN)
- Department of Internal Medicine-Oncology, The Fourth Hospital in Qinhuangdao, Qinhuangdao, China
- Tianjin University (CN)
- Corresponding author
- Zou, Quan
- Venue
- Current Bioinformatics
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2018-02-19
- Volume
- 13
- Issue
- 1
- Pages
- 57-63
- ISSN
- 1574-8936 (ISSN-L)
- OpenAlex
- W2427122612
- Cited by
- 130
- References
- 0
- Citations by year
- 2025: 2 · 2024: 6 · 2023: 10 · 2022: 13 · 2021: 19 · 2020: 46 · 2019: 26 · 2018: 8
- FWCI
- 4.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Bioinformatics (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Keywords
- isomiRs, microRNA isoforms, cancer diagnosis, random forest classification, RNA-seq, invasive breast cancer, lung adenocarcinoma, lung squamous cell carcinoma, gastric adenocarcinoma, thyroid cancer, uterine corpus endometrial carcinoma, TCGA
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