Identifying Potential miRNA Biomarkers for Gastric Cancer Diagnosis Using Machine Learning Variable Selection Approach

被引:13
|
作者
Gilani, Neda [1 ]
Arabi Belaghi, Reza [2 ,3 ]
Aftabi, Younes [4 ]
Faramarzi, Elnaz [5 ]
Edguenlue, Tuba [6 ]
Somi, Mohammad Hossein [5 ]
机构
[1] Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran
[2] Uppsala Univ, Dept Math, Uppsala, Sweden
[3] Univ Tabriz, Fac Math Sci, Dept Stat, Tabriz, Iran
[4] Tabriz Univ Med Sci, TB & Lung Dis Res Ctr, Tabriz, Iran
[5] Tabriz Univ Med Sci, Liver & Gastrointestinal Dis Res Ctr, Tabriz, Iran
[6] Mugla Sitki Kocman Univ, Fac Med, Dept Med Biol, Mugla, Turkey
关键词
miRNA; machine learning; boruta algorithm; gastric cancer; hsa-miR-1343-3p; AUC; GSE106817; GSE113486; MICRORNAS; ADENOCARCINOMA; PROGRESSION; CARCINOMA;
D O I
10.3389/fgene.2021.779455
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease.Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships.Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision (AUC; 100%, sensitivity; 100%, specificity; 100% ROC; 100%, Kappa; 100) using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events.Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning
    Marquardt, Andre
    Landwehr, Laura-Sophie
    Ronchi, Cristina L.
    di Dalmazi, Guido
    Riester, Anna
    Kollmannsberger, Philip
    Altieri, Barbara
    Fassnacht, Martin
    Sbiera, Silviu
    [J]. CANCERS, 2021, 13 (18)
  • [42] Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis
    Wu, Zenan
    Chen, Huan
    Ke, Shiwen
    Mo, Lisha
    Qiu, Mingliang
    Zhu, Guoshuang
    Zhu, Wei
    Liu, Liangji
    [J]. SCIENTIFIC REPORTS, 2023, 13 (01)
  • [43] Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis
    Zenan Wu
    Huan Chen
    Shiwen Ke
    Lisha Mo
    Mingliang Qiu
    Guoshuang Zhu
    Wei Zhu
    Liangji Liu
    [J]. Scientific Reports, 13
  • [44] Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases
    Su, Benzhe
    Wang, Weiwei
    Lin, Xiaohui
    Liu, Shenglan
    Huang, Xin
    [J]. BRIEFINGS IN BIOINFORMATICS, 2024, 25 (01)
  • [45] Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data
    Paul, Dahrii
    Sinnarasan, Vigneshwar Suriya Prakash
    Das, Rajesh
    Sheikh, Md Mujibur Rahman
    Venkatesan, Amouda
    [J]. JOURNAL OF PROTEOMICS, 2024, 309
  • [46] Explainable Machine Learning Approach for Hepatitis C Diagnosis Using SFS Feature Selection
    Ali, Ali Mohd
    Hassan, Mohammad R.
    Aburub, Faisal
    Alauthman, Mohammad
    Aldweesh, Amjad
    Al-Qerem, Ahmad
    Jebreen, Issam
    Nabot, Ahmad
    [J]. MACHINES, 2023, 11 (03)
  • [47] Multi-stage Feature Selection in Identifying Potential Biomarkers for Cancer Classification
    Wong, Yit Khee
    Chan, Weng Howe
    Nies, Hui Wen
    Moorthy, Kohbalan A. L.
    [J]. 2022 2ND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBERNETICS TECHNOLOGY & APPLICATIONS (ICICYTA), 2022, : 6 - 11
  • [48] Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods
    Qingde Zhou
    Lan Lan
    Wei Wang
    Xinchang Xu
    [J]. BMC Medical Informatics and Decision Making, 25 (1)
  • [49] Explainable machine learning models for early gastric cancer diagnosis
    Du, Hongyang
    Yang, Qingfen
    Ge, Aimin
    Zhao, Chenhao
    Ma, Yunhua
    Wang, Shuyu
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01):
  • [50] Identification of potential miRNA biomarkers for breast cancer using TaqMan Pri-miRNA assays
    Liang, Yu
    Markoy, Athina
    Lianidou, Evi
    [J]. CANCER RESEARCH, 2011, 71