Improved predictability of pancreatic ductal adenocarcinoma diagnosis using a blood immune cell biomarker panel developed from bulk mRNA sequencing and single-cell RNA- sequencing

Improved predictability of pancreatic ductal adenocarcinoma diagnosis using a blood immune cell biomarker panel developed from bulk mRNA sequencing and single-cell RNA- sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improved predictability of pancreatic ductal adenocarcinoma diagnosis using a blood immune cell biomarker panel developed from bulk mRNA sequencing and single-cell RNA- sequencing Sung Ill Jang, Hyung Keun Lee, Eun-Ju Chang, Somi Kim, So Young Kim, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2663433/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 May, 2023 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted 7 You are reading this latest preprint version Abstract Background Although pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive form of cancer, there are no validated biomarkers for its diagnosis yet. This study aimed to investigate a PDAC-specific peripheral blood biomarker panel and validate its clinical performance using two cohorts. Methods This prospective, blinded, case-control study included two cohorts. A biomarker panel formula was created using a development cohort and applied to a validation cohort to verify the diagnostic performance of the biomarker panel. The development cohort included healthy controls; patients with a high risk of PDAC; and patients with benign pancreatic disease, PDAC, or other gastrointestinal malignancies. The inclusion criteria for the validation cohort were patients with at least one lesion that could be suspected as PDAC on computed tomography (CT). Results From bulk and single-cell RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from patients with PDAC, three novel immune cell markers, IL-7R, PLD4, and ID3, were selected as specific markers for PDAC. Regarding diagnostic performance of the regression formula for the three biomarker panels, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 84.0%, 78.8%, 47.2%, 95.6%, and 79.8%, respectively. Based on the formula scores for the biomarker panel, the false-negative rate (FNR) of biomarkers was 8% (95% confidence interval [CI]: 3.0–13.0), which was significantly lower than that of CT (29.2%, 95% CI: 20.8–37.6) in the validation cohort. Conclusions The regression formula constructed using three PBMC biomarkers is a cheap, fast, and convenient method that shows clinically usable performance for the diagnosis of PDAC. In particular, it aids in the diagnosis and differential diagnosis of PDAC from pancreatic disease by lowering the FNR of CT. Trial registration: Clinical Research Information Service, KCT0004614. Registered 08 January 2020 - Prospectively registered, Pancreatic ductal adenocarcinoma interleukin-7 receptor phospholipase D4 inhibitor of DNA binding 3 cytokine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and deadly disease with a mortality rate closely paralleling its incidence. The mean 5-year-survival rate has been as low as < 10% in most studies 1 – 3 . This is due to patients being diagnosed when cancer cells have already metastasized, commonly to the liver, lung, and/or peritoneum, coupled with the disease being resistant to therapies. However, PDAC diagnosis lacks sensitive or specific biomarkers for early diagnosis 4 , 5 . Currently used circulating biomarkers such as CA19-9 lack sufficient sensitivity and specificity for diagnostic purposes. Therefore, noninvasive early detection, which can be used for cancer screening, is important for improving the survival of patients with PDAC. However, despite the number of studies, there are still no PDAC-specific biomarkers that are widely used clinically 2 , 5 , 6 . Liquid biopsy holds great promise as a method for noninvasive cancer detection, especially through the analysis of cell-free DNA, cell-free RNA fragments, extracellular vesicles (particularly exosomes), or circulating tumor cells 7 , 8 . In cancer patients, these molecules and vesicles are released into the bloodstream through apoptosis, necrosis, and/or active secretion. However, the sensitive detection of usually very limited amounts of tumor-specific molecules in the blood of patients with early-stage cancers remain an ongoing challenge. Here, we used complementary approaches, bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq), to investigate the transcriptional landscape of peripheral blood mononuclear cells (PBMCs) and to determine the specific immune cell markers that may help in the differential diagnosis of PDAC from other benign pancreatic and gastrointestinal diseases. Although the peripheral blood immune landscapes in individual patients were quite heterogeneous, we selected some common and highly specific PDAC-specific markers (e.g., IL-7R, PLD4, and ID3) from the blood sample of patients with PDAC that were identified in striking contrast to healthy controls and benign pancreatic diseases, including chronic pancreatitis and cystic disease. Clinical performance was found to have remarkable value for the diagnosis and differential diagnosis of PDAC from other pancreatic diseases through two cohorts. In addition, this study provides immune landscape differences between PDAC and benign pancreatic diseases, which may provide a wealth of hypothesis-generating data to benefit pancreatic disease researchers. Methods Study design and registration of clinical research This prospective case-control study was performed using two cohorts (Fig. 1 ). A biomarker panel formula was created using a development cohort and applied to a validation cohort to verify the diagnostic performance of the biomarker panel. The development cohort was approved by the Institutional Review Board of Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (no. 3-2018-0293) and registered at the Clinical Research Information Service ( https://cris.nih.go.kr/cris/en/ ; KCT0004614, accessed on January 8, 2020). The validation cohort was approved by the Institutional Review Board of Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (no. 3-2020-0238). The study was conducted according to the tenets of the Declaration of Helsinki (2008, amended version), and written informed consent was obtained from each patient preoperatively and from all control participants. Patients selection and inclusion criteria of the study The development cohort included patients with PDAC, healthy controls, patients with high risk of PDAC, patients with benign pancreatic disease, and patients with other gastrointestinal (GI) malignancies. The high-risk factors for PDAC included chronic pancreatitis, pancreatic cyst, pancreatic duct dilatation, DM onset over 50 years, and elevated CA19-9 level. Healthy controls with no benign or malignant diseases were also recruited. PDAC and other GI cancers were diagnosed by cytological examination of endoscopic ultrasound (EUS)-guided fine-needle aspiration samples or surgical specimens. Other pancreatic diseases were diagnosed by evaluating clinical symptoms and imaging studies (computed tomography [CT], EUS, and magnetic resonance imaging). Patients with evidence of serious illnesses, immunosuppression, autoimmune or infectious diseases, or those taking immunosuppressive drugs were excluded. The inclusion criteria for the validation cohort were patients who had been diagnosed with PDAC on CT (PDAC-positive group) and who that had at least one lesion that could be suspected of PDAC based on the results of pancreatic CT (pancreatic duct dilatation, focal alteration of parenchymal attenuation, parenchymal atrophy, pancreatic duct interruption, bile duct dilatation, double-duct sign, cystic lesion with high malignant stigma, contour abnormality of pancreatic parenchyma, and peripancreatic lymphadenopathy) in men and women aged over 19 years (PDAC-suspicious group) 9 – 14 . The exclusion criteria were as follows: patients for whom blood sample could not be collected, and patients with a history of immunosuppressive drug use. In addition, inappropriate blood samples were excluded for the following reasons: samples suspected of microbial contamination, improperly stored samples or storage methods that could not be confirmed, and damaged sample containers or without labels. Sample processing and DNA isolation from PBMC Samples from all cases and controls were processed using the method described previously. 15 In brief, peripheral blood samples were collected in EDTA vacutainer tubes and processed within 3 h of collection. PBMCs were isolated from whole blood by Ficoll Paque Plus density-gradient centrifugation, according to the manufacturer’s instructions. Total RNA was extracted from PBMCs using QIAzol (QIAZEN) and reverse-transcribed to cDNA using PrimeScript RT Master Mix (TaKaRa). For quality control of the yielded RNA, the purity and integrity of RNA were evaluated by OD 260/280 ratio and analyzed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). mRNA collection and quantitative RT-PCR Quantitative real-time PCR was performed using a PCR detection system (StepOnePlus Real-Time PCR; Applied Biosystems) and commercial detection kit (Taqman™ Gene expression Master Mix; Applied Biosystems) according to the manufacturer's instructions. The amplification program included an initial denaturation step at 95°C for 10 minutes, followed by 40 cycles of denaturation at 95°C for 15 seconds and annealing and extension at 60°C for 60 seconds. Primer sequences used in this study are listed Supplementary Table 1. Quantitative PCR (qPCR) results were analyzed using the comparative Ct method and normalized to GAPDH levels. mRNA sequencing (mRNA-seq) and data analysis The Affymetrix whole-transcript expression array process was performed according to the manufacturer’s instructions (GeneChip Whole Transcript PLUS reagent Kit). cDNA was synthesized using the GeneChip Whole Transcript (WT) Amplification Kit, according to the manufacturer's instructions. Sense cDNA was then fragmented and biotin-labeled with terminal deoxynucleotidyl transferase using the GeneChip WT Terminal labeling kit. Approximately 5.5 µg of labeled DNA was hybridized to the Affymetrix GeneChip human 2.0 ST Array at 45°C for 16 h. Hybridized arrays were washed and stained on GeneChip Fluidics Station 450 and scanned using the GCS3000 scanner (Affymetrix). Signal values were computed using the Affymetrix® GeneChip™ Command Console software. Bulk RNA-seq data analysis Raw read counts were normalized and log 2 fold changes (log2FC) of genes between conditions were calculated by using the DESeq function of the DESeq2 (v1.34.0) R package 16 with a design formula considering two factor variables, condition and sex. To rank and visualize the effect size of genes between conditions effectively, shrinkage of effect size was calculated per each gene by using the lfcShrink function of the same package. For deconvolution analysis, protein coding genes were used except mitochondrial genes, ribosomal genes and gonosomal genes. The posterior sum over different cell types defined by the published scRNA-seq data 17 was calculated by using the run.prism function of the BayesPrism (v2.0) R package 18 with default parameters. scRNA-seq data analysis Count matrices for 4 human normal PBMC scRNA-seq data were downloaded 17 . Poor quality cells with log 10-scaled counts 20 were discarded using the percellQCMetrics function of the scater (v1.18.6) R package 19 and total 18,895 cells from 4 samples were used for further analysis. Raw UMI counts were normalized in log2-scale by using the logNormCounts function of scran (v1.18.7) R package 20 after removing cell-specific biases by clustering cells and calculating cell-specific size factors using the quickCluster and the computeSumFactors of the same R package. After decomposing gene-specific variance into biological and technical components by using the modelGeneVar function of the same R package, highly variable genes (HVGs) were identified with FDR < 0.05. The top 20 PCs with HVGs were calculated for further cell clustering and visualization. A Shared Nearest Neighbor (SNN) graph was constructed using the FindNeighbors function of Seurat (v4.2.0) R package 21 with the default parameters. Cells were visualized on the two-dimensional UMAP plot using the RunUMAP function. After cell type annotation using expression of canonical markers based on the reference paper, to compare relative gene expression among cell types, the normalized counts were scaled, and the scaled expression was averaged per each cell type. Immunohistochemical staining Immunohistochemical staining (IHC) for PLD4 and ID3 was performed on 5µm histological sections that were cut from the TMA blocks. PLD4/ CD20 and ID3/CD3 immunohistochemical double staining was carried out on a BenchMark Ultra IHC/ISH System (Ventana Medical Systems, Tucson, Az) according to the manufacturer’s instructions. Antibodies for IHC are listed in Supplementary Table 2. Statistical analysis Using the development dataset, the study population characteristics are presented as mean ± standard deviation for continuous variables and frequencies (percentages) for categorical variables. Differences between groups were analyzed using one-way analysis of variance for continuous variables and chi-square tests for categorical variables. Post hoc analyses were conducted using the Bonferroni method. Univariate logistic regression analysis was performed to evaluate the association between pancreatic cancer and CA19-9, IL-7R, PLD4, and ID3 levels. To construct the biomarker panel, multivariate logistic regression was performed to identify independent factors, including IL-7R, PLD4, and ID3. Optimal cutoff values for CA19-9, IL-7R, PLD4, ID3, and the biomarker panel were determined by calculating Youden’s index. In the development and validation datasets, we assessed the performance of CT findings, CA19-9, IL-7R, PLD4, ID3, and the biomarker panel. Diagnostic performance was evaluated based on sensitivity, specificity, accuracy, positive predictive values (PPVs), negative predictive values (NPVs), and false-negative rate (FNR) values. In this study, we used two definitions of FNR. For the development and validation datasets with both positive and negative CT findings, we used the classic FNR definition (FN/[TP + FN]). For the validation dataset with only negative CT, we defined the modified FNR as the percentage of FN in patients with negative CT findings (FN/total N). Additionally, receiver operating characteristic (ROC) curves were constructed, and areas under the curve (AUCs) were calculated. A generalized estimation equation was used to compare the diagnostic performance. Statistical analyses were performed using SAS (version 9.4; SAS Institute, Cary, NC, USA) and R (version 4.0.3; http://www.R-project.org , accessed on January 2, 2020). The significance level was set at a p-value of < 0.05. Results Selection of PDAC-specific PBMC biomarkers from transcriptome data For bulk mRNA-seq, 7 healthy controls and 15 PDAC patients were selected, and their PBMCs were analyzed. We identified 616 up-regulated and 415 down-regulated genes in PDAC patients compared to controls (Fig. 2 A). To link these differentially expressed genes (DEGs) to cell type composition alterations in PDAC patients, we re-analyzed the published human PBMC