Prognostic Role of Secretory Clusterin in Multiple Human Malignant Neoplasms: A Meta-Analysis of 26 Immunohistochemistry Studies

Abstract

Secretory clusterin (sCLU) is a potential prognostic tumour biomarker, but results of different sCLU studies are inconsistent. We conducted this meta-analysis to evaluate the precise predictive value of sCLU. Qualified studies were identified by performing online searches in PubMed, EMBASE, and Web of Science. The selected articles were divided into three groups based on scoring method for clusterin detection. Pooled hazard ratios (HRs) with 95% confidence interval (CI) for patient survival and disease recurrence were calculated to determine the correlation between sCLU expression and cancer prognosis. Heterogeneity was assessed using I2 statistics, and specific heterogeneity in different groups was analysed. Elevated sCLU was significantly associated with recurrence-free survival in groups 1 and 3 (group 1: pooled HR = 1.35, 95% CI = 1.01 to 1.79; group 3: pooled HR = 1.80, 95% CI = 1.22 to 2.65). However, clusterin expression was not associated with overall survival in all three groups. Results showed that only the heterogeneity of group 2 was very strong (p = 0.013, I2 = 76.3%), in which the specimens were scored through sCLU staining intensity only. sCLU is a potential biomarker for tumour prognosis, and IHC methods can be more standardised if both intensity and staining proportion are considered.

Fig 1. Flow diagram of the study selection process.

Fig 2. Sensitivity of each included study in three different groups.

(A) Sensitivity analysis of OS in group 1. (B) Sensitivity analysis of RFS in group 1. (C) Sensitivity analysis of OS in group 2. (D) Sensitivity analysis of RFS in group3.

Fig 3. Forest plots of merged analyses of disease-free survival (DFS)/relapse-free survival (RFS) in association with sCLU expression in different groups.

Forest plots of merged analyses of overall survival (OS). Squares and horizontal lines represent study-specific HRs and 95% CIs, respectively. The areas of the squares correspond to weights, and the diamonds represent overall HRs and 95% CIs. (A) Forest plots of merged analyses of RFS/DFS in group 1. (B) Forest plots of merged analyses of RFS/DFS in group3.

Fig 4. Forest plots of merged analyses of overall survival (OS) in association with sCLU expression in different groups.

Forest plots of merged analyses of disease-free survival (DFS)/relapse-free survival (RFS). Squares and horizontal lines represent study-specific HRs and 95% CIs, respectively. The areas of the squares correspond to weights, and the diamonds represent overall HRs and 95% CIs. (A) Forest plots of merged analyses of overall survival (OS) in group 1. (B) Forest plots of merged analyses of overall survival (OS) in group 2. (C) Forest plots of merged analyses of overall survival (OS) in group3.

Fig 5. Subgroup analyses of group 2.

(A) shows the pathology-type subgroup analysis, and (B) shows the ethnicity subgroup analysis.

Fig 6. Begg’s funnel plots of the publication bias.

Begg’s funnel plots of the publication bias for overall merged analysis of OS or DFS/RFS. Each point represents a separate study. About the OS analysis, (A) and (B) represents group 1 and group 2 respectively. About the DFS/RFS analysis, (C) and (D) represents group 1 and group 3 respectively.