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. 2017 Jun 26;7:4223. doi: 10.1038/s41598-017-01061-3

Prognostic value of CD146 in solid tumor: A Systematic Review and Meta-analysis

Ping Zeng 1,#, Hai Li 1,#, Pei-Hua Lu 2,✉, Li-Na Zhou 1, Min Tang 1, Chao-Ying Liu 2, Min-Bin Chen 1,✉
PMCID: PMC5484668  PMID: 28652617

Abstract

CD146, also known as melanoma cell adhesion molecule, was initially identified as a marker of melanoma progression and metastasis. Recently many clinical studies investigated overexpression of CD146 predict poor prognosis of solid tumor, however, the results was inconclusive, partly due to small numbers of patients included. This present meta-analysis was therefore performed utilizing the results of all clinical studies concerned to determine the prognostic value of CD146 expression in solid tumors. Relevant articles were identified through searching the PubMed, Web of Science and Embase database. In this meta-analysis, 12 studies involving 2,694 participants were included, and we drew the conclusion that strong significant associations between CD146 expression and all endpoints: overall survival (OS) [hazard ratio (HR) = 2.496, 95% confidence interval (95% CI) 2.115–2.946], time to progression (TTP) (HR = 2.445, 95% CI 1.975–3.027). Furthermore, the subgroup analysis revealed that the associations between CD146 overexpression and the outcome endpoints (OS or TTP) were significant in Mongoloid patients and Caucasian patients, as well in patients with lung cancer and digestive system cancer. In conclusion, these results showed that high CD146 was associated with poor survival in human solid tumors. CD146 may be a valuable prognosis predictive biomarker; nevertheless, whether CD146 could be a potential therapeutic target in human solid tumors needs to be further studied.

Introduction

CD146, also referred to as MUC18, MCAM, Mel-CAM, S-Endo-1 and P1H12 antigen1, a transmembrane glycoprotein belonging to the immunoglobulin superfamily that functions as a Ca2+ independent adhesion molecule2, was first identified as a melanoma-specific cell adhesion molecule. CD146 was a 113-kD membrane glycoprotein that contains five immunoglobulin-like domains, a transmembrane region, and a short cytoplasmic tail3. Early research indicated that CD146 is expressed on vascular endothelium, smooth muscle and other cells in normal tissue, and mediates cation-independent adhesion through interactions with an unidentified ligand on the surface of various cells4. Subsequent research manifested CD146 was a multifunctional molecule that participates in several physiological and pathological processes involving in development, immunity, and angiogenesis5. CD146 regulated development of the nervous system, kidney, and retina6–8. Knockdown of CD146 protein expression impeded vascular development whereas overexpression of CD146 in zebrafish induces sprouting angiogenesis9. In recent years, cumulated evidence indicated that CD146 overexpression significantly correlates with the progression, angiogenesis, metastasis of some malignant tumors which was observed in esophageal cancer, melanoma, gallbladder adenocarcinoma, ovarian carcinoma, prostate cancer10–16. However, in oral mucoepidermoid carcinoma, CD146 expression was greater in intermediate/high grade tumors, weaker in patients with local recurrence, regional and distant metastasis17. CD146 was found downregulate in breast cancer and pancreatic cancer progression18, 19. Recently, An increasing number of studies suggested that CD146 is highly expressed in solid tumors, including hepatocellular carcinoma20, leiomyosarcoma21, esophageal squamous cell carcinoma22, lung cancer23–25, colorectal cancer26, clear cell renal cell carcinoma27, gastric cancer28, gallbladder adenocarcinoma13, breast cancer29, epithelial ovarian cancer30. These conspicuous indications on the role of CD146 in cancer indicated that the transmembrane glycoprotein would be further considered as a potential marker for outcome of cancer patients.

