Clinicopathological characteristics and prognostic value of cancer stem cell marker CD133 in breast cancer: a meta-analysis

Figures & data

Figure 1 Flow diagram of the studies selection process.

Table 1 Characteristics of studies included in our meta-analysis

Study	Year	Country	No of patients	Mean age (years)	Stage	Follow-up, months (range)	AB source	AB type	Scoring criteria	Cutoff value	Outcomes	NOS score
Ieni et al^Citation11	2011	Italy	49	61.8	I, II	64.3 (6–136)	Rabbit	Polyclonal	ID score	NR	CF	8
Lin et al^Citation12	2015	Taiwan	49	52	I–III	NR	NR	NR	Percentage	10%	CF	6
Liu et al^Citation13	2009	China	74	49	I–III	41.6 (3–49.3)	NR	Polyclonal	Semiquantitative	10%	CF	6
Currie et al^Citation14	2013	New Zealand	94	NR	I–IV	30.5 (1–60)	Mouse	Monoclonal	Semiquantitative	10%	CF, OS, DFS	8
Di Bonito et al^Citation15	2012	Italy	204	56	I–III	42.5 (2–83)	NR	NR	Semiquantitative	8%	CF	6
Aomatsu et al^Citation16	2012	Japan	102	55	II, III	48 (12–84)	NR	NR	Percentage	10%	CF, OS, DFS	8
Kapucuoglu et al^Citation17	2015	Turkey	105	54	I–IV	45.05 (2–89)	NR	Polyclonal	Percentage	10%	CF	6
Zhao et al^Citation18	2011	China	67	47	I–III	36 (1–64)	Rabbit	Polyclonal	Semiquantitative	10%	OS, DFS	8
Mansour and Atwa^Citation19	2015	Egypt	120	49	I–III	NR	Mouse	Monoclonal	Semiquantitative	10%	CF	6
Kim et al^Citation20	2015	Korea	291	49	I–III	53.8 (4–97)	Rabbit	Polyclonal	Semiquantitative	10%	CF, OS, DFS	7
Liu et al^Citation21	2013	China	134	47.5	NR	NR	NR	NR	NR	NR	CF	6
Lv et al^Citation22	2016	China	120	48.7	NR	NR	Rabbit	Monoclonal	Semiquantitative	10%	CF	7
Han et al^Citation23	2015	China	325	45.6	I–IV	46.8 (15–108)	Mouse	Monoclonal	Semiquantitative	10%	CF, OS	8

Abbreviations: NR, not reported; AB, antibody; ID, intensity-distribution; CF, clinicopathological features; OS, overall survival; DFS, disease-free survival; NOS, Newcastle–Ottawa scale.

Figure 2 Forest plots of ORs for the correlation between CD133 overexpression and ER, PR, and HER2.

Notes: (A) OR for the relation between CD133 overexpression and ER; (B) OR for the relation between CD133 overexpression and PR; and (C) OR for the relation between CD133 overexpression and HER2. Weights are from random-effects analysis.
Abbreviations: OR, odds ratio; CI, confidence interval; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor-2.

Table 2 Stratified analysis of pooled hazard ratios or odds ratios of breast cancer patients with CD133 expression on survival data and clinicopathological features

