Abstract
Background
PCa (Prostate cancer) is the most prevalent urogenital malignancy among men. The genes APOC1(Apolipoprotein C1) and NOP16 (Nucleolar protein 16) have been associated with various types of cancer. This article aims to investigate the potential of APOC1 and NOP16 as preliminary proactive diagnostic markers for PCa.
Methods
Search for APOC1 and NOP16 using the TCGA (The Cancer Genome Atlas) and GEPIA (Gene Expression Profiling Interactive Analysis) databases. Immunohistochemical staining was employed to detect the expression of APOC1 and NOP16 in prostate tissue. The enzyme-linked immunosorbent assay (ELISA) was utilized to quantify the levels of APOC1 and NOP16 proteins. Spearman correlation coefficient and Pearson correlation coefficient were calculated to validate the association between APOC1 and NOP16. A binary logistic regression model was developed to analyze the factors that influence PCa. Predictive models for APOC1 and NOP16 and PSA, and their role in detecting PCa, were established through the construction of receiver operating characteristic (ROC) curves.
Results
APOC1 and NOP16 are up-regulated in PCa tissues, and their expressions are related with the clinical stage of PCa(n = 51). Furthermore, compared with individuals affected by benign prostatic hyperplasia (BPH), the expression levels of APOC1 and NOP16 in the serum of individuals affected by PCa are higher(n = 61). Moreover, in the tissues and serum of the same PCa patient, APOC1 and NOP16 are positively correlated(n = 32). APOC1 and NOP16 are independent influencing factors for PCa(n = 50). APOC1 and NOP16 showed predictive potential for PCa (n = 50, APOC1 AUC = 0.729, NOP16 AUC = 0.777).
Conclusions
APOC1 and NOP16 are anticipated to function as biomarkers for the early proactive diagnosis of PCa.
Keywords: Prostate cancer, APOC1, NOP16, Early diagnosis
Introduction
PCa is an epithelial malignant tumor that occurs in the prostate. It ranks first in the incidence of male genitourinary tumors and continues to rise [1–3]. At present, clinical detection mainly relies on a combination of multiple methods, including prostate biopsy, serum prostate-specific antigen (PSA) test, a digital rectal exam (DRE), and multi-parametric magnetic resonance imaging (mpMRI) [4–6]. However, the substantial increase in the detection rate of clinically indolent low-risk PCa has gradually revealed the limitations of traditional surveillance systems [7]. These limitations pertain to early detection sensitivity, risks associated with invasive procedures, and issues related to overdiagnosis [8–10]. In today’s world, many studies are increasingly focusing on improving the detection and treatment of PCa through precision medicine and minimally invasive methods [11]. Consequently, there is a growing trend in the field of urologic oncology to identify improved and less invasive methods for diagnosing PCa, as well as to develop high-sensitivity, low-trauma molecular diagnostic techniques.
Apolipoprotein C1 (APOC1), as the apolipoprotein member with the smallest molecular weight (6.6 kDa), plays a core role in the regulation of lipid metabolism, it specifically binds to phosphatidylinositol modification sites and participates in the assembly of extremely low-density lipoprotein (VLDL) and the reverse transport of high-density lipoprotein (HDL) cholesterol [12, 13]. In recent years, a growing body of evidence has indicated that APOC1 is associated with the onset and progression of various cancers, including breast cancer, renal clear cell carcinoma, pancreatic cancer, and lung cancer [14–17]. Notably, it has been found that down-regulation of APOC1 expression can arrest the cycle of PCa cells at G2/M phase and promote cell apoptosis [18].
Nucleolar protein 16 (NOP16) is the core component of large ribonucleoprotein particles, which is located in the nucleolar region [19, 20]. Research demonstrates that NOP16 holds a pivotal regulatory function in cancer emergence and advancement [19–24]. For instance, in a breast cancer model, the knockdown of NOP16 through RNA interference significantly hinders the proliferation of malignant cells. In PCa, the downregulation of carboxy-terminal binding protein 2 (CtBP2) leads to a reduction in the levels of c-Myc and its direct transcriptional target NOP16, thereby inhibiting the proliferation of PCa cells [22, 25].
These findings suggest that NOP16 and APOC1 play a pivotal role in various cancers. However, their effects on biological behaviors such as the initiation, progression, propagation, invasion, and metastatic dissemination of PCa, along with the specific molecular mechanisms involved, remain poorly understood. Therefore, this paper aims to provide novel insights for detection and treatment strategies as well as a theoretical foundation for proactive surveillance of PCa by investigating the relationship between APOC1, NOP16, and PCa.