Plenty studies showed that increased CD146 expression in tumor tissues was associated with poor survival of patients with various cancer types. However, the results of those individual studies were not comprehensive. Therefore, we performed this comprehensive meta-analysis aiming to clarify the prognostic value of CD146 in solid tumors and to support that the protein may be a potential therapeutic oncotarget.

Materials and Methods

Publication search

According to the Preferred Reporting Items for Systematics Reviews and Meta-Analyses guidelines31, this present meta-analysis was performed. A comprehensive literature search was implemented by using the electronic databases PubMed, Embase, and Web of Science databases (up to July 20, 2016) with the search terms: ‘CD146’, ‘MCAM’ and “cancer”/“tumor”/“neoplasm”/“carcinoma” and the following limits: Human, article in English. Wholly potentially eligible studies were retrieved and their bibliographies were carefully scanned to identify other eligible studies and extra studies were identified by a hand search of the references cited in the original studies. When multiple studies of the same patient population were identified, we incorporated the published report into the largest sample size.

Inclusion criteria

To be qualified for presence in this meta-analysis and data extraction, studies had to: (a) assess CD146 expression in predicting prognosis in cancer, (b) offer hazard ratios (HRs) with 95% confidence intervals (CIs) or enable calculation of these statistics from the data presented, (c) sort CD146 expression into “high” and “low” or “positive” and “negative”.

Exclusion criteria

Exclusion criteria were: (a) literatures published as letters, editorials, abstracts, reviews, case reports and expert opinions; (b) experiments performed in vitro or in vivo, but not based on patients; (c) articles without the HRs and 95% CI, or not dealing with overall survival, disease-free survival, or the K-M survival curves; (d) studies in which the follow-up duration was shorter than 3 years.

Data extraction

All data included in the present meta-analysis were extracted independently and carefully by two reviewers using a standardized form. Disagreement was resolved through independently extracting data from the original article by the third author, and consensus was reached by discussions. The meta-analysis of CD146 expression was based on two outcome endpoints: OS (overall survival), TTP (time to progression). DFS (disease-free survival) and TTR (time to recurrence) that similar in meaning were combined to use a unified prognostic parameter: time to tumor progression (TTP). Several different parameters, if reported, were extracted from each paper, including the first author’s surname, publication year, country of origin, number of patients analyzed, types of measurement, and score for CD146 assessment, cut-off values to determine CD146 overexpression, OS, TTP. The main features of these eligible studies were summarized in Table 1. The multivariate HR was extracted to assess prognostic value of CD146 expression. For the articles in which prognosis was plotted only with the Kaplan-Meier curves, the Engauge Digitizer V4.1 was then used to extract survival data, and the estimation of the HRs and 95% CIs were calculated with Tierney’s method32. All studies were assessed via Newcastle-Ottawa Scale (NOS). The quality scores ranged from 6 to 9, suggesting that the methodological quality was high.

Table 1.

Characteristics of studies included in the meta-analysis.

Author Year Country Case Disease Method Cut off value Endpoints N OS
Jiang G20 2016 China 120 hepatocellular carcinoma qRT-PCR averaged 2−ΔCT value OS, TTP 8
Zhou Y21 2015 China 36 leiomyosarcoma IHC scores of (++) or (+++) OS, TTP 8
Li Y22 2014 China 63 esophageal squamous cell carcinoma IHC positive cells ≥25% and/or scores ≥2 OS 6
Zhang X23 2013 China 118 non-small cell lung cancer IHC score of 3–6 (range of 0–6) OS 8
Tian B26 2013 China 1080 colorectal cancer IHC scored as 1+ or 2+ OS 8
Oka S24 2012 Japan 183 lung adenocarcinoma IHC cellular membrane staining ≥10% OS 9
Feng G27 2012 China 84 clear cell renal cell carcinoma qRT-PCR >0.0362 TTP 7
Liu WF28 2012 China 144 gastric cancer IHC mild staining cells >10% of tumor cells OS 6
Wang W13 2011 China 67 gallbladder adenocarcinoma IHC positive cells ≥25% and/or scores ≥2 OS 7
Zabouo G29 2009 France 635 breast cancer IHC scores of 2+ and 3+ OS 8
Aldovini D30 2006 Italy 126 epithelial ovarian cancer IHC score greater/equal to 2 (range of 0–7) OS, TTP 8
Kristiansen G25 2003 Germany 38 lung adenocarcinoma IHC score of 2+ OS 6
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ICH: Immunohistochemistry; qRT-PCR: quantitative Real-time polymerase chain reaction.

