Graphical abstract

INTRODUCTION

Prostate cancer (PCa) ranks as the second most prevalent malignancy affecting men, with an estimated 1.4 million diagnoses in 2020 [1]. Digital rectal examinations and prostate-specific antigen (PSA) tests have become standard practices for early identification of PCa. If an individual has elevated PSA values, additional imaging and biopsy procedures are recommended [2]. Among individuals categorized as low risk on magnetic resonance imaging results, 11 to 28 of every 100 have clinically significant cancer.

Underdiagnosed PCa may result in delayed initiation of early treatment, potentially leading to significant adverse events for patients [3]. As PCa may not be distinguishable from benign prostate glands, its identification can occasionally present a diagnostic challenge [4]. Therefore, to augment the discriminatory capability of hematoxylin and eosin (H&E) staining in distinguishing between small foci of benign and malignant glands and numerous complex cases in which the pathologist exhibits no confidence in diagnosing solely based on H&E staining [5], immunohistochemical (IHC) markers of PCa are used as supplementary tools to reinforce critical diagnostic decisions, offering heightened diagnostic accuracy [3].

Alpha-methylacyl-CoA racemase (AMACR) or P504S is an enzyme critical for stereochemical inversion in the metabolic pathways of several proteins and pharmaceutical agents. Located in both peroxisomes and mitochondria, AMACR has been shown to include splice variants with potential roles in cancer development, particularly PCa [6].

While PSA serves as an important initial clinical biomarker, elevated levels still necessitate a pathological biopsy to confirm PCa and allow further classification through Gleason scoring. Differentiating glands with atypical, bland-appearing features from those with PCa is very difficult, particularly for cases with low Gleason scores (e.g., 3). Presently, prostate needle biopsy remains the gold standard for diagnosis of PCa; however, these are not entirely free of diagnostic inaccuracies. The current challenge faced by pathologists in the interpretation of prostate needle biopsy specimens is the increasing prevalence of samples containing a limited number of suspicious glands and minimal atypia [7]. The increasing prevalence of samples containing a limited number of suspicious glands and minimal atypia may be attributed to improved biopsy techniques, more widespread PSA screening, and enhanced pathological scrutiny. In this context, AMACR has diagnostic utility in diagnosis of PCa. These differences may be due to variations in diagnostic methods and potential differences among ethnic and racial groups [8].

Recent research has shown that AMACR has a high sensitivity and specificity for PCa compared to routine H&E staining in resolving suspicious lesions and excluding benign mimickers. Therefore, AMACR in PCa is a promising candidate as a diagnostic marker [9]. A previous meta-analysis conducted by Jiang et al. (2013) [3] observed that the expression of AMACR was associated with a significantly increased risk of PCa. However, its sensitivity and specificity in terms of diagnostic accuracy were not evaluated. Therefore, given the lack of a comprehensive systematic review and meta-analysis, we performed the first meta-analysis to determine the diagnostic test accuracy (DTA) of AMACR as an essential marker in PCa diagnosis.

MATERIALS AND METHODS

Source and search strategy

The current investigation was registered in PROSPERO under registration number CRD42024495450. Systematic exploration published from January 2003 until December 2023 was performed across five databases: MEDLINE, ScienceDirect, ProQuest, Google Scholar, and the Cochrane Library. Key search terms used were ‘PCa,’ ‘AMACR,’ and ‘biomarkers.’ Additionally, manual screening of reference lists from included studies and a snowballing method were implemented to identify useful studies (Supplementary Table S1).

Selection criteria

Studies were deemed eligible when they fulfilled the following criteria: (1) examination of AMACR and its association with PCa, (2) inclusion of patients without other types of cancer, (3) performance of testing among benign or biopsy-negative patients, (4) utilization of pathology as the reference standard, (5) provision of relevant data amenable to accurate extraction, and (5) non-randomized studies of interventions.

Studies were considered ineligible if they were reviews, conference proceedings, book sections, commentaries, editorials, letters, abstract-only publications, or in vitro or in silico investigations or if they presented inseparable independent AMACR data using mixed or cocktail diagnostic modalities. We used a population, intervention, comparison, outcome, timing, setting, and study design (PICOTS-SD) framework for the systematic review (Supplementary Table S2). The excluded potentially relevant studies are documented in the supplementary material (Supplementary Table S3).