Methods
TCGA and GEPIA database
After conducting a comprehensive review of the literature, we decided to explore APOC1 and NOP16. They were first searched in TCGA and GEPIA databases to obtain boxplots of APOC1 and NOP16 transcript levels in PCa and normal prostate. Secondly, the relationship between the transcriptional levels of APOC1 and NOP16 and the disease-free survival rate of patients with PCa was investigated.
Tissue samples and serum samples
Inclusion: (a) PCa/BPH confirmed by pathology; (b) complete case information.
Exclusion: (a) concomitant primary tumors at other sites; (b) unwilling/unable to cooperate.
Prostate tissue specimens from prostate surgeries performed in our hospital from 2022 to 2024 were collected through continuous enrollment. Serum samples of PCa patients who were hospitalized in our hospital from 2023 to 2024 were collected. Tissue samples and serum samples were from different patients. The collected serum samples were stored in a refrigerator at −80 ° C by number for subsequent studies.
Consent was obtained from all patients or their families and approved by the Ethics Committee of Xiaogan Central Hospital for all materials used in this study. (Ethics batch number: KY-2025010501)
Immunohistochemistry
Firstly, the paraffin-embedded tissue is sectioned, dewaxed (xylene), and hydrated. Then antigen retrieval was performed to expose the masked antigenic epitopes. Subsequently, the block was dropped in, and a specific primary antibody was added (usually incubated overnight at 4℃) to bind to the target antigen. After thorough washing, add the secondary antibody and incubate at room temperature to bind to the primary antibody. After thorough washing again, add DAB for color development. Finally, hematoxylin was counterstained, washed, blued, dehydrated, sealed, and the localization and expression of the specific antigen were observed under a light microscope.
APOC1 primary antibody (Concentration: 1:150, ab198288, Abcam, USA), NOP16 primary antibody (Concentration: 1:100, sc-81866, Santa Cruz Biotechnology, USA). The intensity scores of the two staining in prostate tissue were 0 (negative), 1 (weak brown), 2 (medium brown), and 3 (strong brown). The staining degree scores were 0 (≤ 5%), 1 (5–25%), 2 (25–50%), and 3 (> 50%). Multiply the two fractions (intensity and proportion) to obtain the final score. The final expression classification is as follows: a score of 0–3 is considered a negative expression, and a score of 4–9 is considered a positive expression. The immunohistochemical scoring findings of this time were independently evaluated by two pathologists, respectively. The staining intensity was observed at a magnification of X40 and the staining ratio at a magnification of X100. The number of fields of view under the microscope was 22 mm.
ELISA
We performed an evaluation of APOC1 and NOP16 levels in serum samples sourced from individuals affected by BPH and those suffering from PCa by employing specific APOC1 and NOP16 enzyme-linked immunosorbent assay kits (APOC1: DM4971, NOP16: DM7061, Dumabio, China), absorbance (OD value) was determined at a wavelength of 450 nm using Micro-plate Reader (Kehua Biotech ST-360, China). The APOC1 and NOP16 ELISA kits employ the double antibody sandwich method. The standard curve was drawn with the gradient dilution standard concentration as the horizontal axis and the corresponding OD value as the vertical axis, and then the sample concentration was calculated using the OD value of the sample.
Statistical analysis
Employing SPSS version 27.0.0, statistical analysis ensued. The independent sample t-test facilitated comparisons among groups featuring measurement data. Comparison between count data sets using the chi-square test. The comparison between groups of grade data is conducted using the non-parametric rank sum test. Spearman correlation coefficient and Pearson correlation coefficient were used for association analysis. Univariate and multivariate analyses of the influencing factors of PCa were conducted using the binary Logistic regression model. GraphPad Prism 10.1.2 was used for plotting. The ROC curve was drawn to establish a PCa prediction model. All the statistical results in the text are marked with corresponding statistics and confidence intervals. All tests were two-tailed, P < 0.05 served as an indicator of statistical significance.
Results
APOC1 and NOP16 are overexpressed genes in PCa
We discovered through the TCGA database that, in comparison to normal prostate tissues, both APOC1 and NOP16 were significantly upregulated in PCa tissues (Fig. 1a, b). Furthermore, survival analysis indicated that individuals with elevated levels of APOC1 and NOP16 exhibited lower disease-free survival rates compared to those with reduced levels of these markers (Fig. 1c, d).
Fig. 1.
Transcriptional levels of APOC1 (a) and NOP16 (b) in PCa (T) and normal prostate (N) were shown in the TCGA database (http://gepia.cancer-pku.cn/). There is an association between the transcriptional levels of APOC1(c) and NOP16(d) and the disease-free survival rate of individuals affected by PCa
APOC1 and NOP16 are highly expressed in PCa tissues
To verify the expression of APOC1 and NOP16 in PCa tissues, 61 prostate tissue specimens preserved in our hospital were collected, including 51 cases of PCa and 10 cases of BPH. In the subgroup of PCa, there were 25 cases in the GS (Gleason score) 6–7 group and 26 cases in the GS 8–10 group. According to TNM staging, there were 15 individuals affected by stage I-II cancer and 36 individuals affected by stage III-IV cancer. 16 patients with PCa were randomly selected and classified according to cancer tissues and adjacent non-cancerous tissues.