Statistical analysis

The data collected from each eligible article was used to evaluate the relations between CD146 expression and solid cancer prognosis by meta-analysis. Pooled HRs and 95% CIs for these outcome endpoints (OS, TTP) were calculated. Subgroup analysis was performed when there were at least three studies in each subgroup. Statistical heterogeneity was assessed using the Q test, and a P value > 0.10 suggested a lack of heterogeneity among studies. We also quantified the effect of heterogeneity using I 2 = 100% × (Q − df)/Q. I 2 values of <25% may be considered “low”, values of about 50% may be considered “moderate” and values of >75% may be considered “high”33. According to the absence or presence of heterogeneity, random effects model or fixed effects model was used to merge the HR, respectively. Without statistical heterogeneity, a fixed effects model was employed to calculate the pooled HRs, otherwise random effects model was used34. Funnel plots and the Egger’s test were used to estimate the possible publication bias35. If a publication bias did exist, the Duval and Tweedie’s trim and fill method was then used to assess its influence on the overall effect36. Sensitivity analysis was also conducted to find out if certain individual article could influence the overall result. The Stata 14.0 (StataCorp, College Station, TX) was used to conduct statistical analyses. P values for all comparisons were two-tailed and statistical significance was defined as p < 0.05 for all tests, except those for heterogeneity.

Results

Demographic characteristics

Through a literature search of the PubMed, Embase, and Web of Science databases, a total of 432 articles were retrieved, using different combinations of key terms, one additional article identified by scanning the reference of the selected studies As showed in the search flow diagram (Fig. 1), 432 records were originally acquired using the predefined search strategy. Owing to repeated data, 71 records were removed. After glancing over the retrieved titles and abstracts, 315 records were excluded because of no relevant endpoint provided. The rest of 47 records were downloaded with full-text and carefully retrieved one by one. Among them, 35 studies were eliminated, including two studies that were experimental study, one was case report, and six without prognosis data, 26 were unrelated. Consequently, 12 published studies counting 2,694 patients that up to the inclusion norm were finally elected for the meta-analysis, which were then used to assess the relevance between CD146 expression and solid tumor prognosis. The median sample-size of these testers was 118, with a wide range from 36 to 1080. Among all cohorts, Mongoloid (n = 9) became the major race of literatures, followed by Caucasian (n = 3). As for the cancer type, one study evaluated hepatocellular carcinoma, one study evaluated uterine leiomyosarcoma, one study evaluated esophageal squamous cell carcinoma, there studies evaluated lung cancer, one study evaluated colorectal cancer, one study evaluated clear cell renal cell carcinoma, one study evaluated gastric cancer, one study evaluated gallbladder cancer, one study evaluated breast canser, one study evaluated epithelial ovarian cancer. Overall, 11 studies focused on OS, four studies focused on TTP.

Figure 1.

Figure 1

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The flow chart of the selection process in our meta-analysis.