Data extraction and quality assessment

Search results were downloaded and compiled into Zotero, a review database. Subsequently, each study was screened by all authors. NDW, SA, FXR, and AFI independently conducted data extraction from all included studies. For validation purposes, JCP and SKL performed another round of data extraction independently on respective halves of the included studies. Any discrepancies regarding study inclusion were resolved through adjudication by JCP. The following data metrics were extracted into a spreadsheet program: first author; country; year; study title; source of sample; control source; and percentages of true-positive, false-positive, false-negative, and true-negative tests. Documented cases of high-grade prostatic intraepithelial neoplasia (HGPIN) or atypical lesions progressing to PCa were also extracted.

Transurethral resection of the prostate, radical prostatectomy, or needle biopsies were used to retrieve biopsy samples from patients diagnosed with prostate cancer. IHC or real-time polymerase chain reaction (RT-PCR) was then used to evaluate the biopsy samples. Patient blood samples and urinary sediments following digital rectal examination were analyzed using RT-PCR. Additionally, some studies reported analysis of urine samples using a chemiluminescence detection system or enzyme-linked immunosorbent assay. Semen ejaculates from biopsy-proven PCa patients before radical prostatectomy were also occasionally used as specimens.

We referenced guidelines outlined in the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) 2020 statement [10] for the literature selection process and used the comprehensive PRISMA 2020 checklist (Supplementary Table S4). Included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool [11]. Each author independently evaluated the studies, with a balanced workload. Any discrepancies were addressed through deliberation and consultation with JCP.

Outcome measures

Diagnostic accuracy was assessed based on sensitivity and specificity, positive and negative predictive values (PPV and NPV), positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of AMACR. Specifically, outcomes related to AMACR were evaluated based on evaluation method, i.e., IHC staining, RT-PCR, echo chemiluminescence detection system (ECL-DS), or chemiluminescence immunoassay (CLIA) of samples obtained from biopsy, urine, serum, and/or semen samples.

Statistical analysis

Data were analyzed to calculate true-positive, false-negative, false-positive, and true-negative values. We generated a hierarchical summary receiver operating characteristic (HSROC) curve to evaluate diagnostic performance across studies because it accounts for variability in meta-analyses of DTA. In more detail, the HSROC model, a random-effects model, incorporates both within- and between-study variability to provide a more reliable summary of test accuracy, particularly in the presence of threshold effects [12].

A bivariate mixed-effects regression was used to calculate combined PPV and NPV, along with their corresponding 95% confidence intervals (CIs). Region and subgroup analyses were also performed to explore variations in DTA. Heterogeneity was evaluated using the I² statistical method, where I² > 50% or a p-value <.05 was considered to signify significant heterogeneity [13]. Publication bias was assessed through Deek’s funnel plot, with p < .05 considered statistically significant.

All statistical analyses were conducted using Cochrane Review Manager software ver. 5.4 and the Metandi [14] and MIDAS [15] modules of Stata software ver. 17.0 (Stata Corp., College Station, TX, USA) and Meta-disc ver. 2.0 [16]. Measurements with a p-value less than .05 were regarded as statistically significant.

RESULTS

Study selection and characteristics

After deduplication, a comprehensive screening process of 505 studies was performed. A subsequent assessment of titles and abstracts led to the exclusion of 411 studies, resulting in a thorough examination of the full text of 94 studies. Eventually, 37 studies were deemed suitable for inclusion in the analysis. Comprehensive details regarding the exclusion of full texts are provided in Fig. 1.

Among the 31 investigations using biopsy samples, 27 [5,17-41] were analyzed with IHC and three using RT-PCR. Furthermore, urine, serum, and semen samples were used in four, two, and one study, respectively. There were 5,898 samples in total, ranging from 19 to 731 per study [5,17-52]. The majority of specimens (2,311) was collected from patients enrolled in 16 studies in the United States [5,17-23,34,42,43,45,46,48,49,51]. Four studies each were conducted in Canada [25,28,29,32] and India [31,38,41,52], while one to two studies each were conducted in other countries.

Pathological testing served as the reference standard, with 32 studies using benign controls as the reference and five studies using biopsy-negative results. Thirteen studies had no conflicts of interest, while seven studies acknowledged academic conflicts of interest for grants or patents. Seventeen studies did not disclose conflicts of interest. Baseline characteristics of the included studies are outlined in Table 1.

