Common molecular markers between circulating tumor cells and blood exosomes in colorectal cancer: a systematic and analytical review

Figures & data

Table 1 Search strategy of CTC and exosome in colorectal cancer

Search strategy		No. of papers
2019 24 July
SCOPUS
1	(TITLE-ABS-KEY (cecum OR colon OR sigmoid OR rectum OR anal)) AND (TITLE-ABS-KEY ((neoplasm OR cancer OR tumor OR tumors OR carcinoma))) OR (TITLE-ABS-KEY ((colorectal AND neoplasms OR crc)))	258,569
2	(TITLE-ABS-KEY (circulating AND tumor AND cell)) OR (TITLE-ABS-KEY (circulating AND neoplastic AND cells)) OR (TITLE-ABS-KEY (neoplasm AND micro-metastasis)) OR (TITLE-ABS-KEY (ctc OR ctm OR dtc))	60,012
3	((TITLE-ABS-KEY (gene AND expression AND profiling)) OR (TITLE-ABS-KEY (messenger AND rna)) OR (TITLE-ABS-KEY (rna OR transcriptome OR mrna))) AND ((TITLE-ABS-KEY (early AND diagnosis)) OR (TITLE-ABS-KEY (early AND detection)) OR (TITLE-ABS-KEY (prognosis OR diagnosis OR biomarkers OR screening OR diagnostic OR prognosis OR prognostic)))	209,207
4	(TITLE-ABS-KEY (extracellular AND vesicle)) OR (TITLE-ABS-KEY (cell-derived AND microparticles)) OR (TITLE-ABS-KEY (extracellular AND vesicles)) OR (TITLE-ABS-KEY (ev OR microvesicle OR exosomes))	274,615
1 & 2 & 3	(((TITLE-ABS-KEY (gene AND expression AND profiling)) OR (TITLE-ABS-KEY (messenger AND rna)) OR (TITLE-ABS-KEY (rna OR transcriptome OR mrna))) AND ((TITLE-ABS-KEY (early AND diagnosis)) OR (TITLE-ABS-KEY (early AND detection)) OR (TITLE-ABS-KEY (prognosis OR diagnosis OR biomarkers OR screening OR diagnostic OR prognosis OR prognostic)))) AND ((TITLE-ABS-KEY (circulating AND tumor AND cell)) OR (TITLE-ABS-KEY (circulating AND neoplastic AND cells)) OR (TITLE-ABS-KEY (neoplasm AND micrometastasis)) OR (TITLE-ABS-KEY (ctc OR ctm OR dtc))) AND ((TITLE-ABS-KEY (cecum OR colon OR sigmoid OR rectum OR anal)) AND (TITLE-ABS-KEY ((neoplasm OR cancer OR tumor OR tumors OR carcinoma))) OR (TITLE-ABS-KEY ((colorectal AND neoplasms OR crc)))) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “no”) OR LIMIT-TO (DOCTYPE, “le”)) AND (LIMIT-TO (EXACTKEYWORD, “Human”))	118
1 & 2 & 4	(((TITLE-ABS-KEY (gene AND expression AND profiling)) OR (TITLE-ABS-KEY (messenger AND rna)) OR (TITLE-ABS-KEY (rna OR transcriptome OR mrna))) AND ((TITLE-ABS-KEY (early AND diagnosis)) OR (TITLE-ABS-KEY (early AND detection)) OR (TITLE-ABS-KEY (prognosis OR diagnosis OR biomarkers OR screening OR diagnostic OR prognosis OR prognostic)))) AND ((TITLE-ABS-KEY (extracellular AND vesicle)) OR (TITLE-ABS-KEY (cell-derived AND microparticles)) OR (TITLE-ABS-KEY (extracellular AND vesicles)) OR (TITLE-ABS-KEY (ev OR microvesicle OR exosomes))) AND ((TITLE-ABS-KEY (cecum OR colon OR sigmoid OR rectum OR anal)) AND (TITLE-ABS-KEY ((neoplasm OR cancer OR tumor OR tumors OR carcinoma))) OR (TITLE-ABS-KEY ((colorectal AND neoplasms OR crc)))) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “ip”)) AND (LIMIT-TO (EXACTKEYWORD, “Human”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”))	37
PUBMED
1	((Colorectal Neoplasms[Title/Abstract] OR “Colorectal Neoplasms”[Mesh] OR CRC[Title/Abstract]) OR ((“Cecum”[Mesh] OR “Colon”[Mesh] OR “Colon, Sigmoid”[Mesh] OR “Rectum”[Mesh] OR “Anal Canal”[Mesh]) AND (“Neoplasms”[Mesh] OR “Carcinoma”[Mesh])) OR ((cecum[Title/Abstract] OR colon[Title/Abstract] OR sigmoid[Title/Abstract] OR rectum[Title/Abstract] OR anus[Title/Abstract]) AND (neoplasm[Title/Abstract] OR cancer[Title/Abstract] OR tumor[Title/Abstract] OR tumors[Title/Abstract] OR carcinoma[Title/Abstract]))	251,819
2	(“Neoplastic Cells, Circulating”[Mesh] OR Circulating Tumor Cell[Title/Abstract] OR “Neoplasm Micrometastasis”[Mesh] OR CTC[Title/Abstract] OR CTM[Title/Abstract] OR DTC[Title/Abstract]	20,001
3	(“Prognosis”[Mesh] OR “Diagnosis”[Mesh] OR “Early Diagnosis”[Mesh] OR “Early Detection of Cancer”[Mesh] OR “Biomarkers, Tumor”[Mesh]) OR (“screening”[Title/Abstract] OR “early detection”[Title/Abstract] OR “Diagnosis”[Title/Abstract] OR “Diagnostic”[Title/Abstract] OR “Prognosis”[Title/Abstract] OR “Prognostic”[Title/Abstract]) AND (“RNA, Messenger”[Mesh] OR “RNA”[Mesh] OR “Transcriptome”[Mesh] OR “Gene Expression Profiling”[Mesh] OR “mRNA” OR “RNA” OR “Transcriptome” OR “gene expression profiling”)	376,269
4	(“extracellular vesicles”[Mesh] OR “Cell-Derived Microparticles”[Mesh] OR “EV” OR “microvesicle” OR “extracellular vesicle” OR “Exosomes”[Mesh] OR Exosome)	41,831
1 & 2 & 3	Search ((((((Colorectal Neoplasms[Title/Abstract] OR “Colorectal Neoplasms”[Mesh] OR CRC[Title/Abstract]) OR ((“Cecum”[Mesh] OR “Colon”[Mesh] OR “Colon, Sigmoid”[Mesh] OR “Rectum”[Mesh] OR “Anal Canal”[Mesh]) AND (“Neoplasms”[Mesh] OR “Carcinoma”[Mesh])) OR ((cecum[Title/Abstract] OR colon[Title/Abstract] OR sigmoid[Title/Abstract] OR rectum[Title/Abstract] OR anus[Title/Abstract]) AND (neoplasm[Title/Abstract] OR cancer[Title/Abstract] OR tumor[Title/Abstract] OR tumors[Title/Abstract] OR carcinoma[Title/Abstract])))) AND ((“Neoplastic Cells, Circulating”[Mesh] OR Circulating Tumor Cell[Title/Abstract] OR “Neoplasm Micrometastasis”[Mesh] OR CTC[Title/Abstract] OR CTM[Title/Abstract] OR DTC[Title/Abstract])) AND (((“Prognosis”[Mesh] OR “Diagnosis”[Mesh] OR “Early Diagnosis”[Mesh] OR “Early Detection of Cancer”[Mesh] OR “Biomarkers, Tumor”[Mesh]) OR (“screening”[Title/Abstract] OR “early detection”[Title/Abstract] OR “Diagnosis”[Title/Abstract] OR “Diagnostic”[Title/Abstract] OR “Prognosis”[Title/Abstract] OR “Prognostic”[Title/Abstract]) AND (“RNA, Messenger”[Mesh] OR “RNA”[Mesh] OR “Transcriptome”[Mesh] OR “Gene Expression Profiling”[Mesh] OR “mRNA” OR “RNA” OR “Transcriptome” OR “gene expression profiling”)))) Filters: Humans; English	164
1 & 2 &4	Search (((((((Colorectal Neoplasms[Title/Abstract] OR “Colorectal Neoplasms”[Mesh] OR CRC[Title/Abstract]) OR ((“Cecum”[Mesh] OR “Colon”[Mesh] OR “Colon, Sigmoid”[Mesh] OR “Rectum”[Mesh] OR “Anal Canal”[Mesh]) AND (“Neoplasms”[Mesh] OR “Carcinoma”[Mesh])) OR ((cecum[Title/Abstract] OR colon[Title/Abstract] OR sigmoid[Title/Abstract] OR rectum[Title/Abstract] OR anus[Title/Abstract]) AND (neoplasm[Title/Abstract] OR cancer[Title/Abstract] OR tumor[Title/Abstract] OR tumors[Title/Abstract] OR carcinoma[Title/Abstract])))) AND (((“Prognosis”[Mesh] OR “Diagnosis”[Mesh] OR “Early Diagnosis”[Mesh] OR “Early Detection of Cancer”[Mesh] OR “Biomarkers, Tumor”[Mesh]) OR (“screening”[Title/Abstract] OR “early detection”[Title/Abstract] OR “Diagnosis”[Title/Abstract] OR “Diagnostic”[Title/Abstract] OR “Prognosis”[Title/Abstract] OR “Prognostic”[Title/Abstract]) AND (“RNA, Messenger”[Mesh] OR “RNA”[Mesh] OR “Transcriptome”[Mesh] OR “Gene Expression Profiling”[Mesh] OR “mRNA” OR “RNA” OR “Transcriptome” OR “gene expression profiling”)))) AND (((“extracellular vesicles”[Mesh] OR “Cell-Derived Microparticles”[Mesh] OR “EV” OR “microvesicle” OR “extracellular vesicle” OR “Exosomes”[Mesh] OR Exosome))))) Filters: Humans; English	66
Embase
1	(cecum OR sigmoid OR rectum OR anal) AND (neoplasm OR cancer OR tumor OR tumors OR carcinoma) OR “colorectal cancer” OR crc	323,384
2	ctc OR ctm OR dtc OR (circulating AND neoplastic AND cells) OR (circulating AND tumor AND cell) OR (neoplasm AND “micro-metastasis”)	54,423
3	(early AND diagnosis) OR (early AND detection) OR biomarkers OR screening OR diagnostic OR prognosis OR prognostic) AND (messenger AND rna) OR (gene AND expression AND profiling) OR mrna OR transcriptome	101,305
4	“membrane microparticle” OR “exosome”	25,614
1 & 2 & 3	#1 AND #2 AND #3 AND ([article]/lim OR [article in press]/lim OR [letter]/lim OR [note]/lim) AND [english]/lim AND [humans]/lim AND [embase]/lim	135
1 & 2 & 4	#1 AND #2 AND #4 AND ([article]/lim OR [article in press]/lim OR [letter]/lim OR [note]/lim) AND [english]/lim AND [humans]/lim AND [embase]/lim	52
Web of Science
1	TI=(Cecum OR Colon OR Colon Sigmoid OR Rectum OR Anal) AND (neoplasm OR cancer OR tumor OR tumors OR carcinoma) OR TI=(Colorectal Neoplasms OR CRC)	43,039
2	TS=(Circulating Neoplastic Cells OR Circulating Tumor Cell OR Neoplasm Micrometastasis OR CTC OR CTM OR DTC)	44,339
3	TS=(Prognosis OR Diagnosis OR Early Diagnosis OR Early Detection OR Biomarkers OR screening OR Diagnostic OR Prognosis OR Prognostic) AND TS=(Messenger RNA OR RNA OR Transcriptome OR Gene Expression Profiling OR mRNA)	138,133
4	TS=(extracellular vesicles OR Cell-Derived Microparticles OR EV OR microvesicle OR extracellular vesicle OR Exosomes)	212,089
1 & 2 & 3	#1 AND #2 AND #3 (#8 AND #7 AND #3) AND LANGUAGE: (English) AND DOCUMENT TYPES: (Article)	19
1 & 2 & 4	#1 AND #2 AND #4 AND (#8 AND #7 AND #3) AND LANGUAGE: (English)	15