Evidence synthesis

The meta-analysis of CD146 expression was established in two outcome endpoints: OS, TTP. 11 articles were involved in OS of the meta-analysis. A fixed effects model was utilized to calculate the pooled hazard ratio (HR) and 95% confidence interval (CI) due to the heterogeneity test testified a P value of 0.433 and an I 2 values of 0.8%. The results suggested that CD146 overexpression was related with poor OS of solid tumors (pooled HR = 2.496, 95% CI = 2.115–2.946, P = 0.000) (Fig. 2). Four studies were included in TTP of the meta-analysis. On account of the heterogeneity test reported a P value of 0.745 and an I 2 values of 0.0%, a fixed-effects model was then used. The results showed a significant association between CD146 expression and TTP (pooled HR = 2.445, 95% CI = 1.975–3.027, P = 0.000) (Fig. 3). Subgroup study was then performed, the results suggested that the associations between CD146 overexpression and poor OS and poor TTP were significant in Mongoloid patients (OS: pooled HR = 2.508, 95% CI 2.073–3.034, P < 0.001; TTP: pooled HR = 2.544, 95% CI = 1.998–3.238, P = 0.000), as well as in Caucasian (OS: pooled HR = 2.461, 95% CI 1.759–3.444, P < 0.001). The significant correlation was also spotted between CD146 overexpression and poor OS in patients with lung cancer (OS: pooled HR = 2.172, 95% CI = 1.453–3.246, P < 0.001) and digestive system cancer (pooled HR = 2.661, 95% CI = 2.149–3.295, P < 0.001).

Figure 2.

Figure 2

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The correlation between CD146 expression and overall survival (OS) in solid tumors.

Figure 3.

Figure 3

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The correlation between CD146 expression and time to progression (TTP) in solid tumors.

Publication bias and sensitivity analysis

Begg’s funnel plot and Egger’s test were utilized to evaluate the publication bias of these involved literatures. As shown in (Fig. 4), the shapes of the funnel plots for the OS and TTP showed no evidence of obvious heterogeneity, and Egger’s tests revealed no publication bias regarding both OS (P = 0.636) and TTP (P = 0.887). Sensitivity analyses were further done to determine the sturdiness of the results depicted above. No single study controlled this meta-analysis, and removal of any individual study had no significant effect on the overall conclusion (Fig. 5).

Figure 4.

Figure 4

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Begg’s funnel plots for the studies involved in the meta-analysis. (A) Overall survival. (B) Time to progression (TTP). Abbreviations: loghr, logarithm of hazard ratios; s.e., standard error.

Figure 5.

Figure 5

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Sensitivity analysis of the meta-analysis. (A) Overall survival. (B) Time to progression (TTP).

Discussions

Overexpression of CD146 had been reported to promote cancer progression and predict poor prognosis of cancer patient. Many clinical studies investigated the prognostic value of CD146 over-expression. Nevertheless, most of these studies, including limited number of patients, remain have incomprehensive conclusions. This current meta-analysis is the first complete overview of all published clinical studies exploring the impact of CD146 expression on prognosis of many solid tumors.

We scientifically estimated survival data of 2,694 solid tumor patients included in 12 different studies. In general, these results clearly indicated that high CD146 expression was a poor prognostic factor in solid tumors, with both results of poor OS (pooled HR = 2.496, 95% CI = 2.115–2.946, P = 0.000) and poor TTP (pooled HR = 2.445, 95% CI = 1.975–3.027, P = 0.000). Similarly, subgroup analysis revealed the associations between CD146 overexpression and poor OS were significant within Mongoloid and Caucasian, and poor TTP within Mongoloid. When data was stratified according to cancer types, the results showed the prognostic value of CD146 over-expression was significant in digestive system neoplasms and lung cancer.

As far as we known, this present study is the first and most all-sided meta-analysis systematically discovering the possible prognostic role of CD146 up-regulation in solid tumors. Our assessable results strongly reinforced the current mainstream perspective that an adverse impact of CD146 redundancy was associated with the OS and TTP. Also, several important insinuations in this meta-analysis were presented. First, high CD146 expression may be a common poor prognostic marker in solid tumors. In this meta-analysis, we incorporated ten different cancer types, including hepatocellular carcinoma20, leiomyosarcoma21, esophageal squamous cell carcinoma22, lung cancer23–25, colorectal cancer26, clear cell renal cell carcinoma27, gastric cancer28, gallbladder adenocarcinoma13, breast cancer29, epithelial ovarian cancer30. The pooled results from these cancer types confirmed that high CD146 expression was associated with poor OS and TTP, and this finding can be extended to all solid tumors. Second, we verified that high CD146 expression associated with poor OS in Mongoloid and Caucasian patients, as well in digestive system neoplasms and lung cancer, and TTP in Mongoloid patients. Finally, it underlines the potential to develop CD146 as a valuable therapeutic target and prognostic biomarker for solid tumors, though it needs to be further studied.