We found that the expressions of APOC1 (P < 0.01, t = 5.337, 95%CI:2.322–5.109) and NOP16 (P < 0.01, t = 4.813, 95%CI:1.510–3.659) were significantly higher in PCa tissues compared with BPH tissues. (Fig. 2a, b). Subsequently, among the 16 randomly selected individuals affected by PCa, we found that the expression of both APOC1 (P < 0.01, t = 4.922, 95%CI:1.682–4.068) and NOP16 (P < 0.01, t = 5.412, 95%CI:1.673–3.702) was higher in cancer tissues than in adjacent non-cancerous tissues (Fig. 2c, d). Finally, we analyzed the differences in the expressions of APOC1 and NOP16 in 51 PCa tissues with different GS and TNM stages. The findings showed that APOC1(P < 0.05, t = 2.253, 95%CI:0.139–2.433) and NOP16(P < 0.05, t = 2.979, 95%CI:0.399–2.051) levels were higher in GS 8–10 PCa tissues than GS 6–7 PCa tissues (Fig. 2e, f). The expression levels of APOC1 (P < 0.01, t = 3.386, 95%CI: 0.815–3.196) and NOP16 (P < 0.01, t = 5.629, 95%CI: 1.382–2.918) were higher in stage III-IV PCa tissues than in stage I-II tissues (Fig. 2 g, h). The immunohistochemical staining of APOC1and NOP16 in prostate tissue is shown in Figure 3 (Fig. 3).
Fig. 2.
Expressions of APOC1 (a) and NOP16 (b) in prostate tissues of individuals affected by PCa and BPH. Expression of APOC1 (c) and NOP16 (d) in PCa tissues and adjacent non-cancerous tissues of patients with PCa. The expressions of APOC1 (e) and NOP16 (f) in PCa tissues of different GS. The expressions of APOC1 (g) and NOP16 (h) in PCa tissues at different TNM stages
Fig. 3.
Immunohistochemical staining of APOC1 and NOP16 in prostate tissue: APOC1 BPH tissue (-): a×200 and b×400; PCa tissues (++++): c×200 and d×400. NOP16 BPH tissue (-): e×200 and f×400; PCa tissues (++++): g×200 and h×400
APOC1 and NOP16 are highly expressed in the serum of individuals affected by PCa
Based on the previous research, we tested and analyzed the serum of 32 PCa patients and 18 BPH patients. In the PCa subgroup, there were 15 cases with GS 6–7 points and 17 cases with 8–10 points. According to the TNM staging, it included 14 patients in stages I to II and 18 patients in stages III to IV.
The findings showed that the expressions of APOC1(P < 0.01, t = 2.845, 95%CI:2.029–11.80) and NOP16(P < 0.01, t = 3.355, 95%CI:1.470–5.865) in the serum of PCa patients significantly increased compared with individuals affected by BPH (Fig. 4a, b). In addition, individuals affected by GS 8–10 PCa had higher serum levels of APOC1(P < 0.05, t = 2.426, 95%CI:0.933–10.86) and NOP16(P < 0.05, t = 2.628, 95%CI:0.768–6.125) than those with GS 6–7 PCa (Fig. 4c, d). Individuals affected by stage III-IV PCa had higher serum APOC1(P < 0.05, t = 3.665, 95%CI:3.607–12.68) and NOP16(P < 0.05, t = 2.320, 95%CI:0.375–5.878) expression levels than individuals affected by stage I-II PCa (Fig. 4e, f).
Fig. 4.
Illustrates the expression profiles(pg/ml) of APOC1 (a) and NOP16 (b) in serum samples obtained from individuals with PCa and BPH. Additionally, the graphs depict APOC1 (c) and NOP16 (d) levels in the serum of PCa patients, stratified by their respective GS. Furthermore, the expression patterns of APOC1 (e) and NOP16 (f) in serum samples derived from PCa patients are presented, with patients categorized based on distinct TNM stages
The expression levels of APOC1 and NOP16 in the tissues and serum of the same PCa patient were positively correlated
Firstly, the expression levels of APOC1 and NOP16 were analyzed in 51 cases of PCa tissue samples using the Spearman correlation coefficient (Fig. 5a, P < 0.01, r = 0.398, 95%CI:0.129–0.612), and then utilizing Pearson correlation analysis, we investigated the relationship between APOC1 and NOP16 expressions in serum samples obtained from 32 PCa patients (Fig. 5b, P < 0.01, r = 0.5625, 95%CI:0.266–0.762). Our findings unveiled a positive association between the expression levels of APOC1 and NOP16 in these samples.