Abbreviation: CTC, Circulating tumor cells.

Table 2 The biomarkers which worked for diagnostic of CRC in circulating tumor cells

Biomarker	Technique of isolation/detection of CTC	Technique of validation/related one	Related marker	Cutoff	Patients (number/type)	Patient stage	Author/ year	CTCs positive rate	PMID
CK20	Nested RT-PCR^Citation1	SWI 116, HT29 cell spiking	–	5 mL	57 patients, 2 controls^Citation2/Blood	I–IV^Citation3	Soeth, 1996.^Citation28	35%	8,797,868
	Nested RT-PCR	A818-4 cell spiking	–	5 mL	39 patients, 12 controls/Blood	I–IV	Soeth, 1997.^Citation27	24%	9,242,433
	RT-PCR	HT29 cell spiking, Immunohistochemistry	PBGD	5–10 m	30 patients, 16 controls/Blood	I–IV	Vlems, 2002.^Citation29	30%	12,032,226
	CD45 Immune magnetic beads,/or Ber-EP4 immuno magnetic beads	LS174T cell spiking		5 mL	40 patients, 10 controls/Blood	A–D Dukes^Citation3	Guo, 2005.^Citation30	80.0%, 82.5%, 72.5%	16,048,578
	RT-PCR	–	–	5–10 mL	58 patients, 12 controls (abnormal)/Blood	A–C Dukes	Zhang, 2005.^Citation25	44.8% to 69.0%	15,637,763
RT-PCR		CEA, CK19	15 mL	57/Blood	A–D Dukes	Katsumata, 2006.^Citation26	42.1%	17,058,136
CEA	CD45 Immune magnetic beads and/or Ber-EP4 immuno magnetic beads	LS174T cell spiking		5 mL	25 patients, 10 controls/Blood	A–D Dukes	Guo, 2004.^Citation36	25.0%, 83.3%, 88.9%	15,490,093
	RT-PCR	Southern blotting, Colo201, HT1 16, HT29, and HT115 cell spiking	–	14 mL	31 patients, 22 controls/Blood	Liver metastasis	Jonas, 1996.^Citation31	58%	9,014,772
	RT-PCR	Cell spiking	–	10 mL	95 patients, 11 controls/Blood	I–IV	Castells, 1998.^Citation32	41%	9,823,981
	RT-PCR	Colo201 cell spiking	–	14 mL	24 patients, 9 controls/Blood	B, C, D Dukes	Noh, 1999.^Citation33	41.1%	10,642,939
	Nested RT-PCR	In-vivo assay	CA19.9, CA72-4	7 mL	51 patients, 40 controls, 18 patients with benign colorectal disease/Blood	A–D Dukes	Guadagni, 2001.^Citation34	67%	11,289,125
	RT-PCR	HT29 and LS147T cell spiking, Sequence analysis	CK20	20 mL	32 patients, 17controls/Blood	–	Hampton, 2002.^Citation35	36%	12,420,218
CEA, CK19	Semi-quantitative RT-PCR	Southern blotting, SK-BR-3 cell spiking	–	20 mL	33 patients, 26 controls/Blood	B–D Dukes	Wong, 2001.^Citation38	64%, 88%	11,121,864
RT-PCR	-	–	3 mL	53 patients, 25 controls/Blood	I–III	Silva, 2002.^Citation37	73.6% 32%	11,889,075
CEA, EPCAM.	Adnatest ColonCancerSelect & Detect.	Multiplex RT-PCR	–	–	50 patients, 40 controls/Blood	I–III	Mourtzikou, 2012.^Citation41	66%, 6%	10.6051/j.issn.2224–3992.2012.01.070
EPCAM	Multigene qRT-PCR, flow cytometry		CK19, CK20, CEA, EGFR.	7.5 mL	49 patients/Blood	I–IV	Cohen, 2006.^Citation39	80%	16,945,168
EPCAM	Microfluidic device, FISH, Cellsearch		Pan CK, EPCAM	2 mL	5 patients, 200 controls/blood	With metastasis	Gogoi, 2016.^Citation40	100%	26,808,060
EPCAM	CTC-chip	NCI-H1650 cell spiking	–	2.7 mL	10 patents/Blood	Advanced	Nagrath, 2007.^Citation43	67%	18,097,410
CEA, TEM-8	RT-PCR	MAD-MB231 and HT29 cell spiking		5 mL	40 patients, 40 controls/Blood	I–III	Raeisossadati, 2011.^Citation44	55%, 22.5%	21,573,768
CD44	RT-PCR	Southern blotting, HCTl16 cell spiking, Restriction enzyme analysis	–	15 mL	24 patients, 8 controls/Blood	B, C Dukes	Wong, 1997.^Citation45	16%	10.1046/j.1365–2168.1997.02685
GCC	Nested RT-PCR	–	PSA, PSMA, CEA, CK-19, CK-20, mucin 1, GA733.2.	-	24 patients, 20 controls/Blood	D Dukes	Fava, 2001.^Citation46	100%	11,579,116
	Nested Duplex RT-PCR	Immuno histochemistry	CD31	10 mL	58 patients, 11controls/Blood	B–D Dukes	Tien, 2001.^Citation47	52%	11,410,499
Nested Duplex RT-PCR	CCL-220 cell spiking, Immunohistochemistry, Western blotting	–	10 mL	68 patients, 11controls/Blood	A–D Dukes	Tien, 2004.^Citation48	58.8%	15,192,312
BMP4, CycD, FAM3D, GPA33, ZPX2, LGALS4, TACSTD1, hTERT, TFF3, TM4SF3, UGT1A9, VIL1, FLJ20127.	RT-PCR	–	B2M	10–15 mL	16 pooled patients, 16 controls/Blood	I–IV	Solmi, 2004.^Citation49	-, 100%, 100%, -, 100%, 100%, 100%, 100%, 100%, 37.5%, 83%, -, 36.3%	15,375,555
CK-20, CEA, CK-19, REG4, uPA, TIAM1.	RT-PCR	–	–	–	80 patients, 98 controls/Blood	I–II	Yeh, 2006.^Citation50	82.5%, 78.8%, 82.5%, 80.0%, 78.8%, 80.0%.	16,391,796
TMEM69, RANBP3, PRSS22.	Microarray screening,	QRT-PCR	–	10–15 mL	2 patients, 4 controls/Blood	TNM stage	Solmi, 2006.^Citation51	~3-fold	17,054,783
LOC644844, FABP1, CEACAM5, MUC13, GUCA2A, ABP1, SLC26A3	Digital Gene Expression Displayer (DGED), RT-PCR	–	–	5 mL	8 patients, 9 controls/Blood	–	Lauriola, 2010.^Citation52	–	20,596,680
SERPINB5	qRT-PCR	SW480 and T84 cell spiking	VSNL1, DPEP1, STC1.	5 mL	818 patients, 4 IBD, 8 controls, 36 control without malignant disease/Blood	TNM stage	Findeisen, 2008.^Citation57	36%	18,949,363
CK20, CK19, EGFR	Multiplex-PCR	–	–	6 mL	81 patients, 38 controls/Blood	0–IV	Vaiopoulos, 2014.^Citation58		24,922,677
CK20,CEA, EGFR,	Nested RT-PCR	–	–	–	36 patients, 18 controls/Blood	I–IV	Teama, 2010.^Citation59	41.7, 61.1%, 66.7%	10.1016/j.ejmhg.2009.10.001
EGFR	AdnaTest Colon Cancer Select, AdnaTest Colon Cancer detect	COLO 205, HCC-2998, HCT-116, LoVo, WiDr, CACO-2, HT-29, SW-480, T84, DLD-1, SW-948, SW-1116 cell spiking, IHC, Multiplex RT-PCR.	EPCAM, CEA.	15 mL	20 patients, 22 controls/Blood	TNM stage	Lankiewicz, 2008.^Citation60	18%	18,936,523
ECT2	Nested qPCR	–	CEA	4 mL	90 patients, 151controls/blood	I–IV	Chen, 2017.^Citation61	–	28,362,321
FAM172A	Filtration	In situ hybridization	EpCAM, CK8, CK18, CK19, Vimentin, Twist, CD45^−.	5mL	45/Blood	I–IV	Cui, 2017.^Citation62	75.6%	28,618,931
A3 adenosine receptors	Real-time RT-PCR	Immunocytochemistry	–	40 mL	30/Blood	I–IV	Gessi, 2003.^Citation63	–	15,355,922
TGFβ1, APP, CD9, CLU, ITGB5, LIMS1,RSU 1, TIMP1, TLN1, VCL, BMP6.	CELLection^TM, Agilent expression arrays	Real-time RT-PCR	EPCAM	7.5 mL	28 patients, 10 controls/Blood	Primary and metastasis	Barbazan, 2012.^Citation53		22,811,761
VIL1, TBX20, GPA33, FAM132A	CELLection^TM	Real-time RT-PCR, HT29 and HCT116 cell spiking	CD45^−, EPCAM	7.5 mL	44 patients, 22 controls/Blood	IV	Barbazan, 2012.^Citation54	77.2%	22,304,365
TSPAN8, LGALS4.	qRT-PCR	TRAM based data set meta-analysis	EPCAM, SPINK1, COL3A1, CEACAM5, COL1A2, CDH1, CKT18, SLC26A3, REG1A, FN1, LUM, CEACAM6, CK20	5 mL	67 patients, 67 controls/Blood	I–III	Rodia, 2016.^Citation64	–	26,993,598
LOXL3, ZEB2, VIL1, TIMP1, CLU, TLN1	AdnaTest colon cancer	–	CD45-, EPCAM, CK 8, 18, and/or 19	7.5 mL	50 patients/Blood	Advanced	Alonso-Alconada 2017.^Citation55	–	29,058,262
VIL1, CLU, TIMP1, LOXL3 and ZEB2	CELLection^TM	qRT-PCR	EPCAM	7.5 mL	50 patients/Blood		Barbazan 2012.^Citation56	–	24,752,533

