Introduction
Pancreatic adenocarcinoma is the fourth leading cause of cancer-related mortality, and it is projected to become the second leading cause of cancer death by 2030 [
1]. Pancreatic cancer (PCa) is characterized by extremely poor outcomes, with the 5-year relative survival rate of approximately 10% [
2]. This low cancer survival rate is attributable to the difficulty in early diagnosis. Because of the lack of typical symptoms, more than two-thirds of patients with PCa are diagnosed with either regional or distant metastasis [
3]. Early detection is a critical strategy for improving the overall survival of patients with PCa.
Unfortunately, current clinical diagnostic approaches for PCa are ineffective owing to their low sensitivity and/or specificity. Clinical diagnostic methods include imaging techniques and blood-based biomarkers. Imaging modalities, such as multidetector computed tomography, magnetic resonance imaging, and endoscopic ultrasonography with fine-needle aspiration, were limited by their disadvantages [
4]. Carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 242 (CA242), carbohydrate antigen 125 (CA125), and carcinoembryonic antigen (CEA) are the common blood-based biomarkers for the clinical diagnosis of PCa [
5]. However, the accuracy of these biomarkers is not strong because of their non-specific aberration in cancers other than PCa [
6]. Though CA19-9 is the best-validated biomarker with a sensitivity of approximately 80%, it is limited by false-positive results in patients with inflammation and non-PCa lesions and false-negative results in Lewis-negative individuals.
In recent years, liquid biopsy based on biomarkers including circulating tumor cells, circulating tumor DNA, microRNAs (miRNAs), and exosomes in blood has proven to be a invasive and effective approach for the detection of cancer in its early stages [
7]. Notably, prior research found that miRNAs-based biomarkers have been used for the diagnosis of patients with PCa. MiRNAs are small non-coding RNAs composed of 17–25 nucleotides, which are relative stable in blood and play important roles in various cancer-associated biological processes. For example, miR-132 was reported to function as a oncogene in pancreatic ductal adenocarcinoma (PDAC), a main subtype accounting for 90% of all subjects with PCa and promote the proliferation, invasion and migration of human pancreatic carcinoma cells [
8,
9]. High expression of miR-30 family promoted migration and invasion of PCa stem cells [
10,
11]. MiR-24 was shown to promote tumor growth and angiogenesis by suppressing Bim expression in vivo [
12]. These miRNAs were included in the panel by identification in our work.
In this study, we identified a panel of four elevated serum miRNAs and assessed the clinical utility of the panel as noninvasive biomarker for the detection of early stage PCa in subjects from multiple centers.
Materials and Methods
Study Design and Participants
This study included a biomarker discovery stage in formalin-fixed, paraffin-embedded (FFPE) tissues, as well as clinical training and validation cohorts in retrospectively collected serum specimens. The cohort study was conducted according to the Technical Guidelines for Clinical Trials of In Vitro Diagnostic Reagents and the Administrative Measures for Registration and Filing of In Vitro Diagnostic Reagents.
For the biomarker discovery stage, miRNA arrays was performed to identify miRNA candidates, which were approved by the institutional review board (IRB) of both institutions (IRB#08-15183; IRB#10-15627). 300 FFPE tissues from the University of Nebraska Medical Center and Creighton University Medical Center, were grouped into normal (benign), early stage [pancreatic intraepithelial neoplasia (PanIN) I/II/III to TNM stage IIA], and advanced-stage (> IIA) groups. Each group contained 100 tissue cores with ≥ 95% statistical power. Then, RT-PCR assays were performed to verify the levels of candidate miRNAs in FFPE tissues.
