Machine learning and experimental analyses identified miRNA expression models associated with metastatic osteosarcoma

被引:1
作者
Abedi, Samira [1 ,5 ]
Behmanesh, Ali [2 ]
Mazhar, Farid Najd [2 ]
Bagherifard, Abolfazl [2 ]
Sami, Sam Hajialiloo [2 ]
Heidari, Negar [1 ,5 ]
Hossein-Khannazer, Nikoo [3 ,10 ]
Namazifard, Saina [4 ]
Arki, Mandana Kazem [3 ]
Shams, Roshanak [2 ]
Zarrabi, Ali [6 ,7 ,8 ]
Vosough, Massoud [5 ,9 ]
机构
[1] Univ Sci & Culture, Fac Sci & Adv Technol Biol, Dept Cellular & Mol Biol, Tehran, Iran
[2] Iran Univ Med Sci, Bone & Joint Reconstruct Res Ctr, Sch Med, Dept Orthoped, Tehran, Iran
[3] Shahid Beheshti Univ Med Sci, Res Inst Gastroenterol & Liver Dis, Gastroenterol & Liver Dis Res Ctr, Tehran, Iran
[4] Univ Texas Arlington, Dept Mech & Aerosp Engn, Arlington, TX USA
[5] ACECR, Dept Regenerat Med, Royan Inst Stem Cell Biol & Technol, Cell Sci Res Ctr, Tehran, Iran
[6] Istinye Univ, Fac Engn & Nat Sci, Dept Biomed Engn, TR-34396 Istanbul, Turkiye
[7] Yuan Ze Univ, Grad Sch Biotechnol & Bioengn, Taoyuan 320315, Taiwan
[8] Saveetha Univ, Saveetha Inst Med & Tech Sci, Saveetha Dent Coll & Hosp, Dept Res Analyt, Chennai 600077, India
[9] Karolinska Inst, Dept Lab Med, Expt Canc Med, Stockholm, Sweden
[10] Shahid Beheshti Univ Med Sci, Sch Adv Technol Med, Dept Tissue Engn & Appl Cell Sci, Tehran, Iran
来源
BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE | 2024年 / 1870卷 / 07期
关键词
Metastatic osteosarcoma; Early biomarkers; Machine learning algorithms; Differentially expressed miRNAs; TUMOR-SUPPRESSOR; SURVIVAL; OVEREXPRESSION; MICRORNAS; PROGNOSIS; MIR-154; IMPACT; GENES;
D O I
10.1016/j.bbadis.2024.167357
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Osteosarcoma (OS), as the most common primary bone cancer, has a high invasiveness and metastatic potential, therefore, it has a poor prognosis. This study identified early diagnostic biomarkers using miRNA expression profiles associated with osteosarcoma metastasis. In the first step, we used RNA-seq and online microarray data from osteosarcoma tissues and cell lines to identify differentially expressed miRNAs. Then, using seven feature selection algorithms for ranking, the first-ranked miRNAs were selected as input for five machine learning systems. Using network analysis and machine learning algorithms, we developed new diagnostic models that successfully differentiated metastatic osteosarcoma from non-metastatic samples based on newly discovered miRNA signatures. The results showed that miR-34c-3p and miR-154-3p act as the most promising models in the diagnosis of metastatic osteosarcoma. Validation for this model by RT-qPCR in benign tissue and osteosarcoma biopsies confirmed the lower expression of miR-34c-3p and miR-154-3p in OS samples. In addition, a direct correlation between miR-34c-3p expression, miR-154-3p expression and tumor grade was discovered. The combined values of miR-34c-3p and miR-154-3p showed 90 % diagnostic power (AUC = 0.90) for osteosarcoma samples and 85 % (AUC = 0.85) for metastatic osteosarcoma. Adhesion junction and focal adhesion pathways, as well as epithelial-to-mesenchymal transition (EMT) GO terms, were identified as the most significant KEGG and GO terms for the top miRNAs. The findings of this study highlight the potential use of novel miRNA expression signatures for early detection of metastatic osteosarcoma. These findings may help in determining therapeutic approaches with a quantitative and faster method of metastasis detection and also be used in the development of targeted molecular therapy for this aggressive cancer. Further research is needed to confirm the clinical utility of miR-34c-3p and miR-154-3p as diagnostic biomarkers for metastatic osteosarcoma.
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页数:19
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