Integrative bioinformatics analysis of miRNA and mRNA expression profiles identified some potential biomarkers for breast cancer

Rezaeijo, Seyed Masoud; Goharifar, Naieme; Poursheikhani, Arash; Rezaie, Monireh; Shayankia, Ghazal; Heydarheydari, Sahel; Saberi, Alihossein; Taghizadeh, Eskandar

doi:10.21203/rs.3.rs-1839953/v1

2022

DOI: 10.21203/rs.3.rs-1839953/v1

|View full text |Cite

Preprint

Integrative bioinformatics analysis of miRNA and mRNA expression profiles identified some potential biomarkers for breast cancer

Seyed Masoud Rezaeijo

Naieme Goharifar

Arash Poursheikhani

et al.

Abstract: Background: Breast cancer is common cause of cancer death among women with a complex and heterogeneous picture in histological, molecular and clinical features. The aim of this study was to identify hub genes and related pathways in breast cancer.Methods: We selected screening methods for differentially expressed mRNAs and miRNAs using expression profile data of Breast cancer from The Cancer Genome Atlas (TCGA). with using some databases for annotation, the functional and pathway enrichment for differential ex… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background We used a hybrid machine learning systems (HMLS) strategy that includes the extensive search for the discovery of the most optimal HMLSs, including feature selection algorithms, a feature extraction algorithm, and classifiers for diagnosing breast cancer. Hence, this study aims to obtain a high-importance transcriptome profile linked with classification procedures that can facilitate the early detection of breast cancer. Methods In the present study, 762 breast cancer patients and 138 solid tissue normal subjects were included. Three groups of machine learning (ML) algorithms were employed: (i) four feature selection procedures are employed and compared to select the most valuable feature: (1) ANOVA; (2) Mutual Information; (3) Extra Trees Classifier; and (4) Logistic Regression (LGR), (ii) a feature extraction algorithm (Principal Component Analysis), iii) we utilized 13 classification algorithms accompanied with automated ML hyperparameter tuning, including (1) LGR; (2) Support Vector Machine; (3) Bagging; (4) Gaussian Naive Bayes; (5) Decision Tree; (6) Gradient Boosting Decision Tree; (7) K Nearest Neighborhood; (8) Bernoulli Naive Bayes; (9) Random Forest; (10) AdaBoost, (11) ExtraTrees; (12) Linear Discriminant Analysis; and (13) Multilayer Perceptron (MLP). For evaluating the proposed models' performance, balance accuracy and area under the curve (AUC) were used. Results Feature selection procedure LGR + MLP classifier achieved the highest prediction accuracy and AUC (balanced accuracy: 0.86, AUC = 0.94), followed by an LGR + LGR classifier (balanced accuracy: 0.84, AUC = 0.94). The results showed that achieved AUC for the LGR + LGR classifier belonged to the 20 biomarkers as follows: TMEM212, SNORD115-13, ATP1A4, FRG2, CFHR4, ZCCHC13, FLJ46361, LY6G6E, ZNF323, KRT28, KRT25, LPPR5, C10orf99, PRKACG, SULT2A1, GRIN2C, EN2, GBA2, CUX2, and SNORA66. Conclusions The best performance was achieved using the LGR feature selection procedure and MLP classifier. Results show that the 20 biomarkers had the highest score or ranking in breast cancer detection.

show abstract

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Integrative bioinformatics analysis of miRNA and mRNA expression profiles identified some potential biomarkers for breast cancer

Cited by 1 publication

References 28 publications

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

Contact Info

Product

Resources

About