In the training and validation cohorts, dual-channel RT-PCR was performed in 1273 specimens enrolled from four medical centers, including Peking Medical Union College Hospital affiliated to the Chinese Medical Academy of Sciences, Liaoning Cancer Hospital & Institute, Shanghai Renji Hospital affiliated to Shanghai Jiaotong University, and Xiangya Hospital affiliated to Central South University between 2011 and 2021 who met the inclusion criteria (Supplementary Material 1). Subjects in both cohorts were classified into four groups: healthy control (HC), PDAC, chronic pancreatitis (CP), and pancreatic cystic neoplasms (PCN). In the PDAC group, all the tumors were histologically proven adenocarcinomas. All the cysts included in the PCN group were surgically confirmed not cancerous. Subjects were randomly allocated into two cohorts (training cohort, n = 635; validation cohort, n = 638). The cohort study was approved by the local ethics committees of Dalian University of Technology.
Externally, the discriminative model was further validated using a set of 51 non-PCa digestive tumors, including 29 colorectal, 7 hepatic, 6 esophageal, and 9 gastric carcinomas, respectively, to assess the tumor specification of the panel.
Microarray Expression Profiling of MiRNAs in FFPE Tissues
miRNAs were extracted from selected FFPE tissues using the mirVana™ miRNA isolation kit (Ambion, USA) according to the manufacturer’s protocol. miRNA microarray profiling was performed using Affymetrix GeneChip miRNA arrays (Affymetrix, Santa Clara, CA, USA) according to the manufacturer’s protocol. RNA was labeled by the addition of polyA polymerase using the Genisphere FlashTag HSR kit (Genisphere, Hatfield, PA, USA) following the manufacturer’s instructions. Labeled RNA was hybridized to the Affymetrix miRNA array 1.0. Chips were washed and stained in Fluidic Station 450 (Affymetrix, USA). Each chip was scanned using the GeneChip Scanner 300 7G system (Affymetrix, USA) to control the image scanning.
Serum RNA Isolation
Serum samples were assayed in a blinded manner. Total RNA was extracted from 0.25-mL serum samples using RNAiso Blood (Takara, Japan) according to the manufacturer’s instruction. Briefly, RNAiso Blood reagent (threefold more than the sample volume) was added to each serum sample, which was then lysed thoroughly via violent vortexes. Next, chloroform and isopropyl alcohol were used to precipitate RNA. The obtained RNA precipitate was then washed with 75% ethanol. Finally, RNA was quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
Dual-Channel RT-PCR
The serum levels of miRNAs and endogenous U6 as an internal control were detected by dual-channel RT-PCR. RNA was transcribed into cDNA using miRNA-specific stem-loop primers and a PrimeScript™ RT reagent kit with gDNA Eraser (Perfect Real Time) (Takara, Japan) in a scaled-down (10 μL) reverse transcription reaction. Each reaction contained primers for both the internal control and one miRNA. RT-PCR was performed using Probe qPCR Mix (Takara, Japan) and pre-designed miRNA-specific probes and primers. To collect the signals of both the target miRNA and U6 simultaneously, miRNAs and U6 probes were labeled with different fluorophores. Each reaction was prepared in triplicates and performed on the LightCycler 480 II System (Roche, Switzerland) through 45-cycle amplification.
Statistical Analysis
To screen the miRNA candidates from the discovery cohort, differential miRNA expression analysis was performed using the limma package in R software, version 4.1.1. The miRNA levels detected by microarray were first log2-transformed. MiRNA levels detected by RT-PCR were represented as the delta cycle threshold (Ct) value, which was the relative Ct value of each miRNA normalized to that of U6. Lower delta Ct values indicate higher miRNA expression. For continuous variables, data were expressed as the mean ± standard deviation (SD) and compared using Student’s t-test or one-way or two-way ANOVA performed in GraphPad Prism software, version 9.0. For categorical variables, the Chi-squared test was used to compare differences between two groups. Multiple linear regression was used to construct disease discrimination models using R software, version 4.1.1. Receiver operating characteristic (ROC) curves were used to evaluate the performance of these models. In addition, both univariate and multivariate logistic regression analyses were used to evaluate the relevant risk factors for PCa. Unless stated otherwise, p < 0.05 was considered statistically significant.
