Table 4.

The biomarker potential of exosomal ncRNAs in Alzheimer’s diseases, CNS tumors, multiple sclerosis, epilepsy, and amyotrophic lateral sclerosis.

Diseases	Classification of ncRNA	ncRNAs	Regulation	ROC analysis	Biomarker potential	Source of exosome	Reference
Alzheimer’s disease	miRNA	miR-135a	Up	-	A biomarker for AD early stages diagnosis	Serum, CSF	Liu C. G. et al. (2021)
		miR-193b	Down	-	A blood-based biomarker for MCI and DAT patients	Blood, CSF	Liu et al. (2014)
		miR-34b, miR-29a	Up	AUC = 0.812, sensitivity = 83% (vs. VaD) specificity = 74% (vs. VaD); AUC = 0.832 sensitivity = 63% (vs. VaD) specificity = 96% (vs. VaD)	Biomarkers to discriminate clinically similar neurodegenerative and vascular-related diseases	Serum	Barbagallo et al. (2020)
		miR-16-5p, miR-451a, miR-605-5p	Down	AUC = 0.760 AUC = 0.951 AUC = 0.706	A biomarker for YOAD diagnosis	CSF	McKeever et al. (2018)
		miR-125b-5p	Up	AUC = 0.723
		miR-384	Up	AUC = 0.991 (vs. PDD) sensitivity = 99.07% (vs. PDD) specificity = 100% (vs. PDD) AUC = 0.991 (vs. VaD) sensitivity = 99.10% (vs. VaD) specificity = 100% (vs. VaD)	A biomarker for AD diagnosis and discrimination between AD, VaD, and PDD	Serum	Yang T. T. et al. (2018)
		miR-135a	Up	AUC = 0.598 (vs. PDD) sensitivity = 75.70% (vs. PDD) specificity = 46.67% (vs. PDD); AUC = 0.721 (vs. VaD) sensitivity = 89.70% (vs. VaD) specificity = 55% (vs. VaD)
	lncRNA	Bace1-AS	Up	AUC = 0.761 sensitivity = 87.5% specificity = 61.3%	A novel biomarker for AD diagnosis	Plasma	Wang D. et al. (2020)
	circRNA	KIAA1586	Up	-	A potential biomarker for AD diagnosis	Blood	Zhang et al. (2019)
	piRNA	piR_019949	Up	AUC = 0.96	Predict conversion from MCI to AD dementia	CSF	Jain et al. (2019)
		piR_020364		AUC = 0.89	A biomarker for classifying AD dementia patients
		piR_019324	Down
CNS tumor	miRNA	miR-454-3p	Down	AUC = 0.866 sensitivity = 79.17% specificity = 91.67%	An exosomal biomarker for glioma diagnosis and prognosis	Serum	Shao et al. (2019)
		miR-21	Up	AUC = 0.927 (vs. health) AUC = 0.872 (grade III/VI vs. II) AUC = 0.751 (grade VI vs. II)	A biomarker for glioma diagnosis, prognosis and different grade	CSF	Shi et al. (2015)
		RNU6	Up	AUC = 0.852	A biomarker for GBM diagnosis	Serum	Manterola et al. (2014)
		miR-574-3p	Up	AUC = 0.738
		miR-320	Up	AUC = 0.719
	lncRNA	HOTAIR	Up	AUC = 0.913 sensitivity = 86.1% specificity = 87.5%	A biomarker for GBM diagnosis	Serum	Tan et al. (2018)
		HOTAIR	Up	-	Promising prognostic predictors for GBM	Serum	Wang Z. et al. (2021)
		SOX21-AS1	Down
		STEAP3-AS1	Up
	circRNA	circNFIX	Up	AUC = 0.885	A biomarker for glioma diagnosis and prognosis	Serum	Ding et al. (2020)
		circHIPK3	Up	-	A potential biomarker for the TMZ-resistant glioma diagnosis	Serum	Yin and Cui (2021)
		circMMP1	Up	AUC = 0.8144	A biomarker for glioma diagnosis and prognosis	Serum	Yin and Liu (2020)
Multiple sclerosis	miRNA	miR-15b-5p	Up	AUC = 0.740	Biomarkers for RRMS diagnosis		Ebrahimkhani et al. (2017)
		miR-122-5p	Down	AUC = 0.878	Biomarkers for RRMS diagnosis	Serum	Selmaj et al. (2017)
		hsa-miR-196b-5p	Down	AUC = 0.866
		miR-19b, miR-25 and miR-92a	Up	-	Potential exosomal biomarkers for MS diagnosis	Plasma	Kimura et al. (2018)
		miR-326	Up	-	A biomarker for MS diagnosis	Serum	Azimi et al. (2019)
		miR-22-3p, miR-660-5p	Down	-	Biomarkers for MS diagnosis and the response after IFN-b therapy	Serum	Manna et al. (2018)
Epilepsy	miRNA	miR-328-3p	Up	AUC = 0.63 (vs. TLE) AUC = 0.90 (EAS vs. EBS)	A novel biomarker for epilepsy diagnosis and different subtypes	Plasma	Raoof et al. (2018)
		miR-8071	Down	AUC = 0.932 sensitivity = 83.33% specificity = 96.67%	A novel biomarker for TLE-HS diagnosis	Plasma	Yan et al. (2017)
		miR-451a, miR-21-5p, miR-19b-3p	Up Down Up	AUC = 0.80	Biomarkers for TLE diagnosis	CSF	Raoof et al. (2017)
ALS	miRNA	miR-27a-3p	Down	-	Biomarkers for ALS diagnosis	Serum	Xu et al. (2018)
		miR-124-3p	Down	-	A disease stage indicator in ALS	CSF	Yelick et al. (2020)
		miR-199a-3p miR-151a-5p	Up	-	Biomarkers for ALS/MND diagnosis	plasma	Banack et al. (2020)