Progression of positive AMACR cases to PCa

Across the included studies, seven [5,19,22,28,35,40,49] evaluated the progression of HGPIN or atypical cases with positive AMACR into PCa (Table 2). Stewart et al. [28] reported that HGPIN cases were five times more likely to progress to PCa, while Ayowole et al. [40] reported a 75% conversion rate of HGPIN to PCa in AMACR-positive cases. Additionally, nearly half of atypical cases also progressed to PCa [19,22,49]. Overall, the findings indicated that more than half of the cases with positive AMACR progressed to PCa.

Research suggests that, when AMACR is absent, HGPIN or atypical cases are unlikely to develop into PCa. For example, Zhou et al. (2004) [19] followed 81 atypical cases and found all 39 AMACR-negative cases to be benign. Similarly, Ayowole et al. (2021) [40] explored the link between AMACR staining and histological diagnosis, showing that every AMACR-negative case was either normal prostate tissue (100%) or benign prostatic hyperplasia (14%), with none progressing to prostate cancer. These findings highlight the potential of AMACR as a reliable marker for distinguishing between benign and malignant prostate conditions.

Quality assessment of studies

Application of the QUADAS-2 tool for bias assessment revealed several potential areas of bias in the studies included. Thirty studies had a low risk of bias (ROB) in the patient selection domain, while the remaining seven studies were classified as high and unclear risk due to insufficient details regarding participant recruitment and exclusion criteria. ROB was rated high in 16 studies and unclear in 11 studies in terms of the index test domain, where most of the index test outcomes were obtained with knowledge of the reference standard test. No studies had a high ROB in the reference standard domain. Regarding the flow and time domains, only one study received a high ROB rating. All studies had low concern for applicability in all domains, as shown in Supplementary Fig. S1.

Deek’s funnel-plot asymmetry test was employed to assess the presence of publication bias, yielding a non-significant result (p = .315), indicating the absence of such bias (Fig. 2).

Diagnostic test accuracy

Coupled Forest plots illustrating the sensitivity and specificity of each study are depicted in Fig. 3. The estimated combined sensitivity was 0.90 (95% CI, 0.86 to 0.93) with significant heterogeneity (I² = 81.95, p < .01). Correspondingly, the combined specificity was 0.91 (95% CI, 0.83 to 0.95), also with significant heterogeneity (I² = 93.56, p < .01).

Area under the HSROC curve was determined to be 0.95 (95% CI, 0.93 to 0.97). Notably, the close alignment of the main solid curve to the top left indicates a high overall accuracy (Fig. 4). Moreover, the PLR was 9.6 (95% CI, 5.3 to 17.4), the NLR was 0.11 (95% CI, 0.08 to 0.15), and the DOR was 88 (95% CI, 42 to 181). A Fagan plot is presented in Supplementary Fig. S2.

Regional analyses were conducted according to the research country of origin (Fig. 5). From highest to lowest, the AUC for each region was 0.98 (95% CI, 0.96 to 0.98) for Asia, 0.95 (95% CI, 0.92 to 0.96) for North America, and 0.85 (95% CI, 0.82 to 0.88) for Europe.

Results of subgroup analysis according to sample type are presented in Table 1. Rogers et al. [49], who used an echo CLIA of urine samples; Sroka et al. [50], who used CLIA of urine samples; and Etheridge et al. [51], who used CLIA analysis of semen samples, were grouped under ‘others’ due to the limited number of studies using these sample types.

DISCUSSION

Summary of findings

In this analysis, we evaluated the prospective diagnostic accuracy of AMACR for PCa diagnosis among participants attending healthcare facilities. We systematically reviewed and analyzed 37 observational studies regardless of methodology and found that AMACR had a sensitivity and specificity of at least 88% for diagnosis of PCa (Supplementary Table S4). In 27 studies with uniform sample sources (biopsy) and testing methodologies (IHC), a minimum sensitivity and specificity of 90% were attained. RT-PCR methods demonstrated a sensitivity of 83% and a specificity of 91%; however, in urine and serum samples, both sensitivity and specificity were significantly lower. Notably, these findings are based on limited data, as only 2–3 studies have evaluated RT-PCR performance for these sample types, limiting the generalizability of these results.

