mRNA and microRNA selection for breast cancer molecular subtype stratification using meta-heuristic based algorithms

Cancer subtype stratification, which may help to make a better decision in treating cancerous patients, is one of the most crucial and challenging problems in cancer studies. To this end, various computational methods such as Feature selection, which enhances the accuracy of the classification and i...

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Vydáno v:Genomics (San Diego, Calif.) Ročník 112; číslo 5; s. 3207 - 3217
Hlavní autoři: MotieGhader, Habib, Masoudi-Sobhanzadeh, Yosef, Ashtiani, Saman Hosseini, Masoudi-Nejad, Ali
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Inc 01.09.2020
Témata:
algorithms
biomarkers
breast neoplasms
computational methodology
human diseases
messenger RNA
microRNA
patients
ACC
PRE
ICA
HTS
SPC
FPR
SVM
LCA
PSO
ACO
DSOS
LA
WCC
CUK
FOA
GA
AL_NAME
SEN
ISSN:0888-7543, 1089-8646, 1089-8646
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Shrnutí:Cancer subtype stratification, which may help to make a better decision in treating cancerous patients, is one of the most crucial and challenging problems in cancer studies. To this end, various computational methods such as Feature selection, which enhances the accuracy of the classification and is an NP-Hard problem, have been proposed. However, the performance of the applied methods is still low and can be increased by the state-of-the-art and efficient methods. We used 11 efficient and popular meta-heuristic algorithms including WCC, LCA, GA, PSO, ACO, ICA, LA, HTS, FOA, DSOS and CUK along with SVM classifier to stratify human breast cancer molecular subtypes using mRNA and micro-RNA expression data. The applied algorithms select 186 mRNAs and 116 miRNAs out of 9692 mRNAs and 489 miRNAs, respectively. Although some of the selected mRNAs and miRNAs are common in different algorithms results, six miRNAs including miR-190b, miR-18a, miR-301a, miR-34c-5p, miR-18b, and miR-129-5p were selected by equal or more than three different algorithms. Further, six mRNAs, including HAUS6, LAMA2, TSPAN33, PLEKHM3, GFRA3, and DCBLD2, were chosen through two different algorithms. We have reported these miRNAs and mRNAs as important diagnostic biomarkers to the stratification of breast cancer subtypes. By investigating the literature, it is also observed that most of our reported mRNAs and miRNAs have been proposed and introduced as biomarkers in cancer subtypes stratification. •Molecular subtyping or intrinsic stratification of breast cancer is an important problem in breast cancer researches.•miR-190b, miR-18a, miR-301a, miR-34c-5p, miR-18b, and miR-129-5p are introduced as breast cancer subtype stratification.•HAUS6, LAMA2, TSPAN33, PLEKHM3, GFRA3 and DCBLD2 5p are proposed as breast cancer subtype stratification.•Selection of an optimal subset of genes is a nondeterministic polynomial (NP) problem.•Eleven different optimization algorithms along with SVM classifier are used for selecting an efficient subset of mRNAs and miRNA in breast cancer subtype classification.•Most of the discovered miRNAs are from miR-190, miR-129, miR-34, and miR-181 family.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0888-7543
1089-8646
1089-8646
DOI:10.1016/j.ygeno.2020.06.014