Abbreviations: RT-PCR, real-time polymerase chain reaction; Controls, healthy volunteer/donors; I–IV, TNM classification of malignant tumors (TNM); A–D Dukes, Dukes staging system is a classification system for colorectal cancer.

Table 3 The biomarkers which worked for diagnostic of CRC in Exosome

Biomarker	Technique of exosome isolation	Technique of exosome validation	Technique of markers detection or validation	Related marker	Patients (number/type)	Patient stage	Author/year:	PMID
KRTAP5-4, MAGEA3	Centrifugation syringe filter	TEM, NTA, light microscope	Bioinformatic Analysis, RT-PCR	lncRNA	30 patients, 30 control/Blood	I–IV	Dong, 2016.^Citation79	27,197,301
GPC1	ExoCapTM	TEM, Flow cytometry, Western blotting	Flow cytometry, Western blot analysis	miR‐96‐5p, miR‐149, miR‐182‐5p	102 patients, 89 control/tissue and Blood, Cell line (HT‐29 & HCT‐116), Mouse	I–II	Li, 2017.^Citation78	28,233,416
EPCAM	PEG	ELISA, SEM	qRT-PCR, SEM, DLS, ELISA	–	HCT‐116 Cell line	–	Manri, 2016.^Citation77	27,917,441
UBC, H3F3A, HIST2H2AA3, AKT3, HSPA1B			GSE100206, GSE100063, GSE32323 (Bioinformatic Analysis)	–	29 patients, 49 control/tissue and Blood	–	Huang, 2018.^Citation81	doi: 10.21037/tcr.2018.05.32
OATP1B3	Exosome Isolation kit (Thermo Fisher Scientific), PVDF filter and Differential centrifugation	TEM, Western blotting	qRT-PCR, Western blotting	–	HCT116, HT-29, and SW480 cell line, Blood of Mouse	–	Morio, 2018.^Citation80	29,491,222

Abbreviations: TEM, transmission electron microscopy; NTA, nanoparticle tracking analyzer; PEG, polyethylene glycol polymer; ELISA, enzyme-linked immunosorbent assay; SEM, scanning electron microscope; DLS, dynamic light-scattering.

Table 4 The biomarkers which worked for prognostic of CRC in circulating tumor cells