Fig. 5.
Positive association between APOC1 and NOP16 expression in tissues(a) and serum(b) of the same PCa patients
Association of APOC1 and NOP16 expression with general clinicopathological features
The associations of APOC1 and NOP16 expressions with various clinicopathological features, including age, family history, smoking history, GS, PSA, and TNM stage, were analyzed in 51 individuals affected by PCa (Tables 1 and 2). The results showed that APOC1 and NOP16 expressions were correlated with age, GS, TNM stage, and PSA, independent of smoking history and family history. The expression levels of APOC1 and NOP16 in the tissues of patients with ≥ 65 years old, GS 8–10 and stage III-IV PCa were significantly higher than those of patients with < 65 years old, GS 6–7 and stage I-II PCa.
Table 1.
Analysis of APOC1 expression and clinicopathological features of PCa patients
| APOC1 expression | Total | Statistics | P-value | ||
|---|---|---|---|---|---|
| Negative | Positive | ||||
| The total | 17 | 34 | 51 | ||
| Age | |||||
| ≥ 65 | 7 | 27 | 34 | 7.456 | 0.006** |
| < 65 | 10 | 7 | 17 | ||
| Smoking history | |||||
| yes | 7 | 18 | 25 | 0.628 | 0.428 |
| no | 10 | 16 | 26 | ||
| Family history | |||||
| yes | 7 | 16 | 23 | 0.158 | 0.691 |
| no | 10 | 18 | 28 | ||
| GS | |||||
| GS = 6–7 | 12 | 13 | 25 | 4.747 | 0.029* |
| GS = 8–10 | 5 | 21 | 26 | ||
| TNM staging | |||||
| Stage I-II | 10 | 5 | 15 | 10.625 | 0.001** |
| Stage III-IV | 7 | 29 | 36 | ||
| PSA | 24.000 (13.000, 28.500) | 56.200 (19.670, 96.100) | 3.388 | 0.001** | |
*P < 0.05
**P < 0.01
Table 2.
Analysis of NOP16 expression and clinicopathological features of PCa patients
| NOP16 expression | Total | Statistics | P-value | ||
|---|---|---|---|---|---|
| Negative | Positive | ||||
| Positive | |||||
| The total | 19 | 32 | 51 | ||
| Age | |||||
| ≥ 65 | 9 | 25 | 34 | 5.075 | 0.024* |
| < 65 | 10 | 7 | 17 | ||
| Smoking history | |||||
| yes | 8 | 17 | 25 | 0.579 | 0.447 |
| no | 11 | 15 | 26 | ||
| Family history | |||||
| yes | 9 | 14 | 23 | 0.063 | 0.802 |
| no | 10 | 18 | 28 | ||
| GS | |||||
| GS = 6–7 | 15 | 10 | 25 | 10.853 | 0.001** |
| GS = 8–10 | 4 | 22 | 26 | ||
| TNM staging | |||||
| Stage I-II | 13 | 2 | 15 | 22.195 | 0.001** |
| Stage III-IV | 6 | 30 | 36 | ||
| PSA | 25.000 (11.200, 39.000) | 51.650 (19.435, 95.350) | 51 | 2.728 | 0.006** |
*P < 0.05
**P < 0.01
The expressions of APOC1 and NOP16 are independent influencing factors of PCa
To investigate the relationship between the expressions of APOC1 and NOP16 and PCa, we used Elisa samples. This analysis included 32 samples of PCa and 18 samples of BPH for both univariate and multivariate analyses (Table 3). After conducting univariate analyses on the expressions of APOC1 and NOP16, PSA, age, family history, and smoking history, multivariate analyses were performed on the expressions of APOC1 and NOP16 based on the principle of stepwise regression analysis. The results of stepwise regression analysis indicated that the expressions of APOC1 and NOP16 were independent influencing factors of PCa.
Table 3.