scRNA-seq dataset as a single-cell reference. Based on 9 distinct cell types and 1 unknown cluster identified from the scRNA-seq data (Fig. 2 C), we deconvolved each bulk RNA-seq sample into 10 cell types by using BayersPrism. scRNA-seq and bulk mRNA-seq were performed using PBMCs from patients with PDAC and healthy controls and compiling the data from both studies. For scRNA-seq, 24,819 cells passed our stringent quality control criteria and were represented in a low-dimensional space using the uniform manifold approximation and projection algorithm 22 . We applied the SingleR algorithm 23 to identify four major immune cell types, B, T, natural killer cells, and monocytes/macrophages, which were confirmed by the expression of cell type-specific markers. Compared with the control group, the analysis showed that CD4 T cells were significantly expanded in PDAC patients (Fig. 2 B and Supplementary Fig. 1), suggesting that the expansion of CD4 T cells is important for explaining the observed DEGs between PDAC and healthy patients. Therefore, we chose two genes as positive markers, which are up-regulated in PDAC patients and expressed in CD4 T cells ( IL7R and ID3 ). As a negative marker, PLD4 that is down-regulated in PDAC patients and not expressed in CD4 T cells was also chosen (Fig. 2 D). To determine whether the selected markers can more precisely distinguish PDAC from other benign pancreatic diseases and controls, we determined the mRNA expression levels of these 57 genes by qPCR among 120 patients. qPCR assays of each marker, IL-7R1, PLD4, and ID3, revealed them to be novel PDAC markers (Fig. 2 E). The expression of IL-7R and its functional role were recently published 15 , 24 . The qPCR assay showed that PLD4 was downregulated in PDAC patients' blood cells, and ID3 was significantly upregulated in PDAC. Moreover, we confirmed that PLD4- and ID3-expressing immune cells infiltrated PDAC tissue (Fig. 2 F). Demographic and laboratory data of the developing cohort population To determine the clinical performance of these three markers, we constructed a clinical cohort. Of the 552 screened patients, 250 were excluded (e.g., incomplete dataset), and 272 patients were enrolled in the development cohort and their data were analyzed (Fig. 3 ). These patients were classified into five groups: PDAC (n = 50), healthy controls (n = 61), high-risk group (n = 56), benign pancreatic disease (n = 51), and other GI malignancies (n = 54). Demographic characteristics were not significantly different between the PDAC, healthy control, high-risk, benign pancreatic disease, and other GI malignancy groups, except that the PDAC group and other GI malignancy groups were older than other groups (Table 1 ). Bilirubin, aspartate transaminase, alanine transferase, and CA19-9 levels were higher in the PDAC group than in the other groups owing to biliary obstruction by PDAC. CA19-9 levels were also higher than those in the other groups. Table 1 Characteristics of the study population in development cohort. Pancreatic cancer (n = 50) Control (n = 61) High risk group (n = 56) Benign pancreatic disease (n = 51) Other GI malignancy (n = 54) Age , years (mean ± SD) 68.5 ± 10.9 52.0 ± 12.5 † 63.9 ± 10.6 † 54.9 ± 16.2 † 66.9 ± 12.3 Male : female 26:24 29:32 26:30 34:17 30:24 BMI , kg/m 2 (mean ± SD) 22.5 ± 2.7 24.5 ± 4.0 † 23.5 ± 2.5 25.5 ± 4.3 † 23.9 ± 3.3 † DM , n (%) 24 (48.0) NA 13 (23.2) 12 (23.6) 14 (25.9) WBC , count/uL (mean ± SD) 7496.4 ± 3568.9 6228.7 ± 1952.1 † 6159.5 ± 1808.2 † 7295.9 ± 3479.2 6868.9 ± 2747.7 Neutrophil 5107.0 ± 3181.2 3404.4 ± 1342.6 † 3633.4 ± 2991.3 † 4721.8 ± 3427.4 5781.8 ± 9791.2 Lymphocyte 1592.5 ± 699.9 2170.2 ± 635.0 † 2036.1 ± 763.3 † 1836.9 ± 706.9 1531.5 ± 571.3 Monocyte 583.9 ± 304.8 438.2 ± 171.1 † 465.2 ± 160.2 † 551.4 ± 228.9 601.5 ± 287.1 Eosinophil 142.6 ± 121.8 169.2 ± 158.8 190.7 ± 164.2 175.6 ± 198.9 213.3 ± 192.8 † Basophil 35.4 ± 18.5 41.1 ± 22.4 40.5 ± 23.3 36.1 ± 19.1 36.3 ± 18.5 Platelet count 10 3 / µL (mean ± SD) 235.8 ± 88.9 NA 238.5 ± 90.6 228.1 ± 73.5 226.9 ± 87.6 Protein , g/dL (mean ± SD) 6.7 ± 1.1 7.1 ± 0.5 † 6.9 ± 0.5 6.9 ± 0.9 6.4 ± 0.8 Albumin , g/dL (mean ± SD) 3.9 ± 0.5 4.3 ± 0.6 † 4.2 ± 0.4 † 4.1 ± 0.6 3.6 ± 0.7 † Bilirubin , mg/dL (mean ± SD) 2.5 ± 4.0 0.7 ± 0.3 † 0.7 ± 0.3 † 1.1 ± 0.7 † 3.4 ± 6.1 AST , IU/L (mean ± SD) 94.9 ± 155.2 26.2 ± 12.0 † 27.5 ± 10.5 † 45.0 ± 40.3 † 48.7 ± 54.3 † ALT , IU/L (mean ± SD) 112.9 ± 176.5 22.2 ± 13.7 † 22.9 ± 14.1 † 52.2 ± 64.6 † 57.9 ± 85.3 † CRP , mg/L (mean ± SD) 20.5 ± 43.4 NA 3.5 ± 15.7 † 38.1 ± 63.6 19.8 ± 38.1 CEA , ng/L (mean ± SD) 70.1 ± 402.5 1.8 ± 0.9 3.1 ± 3.0 2.5 ± 2.0 173.8 ± 840.2 CA19-9 , U/mL (mean ± SD) 2254.6 ± 5357.2 7.2 ± 6.0 † 23.7 ± 51.5 † 11.0 ± 15.2 † 1059.3 ± 3101.2 † IL-7R , (mean ± SD) 1235.8 ± 597.2 947.7 ± 396.4 † 1188.6 ± 566.7 938.6 ± 578.4 † 929.6 ± 633.9 † PLD4 , (mean ± SD) 11.7 ± 6.3 26.4 ± 7.8 † 18.7 ± 8.6 † 14.9 ± 8.9 15.1 ± 9.7 † ID3 , (mean ± SD) 10.4 ± 6.8 7.0 ± 3.8 † 7.6 ± 4.5 † 7.9 ± 7.8 † 5.8 ± 4.7 † GI, gastrointestinal; SD, standard deviation; BMI, body mass index; DM, diabetes mellitus; NA, non−available; WBC, whole blood cell; AST, aspartate transaminase; ALT, alanine transaminase; CRP, c−reactive protein; CEA, carcino−embryonic antigen; CA19−9, carbohydrate antigen 19−9; IL−7R, Interleukin−7 receptor, PLD 4 , Phospholipase D 4 ; ID 3 , inhibitor of DNA binding 3 †, P−value<0.005 comparing with pancreatic cancer Among the selected biomarkers, IL-7R mRNA levels were significantly higher in the PDAC group than in the other groups. PLD4 mRNA levels were significantly higher in the PDAC group than the healthy control, high-risk, and other GI malignancy groups. The ID3 mRNA levels were significantly higher in the PDAC group than in the other groups. Formula and diagnostic performance of the biomarker panel Serum CA19-9, IL-7R, PLD4, and ID3 levels were significantly higher in PDAC patients than in non-pancreatic cancer patients (Table 2 ). Furthermore, the IL-7R, PLD4, and ID3 markers were statistically significant in both univariate and multivariate models for differentiating between pancreatic cancer and non-pancreatic cancer. Therefore, combinations of these three biomarkers were used to establish the formula for the biomarker panel. Table 2 CA 19 − 9, IL-7R, PLD4 and CA19-9 between pancreatic cancer and non-pancreatic cancer. Variable (mean ± SD) Pancreatic cancer (n = 50) Non-pancreatic cancer (n = 222) p- value Univariable model Multivariable model OR (95% CI) p- value OR(95% CI) p- value CA19-9 2254.6 ± 5357.3 226.3 ± 1446.4 0.0106 1.000 (1.000–1.000) 0.0096 IL7R 1235.8 ± 597.2 1013.7 ± 580.6 0.0157 0.908 (0.872–0.946) < .0001 0.795(0.736–0.859) < .0001 PLD4 11.7 ± 6.3 19.1 ± 9.9 < .0001 1.001 (1.000-1.001) 0.0179 1.001(1.000-1.002) 0.0025 ID3 10.4 ± 6.8 7.3 ± 5.8 0.001 1.075 (1.026–1.126) 0.0022 1.144(1.059–1.235) 0.0006 CA19−9, carbohydrate antigen 19−9; IL−7R, Interleukin−7 receptor, PLD 4 , Phospholipase D 4 ; ID 3 , inhibitor of DNA binding 3 ; SD, standard deviation $$\text{A}=-0.789445-0.229438\times \left(\text{P}\text{L}\text{D}4\right)+0.001251\times \left(\text{I}\text{L}7\text{R}\right)+0.134571\times \left(\text{I}\text{D}3\right)$$ $$\text{Pr}\left(Y=pancreatic cancer\right)=\frac{1}{1+\text{e}\text{x}\text{p}(-A)}$$ The AUC for the biomarker panel was significantly greater than that for IL-7R, PLD4, and ID3 (Fig. 4 ). However, the AUC for CA19-9 did not differ from that for the biomarker panel. The diagnostic performance of the biomarker panel was superior to that of either marker alone (IL-7R, PLD4, or ID3) in the development cohort (Table 3 ). The sensitivity, specificity, PPV, NPV, and accuracy of the biomarker panel were 84.0%, 78.8%, 47.2%, 95.6%, and 79.8%, respectively. Notably, these values were significantly higher than those for IL-7R (66%, 56.8%, 25.6%, 88.1%, and 58.5%, respectively), PLD4 (90%, 52.3%, 29.8%, 95.9%, and 59.2%, respectively), and ID3 (62%, 71.6%, 33%, 89.3%, and 69.9%, respectively) alone (p < 0.001). Table 3 Diagnostic performance of multi-panel biomarkers and CA19-9 for pancreatic adenocarcinoma in development cohort Marker Sensitivity (95% CI) Specificity (95% CI) Accuracy (95% CI) PPV (95% CI) NPV (95% CI) Classic FNR † (95% CI) AUC (95% CI) p-value ‡ CT 48.0 (34.2–61.8) 100.0 (100.0-100.0) 90.4 (86.9–93.9) 100.0 (100.0-100.0) 89.5 (85.7–93.3) 52.0 (38.2–65.8) 74.0 (67.0–81.0) < .0001 CA19-9 76.0 (64.2–87.8) 82.2 (76.9–87.5) 81.0 (76.1–85.8) 51.4 (40.0-62.7) 93.3 (89.6–96.9) 24.0 (12.2–35.8) 79.1 (72.6–85.6) 0.2795 IL-7R 66.0 (52.9–79.1) 56.8 (50.2–63.3) 58.5 (52.6–64.3) 25.6 (18.1–33.1) 88.1 (82.8–93.4) 34.0 (20.9–47.1) 61.4 (54.0-68.8) 0.0217 PLD4 90.0 (81.7–98.3) 52.3 (45.7–58.8) 59.2 (53.4–65.0) 29.8 (22.5–37.1) 95.9 (92.3–99.4) 10.0 (1.7–18.3) 71.1 (65.8–76.5) 0.3618 ID3 62.0 (48.5–75.5) 71.6 (65.7–77.6) 69.9 (64.4–75.3) 33.0 (23.5–42.5) 89.3 (84.8–93.9) 38.0 (24.5–51.5) 66.8 (59.4–74.2) 0.0107 Biomarker panel 84.0 (73.8–94.2) 78.8 (73.5–84.2) 79.8 (75.0-84.6) 47.2 (36.8–57.6) 95.6 (92.7–98.6) 16.0 (5.8–26.2) 81.4 (75.6–87.2) reference Values are % (95% confidence interval). Cut-off points: CA19-9 > 37.0, IL-7R > 1025.40733, PLD4 8.7021, Multi-panel > 0.22016 CT, computed tomography; PPV, positive predictive value; NPV, negative predictive value; FNR, False negative rate; AUC, area under curve; CA19-9, carbohydrate antigen 19 − 9; IL-7R, Interleukin-7 receptor, PLD4, Phospholipase D4; ID3, inhibitor of DNA binding 3 †, Classic FNR calculated False Negative/(True Positive + False Negative) ‡, P−value comparing with multi−panel biomarker for FNR Patient characteristics and diagnostic performance in the validation cohort Of the 195 screened patients, 39 were excluded and 156 patients were enrolled in the validation cohort and their data were analyzed (Fig. 5 ). These patients included those with PDAC (n = 76), high-risk group (n = 38), benign pancreatic disease (n = 28), and other malignancies (n = 14). The patients were classified into the PDAC-positive group (n = 43) and PDAC-suspicious group (n = 113) according to the CT results (Table 4 ). There was no difference in patient characteristics between the two groups, except that the values of the biomarker panel of PDAC were significantly higher than those of the PDAC-suspicious group (p = 0.001). The proportion of patients finally diagnosed with PDAC was 100% (43/43) in the PDAC-positive group and 29.2% (33/113) in the PDAC-suspicious group. Table 4 Characteristics of the study population in validation cohort. PDAC-positive group (n = 43) PDAC-suspicious group (n = 113) P-value Age , years (mean ± SD) 64.9 ± 13.6 61.3 ± 15.7 0.175 Male : female 25:18 61:52 0.643 BMI , kg/m 2 (mean ± SD) 23.3 ± 3.3 23.4 ± 3.5 0.773 DM , n (%) 16 (37.2) 33 (29.2) 0.293 WBC , count/uL (mean ± SD) 7361.6 ± 2818.9 6911.5 ± 3061.1 0.404 Neutrophil 6613.5 ± 11886.3 4751.5 ± 2913.8 0.274 Lymphocyte 1531.2 ± 816.2 1501.1 ± 575.4 0.797 Monocyte 573.3 ± 247.7 548.5 ± 593.8 0.792 Eosinophil 167.2 ± 172.9 179.8 ± 157.6 0.664 Basophil 34.2 ± 19.7 32.2 ± 18.4 0.557 Platelet count 10 3 / µL (mean ± SD) 233.1 ± 88.1 245.3 ± 109.5 0.511 Protein , g/dL (mean ± SD) 6.7 ± 0.7 6.6 ± 0.6 0.354 Albumin , g/dL (mean ± SD) 3.8 ± 0.6 3.9 ± 0.4 0.589 Bilirubin , mg/dL (mean ± SD) 3.3 ± 5.1 1.3 ± 1.9 0.016 AST , IU/L (mean ± SD) 76.6 ± 108.0 46.1 ± 70.9 0.091 ALT , IU/L (mean ± SD) 73.6 ± 119.4 51.1 ± 89.6 0.267 CRP , mg/L (mean ± SD) 24.0 ± 40.2 28.4 ± 60.5 0.609 CEA , ng/L (mean ± SD) 22.8 ± 47.4 9.5 ± 58.6 0.148 CA19-9 , U/mL (mean ± SD) 2671.9 ± 5431.2 908.2 ± 3760.7 0.055 IL-7R , (mean ± SD) 1311.7 ± 823.5 1123.2 ± 653.3 0.137 PLD4 , (mean ± SD) 13.4 ± 7.9 15.2 ± 8.3 0.229 ID3 , (mean ± SD) 10.5 ± 7.2 10.3 ± 6.6 0.848 Biomarkers panel , (mean ± SD) 0.3347 ± 0.1756 0.2318 ± 0.1669 0.001 Final diagnosis, PDAC, n (%) 43 (100) 33 (29.2) < 0.001 PDAC, pancreatic ductal adenocarcinoma; SD, standard deviation; BMI, body mass index; DM, diabetes mellitus; NA, non-available; WBC, whole blood cell; AST, aspartate transaminase; ALT, alanine transaminase; CRP, c-reactive protein; CEA, carcino-embryonic antigen; CA19-9, carbohydrate antigen 19 − 9; IL-7R, Interleukin-7 receptor, PLD4, Phospholipase D4; ID3, inhibitor of DNA binding 3 The diagnostic performance of the biomarker panel in the validation cohort did not differ from that in the development cohort (Table 5 ). The sensitivity, specificity, PPV, NPV, and accuracy for the biomarker panel were 80.3%, 78.8%, 78.2%, 80.8%, and 79.5%, respectively. However, the diagnostic performance in terms of sensitivity of CA19-9 in the validation cohort was lower than that in the development cohort (56.6 vs 76.0). Table 5 Diagnostic performance of multi-panel biomarkers and CA19-9 for pancreatic adenocarcinoma in validation cohort Marker Sensitivity (95% CI) Specificity (95% CI) Accuracy (95% CI) PPV (95% CI) NPV (95% CI) Classic FNR † (95% CI) AUC (95% CI) p-value ‡ CT 56.6 (45.4–67.7) 100.0 (100.0-100.0) 78.8 (72.4–85.3) 100.0 (100.0-100.0) 70.8 (62.4–79.2) 43.4 (32.3–54.6) 78.3 (72.7–83.9) 0.0004 CA19-9 68.4 (58.0-78.9) 77.5 (68.3–86.7) 73.1 (66.1–80.0) 74.3 (64.0-84.5) 72.1 (62.6–81.6) 31.6 (21.1–42.0) 73.0 (66.0–80.0) 0.0658 IL-7R 52.6 (41.4–63.9) 52.5 (41.6–63.4) 52.6 (44.7–60.4) 51.3 (40.2–62.4) 53.8 (42.8–64.9) 47.4 (36.1–58.6) 52.6 (44.7–60.5) 0.0001 PLD4 75.0 (65.3–84.7) 34.2 (23.7–44.6) 54.2 (46.3–62.0) 52.3 (42.9–61.7) 58.7 (44.5–72.9) 25.0 (15.3–34.7) 54.6 (47.4–61.8) 0.3141 ID3 50.0 (38.8–61.2) 47.5 (36.6–58.4) 48.7 (40.9–56.6) 47.5 (36.6–58.4) 50.0 (38.8–61.2) 50.0 (38.8–61.2) 51.3 (43.4–59.1) < .0001 Biomarker panel 80.3 (71.3–89.2) 78.8 (69.8–87.7) 79.5 (73.2–85.8) 78.2 (69.0-87.4) 80.8 (72.0-89.5) 19.7 (10.8–28.7) 79.5 (73.1–85.9) reference Values are % (95% confidence interval). Cut-off points: CA19-9 > 37.0, IL-7R > 1025.40733, PLD4 8.7021, Multi-panel > 0.22016 PPV, positive predictive value; NPV, negative predictive value; FNR, False negative rate; AUC, area under curve; CA19-9, carbohydrate antigen 19 − 9; IL-7R, Interleukin-7 receptor, PLD4, Phospholipase D4; ID3, inhibitor of DNA binding 3 †,Classic FNR calculated False Negative/(True Positive + False Negative) ‡, P−value comparing with multi−panel biomarker for FNR Abnormal findings on abdominal CT in PDAC-suspicious group Abnormal findings on abdominal CT in the patients enrolled in the validation cohort are summarized in Table 6 . The most common abnormal CT finding was focal alteration of parenchymal attenuation, which was identified in 76 patients (67.3%). Other findings included pancreatic duct dilatation in 39 patients, cystic lesions with high malignant stigma in 28 patients, parenchymal atrophy in 27 patients, contour abnormality of the pancreatic parenchyma in 18 patients, peripancreatic lymphadenopathy in 18 patients, bile duct dilatation in 12 patients, double-duct sign in 12 patients, and pancreatic duct interruption in 1 patient. There were cases of multiple abnormal CT findings in one patient, and the mean number of findings was 2.04 ± 0.87. Table 6 Abnormal findings on abdominal computed tomography in suspicious PDAC patients Computed tomography findings No. of patients (%) Focal alteration of parenchymal attenuation 76 (67.3) Pancreatic duct dilatation 39 (34.5) Cystic lesion with high malignant stigma 28 (24.8) Parenchymal atrophy 27 (23.9) Contour abnormality of pancreatic parenchyma 18 (15.9) Peripancreatic lymphadenopathy 18 (15.9) Bile duct dilatation 12 (10.6) Double-duct sign 12 (10.6) Pancreatic duct interruption 1 (0.9) Application of the biomarker panel in PDAC-suspicious group Based on the formula scores for the biomarker panel, the 113 patients with suspected PDAC on abdominal CT were divided into high-risk (positive) (Pr ≥ 0.22016) and low-risk (negative) (Pr < 0.22016) groups. Of the 41 high-risk cases, 24 (58.5%) were PDAC cases and 17 (41.5%) were benign cases. The 72 cases categorized as low risk included 9 (12.5%) PDAC cases and 63 (87.5%) benign cases. The sensitivity, specificity, PPV, NPV, and accuracy for the biomarker panel were 72.7%, 78.8%, 58.5%, 87.5%, and 77%, respectively (Table 7 ). Thirty-three patients were diagnosed with PDAC among the 113 patients who tested negative for PDAC on CT. Therefore, the FNR of CT was 29.2% (95% confidence interval [CI]: 20.8–37.6). Nine patients with PDAC were diagnosed from the 113 patients who tested negative for the biomarker panel, and the FNR of biomarkers was 8% (95% CI: 3.0–13.0), which was statistically lower than the FNR of CT (p < 0.001) and reduced that of CT by 72.4%. Table 7 Diagnostic performance of multi-panel biomarkers and CA19-9 for pancreatic adenocarcinoma in suspicious PDAC group. Marker Sensitivity (95% CI) Specificity (95% CI) Accuracy (95% CI) PPV (95% CI) NPV (95% CI) Modified FNR † (95% CI) AUC (95% CI) p -value ‡ CT NA NA NA NA 70.8 (62.4–79.2) 29.2 (20.8–37.6) NA < 0.0001 CA19-9 63.6 (47.2–80.0) 77.5 (68.3–86.7) 73.5 (65.3–81.6) 53.8 (38.2–69.5) 83.8 (75.4–92.2) 10.6 (4.9–16.3) 70.6 (61.0-80.1) 0.4039 Biomarkers panel 72.7 (57.5–87.9) 78.8 (69.8–87.7) 77.0 (69.2–84.8) 58.5 (43.5–73.6) 87.5 (79.9–95.1) 8.0 (3.0–13.0) 75.7 (66.8–84.7) reference Values are % (95% confidence interval). Cut-off points: CA19-9 > 37.0, Biomarkers panel > 0.22016 PPV, positive predictive value; NPV, negative predictive value; FNR, False negative rate; AUC, area under curve; CA19-9, carbohydrate antigen 19 − 9; CT, computed tomography; NA, non-available †, Modified FNR calculated False Negative/Total N ‡, P−value comparing with biomarkers panel for FNR Discussion Molecular biomarkers for cancer diagnosis can be classified as direct or indirect. Direct biomarkers are related to or are segments of tumor tissues (e.g., tumor DNA and RNA). However, indirect biomarkers could be reminiscent of known or unknown factors implicated in the deregulation of cell functions. It has long been reported that PBMCs have emerged as a novel source of biomarkers in various disorders, including inflammatory disease and cancers. 25 – 27 PBMCs may mimic the conditions of some tissues in direct contact such as tumor cells. 26 Recent experiments have indicated that gene expression and methylation profiles in PBMCs are altered in the context of malignancies such as non-small-cell lung cancer, renal cell carcinoma, breast, and other cancers. 26 , 28 , 29 In light of this idea, we investigated peripheral blood markers for cancer detection using recent sophisticated detection tools (RNA-seq and scRNA-seq). The field of tumor immunology has focused heavily on local immune responses in the tumor microenvironment; however, immunity is coordinated across tissues. For example, many myeloid cells are frequently replenished from hematopoietic precursors in the bone marrow 30 , and critical T-cell priming events typically occur in lymphoid tissues 31 . Recent clinical and preclinical studies are beginning to unravel the range of systemic immune perturbations that occur during tumor development as well as the crucial contribution of peripheral immune cells to an anti-tumor immune response. Therefore, we were tried to find immune biomarkers for PDAC. Although biomarkers are found in the blood, their detection remains a challenging issue because of the high degree of fragmentation, minute quantity, and a vast amount of non-specific background. 7 , 8 In this context, we have tried to find a more stable and intact source of biomarkers and finally found three markers from PBMCs as the target source of biomarker detection 27 , 32 . IL-7R is a well-known marker for some T cells, including naive and stem cell memory T cells. 33 – 35 Our team recently reported that IL-7R level is elevated in blood cells from patients with PDAC, especially in the early stages of the disease. Although this study focused on the detection and validation of biomarkers, the molecular and biological mechanisms were not investigated. However, considering the hypothesis of tumor immune surveillance 36 , it is reasonable that IL-7R level in T cells is elevated in the early period of PDAC. From the scRNA-seq data, PLD4 was highly expressed in B cells and monocytes from patients with PBMC (Fig. 2 b). Although not much data have been published on the role of PLD4 in cancer, PLD4 has already been reported as a critical factor for tumor as it plays an important role in anti-tumor activities in colon cancer 37 and kidney fibrosis. 38 To the best of our knowledge, no study has reported the role of PLD4 in PDAC development. However, we found that PLD4 level was downregulated in PDAC and correlated well with tumor stage (data not shown). Because PLD4 has anti-tumor activities, the significant reduction of PLD4 level in PBMCs in PDAC compared with that in other benign pancreatic diseases as well as healthy controls may be explainable. ID3 is a member of the ID family of helix-loop-helix proteins and lacks a basic DNA-binding domain that is known to inhibit transcription. ID3 is highly expressed in B and T cells, 39 , 40 especially Th1 type cells 40 and tissue-resident regulatory T cells. 41 Similar to previous studies, ID3 was found to be significantly elevated in T and B/plasma cells in PMBCs from patients with PDAC. Although ID3 has been found to inhibit the metastatic potential of PDAC, 42 , 43 these studies investigated pancreatic cancer cells and cell lines, not blood immune cells from patients. Therefore, the present study is the first to identify the changes in blood cell expression levels in human samples. We believe that these three indirect tumor markers have three clinical values. First, the biomarker panel can improve the FNR of abdominal CT and CA19-9 level. Based on our data, the FNR of CT was 52.0 in the development cohort and 43.4 in the validation cohort. However, the biomarker panel showed 16.0 and 19.7 in each cohort. Moreover, by combining CT and biomarker panel, FNR decreased to 8.0% (Table 7 ). Considering that most patients with suspected PDAC had already undergone abdominal CT, it is quite impressive that this simple and 1-day blood cell examination can significantly improve the diagnostic value. Second, the levels of these three markers were selectively higher in relatively early cases that did not show elevated CA19-9 levels. Using these three marker qPCR data, 21 (42.3%) CA19-9-negative cancer cases were converted to clinically approved PDAC cases. It is well-known that CA19-9 level is elevated in late cases and is correlated with mass size 44 – 46 . Therefore, it is quite meaningful that the three-marker test positively identified non-diagnostic cases based on CA19-9 level and abdominal CT findings. Although the survival rate of patients with PDAC is extremely low and has not been improved over the last several decades, if an accurate diagnosis is made in the early stage, the prognosis significantly improves. Recently, Hanaeda et al. reported an improved 5-year-survival of up to 80% when the cancer size was < 10 mm 4 , 47 . Lastly, as the test is based on the qPCR assay, the test can be easily and quickly performed by each separate laboratory and enhances the convenience of the diagnostic process for both doctors and patients. This study has some limitations. The regression equation markers were developed from the development cohort, their efficacy was determined in a separate verification cohort, and the exact functional role of the markers was not investigated well. We found elevated levels of IL-7R, PLD4, and ID3 in PBMC using a murine syngeneic tumor model. However, the functional role of the marker-expressed immune cells, the spatiotemporal relationship between the markers in tumor development, and the precise mechanisms for marker upregulation remain unclear. As interest in the immune environment of tumor progression is still beginning in most malignancies, these unsolved problems should be investigated in the future. Additionally, whether the tumor-induced immunity suppresses tumorigenesis or supports tumor growth is context dependent; ultimately, the global immune landscape beyond the tumor becomes significantly altered during tumor progression. 48 It is true that over the last several decades, immune system-targeting immunotherapy has revolutionized cancer therapy, such as anti-CTLA4, anti-PD1, and anti-PDL1. Therefore, although our PBMC marker data are helpful and can be useful for clinical PDAC diagnosis, the levels of the markers should also be monitored and determined in a time-dependent manner. This will allow the changes in the levels of markers to be understood comprehensively, and the true value of the regression equation of the three-marker combination can be determined. Conclusion We found PDAC-specific PBMC markers that are upregulated in PDAC cases and can simply be measured by small-volume blood sampling. The logistic equation developed by combining PBMC-related immune markers from PDAC patients, differential diagnosis of undetermined cases of PDAC, reduced FNR of abdominal CT and CA19-9 level, and improved predictability for PDAC diagnosis. Abbreviations PDAC pancreatic ductal adenocarcinoma RNA-seq RNA-sequencing scRNA-seq single-cell RNA-seq PBMC peripheral blood mononuclear cells GI gastrointestinal EUS endoscopic ultrasound CT computed tomography qPCR quantitative PCR mRNA-seq mRNA sequencing WT Whole Transcript RMA robust multi-average DEG differentially expressed gene PPV positive predictive value NPV negative predictive value FNR false-negative rate value AUC area under the curve CI confidence interval Declarations Trial registration: Clinical Research Information Service, KCT0004614. Registered 08 January 2020 - Prospectively registered, This study was conducted on samples that passed QC among the samples collected and stored after receiving approval from the institutional IRB on November 16, 2018. Clinical Trial Registration was done on January 8, 2020 for research registration to use stored samples and collect validation cohort samples. The period of sample collection for additional validation was after January 8, 2020, and the first sample was collected on January 12, 2020. https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&focus=reset_12&search_page=L&pageSize=10&page=undefined&seq=15613&status=5&seq_group=15613 Ethics approval and consent to participate : The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Gangnam Severance Hospital (IRB No. 3-2018-0293, 3-2020-0238). Consent for publication : Informed consent was obtained from all subjects involved in the study. Availability of data and materials : The data presented in this study are available on request from the corresponding author. Competing interests : The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results Funding : This work was supported by the Technology Development Program (S3049730 and S3301290) funded by the Ministry of SMEs and Startups (MSS, Korea) Authors' contributions : Conceptualization, H.-K.L., E.-J.C., J.-K.K. and D.-K.L.; Methodology, S.-Y.K., I.-Y.H. and S.K. ; Validation, S.-I.J. and J.-H.C.; Formal Analysis, H.-S.L., J.-Y.Y. and S.K.; Investigation, S.-I.J., J.-H.C. and D.-K.L.; Resources, S.-Y.K. and I.-Y.H..; Data Curation, S.-I.J., J.-H.C. and J.-K.K.; Writing—Original Draft, S.-I.J. and H.-K.L.; Writing—Review & Editing, S.-I.J., H.-K.L., E.-J.C, J-K.K. and D.-K.L.; Supervision, H.-K.L. and D.-K.L. All authors have read and agreed to the published version of the manuscript. Authors details Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea ; [email protected] (S.-I.J.) ; [email protected] (D.-K.L.) ; [email protected] (J.-H.C.); [email protected] (I.-Y.H.) Institute of Vision Research, Department of Ophthalmology, Yonsei University College of Medicine, Seoul, Korea; [email protected] (H.-K.L.) ; [email protected] (S.-Y.K.) Department of Biomedical Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; [email protected] (E.-J. C.) Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, Korea; [email protected] (J.-K.K.), [email protected] (S.K) Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea; [email protected] (H.-S.L.) ; [email protected] (J.-Y.Y) AccurasysBio. Co., Ltd., Seoul, Korea; [email protected] (H.-K.L.) ; [email protected] (S.-Y.K.); [email protected] (I.-Y.H.) 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Supplementary Files Supplementarydata.docx Cite Share Download PDF Status: Published Journal Publication published 10 May, 2023 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted Editorial decision: Major revision 24 Mar, 2023 Reviews received at journal 09 Mar, 2023 Reviewers agreed at journal 07 Mar, 2023 Reviewers invited by journal 07 Mar, 2023 Editor assigned by journal 07 Mar, 2023 Submission checks completed at journal 07 Mar, 2023 First submitted to journal 06 Mar, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2663433","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":181562008,"identity":"c542acea-5edf-47dd-a7bd-4b6cb5b27b4d","order_by":0,"name":"Sung Ill Jang","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sung","middleName":"Ill","lastName":"Jang","suffix":""},{"id":181562009,"identity":"0c4a6219-4f70-45a9-b12f-d5ae17423fb8","order_by":1,"name":"Hyung Keun Lee","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hyung","middleName":"Keun","lastName":"Lee","suffix":""},{"id":181562010,"identity":"c195b481-ee09-4880-883c-a905b0fad038","order_by":2,"name":"Eun-Ju Chang","email":"","orcid":"","institution":"University of Ulsan College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eun-Ju","middleName":"","lastName":"Chang","suffix":""},{"id":181562011,"identity":"c47cd441-ef72-4bd8-965c-8fff5306d0c0","order_by":3,"name":"Somi Kim","email":"","orcid":"","institution":"Pohang University of Science and Technology (POSTECH)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Somi","middleName":"","lastName":"Kim","suffix":""},{"id":181562012,"identity":"e6eb85d4-1111-4f06-bbe9-9abff70b1d76","order_by":4,"name":"So Young Kim","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"So","middleName":"Young","lastName":"Kim","suffix":""},{"id":181562013,"identity":"498ef09f-ab61-4052-a060-27461cc17f1b","order_by":5,"name":"In Young Hong","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"In","middleName":"Young","lastName":"Hong","suffix":""},{"id":181562014,"identity":"dedd0cd0-9464-4996-8fdb-b8c4fbcdca9c","order_by":6,"name":"Jong Kyoung Kim","email":"","orcid":"","institution":"Pohang University of Science and Technology (POSTECH)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jong","middleName":"Kyoung","lastName":"Kim","suffix":""},{"id":181562015,"identity":"e3ffb566-51e3-4a08-b7ea-8826c3fd7826","order_by":7,"name":"Hye Sun Lee","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hye","middleName":"Sun","lastName":"Lee","suffix":""},{"id":181562017,"identity":"110d74e7-b1db-4dad-96e9-6c9a36710710","order_by":8,"name":"Juyeon Yang","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juyeon","middleName":"","lastName":"Yang","suffix":""},{"id":181562019,"identity":"41d6d076-3128-4bba-a975-d850e5e370e0","order_by":9,"name":"Jae Hee Cho","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jae","middleName":"Hee","lastName":"Cho","suffix":""},{"id":181562021,"identity":"aede63dc-717f-4816-9442-2cba5a1d5b0b","order_by":10,"name":"Dong Ki Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYDCCM8wNDAwVDAwGJGhhBGo5Q7IWxjZStPCdOdj44OO8w4nb+Q8wfvhBjBbJs43NhjO3HU7cOSOBWbKHGC0G5xnbpHmBWjbcYGCQJsphQC3tv//OAWo5f4D5N3Fazja2MTM2ALUcSGAjzhbJMwebJXuOpRtvuJHYZkmUX/jOJB/88KPGWnbD+cOHbxAVYlDQDMSgOCUB1JGkehSMglEwCkYYAAAKNjwBZ7IZXgAAAABJRU5ErkJggg==","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Dong","middleName":"Ki","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2023-03-07 04:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2663433/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2663433/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00262-023-03458-8","type":"published","date":"2023-05-10T20:48:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":34234946,"identity":"386d5bdd-f39b-46f8-b754-a0d57563db27","added_by":"auto","created_at":"2023-03-14 14:37:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic flowchart of the study design and patient flowchart. \u003c/strong\u003eGI, gastrointestinal; CT, computed tomography; PPV, positive predictive value; NPV, negative predictive value; PDAC, pancreatic ductal adenocarcinoma.