Biomarker	Technique of isolation/detection of CTC	Technique of validation/related one	Related marker	Cutoff	Patients (number/type)	Patient stage	Author/year	CTCs positive rate	PMID
CK20	RT-PCR	Colo205 cell spiking	–	2 mL	8 patients, 3 controls/Blood	III–IV	Funaki, 1997.^Citation84	36%	9,048,967
	RT-PCR	HT29 cell spiking	–	10 cells/2 mL	26 patients, 12 controls/Blood	B, C Dukes stage	Wyld, 1998.^Citation82	48%	9,645,353
	RT-PCR	–	–	10 mL	108 patients, 38 controls/Blood	I–IV	Hinz, 2012.^Citation83	25%	22,395,998
	qRT-PCR	–	–	5 mL	95 patients, 23 controls/Blood	I–IV	Samija, 2013^Citation85	–	23,558,939
RT-PCR	–	–	5 mL	95 patients, 23 controls/Blood	I–IV	Kust, 2016.^Citation86	–	27,144,776
CEA	RT-PCR	Southern blot hybridization	–	7 mL	69 patients, 16 controls/Blood	I–IV	Piva, 2000.^Citation87	34%	11,096,345
qRT-PCR	COLM-2 cell spiking	–	5–7 mL	99 patients, 20 controls/Blood	I–III	Ito, 2002.^Citation88	44.4%	12,065,095
	RT-PCR			5 mL	108 patients, 76 controls/Blood	III–IV	Kanellos, 2006.^Citation89	11.1%	16,788,936
	Membrane arrays	RT-PCR	–	4 mL	141 patients/Blood	II–III	Lu, 2011.^Citation90	33.3%	21,343,933
	CellSearch (EPCAM)	CellTracks® Analyzer II	CD45^−	7.5 mL	20 patients/Blood	I–III	Thorsteinsson, 2011.^Citation91	5%	21,378,346
CK20, CEA.	RT-PCR	Colo320 cell spiking	–	10 mL	52 patients, 10 controls/Blood	I–IV	Yamaguchi, 2000.^Citation92	38.4%, 36.5%	10,862,196
	RT-PCR	HT29 or HT115 cell spiking	–	14 mL	33 patients, 70 controls/Blood	I–IV	Mathur, 2001.^Citation93	85%	11,417,979
	RT-PCR	LS 180 and C205; ATCC CL-187 and CCL-222 cell spiking	–	12 mL	39 patients,13 controls (abnormal)/Blood	I–III	Guller, 2002.^Citation94	28%	12,454,515
	qRT-PCR	–	–	–	167 patients, 25 controls/Blood	I–IV	Iinuma, 2006.^Citation95	22%	16,391,782
qRT-PCR	HT29 cell spiking	CA19-9	10 mL	46 patients, 23 controls/Blood	I–IV	Liu,2012.^Citation96	65.21%, 36.95%	22,414,974
CK20, CK19	RT-PCR	Cell Spiking	K-ras, p53	20 mL	35 patients, 23 controls/Blood	I–IV	Nakamori, 1997.^Citation98	26%	9,378,009
CK20, CEA, CK19.	Nested RT-PCR	–	–	–	62 patients, 12 controls/Blood	I–IV	Huang, 2003.^Citation97	35.5%, 48.4%, 51.6%	12,684,893
CK20, GCC.	RT-PCR	–	CEA, CA199	5 mL	100 patients, 5 controls/Blood	I–III	Liu, 2017.^Citation103	–	28,418,917
qRT-PCR	–	CEA	5 mL	69 patients, 23 controls//Blood	I–III	Liu, 2013.^Citation104		23,150,200
CK, CEA, CD133.	qRT-PCR	–	CK19, CK20	10 mL	735 patients/Blood	B–C Dukes	Iinuma, 2011^Citation106	24.52%	21,422,427
CK20, CEA, CK19, CD133	qRT-PCR	–	–	–	197 patients, 20 controls (benign diseases)/Blood	B–C Dukes	Shimada, 2012.^Citation100	63%	22,267,181
CEA, EPCAM.	CellSearch, TRC method	DLD1 cell spiking	–	7.5 mL	67 patients/Blood	Metastatic	Sato, 2012.^Citation108	9.0±23.4%, 64.3%	21,732,137
CK20, CEA, Survivin.	CD45 immuno magnetic beads + Ber-EP4 immuno magnetic beads	Lovo cell spiking, Real-time RT-PCR	–	10 mL	156 patients, 40 benign patients, 40 healthy/Blood	A–D Dukes	Shen, 2008.^Citation111	47.4%, 39.1%, 57.7%.	18,845,519
CK20,CEA, ProtM,	Real-time RT-PCR	COLO 205, LS-174-T, CX 2, CX 94, HCT 116, HT 29, CaCo2 cell spiking	PBGD	10 mL	129 patients, 47 controls/Blood	0–IV	Schuster, 2004.^Citation112	88%, 86%; 17%	14,639,606
CK19, CK20, MUC1, MUC2.	Immunobead RT-PCR	SW48, SW480, HT29, LIM-2412, LIM-1215, LIM-2099, LIM-2405, LIM-1899, LIM-2463 and LIM-1863 cell spiking	–	20 mL	94 patients, 20 controls/Blood	A–D Dukes	Hardingham, 2000.^Citation99	20%	10,719,724
CK-19, CK-20, CEA, hTERT.	Membrane arrays	RT-PCR	–	4 mL	72 patients, 30 controls/Blood	I–IV	Wang, 2006.^Citation113	66.7%, 52.8%, 72.2%, 69.4%	16,736,329
CGM2 (CEACAM7)	RT-PCR	CACO-2 and HT-29 cell spiking	–	20 mL	78 patients, 115 controls/Blood	A–D Dukes	Douard, 2001.^Citation116	59%	11,331,451
CEACAM5, CEACAM7	Immuno bead multiplex RT-PCR	–	HBB	20 mL	84 patients, 41 controls, 32 non CRC patients/Blood	I–IV	Douard, 2005.^Citation115	55%, 45%	15,843,204
EPCAM, EGFR.	Immuno magnetic selection (IMS), multiplex RT-PCR	T84, HT29, SW948 and SW1116 cell spiking	CEA	5 mL	76 patients, 106 controls/Blood	I–IV	Zieglschmid, 2007.^Citation107	88%, 12%.	17,649,779
EGFR	RT-PCR	Immunohistochemistry (IHC)	CEA (45%), CK-19 (27%)	5 mL	38 patients, 38 controls/Blood	B, C Dukes	De luca, 2000.^Citation118	(73%)	10,778,975
RT-PCR	–	–	3 mL	16 patients, 23 controls/Blood	Advanced-stage	Clarke, 2003.^Citation119	12.5%	12,527,944
EGFR, HLM	RT-PCR	Northern blotting, HT11C cell spiking	–	3 mL	1 patients, 9 controls/Blood	Metastatic	Fournier, 1999.^Citation120	100%	10,446,991
EGFR, Tenascin C.		–	–	5 mL	41 patients, 40 controls/Blood	I–IV	Gazzaniga, 2005.^Citation121	49%	16,211,285
PLS3	RT-PCR	Fluorescent immunocytochemistry	CEA	–	711 patients, 25 controls/Blood	Dukes A, B, C, and D	Yokobori, 2013.^Citation122	25%	23,378,342
PLS3, AQP5	RT-PCR	Fluorescent Immuno cytochemistry	CD45 (−)	10 mL	177 patients, 25 controls/Blood	Dukes A, B, C, and D	Sugimachi, 2014.^Citation123	-	24,217,791
CD45 magnetic bead depletion	FISH Immunofluorescent	CEP8≥3 and	7.5 mL	45 patients, 25 controls/Blood	I–IV	Shan, 2014^Citation131	55%	25,109,507
PKP3, AGR2.	Bioinformatic analysis and RT-PCR	Gp5d, LoVo, DLD1, LS513, HT29, OJC4, OJC5, OJC6 cell spiking	S100A16, S100A6, LGALS4, CLDN3.	10 mL	21 patients and controls/Blood	III–IV	Valladares-Ayerbes, 2008.^Citation124	40%, 81.8%	18,801,625
AGR2, LGR5.	qRT-PCR	–	–	10 mL	54 patients, 19 controls/Blood	I–IV	Valladares-Ayerbes, 2012.^Citation126	84.9%, 90.5%	22,605,983
DCLK1, LGR5	qRT-PCR	–	–	10 mL	58 patients, 58 controls/Blood	I–IV	Mirzaei, 2015.^Citation127	63.7%	25,631,749
LGR5	mRNA ISH	–	EpCAM, CK8, CK18, CK19 Twist1, Vimentin, AKT2, SNAI1, CD45 (−)	5 mL	66 patients,/Blood	I–IV	Wang, 2018.^Citation128	86.4%	29,949,050
CK20, Tyrosinase, PSA.	RT-PCR, Nucleic acid sequence-based amplification (NASBA assay)	HT-29 cell spiking, In vitro cell assay	–	2 mL	12 patients, 8 controls/Blood	–	Burchill, 2002.^Citation132	–	11,857,020
MAGE-A	Electrochemiluminescence (ECL), RT-PCR	Sequencing analysis	uMAGE-A, M-A1, M-A3, M-A12	10 mL	12 patients, 20 controls/Blood	I–IV	Miyashiro, 2001.^Citation133	29%	11,238,304
DVL1	Microarray and enzymatic chip array (WEnCA)	IHC	PSG2, TMPO, CD55, ELAVL4, PDX1, CTHRC1, CA9, TK1, UBE2C, FOXM1, PDE6D, PSAT1, CHRNB1,CEA,BMI CAP2, MMP13, OLFM4, PTTG1, MYC, MET, ENO2, MUC1, KRT19, BIRC5, HMGB1, KRT20, hTERT, GCNT1, NPM1	4 mL	214 patients/Blood	I–III	Huang, 2013.^Citation134	55%	24,129,181
CD47	Cellsearch	–	EPCAM, CD45 (−)	20–30 mL	72 patients/Blood	I–IV	Steinert, 2014.^Citation135	14%	24,599,131
CD44v9	OncoQuick	qRT-PCR	–	20 mL	150 patients, 15 controls/Blood	I–IV	Katoh, 2015.^Citation136	40%	25,550,556
CK19, AGR2, CK8, CK9.	CellSearchn	–	TSPAN8, LAD1, CK20, IGFBP5, GPX2, FABP1, S100A1,6 CK8, PRSS8, CDX1, CEACA,M5, AKR1C3, RARRES2, REG1A, IGFBP4, CD44, TRIM2, CXCL1, SATB2, NQO1, CK19, MAPT, IGFBP3, COL4A1, FCGBP, SLC6A8, CDH5, CDH17, EGFR, S100P, HOXB9, CDH1, MACROD1,	30 mL	142 patients, 30 controls/Blood	Metastatic colorectal cancer	Mostert, 2015.^Citation125	66%	25,655,581
CK20, CEA, AGR2, MGB2, DLL4, EphA2, Her3, PDGFRα	qRT-PCR	–	–	7.5 mL	24 patients/Blood	III–IV	Bao, 2013.^Citation137	59%	23,990,866
CK-20, CEA, CK-19, hTERT.TM4SF3, CK19.	Membrane arrays	RT-PCR	–	4 mL	157 patients, 80 controls/Blood	I–IV	Wang, 2007.^Citation114	50%	17,406,027
RT-PCR	–	CEA, CK20, TACSTD1,	10 mL	28 patients, 19 controls/Blood	I–IV	Xi, 2007.^Citation101	96.4%	17,525,108
DCLK1, LGR5, EpCAM, CK8, CK9, CK19, Vimentin, Twist	qRT-PCR, IHC	–	–	10 mL	78 patients and controls/Blood	I–IV	Mirzaei, 2016.^Citation109		26,383,518
CanPatrol CTC enrichment	(ISH) assay	–	5 mL	38 patients, 27 controls/Blood	I–IV	Wu, 2015.^Citation110	67%	25,909,322
PSG2, ELAVL4, TK1, UBE2C, PDE6D, PSAT1, CHRNB1, BMI1, CAP2, MMP13, OLFM4, PTTG1, MYC, MET, MUC1, HMGB1, hTERT, BIRC5,	Enzyme immunoassay test kit	–	CEA	3 mL	298 patients/Blood	I–III	Chang, 2016.^Citation138	-	27,701,415
PI3Kα, Akt-2, Twist1 ALDH1	antiCD45 specific antibodies (Dynabeads, Invitrogen)	qRT-PCR and multiplex-PCR	_	8 mL	78 patients, 20 controls/Blood	I–IV	Ning, 2018.^Citation129	55%	27,503,579
CK19, MUC1, CD44, CD133, ALDH1	CD45 Human MicroBeads (Miltenyi Biotec), enrichment of cytokeratin (Miltenyi Biotec)	Flowcytometry, CellSearch, qRT-PCR, Cytomorphology, PC3, MDA-MB-231 and SKBR3 cell spiking	_	7.5 mL	63 patients, 40 controls/Blood	I–III	Bahnassy, 2019.^Citation139	(55.6%), (46.0%), (44.4%), (41.3%) (41.3%)	30,578,762
CEACAM5, CK19, AGR2, LGR5	Inertial microfluidics combined with droplet digital PCR	qRT-PCR, HT-29 and LoVo cell spiking	–	9 mL	Patients and controls/Blood	Advanced	Methai, 2019.^Citation164	-	31,304,099