Initially documented in 2000 in two adults exhibiting neurological disorders linked to its deficiency [53], AMACR has since been identified in numerous other neurological cases [54-56]. Elevated expression levels of AMACR have been observed in 32 types of human cancers, as identified by The Cancer Genome Atlas Program (TCGA) [57,58], including gastrointestinal [59,60], ovarian [61], renal [62], and liver [63] cancers. Notably, AMACR level was most highly elevated in PCa, with a 682-fold increase in mRNA levels compared to control samples [37]. AMACR also exhibits preferential overexpression in approximately 80%–100% of PCa identified through biopsy [64].

In our meta-analysis, AMACR exhibited exceptional sensitivity and specificity as a diagnostic biomarker of PCa, with values of 90% and 91%, respectively, as shown in the Forest plot in Fig. 3. These findings suggest that AMACR is appropriate to use for risk stratification of PCa. Furthermore, the AUC of 0.95 underscores the assay's remarkable discriminatory power (Fig. 4). The Fagan nomogram (Supplementary Fig. S2) corroborated this exceptional accuracy by demonstrating a high PLR of 9.6 and a low NLR of 0.11. Collectively, these results indicate that AMACR is a highly accurate diagnostic tool for differentiating between patients with PCa and those with benign prostatic conditions.

IHC of biopsy samples was the most widely used method to assess AMACR expression in the studies we reviewed, with higher diagnostic accuracy than other sample types. RT-PCR analysis, however, has its own advantages; for example, detection of AMACR expression in various prostatic secretions (urine, serum, semen). Automated RT-PCR analysis also helps reduce error rates in pathological diagnosis, particularly in centers without pathologists specialized in urological pathology [42].

Although diagnostic accuracy was lower for urinary (sensitivity, 72%; specificity, 69%) and serum (sensitivity, 70%; specificity, 39%) samples, these types remain appealing as they can be collected using minimally invasive methods, especially from patients unable to undergo biopsy due to medical or other conditions. Further studies exploring prostatic secretions and serum in combination with PSA or other examinations could enhance the diagnostic accuracy of AMCAR in these sample types and aid in the development of non-invasive diagnostic protocols [65].

This study evaluated the probability of progression from HGPIN and atypical lesions to PCa in cases positive for AMACR expression and found that this was high. AMACR is used to diagnose several tumor markers and their precursor lesions, including PCa [66]. Our findings indicate that AMACR will significantly boost the probability of PCa diagnosis, with post-test values reaching 71% compared to a pre-test baseline of 20%. This suggests its potential as a confirmatory test; however, results outside the confidence region in the HSROC curve should be investigated further.

Cumulative heterogeneity in both the sensitivity and specificity of AMACAR is a considerable limitation of our meta-analysis (Fig. 3). This variability could be due to clinical, methodological, or statistical issues. From a clinical perspective, the difference in the number of samples analyzed, ranging from 20 by Kaic et al. [30] to 612 by Stewart et al. [25], could potentially have resulted in biased estimates.

The larger the sample, the higher the likelihood of effect sizes. From a methodological perspective, the differences in populations across 14 countries and five continents may have contributed to the high observed heterogeneity. From a statistical perspective, variation in the reported outcome data likely contributed to the high heterogeneity. However, despite differences in study design, all studies consistently reported high diagnostic accuracy. This uniformity strengthens the likelihood of a normal distribution of diagnostic accuracy estimates within each study and minimizes concerns about statistical heterogeneity.

Multiple comparable biomarkers were included in several of these studies. For example, the ETS-related gene (ERG) [34] and human telomerase reverse transcriptase [24] displayed perfect specificity but diminished sensitivity levels at 45.1% and 65.96%, respectively. In contrast, Ki-67 [40] and P510s [47] demonstrated flawless sensitivity, yet their specificities were comparatively lower at 66.67% and 25%, respectively. Annexin II (ANXII) [25] and monocarboxylate transporter 2 (MCT2) [35] had a sensitivity of 73.51% and 65.9%, respectively, and specificity of 97.95% and 93.1%.

Another diagnostic accuracy investigation of PSA was performed by Merriel et al. [67], who reported a combined sensitivity and specificity of 93% and 20%, respectively, across 19 studies. The higher sensitivity and specificity values of AMACR highlight its greater diagnostic accuracy for PCa detection than other currently used biomarkers. However, while PSA is assayed in serum, AMACR is usually assessed from biopsy samples.