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2663433/v1/b9ae9e1e5632c391af4ae03e.png"},{"id":34236996,"identity":"ff96b4f5-6294-41d9-a29b-8bcda9435e54","added_by":"auto","created_at":"2023-03-14 14:45:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":608422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetermination of PDAC-specific PBMC marker selection\u003c/strong\u003e. \u003cstrong\u003eA.\u003c/strong\u003e Volcano plot depicting differentially expressed genes (DEGs) between PDAC patients and controls. Blue and red dots are genes satisfying adjusted P value \u0026lt; 0.05 and log2 fold change \u0026lt; -0.25 and \u0026gt; 0.25, respectively. \u003cstrong\u003eB.\u003c/strong\u003e Box plot showing inferred cell type composition in RNA-seq in 15 PDAC patients and 7 controls. \u003cstrong\u003eC.\u003c/strong\u003e UMAP plot showing 18,895 cells of 15 PDAC patients and 7 controls colored by cell types. \u003cstrong\u003eD.\u003c/strong\u003e UMAP plot showing expression of \u003cem\u003eID3\u003c/em\u003e, \u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003ePLD4\u003c/em\u003e. Black dotted lines highlight CD4 T cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE.\u003c/strong\u003e qPCR data for PLD4 and ID3 between controls and PDAC patients. \u003cstrong\u003eF.\u003c/strong\u003e Immunohistochemical staining of ID3-expressing cells. PLD4-expressing immune cells were not found; however, CD4\u003csup\u003e+\u003c/sup\u003eID3\u003csup\u003e+\u003c/sup\u003e-co-expressing cells were found in PDAC tissues.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2663433/v1/84c7bdf56870d27c3c7bac38.png"},{"id":34234952,"identity":"480e2edb-77d0-4d32-ba67-5a62ec46454d","added_by":"auto","created_at":"2023-03-14 14:37:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation data for the development cohort\u003c/strong\u003e. QC, quality control; P-duct, pancreatic duct; DM, diabetes mellitus; NET, neuroendocrine tumor; LN, lymph node; GI, gastrointestinal.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2663433/v1/a2368ca3e8c215df28a67d4c.png"},{"id":34234951,"identity":"8674c628-6948-4e8c-84e7-df97d46f5cef","added_by":"auto","created_at":"2023-03-14 14:37:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":186709,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves for CA19-9, PLD4, IL-7R, ID3, and the biomarker panel for PDAC assessment\u003c/strong\u003e. \u003cstrong\u003eA.\u003c/strong\u003e The AUC for the biomarker panel was significantly higher than that for IL-7R, PDL4, and ID3 individually. \u003cstrong\u003eB.\u003c/strong\u003eThe AUC for CA19-9 did not differ from that of the biomarker panel. ROC, receiver operating characteristics; PDAC, pancreatic ductal adenocarcinoma; AUC, areas under the curve;\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2663433/v1/53747f3a9913b87626100260.png"},{"id":34236997,"identity":"a37df7f7-9581-4ba4-83d0-5dd00660416e","added_by":"auto","created_at":"2023-03-14 14:45:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation data for the validation cohort\u003c/strong\u003e. CCC, cholangiocarcinoma; GB, gallbladder; IPNB, intraductal papillary neoplasm, biliary; PSC, primary sclerosing cholangitis; NET, neuroendocrine tumor; GIST, gastrointestinal stromal tumor.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2663433/v1/cb515c1949897ee8388c1209.png"},{"id":44729633,"identity":"aa85b096-0912-41ba-8bcc-9a8904b65279","added_by":"auto","created_at":"2023-10-16 21:19:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1625770,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2663433/v1/0abc935b-8dae-4a1a-8908-1feba4853c5e.pdf"},{"id":34234948,"identity":"84d30c21-9a6a-4679-aaac-c4cfe089cc75","added_by":"auto","created_at":"2023-03-14 14:37:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33033,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-2663433/v1/4f346e4797048284c67fddde.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improved predictability of pancreatic ductal adenocarcinoma diagnosis using a blood immune cell biomarker panel developed from bulk mRNA sequencing and single-cell RNA- sequencing","fulltext":[{"header":"Background","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) is an aggressive and deadly disease with a mortality rate closely paralleling its incidence. The mean 5-year-survival rate has been as low as \u0026lt;\u0026thinsp;10% in most studies\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This is due to patients being diagnosed when cancer cells have already metastasized, commonly to the liver, lung, and/or peritoneum, coupled with the disease being resistant to therapies. However, PDAC diagnosis lacks sensitive or specific biomarkers for early diagnosis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Currently used circulating biomarkers such as CA19-9 lack sufficient sensitivity and specificity for diagnostic purposes. Therefore, noninvasive early detection, which can be used for cancer screening, is important for improving the survival of patients with PDAC. However, despite the number of studies, there are still no PDAC-specific biomarkers that are widely used clinically\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLiquid biopsy holds great promise as a method for noninvasive cancer detection, especially through the analysis of cell-free DNA, cell-free RNA fragments, extracellular vesicles (particularly exosomes), or circulating tumor cells\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In cancer patients, these molecules and vesicles are released into the bloodstream through apoptosis, necrosis, and/or active secretion. However, the sensitive detection of usually very limited amounts of tumor-specific molecules in the blood of patients with early-stage cancers remain an ongoing challenge.\u003c/p\u003e \u003cp\u003eHere, we used complementary approaches, bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq), to investigate the transcriptional landscape of peripheral blood mononuclear cells (PBMCs) and to determine the specific immune cell markers that may help in the differential diagnosis of PDAC from other benign pancreatic and gastrointestinal diseases. Although the peripheral blood immune landscapes in individual patients were quite heterogeneous, we selected some common and highly specific PDAC-specific markers (e.g., IL-7R, PLD4, and ID3) from the blood sample of patients with PDAC that were identified in striking contrast to healthy controls and benign pancreatic diseases, including chronic pancreatitis and cystic disease. Clinical performance was found to have remarkable value for the diagnosis and differential diagnosis of PDAC from other pancreatic diseases through two cohorts. In addition, this study provides immune landscape differences between PDAC and benign pancreatic diseases, which may provide a wealth of hypothesis-generating data to benefit pancreatic disease researchers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and registration of clinical research\u003c/h2\u003e \u003cp\u003eThis prospective case-control study was performed using two cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A biomarker panel formula was created using a development cohort and applied to a validation cohort to verify the diagnostic performance of the biomarker panel. The development cohort was approved by the Institutional Review Board of Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (no. 3-2018-0293) and registered at the Clinical Research Information Service (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cris.nih.go.kr/cris/en/\u003c/span\u003e\u003cspan address=\"https://cris.nih.go.kr/cris/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; KCT0004614, accessed on January 8, 2020). The validation cohort was approved by the Institutional Review Board of Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (no. 3-2020-0238). The study was conducted according to the tenets of the Declaration of Helsinki (2008, amended version), and written informed consent was obtained from each patient preoperatively and from all control participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatients selection and inclusion criteria of the study\u003c/h2\u003e \u003cp\u003eThe development cohort included patients with PDAC, healthy controls, patients with high risk of PDAC, patients with benign pancreatic disease, and patients with other gastrointestinal (GI) malignancies. The high-risk factors for PDAC included chronic pancreatitis, pancreatic cyst, pancreatic duct dilatation, DM onset over 50 years, and elevated CA19-9 level. Healthy controls with no benign or malignant diseases were also recruited. PDAC and other GI cancers were diagnosed by cytological examination of endoscopic ultrasound (EUS)-guided fine-needle aspiration samples or surgical specimens. Other pancreatic diseases were diagnosed by evaluating clinical symptoms and imaging studies (computed tomography [CT], EUS, and magnetic resonance imaging). Patients with evidence of serious illnesses, immunosuppression, autoimmune or infectious diseases, or those taking immunosuppressive drugs were excluded.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for the validation cohort were patients who had been diagnosed with PDAC on CT (PDAC-positive group) and who that had at least one lesion that could be suspected of PDAC based on the results of pancreatic CT (pancreatic duct dilatation, focal alteration of parenchymal attenuation, parenchymal atrophy, pancreatic duct interruption, bile duct dilatation, double-duct sign, cystic lesion with high malignant stigma, contour abnormality of pancreatic parenchyma, and peripancreatic lymphadenopathy) in men and women aged over 19 years (PDAC-suspicious group)\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The exclusion criteria were as follows: patients for whom blood sample could not be collected, and patients with a history of immunosuppressive drug use. In addition, inappropriate blood samples were excluded for the following reasons: samples suspected of microbial contamination, improperly stored samples or storage methods that could not be confirmed, and damaged sample containers or without labels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample processing and DNA isolation from PBMC\u003c/h2\u003e \u003cp\u003eSamples from all cases and controls were processed using the method described previously.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In brief, peripheral blood samples were collected in EDTA vacutainer tubes and processed within 3 h of collection. PBMCs were isolated from whole blood by Ficoll Paque Plus density-gradient centrifugation, according to the manufacturer\u0026rsquo;s instructions. Total RNA was extracted from PBMCs using QIAzol (QIAZEN) and reverse-transcribed to cDNA using PrimeScript RT Master Mix (TaKaRa). For quality control of the yielded RNA, the purity and integrity of RNA were evaluated by OD 260/280 ratio and analyzed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003emRNA collection and quantitative RT-PCR\u003c/h2\u003e \u003cp\u003eQuantitative real-time PCR was performed using a PCR detection system (StepOnePlus Real-Time PCR; Applied Biosystems) and commercial detection kit (Taqman\u0026trade; Gene expression Master Mix; Applied Biosystems) according to the manufacturer's instructions. The amplification program included an initial denaturation step at 95\u0026deg;C for 10 minutes, followed by 40 cycles of denaturation at 95\u0026deg;C for 15 seconds and annealing and extension at 60\u0026deg;C for 60 seconds. Primer sequences used in this study are listed Supplementary Table\u0026nbsp;1. Quantitative PCR (qPCR) results were analyzed using the comparative Ct method and normalized to GAPDH levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003emRNA sequencing (mRNA-seq) and data analysis\u003c/h2\u003e \u003cp\u003eThe Affymetrix whole-transcript expression array process was performed according to the manufacturer\u0026rsquo;s instructions (GeneChip Whole Transcript PLUS reagent Kit). cDNA was synthesized using the GeneChip Whole Transcript (WT) Amplification Kit, according to the manufacturer's instructions. Sense cDNA was then fragmented and biotin-labeled with terminal deoxynucleotidyl transferase using the GeneChip WT Terminal labeling kit. Approximately 5.5 \u0026micro;g of labeled DNA was hybridized to the Affymetrix GeneChip human 2.0 ST Array at 45\u0026deg;C for 16 h. Hybridized arrays were washed and stained on GeneChip Fluidics Station 450 and scanned using the GCS3000 scanner (Affymetrix). Signal values were computed using the Affymetrix\u0026reg; GeneChip\u0026trade; Command Console software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBulk RNA-seq data analysis\u003c/h2\u003e \u003cp\u003eRaw read counts were normalized and log\u003csub\u003e2\u003c/sub\u003e fold changes (log2FC) of genes between conditions were calculated by using the DESeq function of the DESeq2 (v1.34.0) R package \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e with a design formula considering two factor variables, condition and sex. To rank and visualize the effect size of genes between conditions effectively, shrinkage of effect size was calculated per each gene by using the lfcShrink function of the same package. For deconvolution analysis, protein coding genes were used except mitochondrial genes, ribosomal genes and gonosomal genes. The posterior sum over different cell types defined by the published scRNA-seq data \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e was calculated by using the run.prism function of the BayesPrism (v2.0) R package \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e with default parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003escRNA-seq data analysis\u003c/h2\u003e \u003cp\u003eCount matrices for 4 human normal PBMC scRNA-seq data were downloaded \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Poor quality cells with log 10-scaled counts\u0026thinsp;\u0026lt;\u0026thinsp;2.5 and percentage of UMIs mapped to mitochondria\u0026thinsp;\u0026gt;\u0026thinsp;20 were discarded using the percellQCMetrics function of the scater (v1.18.6) R package \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and total 18,895 cells from 4 samples were used for further analysis. Raw UMI counts were normalized in log2-scale by using the logNormCounts function of scran (v1.18.7) R package \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e after removing cell-specific biases by clustering cells and calculating cell-specific size factors using the quickCluster and the computeSumFactors of the same R package. After decomposing gene-specific variance into biological and technical components by using the modelGeneVar function of the same R package, highly variable genes (HVGs) were identified with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The top 20 PCs with HVGs were calculated for further cell clustering and visualization. A Shared Nearest Neighbor (SNN) graph was constructed using the FindNeighbors function of Seurat (v4.2.0) R package \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e with the default parameters. Cells were visualized on the two-dimensional UMAP plot using the RunUMAP function. After cell type annotation using expression of canonical markers based on the reference paper, to compare relative gene expression among cell types, the normalized counts were scaled, and the scaled expression was averaged per each cell type.