Categories	Region						Sample sizes						Mean age
	Asian			non-Asian			<150			>150			<50			>50
	HR/OR	I^2H	PH	HR/OR	I^2H	PH	HR/OR	I^2H	PH	HR/OR	I^2H	PH	HR/OR	I^2H	PH	HR/OR	I^2H	PH
OS	2.22 (1.38, 3.57)	61.8%	0.049	0.78 (0.32, 1.93)	–	–	1.98 (0.84, 4.68)	71.2%	0.031	1.70(1.05, 2.75)	43.8%	0.182	1.70 (1.05, 2.75)	43.8%	0.182	1.68 (0.40, 6.96)	85.1%	0.01
DFS	4.01 (1.83, 8.79)	72.7%	0.026	0.80 (0.37, 1.73)	–	–	2.68 (0.68, 10.58)	90.2%	0.000	2.72 (1.39, 5.33)	–	–	4.89 (1.54, 15.52)	82.3%	0.017	0.80 (0.37, 1.73)	–	–
ER	0.42 (0.19, 0.96)	83.9%	0.000	0.20 (0.08, 0.47)	11.1%	0.289	0.38 (0.16, 0.88)	69.2%	0.011	0.31 (0.07, 1.36)	93.7%	0.000	0.48 (0.19, 1.21)	87.4%	0.000	0.28 (0.11,0.68)	0%	0.689
PR	0.61 (0.46, 0.82)	42.3%	0.139	0.30 (0.14, 0.67)	0%	0.904	0.49 (0.31,0.78)	54.5%	0.067	0.60 (0.43, 0.85)	0%	0.569	0.64 (0.48, 0.87)	35.1%	0.202	0.26 (0.10, 0.64)	0%	0.805
HER2	1.70(1.25, 2.32)	25.4%	0.252	9.29 (1.57, 54.77)	–	–	1.57 (0.92, 2.67)	53.3%	0.093	1.94(1.34, 2.81)	41.4%	0.191	1.65 (1.20, 2.27)	37.5%	0.187	4.28 (1.58, 11.57)	17.7%	0.270
Age	1.01 (0.70, 1.47)	22.6%	0.264	0.94 (0.32, 2.70)	80.5%	0.006	0.98 (0.52, 1.82)	67.3%	0.009	1.00 (0.70, 1.44)	0%	0.460	1.31 (0.90, 1.91)	24.6%	0.250	0.77 (0.38, 1.58)	44.7%	0.179
T	1.50 (0.86, 2.61)	64.1%	0.010	0.83 (0.44, 1.57)	12.4%	0.285	1.04 (0.48, 2.27)	66.7%	0.010	1.70(1.04, 2.79)	52.7%	0.121	1.98 (1.27, 3.07)	39.9%	0.155	0.68 (0.27, 1.75)	54.6%	0.111
N	2.42(1.67, 3.51)	49.2%	0.046	1.11 (0.45, 2.75)	72.1%	0.028	2.14 (1.33, 3.44)	57.7%	0.015	1.65 (0.73, 3.71)	83.4%	0.002	2.74 (1.89, 3.97)	45.4%	0.089	1.23 (0.62, 2.45)	52.4%	0.098
G	1.85 (1.40, 2.46)	33.1%	0.175	1.50 (0.99, 2.28)	58.7%	0.046	2.30 (1.66, 3.20)	40.2%	0.100	1.29 (0.92, 1.81)	0%	0.413	1.70 (1.29, 2.22)	41.5%	0.129	1.32 (0.79, 2.21)	13.6%	0.328

Abbreviations: OS, overall survival; DFS, disease-free survival; HR, hazard ratio; OR, odds ratio; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor-2; T, tumor size; N, lymph node; G, tumor histological grade; H, heterogeneity.

Figure 3 Forest plots of ORs for the correlation between CD133 overexpression and age, tumor size, lymph node metastasis, and tumor histological grade.

Notes: (A) OR for the relation between CD133 overexpression and age; (B) OR for the relation between CD133 overexpression and tumor size; (C) OR for the relation between CD133 overexpression and lymph node metastasis; and (D) OR for the relation between CD133 overexpression and tumor histological grade. Weights are from random-effects analysis.
Abbreviations: OR, odds ratio; CI, confidence interval.

Figure 4 Forest plots of HRs for the association between CD133 overexpression and survival.

Notes: (A) HR for the relation between CD133 overexpression and OS and (B) HR for the relation between CD133 overexpression and DFS. Weights are from random-effects analysis.
Abbreviations: HR, hazard ratio; CI, confidence interval; OS, overall survival; DFS, disease-free survival.

Figure 5 (A) Begg’s funnel plot for the assessment of publication bias in analysis of OS; (B) Begg’s funnel plot for the assessment of publication bias in analysis of DFS.

Abbreviations: In, napierian logarithm; HR, hazard ratio; SE, standard error; OS, overall survival; DFS, disease-free survival.

Table S1 Newcastle–Ottawa quality assessment scale (cohort studies)

