S100P in Duodenal Fluid Is a Useful Diagnostic Marker for Pancreatic Ductal Adenocarcinoma

Matsunaga, Taketo; Ohtsuka, Takao; Asano, Koichi; Kimura, Hideyo; Ohuchida, Kenoki; Kitada, Hidehisa; Ideno, Noboru; Mori, Yasuhisa; Shoji, Tetsuo; Oda, Yoshinao; Guha, Sushovan; Raimondo, Massimo; Nakamura, Masafumi; Tanaka, Masao

doi:10.1097/mpa.0000000000000940

Cited by 20 publications

(28 citation statements)

References 27 publications

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“…Thus, measuring the expression level of S100P in pancreatic juice might help distinguish pancreatitis from tumor disease for early screening and diagnosis of PC (85). Since S100P is a specific and sensitive marker, it is possible to detect the concentration of S100P in duodenal fluid (DF) based on upper gastrointestinal endoscopy (GIE) or endoscopic ultrasonography, which may be helpful for early PDAC screening (88). In addition, evaluation of S100P expression can be combined with EUS-FNA quantitative analysis to improve the accuracy of diagnosis of PC and nontumor lesions (89)(90)(91) or be applied in fine-needle aspiration biopsy (FNAB) specimens prepared in cell blocks and smears to test for PDAC (92,93).…”

Section: S100p: An Indicator Of Early Pc Occurrencementioning

confidence: 99%

S100 Proteins in Pancreatic Cancer: Current Knowledge and Future Perspectives

Zhou

Guo

et al. 2021

Front. Oncol.

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Pancreatic cancer (PC) is a highly malignant tumor occurring in the digestive system. Currently, there is a lack of specific and effective interventions for PC; thus, further exploration regarding the pathogenesis of this malignancy is warranted. The S100 protein family, a collection of calcium-binding proteins expressed only in vertebrates, comprises 25 members with high sequence and structural similarity. Dysregulated expression of S100 proteins is a biomarker of cancer progression and prognosis. Functionally, these proteins are associated with the regulation of multiple cellular processes, including proliferation, apoptosis, growth, differentiation, enzyme activation, migration/invasion, Ca2+ homeostasis, and energy metabolism. This review highlights the significance of the S100 family in the diagnosis and prognosis of PC and its vital functions in tumor cell metastasis, invasion and proliferation. A further understanding of S100 proteins will provide potential therapeutic targets for preventing or treating PC.

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Section: S100p: An Indicator Of Early Pc Occurrencementioning

confidence: 99%

S100 Proteins in Pancreatic Cancer: Current Knowledge and Future Perspectives

Zhou

Guo

et al. 2021

Front. Oncol.

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“…We showed that S100P was one of the three proteins detected with high expression based on IHC in HPA (6 of 9 samples). S100P was studied to discriminated normal and PDAC samples using a higher concentration in duodenal fluid in patients with PDAC compared with the control group, presenting an AUC of 0.71 for detecting PDAC [73]. Our results showed an AUC of 0.92 for S100P using mRNA expression (Additional file 4: Figure S2).…”

Section: Discussionmentioning

confidence: 77%

PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression

2020

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Background: Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment. Methods: We performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples. Results: This microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1. Conclusion: The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.

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“…Cystatin 6 (CST6) is overexpressed in pancreatic ductal adenocarcinoma (PDAC) cells and can stimulate PDAC cell growth by reducing the activity of intracellular cathepsin B ( Hosokawa et al, 2008 ). There is evidence that S100 calcium-binding protein P (S100P) can be used as a biomarker in duodenal fluid and fine needle aspiration biopsies for detection of PDAC, and this protein has a diagnostic sensitivity of 84.8% in biopsy samples ( Matsunaga et al, 2017 ; Aksoy-Altinboga et al, 2018 ). S100P secretes matrix metalloproteinase 9 (MMP9) and regulates the invasion of PC cells into the lymphatic endothelial monolayer, thereby promoting tumor cell invasion and metastasis ( Dakhel et al, 2014 ; Nakayama et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer

Zhou

Meng

et al. 2020

Front. Mol. Biosci.

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It is currently difficult for pathologists to diagnose pancreatic cancer (PC) using biopsy specimens because samples may have been from an incorrect site or contain an insufficient amount of tissue. Thus, there is a need to develop a platform-independent molecular classifier that accurately distinguishes benign pancreatic lesions from PC. Here, we developed a robust qualitative messenger RNA signature based on within-sample relative expression orderings (REOs) of genes to discriminate both PC tissues and cancer-adjacent normal tissues from non-PC pancreatitis and healthy pancreatic tissues. A signature comprising 12 gene pairs and 17 genes was built in the training datasets and validated in microarray and RNA-sequencing datasets from biopsy samples and surgically resected samples. Analysis of 1,007 PC tissues and 257 non-tumor samples from nine databases indicated that the geometric mean of sensitivity and specificity was 96.7%, and the area under receiver operating characteristic curve was 0.978 (95% confidence interval, 0.947–0.994). For 20 specimens obtained from endoscopic biopsy, the signature had a diagnostic accuracy of 100%. The REO-based signature described here can aid in the molecular diagnosis of PC and may facilitate objective differentiation between benign and malignant pancreatic lesions.

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S100P in Duodenal Fluid Is a Useful Diagnostic Marker for Pancreatic Ductal Adenocarcinoma

Abstract: Analysis of S100P concentration in DF, in combination with routine screening upper GIE, may facilitate the detection of PDAC.

Cited by 20 publications

References 27 publications

S100 Proteins in Pancreatic Cancer: Current Knowledge and Future Perspectives

S100 Proteins in Pancreatic Cancer: Current Knowledge and Future Perspectives

PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression

Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer

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