miR-214, miR-29b, miR-590 ↑
|
(Qiao et al. 2019), (Yuan et al. 2018)
|
|
Methamidophos
|
peripheral blood
|
miR-125b, miR-141-5p, miR-142a, miR-150 ↓
|
|
|
|
Omethoate
|
peripheral blood lymphocytes
|
miR-145
|
|
(Qiao et al. 2019), (Wang et al. 2019)
|
|
PFOA
|
serum
|
miR-199a, miR-26b ↑
|
|
(Wang et al. 2012)
|
|
Organophosphates
|
serum
|
miR-155 ↓
|
Guillain-Barre Syndrome
|
(Costa et al. 2020), (Yuan et al. 2018)
|
|
Heavy metals
|
|
|
|
|
|
Arsenic
|
urine
|
miR-21, miR-126, miR-155, miR-221↑
|
albuminuria (miR-21)
|
(Kong et al. 2012)
|
|
Arsenic
|
urine
|
miR-205↓
|
|
(Gao et al. 2019)
|
|
Arsenic
|
peripheral blood mononuclear cells
|
miR-21↑
|
skin cancers
|
(Wallace et al. 2020), (Liu et al. 2018)
|
|
Arsenic
|
cord blood
|
has-let-7a, miR-107, miR-126, miR-16, miR-17, miR-195, miR-20a, miR-20b, miR-26b, miR-454, miR-96, miR-98 ↑
|
|
(Rager et al. 2014)
|
|
Arsenic
|
bronchial epithelial cells
|
miR-190 ↑
|
|
(Beezhold et al. 2011)
|
|
Arsenic
|
plasma
|
miR-423-5p, miR-423-5p, miR-142-5p -2, miR-454-5p
|
cardiometabolic disease
|
(Beck et al. 2018)
|
|
Arsenic
|
plasma
|
-320c-1, and -320c
|
|
|
|
Arsenic
|
serum
|
miR-126, miR-155
|
|
(Wallace et al. 2020), (Ruiz-Vera et al. 2019)
|
|
Lead
|
placenta
|
miR-146a, miR-10a, miR-190b, miR-431 ↑
|
|
(Wallace et al. 2020), (Li et al. 2015)
|
|
Lead
|
placenta
|
miR-651 ↓
|
|
|
|
Lead
|
blood
|
miR-21-5p, miR-122-5p ↑
|
|
(Wallace et al. 2020), (Guo et al. 2019)
|
|
Lead (battery factory workers)
|
blood
|
miR-520c-3p, miR-148a, miR-141,and miR-211↑.
|
|
(Wallace et al. 2020), (Xu et al. 2017)
|
|
Lead (battery factory workers)
|
blood
|
miR-572 and miR-130b↓
|
|
(Wallace et al. 2020), (Xu et al. 2017)
|
|
Mercury (female factory workers)
|
blood
|
miR-92a-3p, miR-122-5p, miR-451a, and miR-486-5p↑
|
|
(Wallace et al. 2020), (Ding et al. 2016)
|
|
Mercury (female factory workers)
|
blood
|
miR-16-5p, miR-30c-3p,miR-181a-5p, and let-7e-5p↓
|
|
(Wallace et al. 2020), (Ding et al. 2016)
|
|
Other
|
|
|
|
|
|
100 nm gold nanoparticles
|
cord blood
|
has-let-7a ↑
|
|
(Balansky et al. 2013)
|
|
Asbestos
|
peripheral blood
|
miR-103 ↑
|
mesothelioma
|
(Weber et al. 2012)
|
|
Occupational grain dust
|
serum
|
miR-18a-5p, miR-124-3p and miR-574-3p ↑
|
lung diseases
|
(Straumfors et al. 2020)
|
|
Occupational grain dust
|
serum
|
miR-19b-3p and miR-146a-5p ↓
|
lung diseases
|
(Straumfors et al. 2020)
|
Abbreviations: PM (particulate matter); SNPs (single nucleotide polymorphisms); MV (microvesicles); DEP (diethyl phthalate); TRAP (traffic-related air pollution); O3 (ozone); PAHs (polycyclic aromatic hydrocarbons); PCBs (polychlorinated biphenyls); POPs (persistent organic pollutants); HCB (hexachlorobenzene); DDT (dichloro-diphenyl-trichloroethane); PFOA (perfluorooctanoic acid)
The best miRNA biomarkers are likely to be those that indicate injury or perturbation to a specific cell type, such as the previously discussed hepatocyte-specific miR-122. Other cell-specific miRNAs that may be useful biomarkers of tissue injury include the myomiRs, miRs-133, -206, -208, and -499, which have shown promise through their ability to identify cardiac injury (Jenike and Halushka 2021). In addition, miRNA biomarkers that indicate the disruption of specific nuclear receptor or regulatory pathways as a result of chemical exposure would be desirable. Studies examining the changes in miRNA–mRNA interactions that are due to environmental exposures will be helpful in determining the mechanistic pathways involved. One such study applied a network approach, integrative Joint Random Forest, to investigate the effect of low-dose environmental chemical exposure on normal mammary gland development in rats (Petralia et al. 2017). The researchers detected miRNAs that regulated