Table 5 The biomarkers which worked for prognostic of CRC in exosome

Biomarker	Technique of exosome isolation	Technique of exosome validation	Technique of markers detection or validation	Related marker	Patients (number/type)	Patient stage	Author/year:	PMID
Alix	–	–	GSE37364, GSE10714, GSE4183, GSE18105, GSE4107, GSE9348, GSE8671, IHC	PGK1, PKM, ANXA5, ENO1, HSP90AB1, MSN	72 patients, 27 controls, and 98 sample (literature bioinformatic)	I–IV	Valcz G, 2016.^Citation140	27,150,162
ΔNp73	UC-Exo* centrifugation 120,000 and PVDF filter	Acetylcholinesterase activity, flow cytometry quantification, transmission electron microscopy, Western blot analysis	qRT-PCR, Cell culture and transfection	CEA	69 patients and control tissues, HCT116 cell lines.	I–IV	Soldevilla, 2013.^Citation70	24,067,531
Hsp60	UC-Exo	TEM AChEase:acetylcholinesterase assay, Western blot	IHC, ELISA, immunogold electron microscopy	Hsc70, Alix, CD57, CD68	57 patients and control tissues, 2 blood sample	I–III	Campanella, 2015.^Citation143	26,060,090
RPL13A, HMBS, TBP	UC-Exo	BCA, Western blotting	qRT-PCR	miR-21, miR-34, miR-143, miR-192, miR-215, miR-22	WiDr, HCT-15, SW480 cell lines	–	Chiba, 2012.^Citation71	22,895,844
TSAP6, CEA	UC-Exo	Flow Cytometry, Western blotting	qRT-PCR, IHC, levels of circulating exosomes in plasma		91 patients, 12 controls/tissue and blood	I–IV	Silva, 2012.^Citation142	22,420,032
Alix, TSG101, HSP70, CD9, CD81, ESCRT-III, VPS32C/CHMP4C, VAMP2, EFNB1, EFNB2, EPHA2–8, EPHB1–4, CTNNB1, TNIK, CRK, GRB2	UC-Exo, DG-Exo: OptiPrep™ density gradient exosome, IAC-Exo: EpCAM immunoaffinity capture	Western blotting, EM: Electron microscopy	GeLC–MS/MS (protein profiling)	–	LIM1863 cell line	–	Tauro, 2012.^Citation141	22,285,593
BCL7C, EEF1G, RAB13, RSP3, TPT1, SCARB1, SCD	UC-Exo	A33-Exos and EpCAM-Exos (Dynabeads™), TEM, Western blot	SRP02205476, SRP029880 (Microarray)	–	LIM1863 cell line	–	Chen, 2016.^Citation146	27,917,920
CPNE3	UC-Exo	TEM, NTA, Western blotting		CEA	92 patients, 32 controls/Blood	Sensitivity of 67.5% and a specificity of 84.4%	Sun, 2019.^Citation145	30,078,189
ABCG1	Polymer-based precipitation method	TEM, Zetasizer Nano ZSP, Western blotting,	qRT-PCR, IHC, GSE1753749	–	Murine cell line		Namba, 2018.^Citation144	30,364,132

Abbreviation: UC-Exo, ultracentrifugation exosome.

Figure 3 Network and enrichment analysis visualization. Combined screenshots from the STRING website, showing results obtained upon entering a set of 131 proteins suspected to be involved in circulating tumor cell markers. According on kmeans clustering has been selected, the corresponding protein nodes in three categories automatically highlighted in colors.

Figure 4 Network and enrichment analysis visualization. Combined screenshots from the STRING website, showing results obtained upon entering a set of 45 proteins suspected to be involved in Exosome markers. According on kmeans clustering has been selected, the corresponding protein nodes in three categories automatically highlighted in colors.

Figure 1 Design of PRISMA flow diagram explaining details of our search process was applied during the article selection for circulating tumor cell.

Figure 2 Design of PRISMA flow diagram explained details of our search process that applied during the article selection for Exosome.

Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87–108. doi:10.3322/caac.2126225651787

 PubMed Web of Science ®Google Scholar

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5–29. doi:10.3322/caac.2125425559415

 PubMed Web of Science ®Google Scholar

Punt CJ, Koopman M, Vermeulen L. From tumour heterogeneity to advances in precision treatment of colorectal cancer. Nat Rev Clin Oncol. 2017;14(4):235. doi:10.1038/nrclinonc.2016.17127922044

 PubMed Web of Science ®Google Scholar

Soeth E, Roder C, Juhl H, Kruger U, Kremer B, Kalthoff H. The detection of disseminated tumor cells in bone marrow from colorectal-cancer patients by a cytokeratin-20-specific nested reverse-transcriptase-polymerase-chain reaction is related to the stage of disease. Int J Cancer. 1996;69(4):278–282. doi:10.1002/(SICI)1097-0215(19960822)69:4<278::AID-IJC7>3.0.CO;2-U8797868

 PubMed Web of Science ®Google Scholar

Soeth E, Vogel I, Roder C, et al. Comparative analysis of bone marrow and venous blood isolates from gastrointestinal cancer patients for the detection of disseminated tumor cells using reverse transcription PCR. Cancer Res. 1997;57(15):3106–3110.9242433

 PubMed Web of Science ®Google Scholar

Vlems FA, Diepstra JH, Cornelissen IM, et al. Limitations of cytokeratin 20 RT-PCR to detect disseminated tumour cells in blood and bone marrow of patients with colorectal cancer: expression in controls and downregulation in tumour tissue. Mol Pathol. 2002;55(3):156–163. doi:10.1136/mp.55.3.15612032226

 PubMedGoogle Scholar

Guo J, Xiao B, Jin Z, et al. Detection of cytokeratin 20 mRNA in the peripheral blood of patients with colorectal cancer by immunomagnetic bead enrichment and real-time reverse transcriptase-polymeras chain reaction. J Gastroenterol Hepatol. 2005;20(8):1279–1284. doi:10.1111/j.1440-1746.2005.03894.x16048578

 PubMed Web of Science ®Google Scholar

Zhang XW, Yang HY, Fan P, Yang L, Chen GY. Detection of micrometastasis in peripheral blood by multi-sampling in patients with colorectal cancer. World J Gastroenterol. 2005;11(3):436–438. doi:10.3748/wjg.v11.i3.43615637763

 PubMed Web of Science ®Google Scholar

Katsumata K, Sumi T, Mori Y, Hisada M, Tsuchida A, Aoki T. Detection and evaluation of epithelial cells in the blood of colon cancer patients using RT-PCR. Int J Clin Oncol. 2006;11(5):385–389. doi:10.1007/s10147-006-0590-517058136

 PubMedGoogle Scholar

Guo J, Xiao B, Zhang X, et al. Combined use of positive and negative immunomagnetic isolation followed by real-time RT-PCR for detection of the circulating tumor cells in patients with colorectal cancers. J Mol Med (Berl). 2004;82(11):768–774. doi:10.1007/s00109-004-0590-815490093

 PubMed Web of Science ®Google Scholar

Jonas S, Windeatt S, Fordy C, Allen-Mersh TG. Identification of carcinoembryonic antigen-producing cells circulating in the blood of patients with colorectal carcinoma by reverse transcriptase polymerase chain reaction. Gut. 1996;39(5):717–721. doi:10.1136/gut.39.5.7179014772

 PubMed Web of Science ®Google Scholar

Castells A, Boix L, Bessa X, Gargallo L, Pique JM. Detection of colonic cells in peripheral blood of colorectal cancer patients by means of reverse transcriptase and polymerase chain reaction. Br J Cancer. 1998;78(10):1368–1372. doi:10.1038/bjc.1998.6869823981

 PubMed Web of Science ®Google Scholar

Noh YH, Im G, Ku JH, Lee YS, Ahn MJ. Detection of tumor cell contamination in peripheral blood by RT-PCR in gastrointestinal cancer patients. J Korean Med Sci. 1999;14(6):623–628. doi:10.3346/jkms.1999.14.6.62310642939

 PubMed Web of Science ®Google Scholar

Guadagni F, Kantor J, Aloe S, et al. Detection of blood-borne cells in colorectal cancer patients by nested reverse transcription-polymerase chain reaction for carcinoembryonic antigen messenger RNA: longitudinal analyses and demonstration of its potential importance as an adjunct to multiple serum markers. Cancer Res. 2001;61(6):2523–2532.11289125

 PubMed Web of Science ®Google Scholar

Hampton R, Walker M, Marshall J, Juhl H. Differential expression of carcinoembryonic antigen (CEA) splice variants in whole blood of colon cancer patients and healthy volunteers: implication for the detection of circulating colon cancer cells. Oncogene. 2002;21(51):7817–7823. doi:10.1038/sj.onc.120590612420218

 PubMed Web of Science ®Google Scholar

Wong IH, Yeo W, Chan AT, Johnson PJ. Quantitative relationship of the circulating tumor burden assessed by reverse transcription-polymerase chain reaction for cytokeratin 19 mRNA in peripheral blood of colorectal cancer patients with Dukes’ stage, serum carcinoembryonic antigen level and tumor progression. Cancer Lett. 2001;162(1):65–73. doi:10.1016/s0304-3835(00)00630-311121864

 PubMed Web of Science ®Google Scholar

Silva JM, Rodriguez R, Garcia JM, et al. Detection of epithelial tumour RNA in the plasma of colon cancer patients is associated with advanced stages and circulating tumour cells. Gut. 2002;50(4):530–534. doi:10.1136/gut.50.4.53011889075

 PubMed Web of Science ®Google Scholar

Mourtzikou A, Kroupis C, Poumpouridou N, et al. Molecular detection of circulating tumor cells in peripheral blood of colon cancer patients. J Gastroenterol Hepatol Res. 2012;1(5):74–79.