Univariate and multivariate analysis of diagnostic-related factors in PCa and BPH samples
| Variables | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
| OR | 95%CI | P-value | OR | 95%CI | P-value | |
| APOC1 expression | 5.111 | 1.469–17.780 | 0.010* | 4.386 | 1.137–16.927 | 0.032* |
| NOP16 expression | 6.644 | 1.834–24.077 | 0.004** | 5.834 | 1.501–22.684 | 0.011* |
| PSA | 21.667 | 4.717–99.527 | 0.001** | |||
| Age | 3.000 | 0.907–9.920 | 0.072 | |||
| Family history | 2.667 | 0.500–14.217.500.217 | 0.251 | |||
| Smoking history | 0.313 | 0.094–1.047 | 0.059 | |||
*P<0.05
**P<0.01
APOC1 and NOP16 diagnostic value in PCa
ROC curves were plotted using the contents of serum APOC1 and NOP16 proteins and serum PSA (Fig. 6a, b, c, d), the findings showed that: The area under the ROC curve (AUC) of APOC1 serum was 0.729(P < 0.01, 95%CI:0.583–0.875). When the Youden index was 0.368, the optimal cutoff value of APOC1 protein content was 42.9351 (pg/ml), and the sensitivity and specificity achieved were 71.90% (95%CI:0.529–0.862) and 67.70% (95%CI:0.385–0.882). The positive predictive value was 0.793 (95%CI:0.597–0.925), and the negative predictive value was 0.571 (95%CI:0.343–0.777).
Fig. 6.
The ROC curve was plotted using the protein contents of serum APOC1(a) and NOP16(b), serum PSA(c), and the combined indicators of APOC1 and NOP16(d)
NOP16 serum demonstrated an AUC of 0.777 (P < 0.01, 95%CI:0.650–0.904). With a Youden index of 0.441, the ideal cutoff value for NOP16 protein content was established at 13.3900 (pg/ml), resulting in sensitivity and specificity scores of 71.90% (95%CI:0.529–0.862) and 72.20% (95%CI:0.465–0.903). The positive predictive value was 0.821 (95%CI:0.632–0.931), and the negative predictive value was 0.591 (95%CI:0.366–0.789).
The AUC of PSA serum was 0.808 (P < 0.01, 95%CI:0.682–0.935). When the Youden index was 0.646, the cut-off value of PSA was 14.4250 (pg/ml), and the sensitivity and specificity were 81.30% (95%CI:0.634–0.922) and 83.30% (95%CI:0.586–0.964). The positive predictive value was 0.897 (95%CI:0.731–0.974), and the negative predictive value was 0.714 (95%CI:0.476–0.885).
Most importantly, when the AUC of the predictive probability of APOC1 combined with NOP16 was 0.806 and the Youden index was 0.614, the sensitivity and specificity were 78.10% and 83.30%, respectively (P < 0.01, 95%CI:0.683–0.929). This result indicates that both and their combined indicators have certain predictive potential for PCa.
Discussion
As a significant concern for men’s health, the incidence of PCa continues to increase, posing considerable challenges for clinicians [1, 26, 27]. PSA, mpMRI, digital rectal examination and needle biopsy are the core surveillance methods for PCa at present [4, 5, 8, 9]. Currently, emerging biomarkers are demonstrating significant advantages. The application of these biomarkers can enhance the sensitivity of early PCa detection, reduce complications associated with invasive procedures, and simultaneously lower the overdetection rate of low-risk indolent cancers [4, 5, 8, 9, 28, 29]. Consequently, the discovery of biomarkers will assist clinicians in making decisions that are more beneficial to patients [30].
APOC1 and NOP16 are frequently upregulated in a variety of malignant tumors. Owing to their functional diversity, they can contribute to tumorigenesis and progression through multiple mechanisms. Notably, Hui Ren et al. demonstrated that APOC1 expression is elevated in breast cancer [17]. Furthermore, Liwen Ren found, APOC1 is a kind of immune biomarker; it can regulate macrophage polarization, eventually leading to tumor metastasis [16]. Wenmin Xiong et al. found that knocking down NOP16 to reduce its expression could inhibit a series of biological behaviors of nasopharyngeal carcinoma cells; Notably, the RhoA/PI3K/Akt/c-Myc and IKK/IKB/NF-κB signaling pathways can be inhibited by knocking out NOP16 [24]. In addition, NOP16 significantly enhances the invasive ability of liver metastasis from colorectal cancer by regulating the lipid metabolism reprogramming of cancer-associated fibroblasts in the tumor microenvironment [21]. However, at present, there are relatively few studies on the expression of APOC1 and NOP16 in PCa. For instance, Wei-peng Su et al. knocked down the expression of APOC1 in PCa cell lines through RNA interference technology, verifying that APOC1 promotes the proliferation of PCa cells in vitro. Changwen Zhang et al. found that down-regulating the expression of CtBP2 could reduce the expression levels of c-Myc and its direct transcription target NOP16, but neither has been demonstrated at the clinical level [18, 25]. APOC1 and NOP16 are at different locations in the cell, but the relationship between them and their association with PCa has not been reported in the literature. Therefore, investigating the expression patterns of APOC1 and NOP16 in PCa, and their relationship with clinical parameters such as stage and GS, as well as the correlation between the two genes, has important potential clinical implications.