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical staining\u003c/h2\u003e \u003cp\u003eImmunohistochemical staining (IHC) for PLD4 and ID3 was performed on 5\u0026micro;m histological sections that were cut from the TMA blocks. PLD4/ CD20 and ID3/CD3 immunohistochemical double staining was carried out on a BenchMark Ultra IHC/ISH System (Ventana Medical Systems, Tucson, Az) according to the manufacturer\u0026rsquo;s instructions. Antibodies for IHC are listed in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eUsing the development dataset, the study population characteristics are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables and frequencies (percentages) for categorical variables. Differences between groups were analyzed using one-way analysis of variance for continuous variables and chi-square tests for categorical variables. Post hoc analyses were conducted using the Bonferroni method. Univariate logistic regression analysis was performed to evaluate the association between pancreatic cancer and CA19-9, IL-7R, PLD4, and ID3 levels. To construct the biomarker panel, multivariate logistic regression was performed to identify independent factors, including IL-7R, PLD4, and ID3. Optimal cutoff values for CA19-9, IL-7R, PLD4, ID3, and the biomarker panel were determined by calculating Youden\u0026rsquo;s index.\u003c/p\u003e \u003cp\u003eIn the development and validation datasets, we assessed the performance of CT findings, CA19-9, IL-7R, PLD4, ID3, and the biomarker panel. Diagnostic performance was evaluated based on sensitivity, specificity, accuracy, positive predictive values (PPVs), negative predictive values (NPVs), and false-negative rate (FNR) values. In this study, we used two definitions of FNR. For the development and validation datasets with both positive and negative CT findings, we used the classic FNR definition (FN/[TP\u0026thinsp;+\u0026thinsp;FN]). For the validation dataset with only negative CT, we defined the modified FNR as the percentage of FN in patients with negative CT findings (FN/total N). Additionally, receiver operating characteristic (ROC) curves were constructed, and areas under the curve (AUCs) were calculated. A generalized estimation equation was used to compare the diagnostic performance.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using SAS (version 9.4; SAS Institute, Cary, NC, USA) and R (version 4.0.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on January 2, 2020). The significance level was set at a p-value of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSelection of PDAC-specific PBMC biomarkers from transcriptome data\u003c/h2\u003e \u003cp\u003eFor bulk mRNA-seq, 7 healthy controls and 15 PDAC patients were selected, and their PBMCs were analyzed. We identified 616 up-regulated and 415 down-regulated genes in PDAC patients compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). To link these differentially expressed genes (DEGs) to cell type composition alterations in PDAC patients, we re-analyzed the published human PBMC scRNA-seq dataset as a single-cell reference. Based on 9 distinct cell types and 1 unknown cluster identified from the scRNA-seq data (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), we deconvolved each bulk RNA-seq sample into 10 cell types by using BayersPrism.\u003c/p\u003e \u003cp\u003escRNA-seq and bulk mRNA-seq were performed using PBMCs from patients with PDAC and healthy controls and compiling the data from both studies. For scRNA-seq, 24,819 cells passed our stringent quality control criteria and were represented in a low-dimensional space using the uniform manifold approximation and projection algorithm\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. We applied the SingleR algorithm\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e to identify four major immune cell types, B, T, natural killer cells, and monocytes/macrophages, which were confirmed by the expression of cell type-specific markers. Compared with the control group, the analysis showed that CD4 T cells were significantly expanded in PDAC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Supplementary Fig.\u0026nbsp;1), suggesting that the expansion of CD4 T cells is important for explaining the observed DEGs between PDAC and healthy patients. Therefore, we chose two genes as positive markers, which are up-regulated in PDAC patients and expressed in CD4 T cells (\u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003eID3\u003c/em\u003e). As a negative marker, \u003cem\u003ePLD4\u003c/em\u003e that is down-regulated in PDAC patients and not expressed in CD4 T cells was also chosen (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo determine whether the selected markers can more precisely distinguish PDAC from other benign pancreatic diseases and controls, we determined the mRNA expression levels of these 57 genes by qPCR among 120 patients. qPCR assays of each marker, IL-7R1, PLD4, and ID3, revealed them to be novel PDAC markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The expression of IL-7R and its functional role were recently published\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The qPCR assay showed that PLD4 was downregulated in PDAC patients' blood cells, and ID3 was significantly upregulated in PDAC. Moreover, we confirmed that PLD4- and ID3-expressing immune cells infiltrated PDAC tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and laboratory data of the developing cohort population\u003c/h2\u003e \u003cp\u003eTo determine the clinical performance of these three markers, we constructed a clinical cohort. Of the 552 screened patients, 250 were excluded (e.g., incomplete dataset), and 272 patients were enrolled in the development cohort and their data were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These patients were classified into five groups: PDAC (n\u0026thinsp;=\u0026thinsp;50), healthy controls (n\u0026thinsp;=\u0026thinsp;61), high-risk group (n\u0026thinsp;=\u0026thinsp;56), benign pancreatic disease (n\u0026thinsp;=\u0026thinsp;51), and other GI malignancies (n\u0026thinsp;=\u0026thinsp;54). Demographic characteristics were not significantly different between the PDAC, healthy control, high-risk, benign pancreatic disease, and other GI malignancy groups, except that the PDAC group and other GI malignancy groups were older than other groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Bilirubin, aspartate transaminase, alanine transferase, and CA19-9 levels were higher in the PDAC group than in the other groups owing to biliary obstruction by PDAC. CA19-9 levels were also higher than those in the other groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population in development cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePancreatic cancer\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh risk group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBenign pancreatic disease\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOther GI malignancy\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003csup\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003csup\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale : female\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26:24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29:32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26:30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34:17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30:24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e, kg/m\u003csup\u003e2\u003c/sup\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (25.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC\u003c/b\u003e, count/uL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7496.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3568.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6228.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1952.1\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6159.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1808.2\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7295.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3479.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6868.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2747.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5107.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3181.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3404.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1342.6\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3633.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2991.3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4721.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3427.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5781.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9791.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphocyte\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1592.5\u0026thinsp;\u0026plusmn;\u0026thinsp;699.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2170.2\u0026thinsp;\u0026plusmn;\u0026thinsp;635.0\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2036.1\u0026thinsp;\u0026plusmn;\u0026thinsp;763.3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1836.9\u0026thinsp;\u0026plusmn;\u0026thinsp;706.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1531.5\u0026thinsp;\u0026plusmn;\u0026thinsp;571.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonocyte\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e583.9\u0026thinsp;\u0026plusmn;\u0026thinsp;304.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438.2\u0026thinsp;\u0026plusmn;\u0026thinsp;171.1\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e465.2\u0026thinsp;\u0026plusmn;\u0026thinsp;160.2\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e551.4\u0026thinsp;\u0026plusmn;\u0026thinsp;228.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e601.5\u0026thinsp;\u0026plusmn;\u0026thinsp;287.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEosinophil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142.6\u0026thinsp;\u0026plusmn;\u0026thinsp;121.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169.2\u0026thinsp;\u0026plusmn;\u0026thinsp;158.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190.7\u0026thinsp;\u0026plusmn;\u0026thinsp;164.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175.6\u0026thinsp;\u0026plusmn;\u0026thinsp;198.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e213.3\u0026thinsp;\u0026plusmn;\u0026thinsp;192.8\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBasophil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.1\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.5\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet\u003c/b\u003e count 10\u003csup\u003e3\u003c/sup\u003e/ \u0026micro;L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235.8\u0026thinsp;\u0026plusmn;\u0026thinsp;88.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238.5\u0026thinsp;\u0026plusmn;\u0026thinsp;90.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e228.1\u0026thinsp;\u0026plusmn;\u0026thinsp;73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e226.9\u0026thinsp;\u0026plusmn;\u0026thinsp;87.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein\u003c/b\u003e, g/dL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin\u003c/b\u003e, g/dL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin\u003c/b\u003e, mg/dL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST\u003c/b\u003e, IU/L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.9\u0026thinsp;\u0026plusmn;\u0026thinsp;155.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;40.3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.7\u0026thinsp;\u0026plusmn;\u0026thinsp;54.3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT\u003c/b\u003e, IU/L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.9\u0026thinsp;\u0026plusmn;\u0026thinsp;176.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.2\u0026thinsp;\u0026plusmn;\u0026thinsp;64.6\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;85.3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP\u003c/b\u003e, mg/L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;38.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCEA\u003c/b\u003e, ng/L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.1\u0026thinsp;\u0026plusmn;\u0026thinsp;402.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e173.8\u0026thinsp;\u0026plusmn;\u0026thinsp;840.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCA19-9\u003c/b\u003e, U/mL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2254.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5357.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;51.5\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1059.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3101.2\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-7R\u003c/b\u003e, (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1235.8\u0026thinsp;\u0026plusmn;\u0026thinsp;597.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e947.7\u0026thinsp;\u0026plusmn;\u0026thinsp;396.4\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1188.6\u0026thinsp;\u0026plusmn;\u0026thinsp;566.