 Google Scholar

Cohen SJ, Alpaugh RK, Gross S, et al. Isolation and characterization of circulating tumor cells in patients with metastatic colorectal cancer. Clin Colorectal Cancer. 2006;6(2):125–132. doi:10.3816/CCC.2006.n.02916945168

 PubMed Web of Science ®Google Scholar

Gogoi P, Sepehri S, Zhou Y, et al. Development of an automated and sensitive microfluidic device for capturing and characterizing Circulating Tumor Cells (CTCs) from clinical blood samples. PLoS One. 2016;11(1):e0147400. doi:10.1371/journal.pone.014740026808060

 PubMed Web of Science ®Google Scholar

Nagrath S, Sequist LV, Maheswaran S, et al. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature. 2007;450(7173):1235–1239. doi:10.1038/nature0638518097410

 PubMed Web of Science ®Google Scholar

Raeisossadati R, Farshchian M, Ganji A, et al. Quantitative analysis of TEM-8 and CEA tumor markers indicating free tumor cells in the peripheral blood of colorectal cancer patients. Int J Colorectal Dis. 2011;26(10):1265–1270. doi:10.1007/s00384-011-1230-821573768

 PubMed Web of Science ®Google Scholar

Wong LS, Cantrill JE, Odogwu S, Morris AG, Fraser IA. Detection of circulating tumour cells and nodal metastasis by reverse transcriptase-polymerase chain reaction technique. Br J Surg. 1997;84(6):834–839.9189103

 PubMed Web of Science ®Google Scholar

Fava TA, Desnoyers R, Schulz S, et al. Ectopic expression of guanylyl cyclase C in CD34+ progenitor cells in peripheral blood. J Clin Oncol. 2001;19(19):3951–3959. doi:10.1200/JCO.2001.19.19.395111579116

 PubMed Web of Science ®Google Scholar

Tien YW, Chang KJ, Jeng YM, et al. Tumor angiogenesis and its possible role in intravasation of colorectal epithelial cells. Clin Cancer Res. 2001;7(6):1627–1632.11410499

 PubMed Web of Science ®Google Scholar

Tien YW, Jeng YM, Hu RH, Chang KJ, Hsu SM, Lee PH. Intravasation-related metastatic factors in colorectal cancer. Tumour Biol. 2004;25(1–2):48–55. doi:10.1159/00007772315192312

 PubMedGoogle Scholar

Solmi R, De Sanctis P, Zucchini C, et al. Search for epithelial-specific mRNAs in peripheral blood of patients with colon cancer by RT-PCR. Int J Oncol. 2004;25(4):1049–1056. doi:10.3892/ijo.25.4.104915375555

 PubMed Web of Science ®Google Scholar

Yeh CS, Wang JY, Wu CH, et al. Molecular detection of circulating cancer cells in the peripheral blood of patients with colorectal cancer by using membrane array with a multiple mRNA marker panel. Int J Oncol. 2006;28(2):411–420. doi:10.3892/ijo.28.2.41116391796

 PubMed Web of Science ®Google Scholar

Solmi R, Ugolini G, Rosati G, et al. Microarray-based identification and RT-PCR test screening for epithelial-specific mRNAs in peripheral blood of patients with colon cancer. BMC Cancer. 2006;6:250. doi:10.1186/1471-2407-6-25017054783

 PubMed Web of Science ®Google Scholar

Lauriola M, Ugolini G, Rosati G, et al. Identification by a Digital Gene Expression Displayer (DGED) and test by RT-PCR analysis of new mRNA candidate markers for colorectal cancer in peripheral blood. Int J Oncol. 2010;37(2):519–525. doi:10.3892/ijo_0000070120596680

 PubMed Web of Science ®Google Scholar

Findeisen P, Rockel M, Nees M, et al. Systematic identification and validation of candidate genes for detection of circulating tumor cells in peripheral blood specimens of colorectal cancer patients. Int J Oncol. 2008;33(5):1001–1010. doi:10.3892/ijo_0000008818949363

 PubMed Web of Science ®Google Scholar

Vaiopoulos AG, Kostakis ID, Gkioka E, et al. Detection of circulating tumor cells in colorectal and gastric cancer using a multiplex PCR assay. Anticancer Res. 2014;34(6):3083–3092.24922677

 PubMed Web of Science ®Google Scholar

Teama SH, Agwa SHA. Detection of circulating tumor cells by nested RT-PCR targeting EGFR/CEA/CK20mRNAs in colorectal carcinoma patients. Egypt J Med Human Genet. 2010;11(2):173–180. doi:10.1016/j.ejmhg.2009.10.001

 Google Scholar

Lankiewicz S, Rother E, Zimmermann S, Hollmann C, Korangy F, Greten TF. Tumour-associated transcripts and EGFR deletion variants in colorectal cancer in primary tumour, metastases and circulating tumour cells. Cell Oncol. 2008;30(6):463–471.18936523

 PubMed Web of Science ®Google Scholar

Chen CJ, Sung WW, Chen HC, et al. Early assessment of colorectal cancer by quantifying circulating tumor cells in peripheral blood: ECT2 in diagnosis of colorectal cancer. Int J Mol Sci. 2017;18(4):E743. doi:10.3390/ijms1804074328362321

 PubMed Web of Science ®Google Scholar

Cui CH, Chen RH, Zhai DY, Xie L, Qi J, Yu JL. Detection of FAM172A expressed in circulating tumor cells is a feasible method to predict high-risk subgroups of colorectal cancer. Tumour Biol. 2017;39(6):1010428317699126. doi:10.1177/101042831769912628618931

 PubMedGoogle Scholar

Gessi S, Cattabriga E, Avitabile A, et al. Elevated expression of A3 adenosine receptors in human colorectal cancer is reflected in peripheral blood cells. Clin Cancer Res. 2004;10(17):5895–5901. doi:10.1158/1078-0432.CCR-1134-0315355922

 PubMed Web of Science ®Google Scholar

Barbazan J, Alonso-Alconada L, Muinelo-Romay L, et al. Molecular characterization of circulating tumor cells in human metastatic colorectal cancer. PLoS One. 2012;7(7):e40476. doi:10.1371/journal.pone.004047622811761

 PubMed Web of Science ®Google Scholar

Barbazan J, Vieito M, Abalo A, et al. A logistic model for the detection of circulating tumour cells in human metastatic colorectal cancer. J Cell Mol Med. 2012;16(10):2342–2349. doi:10.1111/j.1582-4934.2012.01544.x22304365

 PubMed Web of Science ®Google Scholar

Rodia MT, Ugolini G, Mattei G, et al. Systematic large-scale meta-analysis identifies a panel of two mRNAs as blood biomarkers for colorectal cancer detection. Oncotarget. 2016;7(21):30295–30306. doi:10.18632/oncotarget.810826993598

 PubMedGoogle Scholar

Alonso-Alconada L, Barbazan J, Candamio S, et al. PrediCTC, liquid biopsy in precision oncology: a technology transfer experience in the Spanish health system. Clin Transl Oncol. 2018;20(5):630–638. doi:10.1007/s12094-017-1760-929058262

 PubMed Web of Science ®Google Scholar

Barbazan J, Muinelo-Romay L, Vieito M, et al. A multimarker panel for circulating tumor cells detection predicts patient outcome and therapy response in metastatic colorectal cancer. Int J Cancer. 2014;135(11):2633–2643. doi:10.1002/ijc.2891024752533

 PubMed Web of Science ®Google Scholar

Dong L, Lin W, Qi P, et al. Circulating long RNAs in serum extracellular vesicles: their characterization and potential application as biomarkers for diagnosis of colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2016;25(7):1158–1166. doi:10.1158/1055-9965.EPI-16-000627197301

 PubMed Web of Science ®Google Scholar

Li J, Chen Y, Guo X, et al. GPC1 exosome and its regulatory miRNAs are specific markers for the detection and target therapy of colorectal cancer. J Cell Mol Med. 2017;21(5):838–847. doi:10.1111/jcmm.1294128233416

 PubMed Web of Science ®Google Scholar

Manri C, Yokoi T, Nishida H. Size-selective harvesting of extracellular vesicles for strategic analyses towards tumor diagnoses. Appl Biochem Biotechnol. 2017;182(2):609–623. doi:10.1007/s12010-016-2348-527917441

 PubMed Web of Science ®Google Scholar

Huang Q, Shen Z, Zang R, Fan X, Yang L, Xue M. Identification of novel genes and pathways in colorectal cancer exosomes: a bioinformatics study. Transl Cancer Res. 2018;7(3):651–658. doi:10.21037/tcr

 Web of Science ®Google Scholar

Morio H, Sun Y, Harada M, et al. Cancer-Type OATP1B3 mRNA in extracellular vesicles as a promising candidate for a serum-based colorectal cancer biomarker. Biol Pharm Bull. 2018;41(3):445–449. doi:10.1248/bpb.b17-0074329491222

 PubMed Web of Science ®Google Scholar

Funaki NO, Tanaka J, Itami A, et al. Detection of colorectal carcinoma cells in circulating peripheral blood by reverse transcription-polymerase chain reaction targeting cytokeratin-20 mRNA. Life Sci. 1997;60(9):643–652. doi:10.1016/s0024-3205(96)00700-x9048967

 PubMed Web of Science ®Google Scholar

Wyld DK, Selby P, Perren TJ, et al. Detection of colorectal cancer cells in peripheral blood by reverse-transcriptase polymerase chain reaction for cytokeratin 20. Int J Cancer. 1998;79(3):288–293. doi:10.1002/(sici)1097-0215(19980619)79:3<288::aid-ijc14>3.0.co;2-49645353

 PubMed Web of Science ®Google Scholar

Hinz S, Bockhorst J, Roder C, et al. Disseminated tumor cells in the bone marrow negatively influence survival after resection of colorectal liver metastases. Ann Surg Oncol. 2012;19(8):2539–2546. doi:10.1245/s10434-012-2291-922395998

 PubMed Web of Science ®Google Scholar

Samija I, Lukac J, Mubrin MK, Kirac I, Kovacevic D, Kusic Z. Detection of cytokeratin-20-positive cells in preoperative and postoperative blood samples from colorectal cancer patients by real-time RT-PCR. Int J Biol Markers. 2013;28(2):174–181. doi:10.5301/jbm.500000323558939