In this study, we first conducted a search of the TCGA database and concluded that APOC1 and NOP16 are associated with PCa. Subsequently, we examined the expression levels of APOC1 and NOP16 in PCa tissues, adjacent non-cancerous tissues, and BPH tissues from different individual PCa patients using immunohistochemical staining. We observed elevated expressions of both APOC1 and NOP16 in PCa tissues, which were particularly pronounced in GS 8–10 and III-IV tumors. Furthermore, a positive correlation was identified between the expressions of APOC1 and NOP16 within the same PCa tissue samples. Secondly, by analyzing the relationship between the expressions of APOC1 and NOP16 and various clinical characteristics of PCa patients, we found that their expression levels were associated with GS, PSA, TNM stage, and age; however, no significant correlations were observed concerning family history or smoking history. This is the same as the result proved by the TCGA database and Wei-peng Su, Changwen Zhang, et al. [18, 25]. These findings suggest that APOC1 and NOP16 hold potential as novel biomarkers for the early diagnosis of PCa.
It is well known that the early detection, diagnosis and treatment of any disease are of vital importance, which can effectively control the development and deterioration of the disease [18, 25]. To this end, we quantified the serum concentrations of APOC1 and NOP16 in patients with BPH or PCa using ELISA. The results demonstrated that both proteins were significantly elevated in PCa patients compared to those with BPH. Moreover, this upregulation was more pronounced in patients with GS 8–10 or III-IV disease. Further analysis revealed a positive correlation between serum APOC1 and NOP16 levels, and identified both as independent factors in PCa. Subsequently, by drawing the ROC curve, it was concluded that APOC1 (AUC = 0.729), NOP16 (AUC = 0.777), and the combination of APOC1 and NOP16 (AUC = 0.806) have certain predictive potential for PCa. Of course, we also plotted the ROC curve of PSA to verify its significance in the diagnosis of PCa (AUC = 0.808). When the content of APOC1 protein in the serum exceeds 42.9351 (pg/ml) and the content of NOP16 protein exceeds 13.3900 (pg/ml), medical attention should be sought promptly. This is the first report demonstrating elevated expressions of APOC1 and NOP16 in serum samples from patients with PCa. Previously, Weipeng Su and Changwen Zhang et al. [14, 21] verified the relationship between APOC1 and NOP16 and PCa through in vitro experiments. Our findings collectively demonstrated that APOC1 and NOP16 were consistently upregulated in both tissue and serum samples from PCa patients, and that their expression levels were positively correlated. These results strongly support the potential of APOC1 and NOP16 as promising biomarkers for the early diagnosis of PCa.
Because PCa has no typical symptoms or almost no symptoms in its initial proactive stage, many PCa patients are already in the advanced or metastatic stage when they are first diagnosed, which also indirectly leads to a poor prognosis of the disease [31, 32]. Data indicates that APOC1 and NOP16 have great potential in the early diagnosis of PCa. For patients presenting with lower urinary tract symptoms secondary to BPH, serum testing for APOC1 and NOP16 could serve as valuable diagnostic triage. A positive test would warrant more definitive investigations, enabling the proactive identification of underlying PCa. This stratified approach holds the potential to improve early detection rates while minimizing unnecessary invasive procedures in patients with negative results.
This study has some limitations. Firstly, in the TNM staging group for immunohistochemistry, the majority of patients had stage III-IV PCa (ratio 2.4:1), which may lead to a loss of statistical power. Secondly, the sample size was insufficient due to the following reasons: serum samples from patients with prostate diseases could not be collected in a timely manner; there was a risk of damage during collection and preservation; and some patients had missing information. This limits the generalizability of the results. The small sample size also resulted in an overly wide confidence interval for PSA in the logistic regression analysis (P < 0.01, OR = 21.667, 95%CI: 4.717–99.527), suggesting an unstable model. Moreover, when PSA, APOC1, and NOP16 were included in a multivariate model, APOC1 and NOP16 were not statistically significant, and the confidence interval for PSA became even wider (95%CI: 3.349–101.720). We therefore considered this multivariate model unstable and included only APOC1 and NOP16 in the final multivariate analysis. Furthermore, the existing data cannot explain the regulatory mechanisms of APOC1 and NOP16 in PCa. To address these issues, we will conduct in vitro experiments for further verification and continue to increase the sample size.
Conclusion
APOC1 and NOP16 are overexpressed in PCa and exhibit positive correlation, suggesting potential utility as complementary diagnostic biomarkers. This will provide a reference for subsequent studies of PCa diagnosis.
Acknowledgements
Not applicable.