Upon further evaluation across geographical regions, the highest sensitivity and specificity were observed in Asia, based on six studies, with a sensitivity of 95% (95% CI, 89% to 97%) and specificity of 91% (95% CI, 79% to 97%). This was followed by North America, which included 13 studies, with a sensitivity of 89% (95% CI, 83% to 90%) and specificity of 93% (95% CI, 80% to 98%). In Europe, the sensitivity was 81% (95% CI, 74% to 86%) and the specificity was 90% (95% CI, 64% to 98%). Due to the limited number of studies, analyses for South America and Africa could not be performed. Detailed diagnostic performance metrics are presented in Fig. 5. Previous studies have shown that PSA levels vary by patient ethnicity. Black men typically exhibit higher PSA levels and have a higher incidence of PCa diagnoses than White or Hispanic men [68,69]. Differences in AMACR expression across regions and ethnicities have not been well-studied. Further research is needed to evaluate these findings.

Basal cell markers

For pathological diagnosis of core needle biopsies, pathologists usually perform high-molecular-weight cytokeratin (HMWCK) (34b12E) in addition to AMACR, so-called “dual IHC staining,” to increase the sensitivity and specificity of IHC. This is a routine process in many pathology laboratories around the world (especially in developed countries).

The International Society of Urologic Pathology (ISUP) currently advises the use of basal cell markers such as HMWCK, p63, and AMACR to diagnose prostatic adenocarcinoma [70]. While adenocarcinoma generally lacks expression of basal cell markers, benign conditions such as adenosis, atrophy, or benign glands may show similar basal cell loss. In contrast, HMWCK staining in a non-basal pattern, and abnormal diffuse expression of p63 can be seen in PCa. Additionally, AMACR stains 5% to 21% of benign prostatic glands and up to 18% of adenosis cases, which reduces its specificity for diagnosing adenocarcinoma. Therefore, the latest recommendations from the ISUP advise using a combination of those cocktails to evaluate small foci of atypical glands suspected to be prostatic adenocarcinoma [71].

Strengths and limitations

This is the first meta-analysis of the diagnostic accuracy of AMACR. We included all sample data regardless of type (tissue biopsy, urine, peripheral blood, and semen) and examination technique (IHC, RT-PCR, ECL-DS, and CLIA). However, a limitation of our study is that, while most studies we reviewed focused on AMACR analysis of biopsy samples via IHC, various other sample types and analysis methods were used; subgroup analyses of HGPIN or Gleason scoring of these studies could not be performed due to their limited numbers.

Implications for future research and practice

Conclusive identification of PCa necessitates pathological validation through prostate biopsy, with AMACR immunohistochemistry staining serving as the prevalent supplementary tool to support diagnostic determinations, particularly in complex cases [5]. Prior research reported higher AMACR protein levels in serum [47,48], urine [45,46,49,50], and semen [51] in PCa cases than control cases, suggesting that analysis of AMACR levels in these sample types may be useful as a non-invasive test for PCa. However, the number of studies that have evaluated AMACR expression in non-invasive samples remains limited, and further investigation is needed. Together, our findings indicate that AMACR facilitates screening and early detection of PCa through its high diagnostic accuracy.

Conclusions

Our findings validate the high diagnostic accuracy of AMACR in detecting PCa, with strong sensitivity and specificity, particularly in biopsy samples analyzed via immunohistochemistry. While AMACR demonstrates utility in challenging cases, such as atypical presentations and HGPIN, the current evidence remains insufficient. Consequently, it cannot serve as a standalone marker and requires complementary scoring methods. Further research exploring alternative sample types, including serum, urine, and semen, across diverse regions is essential to advance our understanding of AMACR’s diagnostic performance in varied clinical contexts.

Supplementary Information

Fig. 1.

PRISMA 2020 flow diagram.

Fig. 2.

Deeks’ publishing bias funnel plot.

Fig. 3.

Forest plot displaying sensitivity and specificity of alpha-methylacyl-CoA racemase detection via immunohistochemistry of prostate cancer biopsy samples [5,17-41,52]. CI, confidence interval.

Fig. 4.

Hierarchical summary receiver operating curve (HSROC) of alpha-methylacyl-CoA racemase detection via immunohistochemistry of prostate cancer biopsy samples. AUC, area under the curve.

Fig. 5.

Forest plots for North America (A), Asia (C), Europe (E), and hierarchical summary receiver operating characteristic (HSROC) curves for North America (B), Asia (D), and Europe (F) for alpha-methylacyl-CoA racemase detection via immunohistochemistry of prostate cancer biopsy samples [5,17-23,25-35,37-39,41,52]. AUC, area under the curve.

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