Aside from the inspiring outcomes, limitations still lay in this assessable meta-analysis. First of all, most of the included studies were designed as retrospective studies, and such studies were more likely to be published if they have positive results than if which have negative one. Furthermore, the method assessing CD146 expression and defining CD146 positivity were inconsistent. Consequently, our estimation of the associations between overexpression of CD146 and outcomes may have been overestimated.

To sum up, high CD146 expression in solid tumor tissues association with poor survival was clearly demonstrated in the present meta-analysis. We suggest that CD146 may be a useful prognostic biomarker, but if it would be a promising therapeutic target for solid tumors still need to be ulteriorly researched. Furthermore, further studies related to specific tumor types and perspectives are required to corroborate the clinical utility of CD146 expression in solid tumors.

Acknowledgements

This work is supported by the National Natural Science Foundation (81472786, 81472305), the Six Talents Peak Project of Jiangsu Province (2012-WSN-012, 2014-WSW-061), Kunshan Science and Technology Program (KS1418, KS1528), the Foundation of tumor clinical and basic research team (KYC005).

Author Contributions

Conceived and designed the experiments: Min-Bin Chen, Pei-Hua Lu. Analyzed the data: Ping Zeng, HaiLi, Pei-Hua Lu. Prepared figures: Ping Zeng, Hai Li, Min-Bin Chen. Contributed to the writing of the paper: Ping Zeng,Hai Li, Min-Bin Chen. Helped edit the manuscript: Li-Na Zhou, Min Tang, Chao-Ying Liu.

Competing Interests

The authors declare that they have no competing interests.

Footnotes

Ping Zeng and Hai Li contributed equally to this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Pei-Hua Lu, Email: lphty1_1@163.com.

Min-Bin Chen, Email: cmb1981@163.com.