Abbreviations
- TCGA
The Cancer Genome Atlas
- GEPIA
Gene Expression Profiling Interactive Analysis
- PCa
Prostate cancer
- APOC1
Apolipoprotein C1
- NOP16
Nucleolar protein 16
- GS
Gleason score
- ELISA
Enzyme-linked immunosorbent assay
- ROC
Receiver operating characteristic curve
- AUC
Area under the ROC curve
- BPH
Benign prostatic hyperplasia
- PSA
Prostate-specific antigen
- mpMRI
Multi-parametric magnetic resonance imaging
- DRE
Digital rectal exam
- CtBP2
Carboxy-terminal binding protein 2
Authors’ contributions
YY conceived this research and drafted the manuscript. STS, ZQF, and ZW are responsible for specimen collection. YY, PL, CY, and BL participated in most of the experiments. QY, CY, and YL analyzed the data. All the authors read and approved the final manuscript.
Funding
This work was supported by the Natural Science Foundation Project of Xiaogan City (XGKJ2024010002).
Data availability
The data supporting result 1 of this study are sourced from [https://gepia.cancer-pku.cn/](https://gepia.cancer-pku.cn).Other datasets used in this study may be provided to the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in compliance with the principles of the Declaration of Helsinki. The methodology and use of all materials were approved by the Medical Ethics Committee of Xiaogan Central Hospital (Code of Ethics Batch Number: KY-2025010501). Informed consent was obtained from all participants or their families prior to their inclusion in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Chang Ye, Email: xgyechang@163.com.
Pan Lu, Email: xgszxyylupan@163.com.
References
- 1.Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. Ca-Cancer J Clin. 2025;75(1):10–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Kratzer TB, Mazzitelli N, Star J, Dahut WL, Jemal A, Siegel RL. Prostate cancer statistics, 2025. CA: Cancer J Clin. 2025;75(6):485–97. [DOI] [PMC free article] [PubMed]
- 3.Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent global patterns in prostate cancer incidence and mortality rates. Eur Urol. 2020;77(1):38–52. [DOI] [PubMed] [Google Scholar]
- 4.Krausewitz P, Ritter M. [Clinical aspects in the diagnosis and treatment of prostate cancer]. Radiologe. 2021;61(9):795–801. [DOI] [PubMed] [Google Scholar]
- 5.Fontana F, Anselmi M, Limonta P. Molecular mechanisms and genetic alterations in prostate cancer: from diagnosis to targeted therapy. Cancer Lett. 2022;534:215619. [DOI] [PubMed] [Google Scholar]
- 6.Diamand R, Peltier A, Albisinni S. Transrectal prostate biopsy: easy, effective and safe. Prostate Cancer P D. 2024;27(3):363–4. [DOI] [PubMed] [Google Scholar]
- 7.Bjornebo L, Discacciati A, Falagario U, Vigneswaran HT, Jaderling F, Gronberg H, Eklund M, Nordstrom T, Lantz A. Biomarker vs MRI-Enhanced strategies for prostate cancer screening: the STHLM3-MRI randomized clinical trial. Jama Netw Open. 2024;7(4):e247131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kretschmer A, Tilki D. Biomarkers in prostate cancer - Current clinical utility and future perspectives. Crit Rev Oncol Hemat. 2017;120:180–93. [DOI] [PubMed] [Google Scholar]
- 9.Nevo A, Navaratnam A, Andrews P. Prostate cancer and the role of biomarkers. Abdom Radiol. 2020;45(7):2120–32. [DOI] [PubMed] [Google Scholar]
- 10.Welch HG, Black WC. Overdiagnosis in cancer. Jnci-J Natl Cancer I. 2010;102(9):605–13. [DOI] [PubMed] [Google Scholar]
- 11.Mckiernan J, Donovan MJ, Margolis E, Partin A, Carter B, Brown G, Torkler P, Noerholm M, Skog J, Shore N, et al. A prospective adaptive utility trial to validate performance of a novel urine exosome gene expression assay to predict High-grade prostate cancer in patients with prostate-specific antigen 2-10ng/ml at initial biopsy. Eur Urol. 2018;74(6):731–8. [DOI] [PubMed] [Google Scholar]
- 12.Yi J, Ren L, Wu J, Li W, Zheng X, Du G, Wang J. Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for gastric cancer. Ann Transl Med. 2019;7(16):380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Fuior EV, Gafencu AV. Apolipoprotein C1: Its Pleiotropic Effects in Lipid Metabolism and Beyond. Int J Mol Sci. 2019;20(23):5939. [DOI] [PMC free article] [PubMed]