 PubMed Web of Science ®Google Scholar

Kust D, Samija I, Kirac I, Radic J, Kovacevic D, Kusic Z. Cytokeratin 20 positive cells in blood of colorectal cancer patients as an unfavorable prognostic marker. Acta Clin Belg. 2016;71(4):235–243. doi:10.1080/17843286.2016.117726427144776

 PubMed Web of Science ®Google Scholar

Piva MG, Navaglia F, Basso D, et al. CEA mRNA identification in peripheral blood is feasible for colorectal, but not for gastric or pancreatic cancer staging. Oncology. 2000;59(4):323–328. doi:10.1159/00001219011096345

 PubMed Web of Science ®Google Scholar

Ito S, Nakanishi H, Hirai T, et al. Quantitative detection of CEA expressing free tumor cells in the peripheral blood of colorectal cancer patients during surgery with real-time RT-PCR on a LightCycler. Cancer Lett. 2002;183(2):195–203. doi:10.1016/s0304-3835(02)00157-x12065095

 PubMed Web of Science ®Google Scholar

Kanellos I, Zacharakis E, Kanellos D, et al. Prognostic significance of CEA levels and detection of CEA mRNA in draining venous blood in patients with colorectal cancer. J Surg Oncol. 2006;94(1):3–8. doi:10.1002/jso.2054916788936

 PubMed Web of Science ®Google Scholar

Lu CY, Uen YH, Tsai HL, et al. Molecular detection of persistent postoperative circulating tumour cells in stages II and III colon cancer patients via multiple blood sampling: prognostic significance of detection for early relapse. Br J Cancer. 2011;104(7):1178–1184. doi:10.1038/bjc.2011.4021343933

 PubMed Web of Science ®Google Scholar

Thorsteinsson M, Soletormos G, Jess P. Low number of detectable circulating tumor cells in non-metastatic colon cancer. Anticancer Res. 2011;31(2):613–617.21378346

 PubMed Web of Science ®Google Scholar

Yamaguchi K, Takagi Y, Aoki S, Futamura M, Saji S. Significant detection of circulating cancer cells in the blood by reverse transcriptase-polymerase chain reaction during colorectal cancer resection. Ann Surg. 2000;232(1):58–65. doi:10.1097/00000658-200007000-0000910862196

 PubMed Web of Science ®Google Scholar

Mathur P, Wharton RQ, Jonas SK, Saini S, Allen-Mersh TG. Relationship between tumour vascularity and circulating cancer cells in patients with colorectal carcinoma. Eur J Surg Oncol. 2001;27(4):354–358. doi:10.1053/ejso.2001.111811417979

 PubMedGoogle Scholar

Guller U, Zajac P, Schnider A, et al. Disseminated single tumor cells as detected by real-time quantitative polymerase chain reaction represent a prognostic factor in patients undergoing surgery for colorectal cancer. Ann Surg. 2002;236(6):768–775; discussion 775–766. doi:10.1097/00000658-200212000-00009

 Web of Science ®Google Scholar

Iinuma H, Okinaga K, Egami H, et al. Usefulness and clinical significance of quantitative real-time RT-PCR to detect isolated tumor cells in the peripheral blood and tumor drainage blood of patients with colorectal cancer. Int J Oncol. 2006;28(2):297–306. doi:10.3892/ijo.28.2.29716391782

 PubMed Web of Science ®Google Scholar

Liu ZP, Li LM, Liu XL, Zhang DX. Comparative analysis of tumor markers and evaluation of their predictive value in patients with colorectal cancer. Onkologie. 2012;35(3):108–113. doi:10.1159/00033681622414974

 PubMedGoogle Scholar

Nakamori S, Kameyama M, Furukawa H, et al. Genetic detection of colorectal cancer cells in circulation and lymph nodes. Dis Colon Rectum. 1997;40(10 Suppl):S29–S36. doi:10.1007/bf020620179378009

 PubMed Web of Science ®Google Scholar

Huang P, Wang J, Guo Y, Xie W. Molecular detection of disseminated tumor cells in the peripheral blood in patients with gastrointestinal cancer. J Cancer Res Clin Oncol. 2003;129(3):192–198. doi:10.1007/s00432-003-0425-y12684893

 PubMed Web of Science ®Google Scholar

Liu Y, Cheng G, Qian J, et al. Expression of guanylyl cyclase C in tissue samples and the circulation of rectal cancer patients. Oncotarget. 2017;8(24):38841–38849. doi:10.18632/oncotarget.1640628418917

 PubMedGoogle Scholar

Liu Y, Qian J, Feng JG, et al. Detection of circulating tumor cells in peripheral blood of colorectal cancer patients without distant organ metastases. Cell Oncol. 2013;36(1):43–53. doi:10.1007/s13402-012-0112-6

 Web of Science ®Google Scholar

Iinuma H, Watanabe T, Mimori K, et al. Clinical significance of circulating tumor cells, including cancer stem-like cells, in peripheral blood for recurrence and prognosis in patients with Dukes’ stage B and C colorectal cancer. J Clin Oncol. 2011;29(12):1547–1555. doi:10.1200/JCO.2010.30.515121422427

 PubMed Web of Science ®Google Scholar

Shimada R, Iinuma H, Akahane T, Horiuchi A, Watanabe T. Prognostic significance of CTCs and CSCs of tumor drainage vein blood in Dukes’ stage B and C colorectal cancer patients. Oncol Rep. 2012;27(4):947–953. doi:10.3892/or.2012.164922267181

 PubMed Web of Science ®Google Scholar

Sato N, Hayashi N, Imamura Y, et al. Usefulness of transcription-reverse transcription concerted reaction method for detecting circulating tumor cells in patients with colorectal cancer. Ann Surg Oncol. 2012;19(6):2060–2065. doi:10.1245/s10434-011-1889-721732137

 PubMed Web of Science ®Google Scholar

Shen C, Hu L, Xia L, Li Y. Quantitative real-time RT-PCR detection for survivin, CK20 and CEA in peripheral blood of colorectal cancer patients. Jpn J Clin Oncol. 2008;38(11):770–776. doi:10.1093/jjco/hyn10518845519

 PubMed Web of Science ®Google Scholar

Schuster R, Max N, Mann B, et al. Quantitative real-time RT-PCR for detection of disseminated tumor cells in peripheral blood of patients with colorectal cancer using different mRNA markers. Int J Cancer. 2004;108(2):219–227. doi:10.1002/ijc.1154714639606

 PubMed Web of Science ®Google Scholar

Hardingham JE, Hewett PJ, Sage RE, et al. Molecular detection of blood-borne epithelial cells in colorectal cancer patients and in patients with benign bowel disease. Int J Cancer. 2000;89(1):8–13. doi:10.1002/(SICI)1097-021510719724

 PubMed Web of Science ®Google Scholar

Wang JY, Wu CH, Lu CY, et al. Molecular detection of circulating tumor cells in the peripheral blood of patients with colorectal cancer using RT-PCR: significance of the prediction of postoperative metastasis. World J Surg. 2006;30(6):1007–1013. doi:10.1007/s00268-005-0485-z16736329

 PubMed Web of Science ®Google Scholar

Douard R, Le Maire V, Wind P, et al. Carcinoembryonic gene member 2 mRNA expression as a marker to detect circulating enterocytes in the blood of colorectal cancer patients. Surgery. 2001;129(5):587–594. doi:10.1067/msy.2001.11248511331451

 PubMed Web of Science ®Google Scholar

Douard R, Moutereau S, Serru V, et al. Immunobead multiplex RT-PCR detection of carcinoembryonic genes expressing cells in the blood of colorectal cancer patients. Clin Chem Lab Med. 2005;43(2):127–132. doi:10.1515/CCLM.2005.02115843204

 PubMed Web of Science ®Google Scholar

Zieglschmid V, Hollmann C, Mannel J, et al. Tumor-associated gene expression in disseminated tumor cells correlates with disease progression and tumor stage in colorectal cancer. Anticancer Res. 2007;27(4a):1823–1832.17649779

 PubMed Web of Science ®Google Scholar

De Luca A, Pignata S, Casamassimi A, et al. Detection of circulating tumor cells in carcinoma patients by a novel epidermal growth factor receptor reverse transcription-PCR assay. Clin Cancer Res. 2000;6(4):1439–1444.10778975

 PubMed Web of Science ®Google Scholar

Clarke LE, Leitzel K, Smith J, Ali SM, Lipton A. Epidermal growth factor receptor mRNA in peripheral blood of patients with pancreatic, lung, and colon carcinomas detected by RT-PCR. Int J Oncol. 2003;22(2):425–430. doi:10.3892/ijo.22.2.42512527944

 PubMed Web of Science ®Google Scholar

Fournier MV, Guimaraes Da Costa F, Paschoal ME, Ronco LV, Carvalho MG, Pardee AB. Identification of a gene encoding a human oxysterol-binding protein-homologue: a potential general molecular marker for blood dissemination of solid tumors. Cancer Res. 1999;59(15):3748–3753.10446991

 PubMed Web of Science ®Google Scholar

Gazzaniga P, Nofroni I, Gandini O, et al. Tenascin C and epidermal growth factor receptor as markers of circulating tumoral cells in bladder and colon cancer. Oncol Rep. 2005;14(5):1199–1202. doi:10.3892/or.14.5.119916211285

 PubMed Web of Science ®Google Scholar

Yokobori T, Iinuma H, Shimamura T, et al. Plastin3 is a novel marker for circulating tumor cells undergoing the epithelial-mesenchymal transition and is associated with colorectal cancer prognosis. Cancer Res. 2013;73(7):2059–2069. doi:10.1158/0008-5472.CAN-12-032623378342