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e938.6\u0026thinsp;\u0026plusmn;\u0026thinsp;578.4\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e929.6\u0026thinsp;\u0026plusmn;\u0026thinsp;633.9\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLD4\u003c/b\u003e, (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eID3\u003c/b\u003e, (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eGI, gastrointestinal; SD, standard deviation; BMI, body mass index; DM, diabetes mellitus; NA, non\u0026minus;available; WBC, whole blood cell; AST, aspartate transaminase; ALT, alanine transaminase; CRP, c\u0026minus;reactive protein; CEA, carcino\u0026minus;embryonic antigen; CA19\u0026minus;9, carbohydrate antigen 19\u0026minus;9; IL\u0026minus;7R, Interleukin\u0026minus;7 receptor, PLD\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, Phospholipase D\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e; ID\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, inhibitor of DNA binding \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003e\u0026dagger;, P\u0026minus;value\u0026lt;0.005 comparing with pancreatic cancer\u003c/b\u003e\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the selected biomarkers, IL-7R mRNA levels were significantly higher in the PDAC group than in the other groups. PLD4 mRNA levels were significantly higher in the PDAC group than the healthy control, high-risk, and other GI malignancy groups. The ID3 mRNA levels were significantly higher in the PDAC group than in the other groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFormula and diagnostic performance of the biomarker panel\u003c/h2\u003e \u003cp\u003eSerum CA19-9, IL-7R, PLD4, and ID3 levels were significantly higher in PDAC patients than in non-pancreatic cancer patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, the IL-7R, PLD4, and ID3 markers were statistically significant in both univariate and multivariate models for differentiating between pancreatic cancer and non-pancreatic cancer. Therefore, combinations of these three biomarkers were used to establish the formula for the biomarker panel.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCA 19\u0026thinsp;\u0026minus;\u0026thinsp;9, IL-7R, PLD4 and CA19-9 between pancreatic cancer and non-pancreatic cancer.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePancreatic cancer (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNon-pancreatic cancer (n\u0026thinsp;=\u0026thinsp;222)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eUnivariable model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMultivariable model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2254.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5357.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e226.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1446.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000 (1.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL7R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1235.8\u0026thinsp;\u0026plusmn;\u0026thinsp;597.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1013.7\u0026thinsp;\u0026plusmn;\u0026thinsp;580.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.908 (0.872\u0026ndash;0.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.795(0.736\u0026ndash;0.859)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e19.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.001 (1.000-1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.001(1.000-1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.075 (1.026\u0026ndash;1.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.144(1.059\u0026ndash;1.235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eCA19\u0026minus;9, carbohydrate antigen 19\u0026minus;9; IL\u0026minus;7R, Interleukin\u0026minus;7 receptor, PLD\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, Phospholipase D\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e; ID\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, inhibitor of DNA binding \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e; SD, standard deviation\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{A}=-0.789445-0.229438\\times \\left(\\text{P}\\text{L}\\text{D}4\\right)+0.001251\\times \\left(\\text{I}\\text{L}7\\text{R}\\right)+0.134571\\times \\left(\\text{I}\\text{D}3\\right)$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\text{Pr}\\left(Y=pancreatic cancer\\right)=\\frac{1}{1+\\text{e}\\text{x}\\text{p}(-A)}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe AUC for the biomarker panel was significantly greater than that for IL-7R, PLD4, and ID3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, the AUC for CA19-9 did not differ from that for the biomarker panel. The diagnostic performance of the biomarker panel was superior to that of either marker alone (IL-7R, PLD4, or ID3) in the development cohort (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The sensitivity, specificity, PPV, NPV, and accuracy of the biomarker panel were 84.0%, 78.8%, 47.2%, 95.6%, and 79.8%, respectively. Notably, these values were significantly higher than those for IL-7R (66%, 56.8%, 25.6%, 88.1%, and 58.5%, respectively), PLD4 (90%, 52.3%, 29.8%, 95.9%, and 59.2%, respectively), and ID3 (62%, 71.6%, 33%, 89.3%, and 69.9%, respectively) alone (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of multi-panel biomarkers and CA19-9 for pancreatic adenocarcinoma in development cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClassic FNR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0\u003c/p\u003e \u003cp\u003e(34.2\u0026ndash;61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003cp\u003e(100.0-100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.4\u003c/p\u003e \u003cp\u003e(86.9\u0026ndash;93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003cp\u003e(100.0-100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.5\u003c/p\u003e \u003cp\u003e(85.7\u0026ndash;93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003cp\u003e(38.2\u0026ndash;65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003cp\u003e(67.0\u0026ndash;81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCA19-9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.0\u003c/p\u003e \u003cp\u003e(64.2\u0026ndash;87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.2\u003c/p\u003e \u003cp\u003e(76.9\u0026ndash;87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003cp\u003e(76.1\u0026ndash;85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003cp\u003e(40.0-62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003cp\u003e(89.6\u0026ndash;96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003cp\u003e(12.2\u0026ndash;35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.1\u003c/p\u003e \u003cp\u003e(72.6\u0026ndash;85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-7R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003cp\u003e(52.9\u0026ndash;79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003cp\u003e(50.2\u0026ndash;63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003cp\u003e(52.6\u0026ndash;64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003cp\u003e(18.1\u0026ndash;33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.1\u003c/p\u003e \u003cp\u003e(82.8\u0026ndash;93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003cp\u003e(20.9\u0026ndash;47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61.4\u003c/p\u003e \u003cp\u003e(54.0-68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLD4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003cp\u003e(81.7\u0026ndash;98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003cp\u003e(45.7\u0026ndash;58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.2\u003c/p\u003e \u003cp\u003e(53.4\u0026ndash;65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003cp\u003e(22.5\u0026ndash;37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.9\u003c/p\u003e \u003cp\u003e(92.3\u0026ndash;99.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003cp\u003e(1.7\u0026ndash;18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.1\u003c/p\u003e \u003cp\u003e(65.8\u0026ndash;76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eID3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003cp\u003e(48.5\u0026ndash;75.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003cp\u003e(65.7\u0026ndash;77.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003cp\u003e(64.4\u0026ndash;75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003cp\u003e(23.5\u0026ndash;42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.3\u003c/p\u003e \u003cp\u003e(84.8\u0026ndash;93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003cp\u003e(24.5\u0026ndash;51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003cp\u003e(59.4\u0026ndash;74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiomarker panel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003cp\u003e(73.8\u0026ndash;94.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003cp\u003e(73.5\u0026ndash;84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.8\u003c/p\u003e \u003cp\u003e(75.0-84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003cp\u003e(36.8\u0026ndash;57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.6\u003c/p\u003e \u003cp\u003e(92.7\u0026ndash;98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003cp\u003e(5.8\u0026ndash;26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.4\u003c/p\u003e \u003cp\u003e(75.6\u0026ndash;87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eValues are % (95% confidence interval).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCut-off points: CA19-9\u0026thinsp;\u0026gt;\u0026thinsp;37.0, IL-7R\u0026thinsp;\u0026gt;\u0026thinsp;1025.40733, PLD4\u0026thinsp;\u0026lt;\u0026thinsp;18.8693, ID3\u0026thinsp;\u0026gt;\u0026thinsp;8.7021, Multi-panel\u0026thinsp;\u0026gt;\u0026thinsp;0.22016\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCT, computed tomography; PPV, positive predictive value; NPV, negative predictive value; FNR, False negative rate; AUC, area under curve; CA19-9, carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9; IL-7R, Interleukin-7 receptor, PLD4, Phospholipase D4; ID3, inhibitor of DNA binding 3\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003e\u0026dagger;, Classic FNR calculated False Negative/(True Positive\u0026thinsp;+\u0026thinsp;False Negative)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e\u003cb\u003e\u0026Dagger;, P\u0026minus;value comparing with multi\u0026minus;panel biomarker for FNR\u003c/b\u003e\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics and diagnostic performance in the validation cohort\u003c/h2\u003e \u003cp\u003eOf the 195 screened patients, 39 were excluded and 156 patients were enrolled in the validation cohort and their data were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These patients included those with PDAC (n\u0026thinsp;=\u0026thinsp;76), high-risk group (n\u0026thinsp;=\u0026thinsp;38), benign pancreatic disease (n\u0026thinsp;=\u0026thinsp;28), and other malignancies (n\u0026thinsp;=\u0026thinsp;14). The patients were classified into the PDAC-positive group (n\u0026thinsp;=\u0026thinsp;43) and PDAC-suspicious group (n\u0026thinsp;=\u0026thinsp;113) according to the CT results (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There was no difference in patient characteristics between the two groups, except that the values of the biomarker panel of PDAC were significantly higher than those of the PDAC-suspicious group (p\u0026thinsp;=\u0026thinsp;0.001). The proportion of patients finally diagnosed with PDAC was 100% (43/43) in the PDAC-positive group and 29.2% (33/113) in the PDAC-suspicious group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population in validation cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDAC-positive group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDAC-suspicious group (n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale : female\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25:18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61:52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e, kg/m\u003csup\u003e2\u003c/sup\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC\u003c/b\u003e, count/uL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7361.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2818.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6911.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3061.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6613.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11886.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4751.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2913.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphocyte\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1531.2\u0026thinsp;\u0026plusmn;\u0026thinsp;816.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1501.1\u0026thinsp;\u0026plusmn;\u0026thinsp;575.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonocyte\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573.3\u0026thinsp;\u0026plusmn;\u0026thinsp;247.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e548.5\u0026thinsp;\u0026plusmn;\u0026thinsp;593.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEosinophil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167.2\u0026thinsp;\u0026plusmn;\u0026thinsp;172.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179.8\u0026thinsp;\u0026plusmn;\u0026thinsp;157.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBasophil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.2\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet\u003c/b\u003e count 10\u003csup\u003e3\u003c/sup\u003e/ \u0026micro;L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233.1\u0026thinsp;\u0026plusmn;\u0026thinsp;88.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245.3\u0026thinsp;\u0026plusmn;\u0026thinsp;109.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein\u003c/b\u003e, g/dL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin\u003c/b\u003e, g/dL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin\u003c/b\u003e, mg/dL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST\u003c/b\u003e, IU/L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.6\u0026thinsp;\u0026plusmn;\u0026thinsp;108.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.1\u0026thinsp;\u0026plusmn;\u0026thinsp;70.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT\u003c/b\u003e, IU/L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.6\u0026thinsp;\u0026plusmn;\u0026thinsp;119.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.1\u0026thinsp;\u0026plusmn;\u0026thinsp;89.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP\u003c/b\u003e, mg/L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;40.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCEA\u003c/b\u003e, ng/L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCA19-9\u003c/b\u003e, U/mL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2671.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5431.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e908.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3760.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-7R\u003c/b\u003e, (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1311.7\u0026thinsp;\u0026plusmn;\u0026thinsp;823.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1123.2\u0026thinsp;\u0026plusmn;\u0026thinsp;653.