References

  • 1.Lehmann JM, Riethmuller G, Johnson JP. MUC18, a marker of tumor progression in human melanoma, shows sequence similarity to the neural cell adhesion molecules of the immunoglobulin superfamily. Proceedings of the National Academy of Sciences of the United States of America. 1989;86:9891–9895. doi: 10.1073/pnas.86.24.9891. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Delorme B, et al. Specific plasma membrane protein phenotype of culture-amplified and native human bone marrow mesenchymal stem cells. Blood. 2008;111:2631–2635. doi: 10.1182/blood-2007-07-099622. [DOI] [PubMed] [Google Scholar]
  • 3.Sers C, Kirsch K, Rothbacher U, Riethmuller G, Johnson JP. Genomic organization of the melanoma-associated glycoprotein MUC18: implications for the evolution of the immunoglobulin domains. Proceedings of the National Academy of Sciences of the United States of America. 1993;90:8514–8518. doi: 10.1073/pnas.90.18.8514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Ouhtit A, et al. Towards understanding the mode of action of the multifaceted cell adhesion receptor CD146. Biochimica et biophysica acta. 2009;1795:130–136. doi: 10.1016/j.bbcan.2009.01.002. [DOI] [PubMed] [Google Scholar]
  • 5.Wang Z, Yan X. CD146, a multi-functional molecule beyond adhesion. Cancer letters. 2013;330:150–162. doi: 10.1016/j.canlet.2012.11.049. [DOI] [PubMed] [Google Scholar]
  • 6.Taira E, Kohama K, Tsukamoto Y, Okumura S, Miki N. Gicerin/CD146 is involved in neurite extension of NGF-treated PC12 cells. Journal of cellular physiology. 2005;204:632–637. doi: 10.1002/jcp.20365. [DOI] [PubMed] [Google Scholar]
  • 7.Takaha N, et al. Expression of gicerin in development, oncogenesis and regeneration of the chick kidney. Differentiation; research in biological diversity. 1995;58:313–320. doi: 10.1046/j.1432-0436.1995.5850313.x. [DOI] [PubMed] [Google Scholar]
  • 8.Tsukamoto Y, et al. Expression of gicerin, a cell adhesion molecule, in the abnormal retina in silver plumage color mutation of Japanese quail (Coturnix japonica) Neuroscience letters. 1999;266:53–56. doi: 10.1016/S0304-3940(99)00256-6. [DOI] [PubMed] [Google Scholar]
  • 9.Chan B, Sinha S, Cho D, Ramchandran R, Sukhatme VP. Critical roles of CD146 in zebrafish vascular development. Developmental dynamics: an official publication of the American Association of Anatomists. 2005;232:232–244. doi: 10.1002/dvdy.20220. [DOI] [PubMed] [Google Scholar]
  • 10.Loges S, et al. Determination of microvessel density by quantitative real-time PCR in esophageal cancer: correlation with histologic methods, angiogenic growth factor expression, and lymph node metastasis. Clinical cancer research: an official journal of the American Association for Cancer Research. 2007;13:76–80. doi: 10.1158/1078-0432.CCR-06-1324. [DOI] [PubMed] [Google Scholar]
  • 11.Lei X, Guan CW, Song Y, Wang H. The multifaceted role of CD146/MCAM in the promotion of melanoma progression. Cancer cell international. 2015;15:3. doi: 10.1186/s12935-014-0147-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Wu GJ, Dickerson EB. Frequent and increased expression of human METCAM/MUC18 in cancer tissues and metastatic lesions is associated with the clinical progression of human ovarian carcinoma. Taiwanese journal of obstetrics & gynecology. 2014;53:509–517. doi: 10.1016/j.tjog.2014.03.003. [DOI] [PubMed] [Google Scholar]
  • 13.Wang W, Yang ZL, Liu JQ, Jiang S, Miao XY. Identification of CD146 expression, angiogenesis, and lymphangiogenesis as progression, metastasis, and poor-prognosis related markers for gallbladder adenocarcinoma. Tumour biology: the journal of the International Society for Oncodevelopmental Biology and Medicine. 2012;33:173–182. doi: 10.1007/s13277-011-0260-8. [DOI] [PubMed] [Google Scholar]
  • 14.Wu GJ, et al. Isolation and characterization of the major form of human MUC18 cDNA gene and correlation of MUC18 over-expression in prostate cancer cell lines and tissues with malignant progression. Gene. 2001;279:17–31. doi: 10.1016/S0378-1119(01)00736-3. [DOI] [PubMed] [Google Scholar]
  • 15.Watson-Hurst K, Becker D. The role of N-cadherin, MCAM and beta3 integrin in melanoma progression, proliferation, migration and invasion. Cancer biology & therapy. 2006;5:1375–1382. doi: 10.4161/cbt.5.10.3241. [DOI] [PubMed] [Google Scholar]