 PubMed Web of Science ®Google Scholar

Sugimachi K, Yokobori T, Iinuma H, et al. Aberrant expression of plastin-3 via copy number gain induces the epithelial-mesenchymal transition in circulating colorectal cancer cells. Ann Surg Oncol. 2014;21(11):3680–3690. doi:10.1245/s10434-013-3366-y24217791

 PubMed Web of Science ®Google Scholar

Shan T, Cui X, Li W, Lin W, Li Y. AQP5: a novel biomarker that predicts poor clinical outcome in colorectal cancer. Oncol Rep. 2014;32(4):1564–1570. doi:10.3892/or.2014.337725109507

 PubMed Web of Science ®Google Scholar

Valladares-Ayerbes M, Díaz-Prado S, Reboredo M, et al. Bioinformatics approach to mRNA markers discovery for detection of circulating tumor cells in patients with gastrointestinal cancer. Cancer Detect Prev. 2008;32(3):236–250. doi:10.1016/j.cdp.2008.08.00218801625

 PubMedGoogle Scholar

Valladares-Ayerbes M, Blanco-Calvo M, Reboredo M, et al. Evaluation of the adenocarcinoma-associated gene AGR2 and the intestinal stem cell marker LGR5 as biomarkers in colorectal cancer. Int J Mol Sci. 2012;13(4):4367–4387. doi:10.3390/ijms1304436722605983

 PubMed Web of Science ®Google Scholar

Mirzaei A, Tavoosidana G, Modarressi MH, et al. Upregulation of circulating cancer stem cell marker, DCLK1 but not Lgr5, in chemoradiotherapy-treated colorectal cancer patients. Tumour Biol. 2015;36(6):4801–4810. doi:10.1007/s13277-015-3132-925631749

 PubMedGoogle Scholar

Wang W, Wan L, Wu S, et al. Mesenchymal marker and LGR5 expression levels in circulating tumor cells correlate with colorectal cancer prognosis. Cell Oncol. 2018;45:1–10. doi:10.1007/s13402-018-0386-4

 Google Scholar

Burchill SA, Perebolte L, Johnston C, Top B, Selby P. Comparison of the RNA-amplification based methods RT-PCR and NASBA for the detection of circulating tumour cells. Br J Cancer. 2002;86(1):102–109. doi:10.1038/sj.bjc.660001411857020

 PubMed Web of Science ®Google Scholar

Miyashiro I, Kuo C, Huynh K, et al. Molecular strategy for detecting metastatic cancers with use of multiple tumor-specific MAGE-A genes. Clin Chem. 2001;47(3):505–512.11238304

 PubMed Web of Science ®Google Scholar

Huang MY, Yen LC, Liu HC, et al. Significant overexpression of DVL1 in Taiwanese colorectal cancer patients with liver metastasis. Int J Mol Sci. 2013;14(10):20492–20507. doi:10.3390/ijms14102049224129181

 PubMed Web of Science ®Google Scholar

Steinert G, Scholch S, Niemietz T, et al. Immune escape and survival mechanisms in circulating tumor cells of colorectal cancer. Cancer Res. 2014;74(6):1694–1704. doi:10.1158/0008-5472.CAN-13-188524599131

 PubMed Web of Science ®Google Scholar

Katoh S, Goi T, Naruse T, et al. Cancer stem cell marker in circulating tumor cells: expression of CD44 variant exon 9 is strongly correlated to treatment refractoriness, recurrence and prognosis of human colorectal cancer. Anticancer Res. 2015;35(1):239–244.25550556

 PubMed Web of Science ®Google Scholar

Mostert B, Sieuwerts AM, Bolt-de Vries J, et al. mRNA expression profiles in circulating tumor cells of metastatic colorectal cancer patients. Mol Oncol. 2015;9(4):920–932. doi:10.1016/j.molonc.2015.01.00125655581

 PubMed Web of Science ®Google Scholar

Bao H, Burke PA, Huang J, et al. Circulating tumor cells: application as a biomarker for molecular characterization and predictor of survival in an all-comer solid tumor phase i clinical study. PLoS One. 2013;8(8):e58557. doi:10.1371/journal.pone.005855723990866

 PubMed Web of Science ®Google Scholar

Wang JY, Lin SR, Wu DC, et al. Multiple molecular markers as predictors of colorectal cancer in patients with normal perioperative serum carcinoembryonic antigen levels. Clin Cancer Res. 2007;13(8):2406–2413. doi:10.1158/1078-0432.CCR-06-205417406027

 PubMed Web of Science ®Google Scholar

Xi L, Nicastri DG, El-Hefnawy T, Hughes SJ, Luketich JD, Godfrey TE. Optimal markers for real-time quantitative reverse transcription PCR detection of circulating tumor cells from melanoma, breast, colon, esophageal, head and neck, and lung cancers. Clin Chem. 2007;53(7):1206–1215. doi:10.1373/clinchem.2006.08182817525108

 PubMed Web of Science ®Google Scholar

Mirzaei A, Tavoosidana G, Rad AA, et al. A new insight into cancer stem cell markers: could local and circulating cancer stem cell markers correlate in colorectal cancer? Tumor Biol. 2016;37(2):2405–2414. doi:10.1007/s13277-015-3989-7

 PubMedGoogle Scholar

Wu S, Liu S, Liu Z, et al. Classification of circulating tumor cells by epithelial-mesenchymal transition markers. PLoS One. 2015;10(4):e0123976. doi:10.1371/journal.pone.012397625909322

 PubMed Web of Science ®Google Scholar

Chang YT, Huang MY, Yeh YS, et al. A prospective study of comparing multi-gene biomarker chip and serum carcinoembryonic antigen in the postoperative surveillance for patients with stage I-III colorectal cancer. PLoS One. 2016;11(10):e0163264. doi:10.1371/journal.pone.016326427701415

 PubMed Web of Science ®Google Scholar

Ning Y, Zhang W, Hanna DL, et al. Clinical relevance of EMT and stem-like gene expression in circulating tumor cells of metastatic colorectal cancer patients. Pharmacogenomics J. 2018;18(1):29–34. doi:10.1038/tpj.2016.6227503579

 PubMed Web of Science ®Google Scholar

Bahnassy AA, Salem SE, Mohanad M, et al. Prognostic significance of circulating tumor cells (CTCs) in Egyptian non-metastatic colorectal cancer patients: A comparative study for four different techniques of detection (Flowcytometry, CellSearch, Quantitative Real-time PCR and Cytomorphology). Exp Mol Pathol. 2019;106:90–101. doi:10.1016/j.yexmp.2018.12.00630578762

 PubMed Web of Science ®Google Scholar

Mathai RAVR, Reddy BS, Thomas L, Udupa K, Kolesar J, Rao M. Potential utility of liquid biopsy as a diagnostic and prognostic tool for the assessment of solid tumors: implications in the precision oncology. J Clin Med. 2019;18(8):3.  doi:10.3390/jcm8030373

 Google Scholar

Valcz G, Galamb O, Krenacs T, et al. Exosomes in colorectal carcinoma formation: ALIX under the magnifying glass. Mod Pathol. 2016;29(8):928–938. doi:10.1038/modpathol.2016.7227150162

 PubMed Web of Science ®Google Scholar

Soldevilla B, Rodriguez M, Millán CS, et al. Tumor-derived exosomes are enriched in ΔNp73, which promotes oncogenic potential in acceptor cells and correlates with patient survival. Hum Mol Genet. 2014;23(2):467–478. doi:10.1093/hmg/ddt43724067531

 PubMed Web of Science ®Google Scholar

Campanella C, Rappa F, Sciumè C, et al. Heat shock protein 60 levels in tissue and circulating exosomes in human large bowel cancer before and after ablative surgery. Cancer. 2015;121(18):3230–3239. doi:10.1002/cncr.2949926060090

 PubMed Web of Science ®Google Scholar

Chiba M, Kimura M, Asari S. Exosomes secreted from human colorectal cancer cell lines contain mRNAs, microRNAs and natural antisense RNAs, that can transfer into the human hepatoma HepG2 and lung cancer A549 cell lines. Oncol Rep. 2012;28(5):1551–1558. doi:10.3892/or.2012.196722895844

 PubMed Web of Science ®Google Scholar

Silva J, Garcia V, Rodriguez M, et al. Analysis of exosome release and its prognostic value in human colorectal cancer. Genes Chromosomes Cancer. 2012;51(4):409–418. doi:10.1002/gcc.2192622420032

 PubMed Web of Science ®Google Scholar

Tauro BJ, Greening DW, Mathias RA, et al. Comparison of ultracentrifugation, density gradient separation, and immunoaffinity capture methods for isolating human colon cancer cell line LIM1863-derived exosomes. Methods. 2012;56(2):293–304. doi:10.1016/j.ymeth.2012.01.00222285593

 PubMed Web of Science ®Google Scholar

Chen MS, Xu R, Ji H, et al. Transcriptome and long noncoding RNA sequencing of three extracellular vesicle subtypes released from the human colon cancer LIM1863 cell line. Sci Rep. 2016;6:38397. doi:10.1038/srep3839727917920

 PubMed Web of Science ®Google Scholar

Sun B, Li Y, Zhou Y, et al. Circulating exosomal CPNE3 as a diagnostic and prognostic biomarker for colorectal cancer. J Cell Physiol. 2019;234(2):1416–1425. doi:10.1002/jcp.2693630078189

 PubMed Web of Science ®Google Scholar

Namba Y, Sogawa C, Okusha Y, et al. Depletion of lipid efflux pump ABCG1 triggers the intracellular accumulation of extracellular vesicles and reduces aggregation and tumorigenesis of metastatic cancer cells. Front Oncol. 2018;8:376. doi:10.3389/fonc.2018.0037630364132

 PubMed Web of Science ®Google Scholar