Study	Selection				Comparability	Outcome			Score
	Representativeness of the exposed cohort	Selection of the nonexposed cohort	Ascertainment of exposure	Demonstration that outcome of interest was not present at start of study	Comparability of cohorts on the basis of the design or analysis	Assessment of outcome	Was follow-up long enough for outcomes to occur?	Adequacy of follow-up of cohorts
	a. Truly representative of the average (described) in the community ★ b. Somewhat representative of the average in the community ★ c. Selected group of users, for example, nurses, volunteers d. No description of the derivation of the cohort	a. Community controls ★ b. Drawn from a different source c. No description of the derivation of the nonexposed cohort	a. Secure record (eg, surgical records) ★ b. Structured interview c. Written self- report d. No description	a. Yes (end point) ★ b. No	a. Study controls for the most important factor ★ b. Study controls for any additional factor. (This criteria could be modified to indicate specific control for a second important factor) ★	a. Independent blind assessment ★ b. Record linkage ★ c. Self-report d. No description	a. Yes (select an adequate follow-up period for outcome of interest) ★ b. No	a. Complete follow-up – all subjects accounted for ★ b. Subjects lost to follow-up unlikely to introduce bias – small number lost >80% (select an adequate %) follow-up, or description provided of those lost ★ c. Follow-up rate <80% (select an adequate %) and no description of those lost d. No statement
Ieni et al^Citation11	b★	★	★	★	a★	a★	★	b★	8
Lin et al^Citation12	b★	★	★	★	a★	a★			6
Liu et al^Citation13	b★	★	★	★	a★	a★			6
Currie et al^Citation14	b★	★	★	★	a★	a★	★	a★	8
Di Bonito et al^Citation15	b★	★	★	★		a★	★		6
Aomatsu et al^Citation16	b★	★	★	★	a★	a★	★	a★	8
Kapucuoglu et al^Citation17	b★	★	★	★	a★	a★			6
Zhao et al^Citation18	b★	★	★	★	a,b★★	a★	★		8
Mansour and Atwa^Citation19	b★	★	★	★	a★	a★			6
Kim et al^Citation20	b★	★	★	★	a★	a★	★		7
Liu et al^Citation21	b★	★	★	★	a★	a★			6
Lv et al^Citation22	b★	★	★	★	a,b★★	a★			7
Han et al^Citation23	b★	★	★	★	a,b★★	a★	★		8

Notes: A study can be awarded a maximum of one star for each numbered item within the “selection” and “outcome” categories. A maximum of two stars can be given for “comparability”.

IeniAGiuffreGAdamoVTuccariGPrognostic impact of CD133 immunoexpression in node-negative invasive breast carcinomasAnticancer Res20113141315132021508381

 PubMedGoogle Scholar

LinCHLiuCHWenCHKoPLChaiCYDifferential CD133 expression distinguishes malignant from benign papillary lesions of the breastVirchows Arch2015466217718425433813

 PubMedGoogle Scholar

LiuQLiJGZhengXYJinFDongHTExpression of CD133, PAX2, ESA, and GPR30 in invasive ductal breast carcinomasChin Med J (Engl)2009122222763276919951611

 PubMed Web of Science ®Google Scholar

CurrieMJBeardsleyBEHarrisGCImmunohistochemical analysis of cancer stem cell markers in invasive breast carcinoma and associated ductal carcinoma in situ: relationships with markers of tumor hypoxia and microvascularityHum Pathol201344340241123036368

 PubMed Web of Science ®Google Scholar

Di BonitoMCantileMCollinaFOverexpression of cell cycle progression inhibitor Geminin is associated with tumor stem-like phenotype of triple-negative breast cancerJ Breast Cancer201215216217122807933

 PubMed Web of Science ®Google Scholar

AomatsuNYashiroMKashiwagiSCD133 is a useful surrogate marker for predicting chemosensitivity to neoadjuvant chemotherapy in breast cancerPLoS One201279e4586523049880

 PubMed Web of Science ®Google Scholar

KapucuogluNBozkurtKKBaspinarSThe clinicopathological and prognostic significance of CD24, CD44, CD133, ALDH1 expressions in invasive ductal carcinoma of the breast: CD44/CD24 expression in breast cancerPathol Res Pract20152111074074726298632

 PubMedGoogle Scholar

ZhaoPLuYJiangXLiXClinicopathological significance and prognostic value of CD133 expression in triple-negative breast carcinomaCancer Sci201110251107111121276138

 PubMed Web of Science ®Google Scholar

MansourSFAtwaMMClinicopathological significance of CD133 and ALDH1 cancer stem cell marker expression in invasive ductal breast carcinomaAsian Pac J Cancer Prev201516177491749626625750

 PubMedGoogle Scholar

KimSJKimYSJangEDSeoKJKimJSPrognostic impact and clinicopathological correlation of CD133 and ALDH1 expression in invasive breast cancerJ Breast Cancer201518434735526770241

 PubMed Web of Science ®Google Scholar

LiuTJSunBCZhaoXLCD133+ cells with cancer stem cell characteristics associates with vasculogenic mimicry in triple-negative breast cancerOncogene201332554455322469978

 PubMed Web of Science ®Google Scholar

LvXWangYSongYPangXLiHAssociation between ALDH1+/CD133+ stem-like cells and tumor angiogenesis in invasive ductal breast carcinomaOncol Lett20161131750175626998072

 PubMedGoogle Scholar

HanZChenZZhengRChengZGongXWangDClinicopathological significance of CD133 and CD44 expression in infiltrating ductal carcinoma and their relationship to angiogenesisWorld J Surg Oncol2015135625889325

 PubMed Web of Science ®Google Scholar