Discussion
In this study, a panel of four serum miRNAs was identified and validated to distinguish PCa, especially at its early stages, from HC and individuals with CP or other pancreatic diseases with high sensitivity and specificity. These findings highlight the potential of circulating miRNAs as noninvasive biomarkers for the early detection of PCa.
Several studies identified circulating miRNAs as biomarkers for distinguishing patients with PDAC from healthy subjects [
15‐
18]. However, they failed to validate the detection ability of biomarkers in distinguish early stage PCa from interfering diseases and high-risk groups, which is an important benchmark to assess the diagnostic ability of biomarkers. Moreover, these single-center studies did not account for regional variations. For example, elevated serum miR-1290 was reported early to distinguish patients with low-stage PCa from healthy and disease controls [
18]. However, its clinical utility was limited as the sample size in the study was small (
n = 133), and the single-target biomarker was not tumor-specific in PCa. It was also reported that miR-1290 may be a potential biomarker of high-grade serous ovarian carcinoma [
19]. Therefore, a biomarker study should be with a large sample size, high-throughput screening, and validation with diverse groups including other pancreatic diseases and other cancers besides PCa.
Similar to previous reports, this study also demonstrated that a panel of multiple markers is usually superior to a single marker. We compared the performance of the four miRNAs individually and in combination (Supplementary Figure
S3). Individually, hsa-miR-30c-5p had the best performance in all three comparison sets. The combination of hsa-miR-30c-5p and hsa-miR-23a-3p displayed superior ability in distinguishing patients with early stage PDAC from those with CP. These findings highlight the advantages of using multiple miRNAs in combination.
This study had several strengths. First, this biomarker study began with a high-throughput screening for differential miRNAs in early stage PCa tissues compared with benign controls. Second, we enrolled a large number of participants from multiple centers in different regions of the country to obtain results that were as representative as possible. In addition, to fully demonstrate the performance of the miRNA panel, we included both healthy individuals and patients with multiple high-risk diseases, including CP and PCN, as controls. We also attempted to distinguish pancreatic mucinous tumors from serous tumors given that mucinous tumors including IPMN and MCN are usually more malignant than the other subtypes of PCN. However, the performance of the miRNA panel was not ideal (AUC = 0.68, 95% CI 0.60–0.75; Supplementary Figure S4). Furthermore, we validated the discrimination model in a group of subjects with other digestive tumors, thereby highlighting the tumor-specific miRNA panel as biomarker.
Our findings also suggest the promise of the miRNA panel in the postoperative treatment of PCa. We tested an independent set including 10 pre-treated patients and 20 patients who underwent surgery, radiotherapy, or chemotherapy for PCa. Of these, 19 patients after receiving treatment were predicted to be PCa-negative, and the levels of all four miRNAs were significantly decreased after treatment (Supplementary Figure S5).
This study had a few limitations. This study analyzed associations of several risk factors, such as gender, age, smoking, drinking, obesity, a history of cancers, and diabetes, with PCa by univariate and multivariate logistic regression analysis (Supplementary Table S5). Similar to previous reports [
20], our results revealed that gender, age, drinking, BMI, a family history of cancer, and diabetes were significantly associated with the risk of PCa (all
p < 0.05). However, multivariate analysis illustrated that only age, a family history of cancer, and diabetes were independent risk factors for PCa while smoking, drinking, and BMI were not independent risk factors for PCa. As a potential reason, smoking and drinking might be biased by sex. Via excluded variable analysis, we found that BMI was the intermediary variable of a history of cancers and diabetes, which resulted in the loss of significance of BMI in multivariate analysis. Furthermore, this cohort study was retrospectively rather than prospectively designed. The diagnostic ability of the miRNA panel was needed to be further accessed in a prospective trial, which is underway by us.
In summary, our findings indicated that the four-miRNA panel could be a superior classifier for discriminating PCa from healthy individuals or those in other at-risk groups. The panel is likely to be an adjunctive method in clinical practice of early diagnosis of PCa.
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