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLD4\u003c/b\u003e, (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eID3\u003c/b\u003e, (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiomarkers panel\u003c/b\u003e, (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3347\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2318\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal diagnosis, PDAC, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePDAC, pancreatic ductal adenocarcinoma; SD, standard deviation; BMI, body mass index; DM, diabetes mellitus; NA, non-available; WBC, whole blood cell; AST, aspartate transaminase; ALT, alanine transaminase; CRP, c-reactive protein; CEA, carcino-embryonic antigen; CA19-9, carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9; IL-7R, Interleukin-7 receptor, PLD4, Phospholipase D4; ID3, inhibitor of DNA binding 3\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe diagnostic performance of the biomarker panel in the validation cohort did not differ from that in the development cohort (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The sensitivity, specificity, PPV, NPV, and accuracy for the biomarker panel were 80.3%, 78.8%, 78.2%, 80.8%, and 79.5%, respectively. However, the diagnostic performance in terms of sensitivity of CA19-9 in the validation cohort was lower than that in the development cohort (56.6 vs 76.0).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of multi-panel biomarkers and CA19-9 for pancreatic adenocarcinoma in validation cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClassic FNR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.6\u003c/p\u003e \u003cp\u003e(45.4\u0026ndash;67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003cp\u003e(100.0-100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003cp\u003e(72.4\u0026ndash;85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003cp\u003e(100.0-100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003cp\u003e(62.4\u0026ndash;79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003cp\u003e(32.3\u0026ndash;54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003cp\u003e(72.7\u0026ndash;83.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCA19-9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.4\u003c/p\u003e \u003cp\u003e(58.0-78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003cp\u003e(68.3\u0026ndash;86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.1\u003c/p\u003e \u003cp\u003e(66.1\u0026ndash;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.3\u003c/p\u003e \u003cp\u003e(64.0-84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.1\u003c/p\u003e \u003cp\u003e(62.6\u0026ndash;81.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003cp\u003e(21.1\u0026ndash;42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.0\u003c/p\u003e \u003cp\u003e(66.0\u0026ndash;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-7R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003cp\u003e(41.4\u0026ndash;63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003cp\u003e(41.6\u0026ndash;63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003cp\u003e(44.7\u0026ndash;60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003cp\u003e(40.2\u0026ndash;62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003cp\u003e(42.8\u0026ndash;64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003cp\u003e(36.1\u0026ndash;58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003cp\u003e(44.7\u0026ndash;60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLD4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003cp\u003e(65.3\u0026ndash;84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003cp\u003e(23.7\u0026ndash;44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003cp\u003e(46.3\u0026ndash;62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003cp\u003e(42.9\u0026ndash;61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003cp\u003e(44.5\u0026ndash;72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003cp\u003e(15.3\u0026ndash;34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003cp\u003e(47.4\u0026ndash;61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eID3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003cp\u003e(38.8\u0026ndash;61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003cp\u003e(36.6\u0026ndash;58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.7\u003c/p\u003e \u003cp\u003e(40.9\u0026ndash;56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003cp\u003e(36.6\u0026ndash;58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003cp\u003e(38.8\u0026ndash;61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003cp\u003e(38.8\u0026ndash;61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003cp\u003e(43.4\u0026ndash;59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiomarker panel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003cp\u003e(71.3\u0026ndash;89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003cp\u003e(69.8\u0026ndash;87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.5\u003c/p\u003e \u003cp\u003e(73.2\u0026ndash;85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.2\u003c/p\u003e \u003cp\u003e(69.0-87.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.8\u003c/p\u003e \u003cp\u003e(72.0-89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003cp\u003e(10.8\u0026ndash;28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.5\u003c/p\u003e \u003cp\u003e(73.1\u0026ndash;85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eValues are % (95% confidence interval).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCut-off points: CA19-9\u0026thinsp;\u0026gt;\u0026thinsp;37.0, IL-7R\u0026thinsp;\u0026gt;\u0026thinsp;1025.40733, PLD4\u0026thinsp;\u0026lt;\u0026thinsp;18.8693, ID3\u0026thinsp;\u0026gt;\u0026thinsp;8.7021, Multi-panel\u0026thinsp;\u0026gt;\u0026thinsp;0.22016\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003ePPV, positive predictive value; NPV, negative predictive value; FNR, False negative rate; AUC, area under curve; CA19-9, carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9; IL-7R, Interleukin-7 receptor, PLD4, Phospholipase D4; ID3, inhibitor of DNA binding 3\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003e\u0026dagger;,Classic FNR calculated False Negative/(True Positive\u0026thinsp;+\u0026thinsp;False Negative)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e\u003cb\u003e\u0026Dagger;, P\u0026minus;value comparing with multi\u0026minus;panel biomarker for FNR\u003c/b\u003e\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAbnormal findings on abdominal CT in PDAC-suspicious group\u003c/h2\u003e \u003cp\u003eAbnormal findings on abdominal CT in the patients enrolled in the validation cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The most common abnormal CT finding was focal alteration of parenchymal attenuation, which was identified in 76 patients (67.3%). Other findings included pancreatic duct dilatation in 39 patients, cystic lesions with high malignant stigma in 28 patients, parenchymal atrophy in 27 patients, contour abnormality of the pancreatic parenchyma in 18 patients, peripancreatic lymphadenopathy in 18 patients, bile duct dilatation in 12 patients, double-duct sign in 12 patients, and pancreatic duct interruption in 1 patient. There were cases of multiple abnormal CT findings in one patient, and the mean number of findings was 2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAbnormal findings on abdominal computed tomography in suspicious PDAC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputed tomography findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of patients (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFocal alteration of parenchymal attenuation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76 (67.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreatic duct dilatation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (34.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystic lesion with high malignant stigma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParenchymal atrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (23.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContour abnormality of pancreatic parenchyma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (15.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripancreatic lymphadenopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (15.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBile duct dilatation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble-duct sign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreatic duct interruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eApplication of the biomarker panel in PDAC-suspicious group\u003c/h2\u003e \u003cp\u003eBased on the formula scores for the biomarker panel, the 113 patients with suspected PDAC on abdominal CT were divided into high-risk (positive) (Pr\u0026thinsp;\u0026ge;\u0026thinsp;0.22016) and low-risk (negative) (Pr\u0026thinsp;\u0026lt;\u0026thinsp;0.22016) groups. Of the 41 high-risk cases, 24 (58.5%) were PDAC cases and 17 (41.5%) were benign cases. The 72 cases categorized as low risk included 9 (12.5%) PDAC cases and 63 (87.5%) benign cases. The sensitivity, specificity, PPV, NPV, and accuracy for the biomarker panel were 72.7%, 78.8%, 58.5%, 87.5%, and 77%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Thirty-three patients were diagnosed with PDAC among the 113 patients who tested negative for PDAC on CT. Therefore, the FNR of CT was 29.2% (95% confidence interval [CI]: 20.8\u0026ndash;37.6). Nine patients with PDAC were diagnosed from the 113 patients who tested negative for the biomarker panel, and the FNR of biomarkers was 8% (95% CI: 3.0\u0026ndash;13.0), which was statistically lower than the FNR of CT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and reduced that of CT by 72.4%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of multi-panel biomarkers and CA19-9 for pancreatic adenocarcinoma in suspicious PDAC group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModified FNR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003cp\u003e(62.4\u0026ndash;79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003cp\u003e(20.8\u0026ndash;37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCA19-9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003cp\u003e(47.2\u0026ndash;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003cp\u003e(68.3\u0026ndash;86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003cp\u003e(65.3\u0026ndash;81.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003cp\u003e(38.2\u0026ndash;69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003cp\u003e(75.4\u0026ndash;92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003cp\u003e(4.9\u0026ndash;16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003cp\u003e(61.0-80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiomarkers panel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003cp\u003e(57.5\u0026ndash;87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003cp\u003e(69.8\u0026ndash;87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003cp\u003e(69.2\u0026ndash;84.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003cp\u003e(43.5\u0026ndash;73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003cp\u003e(79.9\u0026ndash;95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003cp\u003e(3.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75.7\u003c/p\u003e \u003cp\u003e(66.8\u0026ndash;84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eValues are % (95% confidence interval).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCut-off points: CA19-9\u0026thinsp;\u0026gt;\u0026thinsp;37.0, Biomarkers panel\u0026thinsp;\u0026gt;\u0026thinsp;0.22016\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003ePPV, positive predictive value; NPV, negative predictive value; FNR, False negative rate; AUC, area under curve; CA19-9, carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9; CT, computed tomography; NA, non-available\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003e\u0026dagger;, Modified FNR calculated False Negative/Total N\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e\u003cb\u003e\u0026Dagger;, P\u0026minus;value comparing with biomarkers panel for FNR\u003c/b\u003e\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMolecular biomarkers for cancer diagnosis can be classified as direct or indirect. Direct biomarkers are related to or are segments of tumor tissues (e.g., tumor DNA and RNA). However, indirect biomarkers could be reminiscent of known or unknown factors implicated in the deregulation of cell functions. It has long been reported that PBMCs have emerged as a novel source of biomarkers in various disorders, including inflammatory disease and cancers.\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e PBMCs may mimic the conditions of some tissues in direct contact such as tumor cells.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Recent experiments have indicated that gene expression and methylation profiles in PBMCs are altered in the context of malignancies such as non-small-cell lung cancer, renal cell carcinoma, breast, and other cancers.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e In light of this idea, we investigated peripheral blood markers for cancer detection using recent sophisticated detection tools (RNA-seq and scRNA-seq).\u003c/p\u003e \u003cp\u003eThe field of tumor immunology has focused heavily on local immune responses in the tumor microenvironment; however, immunity is coordinated across tissues. For example, many myeloid cells are frequently replenished from hematopoietic precursors in the bone marrow\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and critical T-cell priming events typically occur in lymphoid tissues\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Recent clinical and preclinical studies are beginning to unravel the range of systemic immune perturbations that occur during tumor development as well as the crucial contribution of peripheral immune cells to an anti-tumor immune response. Therefore, we were tried to find immune biomarkers for PDAC. Although biomarkers are found in the blood, their detection remains a challenging issue because of the high degree of fragmentation, minute quantity, and a vast amount of non-specific background.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e In this context, we have tried to find a more stable and intact source of biomarkers and finally found three markers from PBMCs as the target source of biomarker detection\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIL-7R is a well-known marker for some T cells, including naive and stem cell memory T cells.\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Our team recently reported that IL-7R level is elevated in blood cells from patients with PDAC, especially in the early stages of the disease. Although this study focused on the detection and validation of biomarkers, the molecular and biological mechanisms were not investigated. However, considering the hypothesis of tumor immune surveillance\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, it is reasonable that IL-7R level in T cells is elevated in the early period of PDAC. From the scRNA-seq data, PLD4 was highly expressed in B cells and monocytes from patients with PBMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Although not much data have been published on the role of PLD4 in cancer, PLD4 has already been reported as a critical factor for tumor as it plays an important role in anti-tumor activities in colon cancer\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and kidney fibrosis.