  • 16.Garcia S, et al. Poor prognosis in breast carcinomas correlates with increased expression of targetable CD146 and c-Met and with proteomic basal-like phenotype. Human pathology. 2007;38:830–841. doi: 10.1016/j.humpath.2006.11.015. [DOI] [PubMed] [Google Scholar]
  • 17.Pires FR, Shih Ie M, da Cruz Perez DE, de Almeida OP, Kowalski LP. Mel-CAM (CD146) expression in parotid mucoepidermoid carcinoma. Oral oncology. 2003;39:277–281. doi: 10.1016/S1368-8375(02)00115-X. [DOI] [PubMed] [Google Scholar]
  • 18.Shih LM, Hsu MY, Palazzo JP, Herlyn M. The cell-cell adhesion receptor Mel-CAM acts as a tumor suppressor in breast carcinoma. The American journal of pathology. 1997;151:745–751. [PMC free article] [PubMed] [Google Scholar]
  • 19.Zheng, B. et al. CD146 attenuation in cancer-associated fibroblasts promotes pancreatic cancer progression. Molecular carcinogenesis, doi:10.1002/mc.22409 (2015). [DOI] [PubMed]
  • 20.Jiang G, et al. CD146 promotes metastasis and predicts poor prognosis of hepatocellular carcinoma. Journal of experimental & clinical cancer research: CR. 2016;35:38. doi: 10.1186/s13046-016-0313-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Zhou Y, et al. CD146 as an adverse prognostic factor in uterine sarcoma. European journal of medical research. 2015;20:67. doi: 10.1186/s40001-015-0160-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Li Y, et al. Correlation of CD146 expression and clinicopathological characteristics in esophageal squamous cell carcinoma. Oncology letters. 2014;8:859–863. doi: 10.3892/ol.2014.2227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Zhang X, et al. MCAM expression is associated with poor prognosis in non-small cell lung cancer. Clinical & translational oncology: official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico. 2014;16:178–183. doi: 10.1007/s12094-013-1057-6. [DOI] [PubMed] [Google Scholar]
  • 24.Oka S, Uramoto H, Chikaishi Y, Tanaka F. The expression of CD146 predicts a poor overall survival in patients with adenocarcinoma of the lung. Anticancer research. 2012;32:861–864. [PubMed] [Google Scholar]
  • 25.Kristiansen G, et al. Expression of the cell adhesion molecule CD146/MCAM in non-small cell lung cancer. Analytical cellular pathology: the journal of the European Society for Analytical Cellular Pathology. 2003;25:77–81. doi: 10.1155/2003/574829. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Tian B, Zhang Y, Li N. CD146 protein as a marker to predict postoperative liver metastasis in colorectal cancer. Cancer biotherapy & radiopharmaceuticals. 2013;28:466–470. doi: 10.1089/cbr.2012.1426. [DOI] [PubMed] [Google Scholar]
  • 27.Feng G, et al. CD146 gene expression in clear cell renal cell carcinoma: a potential marker for prediction of early recurrence after nephrectomy. International urology and nephrology. 2012;44:1663–1669. doi: 10.1007/s11255-012-0255-4. [DOI] [PubMed] [Google Scholar]
  • 28.Liu WF, et al. CD146 expression correlates with epithelial-mesenchymal transition markers and a poor prognosis in gastric cancer. International journal of molecular sciences. 2012;13:6399–6406. doi: 10.3390/ijms13056399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Zabouo G, et al. CD146 expression is associated with a poor prognosis in human breast tumors and with enhanced motility in breast cancer cell lines. Breast cancer research: BCR. 2009;11:R1. doi: 10.1186/bcr2215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Aldovini D, et al. M-CAM expression as marker of poor prognosis in epithelial ovarian cancer. International journal of cancer. 2006;119:1920–1926. doi: 10.1002/ijc.22082. [DOI] [PubMed] [Google Scholar]
  • 31.Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. International journal of surgery. 2010;8:336–341. doi: 10.1016/j.ijsu.2010.02.007. [DOI] [PubMed] [Google Scholar]
  • 32.Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:16. doi: 10.1186/1745-6215-8-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Bmj. 2003;327:557–560. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.DerSimonian R, Laird N. Meta-analysis in clinical trials. Controlled clinical trials. 1986;7:177–188. doi: 10.1016/0197-2456(86)90046-2. [DOI] [PubMed] [Google Scholar]
  • 35.Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Bmj. 1997;315:629–634. doi: 10.1136/bmj.315.7109.629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56:455–463. doi: 10.1111/j.0006-341X.2000.00455.x. [DOI] [PubMed] [Google Scholar]

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