- 14.Cui Y, Miao C, Hou C, Wang Z, Liu B. Apolipoprotein C1 (APOC1): A novel diagnostic and prognostic biomarker for clear cell renal cell carcinoma. Front Oncol. 2020;10:1436. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Guo Q, Liu X, Jiang N, Zhang W, Guo S, Yang H, Ji Y, Zhou J, Guo J, Zhang J, et al. Decreased APOC1 expression inhibited cancer progression and was associated with better prognosis and immune microenvironment in esophageal cancer. Am J Cancer Res. 2022;12(11):4904–29. [PMC free article] [PubMed] [Google Scholar]
- 16.Ren L, Yi J, Yang Y, Li W, Zheng X, Liu J, Li S, Yang H, Zhang Y, Ge B, et al. Systematic pan-cancer analysis identifies APOC1 as an immunological biomarker which regulates macrophage polarization and promotes tumor metastasis. Pharmacol Res. 2022;183:106376. [DOI] [PubMed] [Google Scholar]
- 17.Zhang H, Wang Y, Liu C, Li W, Zhou F, Wang X, Zheng J. The Apolipoprotein C1 is involved in breast cancer progression via EMT and MAPK/JNK pathway. Pathol Res Pract. 2022;229:153746. [DOI] [PubMed] [Google Scholar]
- 18.Su W, Sun L, Yang S, Zhao H, Zeng T, Wu W, Wang D. Apolipoprotein C1 promotes prostate cancer cell proliferation in vitro. J Biochem Mol Toxic. 2018;32(7):e22158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Castelli RF, Pereira A, Honorato L, Valdez A, de Oliveira HC, Bazioli JM, Garcia AWA, Klimeck TDF, Reis FCG, Staats CC, et al. Correction for castelli extracellular vesicle formation in cryptococcus deuterogattii impacts fungal virulence and requires the NOP16 Gene. Infect Immun. 2024;92(4): e4924. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Takashima K, Lee D, Trovero MF, Rothi MH, Mistry M, Zhang Y, Li Z, Davis CP, Li Z, Natale J, et al. NOP16 is a histone mimetic that regulates Histone H3K27 methylation and gene repression. Biorxiv: The Preprint Server for Biology. 2023:2023–6.
- 21.Zhang C, Wang X, Zhang P, He T, Han J, Zhang R, Lin J, Fan J, Lu L, Zhu W, et al. Cancer-derived Exosomal HSPC111 promotes colorectal cancer liver metastasis by reprogramming lipid metabolism in cancer-associated fibroblasts. Cell Death Dis. 2022;13(1):57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zhang C, Yin C, Wang L, Zhang S, Qian Y, Ma J, Zhang Z, Xu Y, Liu S. HSPC111 governs breast cancer growth by regulating ribosomal biogenesis. Mol Cancer Res. 2014;12(4):583–94. [DOI] [PubMed] [Google Scholar]
- 23.Mu S, Tian Q, Shen L. NOP16 promotes hepatocellular carcinoma progression and triggers EMT through the Keap1-Nrf2 signaling pathway. Technol Health Care. 2024;32(4):2463–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Xiong W, Li D, Ao F, Tu Z, Xiong J. The role and molecular mechanism of NOP16 in the pathogenesis of nasopharyngeal carcinoma. Cell Biochem Funct. 2024;42(2):e3939. [DOI] [PubMed] [Google Scholar]
- 25.Zhang C, Gao C, Xu Y, Zhang Z. CtBP2 could promote prostate cancer cell proliferation through c-Myc signaling. Gene. 2014;546(1):73–9. [DOI] [PubMed] [Google Scholar]
- 26.Van Blarigan EL, Mckinley MA, Washington SLR, Cooperberg MR, Kenfield SA, Cheng I, Gomez SL. Trends in prostate cancer incidence and mortality rates. Jama Netw Open. 2025;8(1):e2456825. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Chu F, Chen L, Guan Q, Chen Z, Ji Q, Ma Y, Ji J, Sun M, Huang T, Song H, et al. Global burden of prostate cancer: age-period-cohort analysis from 1990 to 2021 and projections until 2040. World J Surg Oncol. 2025;23(1):98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Sekhoacha M, Riet K, Motloung P, Gumenku L, Adegoke A, Mashele S. Prostate Cancer Review: Genetics, Diagnosis, Treatment Options, and Alternative Approaches. Molecules. 2022;27(17):5730. [DOI] [PMC free article] [PubMed]
- 29.Kumar Am S, Rajan P, Alkhamees M, Holley M, Lakshmanan V. Prostate cancer theragnostics biomarkers: an update. Investig Clin Urol. 2024;65(6):527–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Mcshane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. REporting recommendations for tumour marker prognostic studies (REMARK). Brit J Cancer. 2005;93(4):387–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. Ca-Cancer J Clin. 2023;73(1):17–48. [DOI] [PubMed] [Google Scholar]
- 32.Rawla P. Epidemiology of prostate cancer. World J Oncol. 2019;10(2):63–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting result 1 of this study are sourced from [https://gepia.cancer-pku.cn/](https://gepia.cancer-pku.cn).Other datasets used in this study may be provided to the corresponding author upon reasonable request.