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e To the best of our knowledge, no study has reported the role of PLD4 in PDAC development. However, we found that PLD4 level was downregulated in PDAC and correlated well with tumor stage (data not shown). Because PLD4 has anti-tumor activities, the significant reduction of PLD4 level in PBMCs in PDAC compared with that in other benign pancreatic diseases as well as healthy controls may be explainable. ID3 is a member of the ID family of helix-loop-helix proteins and lacks a basic DNA-binding domain that is known to inhibit transcription. ID3 is highly expressed in B and T cells,\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e especially Th1 type cells\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and tissue-resident regulatory T cells.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Similar to previous studies, ID3 was found to be significantly elevated in T and B/plasma cells in PMBCs from patients with PDAC. Although ID3 has been found to inhibit the metastatic potential of PDAC,\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e these studies investigated pancreatic cancer cells and cell lines, not blood immune cells from patients. Therefore, the present study is the first to identify the changes in blood cell expression levels in human samples.\u003c/p\u003e \u003cp\u003eWe believe that these three indirect tumor markers have three clinical values. First, the biomarker panel can improve the FNR of abdominal CT and CA19-9 level. Based on our data, the FNR of CT was 52.0 in the development cohort and 43.4 in the validation cohort. However, the biomarker panel showed 16.0 and 19.7 in each cohort. Moreover, by combining CT and biomarker panel, FNR decreased to 8.0% (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Considering that most patients with suspected PDAC had already undergone abdominal CT, it is quite impressive that this simple and 1-day blood cell examination can significantly improve the diagnostic value. Second, the levels of these three markers were selectively higher in relatively early cases that did not show elevated CA19-9 levels. Using these three marker qPCR data, 21 (42.3%) CA19-9-negative cancer cases were converted to clinically approved PDAC cases. It is well-known that CA19-9 level is elevated in late cases and is correlated with mass size\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Therefore, it is quite meaningful that the three-marker test positively identified non-diagnostic cases based on CA19-9 level and abdominal CT findings. Although the survival rate of patients with PDAC is extremely low and has not been improved over the last several decades, if an accurate diagnosis is made in the early stage, the prognosis significantly improves. Recently, Hanaeda et al. reported an improved 5-year-survival of up to 80% when the cancer size was \u0026lt;\u0026thinsp;10 mm\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Lastly, as the test is based on the qPCR assay, the test can be easily and quickly performed by each separate laboratory and enhances the convenience of the diagnostic process for both doctors and patients.\u003c/p\u003e \u003cp\u003eThis study has some limitations. The regression equation markers were developed from the development cohort, their efficacy was determined in a separate verification cohort, and the exact functional role of the markers was not investigated well. We found elevated levels of IL-7R, PLD4, and ID3 in PBMC using a murine syngeneic tumor model. However, the functional role of the marker-expressed immune cells, the spatiotemporal relationship between the markers in tumor development, and the precise mechanisms for marker upregulation remain unclear. As interest in the immune environment of tumor progression is still beginning in most malignancies, these unsolved problems should be investigated in the future. Additionally, whether the tumor-induced immunity suppresses tumorigenesis or supports tumor growth is context dependent; ultimately, the global immune landscape beyond the tumor becomes significantly altered during tumor progression.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e It is true that over the last several decades, immune system-targeting immunotherapy has revolutionized cancer therapy, such as anti-CTLA4, anti-PD1, and anti-PDL1. Therefore, although our PBMC marker data are helpful and can be useful for clinical PDAC diagnosis, the levels of the markers should also be monitored and determined in a time-dependent manner. This will allow the changes in the levels of markers to be understood comprehensively, and the true value of the regression equation of the three-marker combination can be determined.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe found PDAC-specific PBMC markers that are upregulated in PDAC cases and can simply be measured by small-volume blood sampling. The logistic equation developed by combining PBMC-related immune markers from PDAC patients, differential diagnosis of undetermined cases of PDAC, reduced FNR of abdominal CT and CA19-9 level, and improved predictability for PDAC diagnosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePDAC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;pancreatic ductal adenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRNA-seq \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;RNA-sequencing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003escRNA-seq \u0026nbsp; \u0026nbsp; \u0026nbsp;single-cell RNA-seq\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePBMC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;peripheral blood mononuclear cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;gastrointestinal\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEUS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;endoscopic ultrasound\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;computed tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eqPCR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;quantitative PCR\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emRNA-seq \u0026nbsp; \u0026nbsp; \u0026nbsp;mRNA sequencing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Whole Transcript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRMA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;robust multi-average\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;differentially expressed gene\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;positive predictive value\u003c/p\u003e\n\u003cp\u003eNPV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;negative predictive value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFNR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;false-negative rate value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;area under the curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eTrial registration: Clinical Research Information Service, KCT0004614.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRegistered 08 January 2020 - Prospectively registered,\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis study was conducted on samples that passed QC among the samples collected and stored after receiving approval from the institutional IRB on November 16, 2018. Clinical Trial Registration was done on January 8, 2020 for research registration to use stored samples and collect validation cohort samples. The period of sample collection for additional validation was after January 8, 2020, and the first sample was collected on January 12, 2020.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ehttps://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E\u0026amp;focus=reset_12\u0026amp;search_page=L\u0026amp;pageSize=10\u0026amp;page=undefined\u0026amp;seq=15613\u0026amp;status=5\u0026amp;seq_group=15613\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/em\u003e: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Gangnam Severance Hospital (IRB No. 3-2018-0293, 3-2020-0238).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/em\u003e: Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/em\u003e: The data presented in this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/em\u003e: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/em\u003e: This work was supported by the Technology Development Program (S3049730 and S3301290) funded by the Ministry of SMEs and Startups (MSS, Korea)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAuthors\u0026apos; contributions\u003c/u\u003e\u003c/em\u003e: Conceptualization, H.-K.L., E.-J.C., J.-K.K. and D.-K.L.; Methodology, S.-Y.K., I.-Y.H. and S.K. ; Validation, S.-I.J. and J.-H.C.; Formal Analysis, H.-S.L., J.-Y.Y. and S.K.; Investigation, S.-I.J., J.-H.C. and D.-K.L.; Resources, S.-Y.K. and I.-Y.H..; Data Curation, S.-I.J., J.-H.C. and J.-K.K.; Writing\u0026mdash;Original Draft, S.-I.J. and H.-K.L.; Writing\u0026mdash;Review \u0026amp; Editing, S.-I.J., H.-K.L., E.-J.C, J-K.K. and D.-K.L.; Supervision, H.-K.L. and D.-K.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAuthors details\u003c/u\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eDepartment of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea ; [email protected] (S.-I.J.) ; [email protected] (D.-K.L.) ; [email protected] (J.-H.C.); [email protected] (I.-Y.H.)\u003c/li\u003e\n \u003cli\u003eInstitute of Vision Research, Department of Ophthalmology, Yonsei University College of Medicine, Seoul, Korea; [email protected] (H.-K.L.) ; [email protected] (S.-Y.K.)\u003c/li\u003e\n \u003cli\u003eDepartment of Biomedical Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; [email protected] (E.-J. C.) \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDepartment of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, Korea; [email protected] (J.-K.K.), [email protected] (S.K)\u003c/li\u003e\n \u003cli\u003eBiostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea; [email protected] (H.-S.L.) ; [email protected] (J.-Y.Y)\u003c/li\u003e\n \u003cli\u003eAccurasysBio. Co., Ltd., Seoul, Korea; [email protected] (H.-K.L.) ; [email protected](S.-Y.K.); [email protected] (I.-Y.H.)\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2020. \u003cem\u003eCA: a cancer journal for clinicians\u003c/em\u003e 2020;70:7-30.\u003c/li\u003e\n\u003cli\u003eRahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. 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Japan Pancreatic Cancer Registry; 30th year anniversary: Japan Pancreas Society. \u003cem\u003ePancreas\u003c/em\u003e 2012;41:985-992.\u003c/li\u003e\n\u003cli\u003eHiam-Galvez KJ, Allen BM, Spitzer MH. Systemic immunity in cancer. \u003cem\u003eNature reviews Cancer\u003c/em\u003e 2021;21:345-359.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pancreatic ductal adenocarcinoma, interleukin-7 receptor, phospholipase D4, inhibitor of DNA binding 3, cytokine","lastPublishedDoi":"10.21203/rs.3.rs-2663433/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2663433/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\n\u003cp\u003eAlthough pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive form of cancer, there are no validated biomarkers for its diagnosis yet. This study aimed to investigate a PDAC-specific peripheral blood biomarker panel and validate its clinical performance using two cohorts.\u003c/p\u003e\n\u003ch2\u003eMethods\u003c/h2\u003e\n\u003cp\u003eThis prospective, blinded, case-control study included two cohorts. A biomarker panel formula was created using a development cohort and applied to a validation cohort to verify the diagnostic performance of the biomarker panel. The development cohort included healthy controls; patients with a high risk of PDAC; and patients with benign pancreatic disease, PDAC, or other gastrointestinal malignancies. The inclusion criteria for the validation cohort were patients with at least one lesion that could be suspected as PDAC on computed tomography (CT).\u003c/p\u003e\n\u003ch2\u003eResults\u003c/h2\u003e\n\u003cp\u003eFrom bulk and single-cell RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from patients with PDAC, three novel immune cell markers, IL-7R, PLD4, and ID3, were selected as specific markers for PDAC. Regarding diagnostic performance of the regression formula for the three biomarker panels, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 84.0%, 78.8%, 47.2%, 95.6%, and 79.8%, respectively. Based on the formula scores for the biomarker panel, the false-negative rate (FNR) of biomarkers was 8% (95% confidence interval [CI]: 3.0–13.0), which was significantly lower than that of CT (29.2%, 95% CI: 20.8–37.6) in the validation cohort.\u003c/p\u003e\n\u003ch2\u003eConclusions\u003c/h2\u003e\n\u003cp\u003eThe regression formula constructed using three PBMC biomarkers is a cheap, fast, and convenient method that shows clinically usable performance for the diagnosis of PDAC. In particular, it aids in the diagnosis and differential diagnosis of PDAC from pancreatic disease by lowering the FNR of CT.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTrial registration: Clinical Research Information Service, KCT0004614.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRegistered 08 January 2020 - Prospectively registered,\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Improved predictability of pancreatic ductal adenocarcinoma diagnosis using a blood immune cell biomarker panel developed from bulk mRNA sequencing and single-cell RNA- sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-14 14:37:01","doi":"10.21203/rs.3.rs-2663433/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-03-24T05:42:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-03-09T10:27:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"01f8f774-b940-47c0-95fc-2338a243a992","date":"2023-03-08T04:31:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-03-08T04:27:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-03-07T15:28:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-03-07T15:28:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2023-03-07T04:34:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"652f79e2-f924-437e-996f-56d3fcfe6f58","owner":[],"postedDate":"March 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:05:13+00:00","versionOfRecord":{"articleIdentity":"rs-2663433","link":"https://doi.org/10.1007/s00262-023-03458-8","journal":{"identity":"cancer-immunology-immunotherapy","isVorOnly":false,"title":"Cancer Immunology, Immunotherapy"},"publishedOn":"2023-05-10 20:48:10","publishedOnDateReadable":"May 10th, 2023"},"versionCreatedAt":"2023-03-14 14:37:01","video":"","vorDoi":"10.1007/s00262-023-03458-8","vorDoiUrl":"https://doi.org/10.1007/s00262-023-03458-8","workflowStages":[]},"version":"v1","identity":"rs-2663433","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2663433","identity":"rs-2663433","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}