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Vellan, C. Proteomics in Pancreatic Ductal Adenocarcinoma. Encyclopedia. Available online: https://encyclopedia.pub/entry/19999 (accessed on 18 December 2025).
Vellan C. Proteomics in Pancreatic Ductal Adenocarcinoma. Encyclopedia. Available at: https://encyclopedia.pub/entry/19999. Accessed December 18, 2025.
Vellan, Christina. "Proteomics in Pancreatic Ductal Adenocarcinoma" Encyclopedia, https://encyclopedia.pub/entry/19999 (accessed December 18, 2025).
Vellan, C. (2022, February 28). Proteomics in Pancreatic Ductal Adenocarcinoma. In Encyclopedia. https://encyclopedia.pub/entry/19999
Vellan, Christina. "Proteomics in Pancreatic Ductal Adenocarcinoma." Encyclopedia. Web. 28 February, 2022.
Proteomics in Pancreatic Ductal Adenocarcinoma
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This entry is adapted from the peer-reviewed paper 10.3390/ijms23042093

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor prognosis, as the clinical symptoms of this disease are only presented at an advanced stage. At a global level, the incidence of PDAC is expected to continue increasing as observed by the trend in the past consecutive years. On the other hand, the available US Food and Drug Administration-approved biomarker for PDAC, CA 19-9, is not reliable for diagnostic purposes but is rather useful for monitoring treatment response among PDAC patients. Nevertheless, there is an urgent need to identify reliable biomarkers for both diagnosis (specifically for the early detection) and ascertain prognosis, as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies have grown exponentially at an accelerated rate for a wide range of applications in cancer research. Interestingly, myriad of research mainly focused on the identification of potential biomarkers for the use of early detection and/or diagnosis of PDAC. Nonetheless, it is unfortunate that several other studies too have concurrently reported that these ‘identified potential biomarkers’ either as lacking in specificity and/or has prognostic values, instead. Likewise, studies conducted on biomarkers to ascertain the prognosis of PDAC, as well as to monitor treatment response and predict tumour recurrence in PDAC had also evidently shown conflicting results. In view of this, the identification and/or implementation of protein-based biomarkers with improved specificity and sensitivity for clinical utility for PDAC remains much to be desired. On the bright side though, the integration of multi-omics techniques, as well as further research on other novel technologies such as nanoparticle-enabled blood test and artificial intelligence), is hoped to lead to the discovery of superior biomarkers for PDAC that could be implemented into clinical practice in the near future.

pancreatic ductal adenocarcinoma biomarkers CA 19-9 proteomics diagnosis prognosis monitoring treatment response tumour recurrence

1. Introduction

Pancreatic cancer (PanC) is an aggressive malignancy of the digestive and endocrine system that develops in the head of the pancreas most commonly, as well as in the tail and body of the organ [1]. The majority of PanC arises from exocrine glands of the pancreas, in which pancreatic ductal adenocarcinoma (PDAC) is the most common type, while the less common pancreatic tumours are of the endocrine type (e.g., pancreatic neuroendocrine tumour (PNET) [2] (Figure 1)).
Ijms 23 02093 g001 550
Figure 1. Classification of pancreatic cancer.
The incidence of PanC has increased worldwide in recent decades and is expected to continually rise [3][4][5][6][7]. Moreover, PanC is the seventh leading cause of mortality by cancer worldwide [8]. Globally, the incidence and mortality rate of PanC is observed at 4.9% and 4.5%, respectively [9]. In the Malaysian context, the incidence and mortality rate of PanC is rather low (2.2% and 3.6%, respectively) as compared to other Asian countries such as Singapore (3.4% and 6.2%, respectively) and Japan (4.3% and 9.6%, respectively) as well as worldwide [9]. Regardless, its infamous reference as a silent killer steadfastly remains with its aggressive clinical symptoms only being presented at an advanced stage, thus posing a great challenge for intervention at the early stage of disease [10]. Further, unlike cancers of the colorectum [11], breast [12] and lung [13] which was reported to have a reduced mortality rate due to the advancement in their treatment modalities, limited options available for the treatment of patients with advanced PanC confer only a minimal effect to patients’ overall survival [14]. Given this, a myriad of research is being conducted worldwide to develop effective biomarkers for PanC that aids in the early detection of the disease as well as to evaluate prognosis and monitor treatment response of PanC patients, which in turn could make room for unrestricted treatment and management options, in addition to improvement of its current dismal 5-year survival rate [15][16][17][18].
The term ‘biomarker’ refers to the characteristics or signs that are measurable and as such could indicate the normal or pathogenic biological processes or responses to treatment [19]. Along this line, a diagnostic biomarker detects and confirms the presence of disease while a prognostic/predictive biomarker identifies the outcomes of disease and/or tumour recurrence. On the other hand, treatment response biomarkers assess whether a particular treatment is beneficial to an individual.
There are numerous strategies exploited for the exploration and/or identification of biomarkers for PanC [20]. There are those that are based on various DNA, mRNA, microRNA (miR), small nuclear RNA, long noncoding RNA, proteins, circulatory tumour cells, metabolites, and carbohydrates (glycans) (Table 1) using different types of biological sample matrices. The development of cancer is a multistep process including various alterations of protein structures, functions, interactions, and expressions [21]. In light of this, proteomics has emerged as one of the popular methodologies for PanC research [22].
Table 1. Selected recently identified biomarkers for PanC.
Target * Name Clinical Utility References
DNAs K-ras mutation Diagnosis [23]
Methylated ADAMTS1 and BNC1 Early diagnosis [24]
TP53 mutation Prognosis [25]
Mutations of BRCA2, EGFR, ERBB2 and KDR Monitoring treatment response [26]
Peritoneal lavage tumour DNA Prognosis/Monitoring tumour recurrence [27]
mRNAs WASF2 mRNA Early diagnosis [28]
EVL mRNA Prognosis [29]
FAM64A mRNA Prognosis [30]
MicroRNAs (miR) [31] ** miR-181c
miR-210		 Diagnosis [32]
miR-10b
miR-155
miR-216		 Prognosis [33]
miR-196a Prognosis [34]
miR-21 Diagnosis/Prognosis/Monitoring treatment response [32][35][36]
miR-155 Monitoring treatment response [37]
miR-142-5p
miR-506
miR-509-5p
miR-1243		 Monitoring treatment response [36]
miR-451a Prognosis/Monitoring tumour recurrence [38]
Long noncoding RNAs SNHG15 Early diagnosis [39]
HOTAIR
MALAT-1		 Prognosis [40]
LINC00460 Prognosis [41]
PVT1 Monitoring treatment response [42]
Circulating tumour cells   Diagnosis [43]
  Prognosis [44]
Vimentin (surface marker) Monitoring treatment response [45]
  Monitoring tumour recurrence [46]
Metabolites Panel of acetylspermidine, diacetylspermine, indole-derivative and two lysophosphatidylcholines Early diagnosis [47]
Polyamines Diagnosis [48]
Ethanolamine Prognosis [49]
Lactic acid
L-Pyroglutamic acid		 Monitoring treatment response [50]
Carbohydrates (glycan) Alpha-2,6-linked sialylation and fucosylation of tri- and tetra-antennary N-glycans Diagnosis [51]
N-glycan branching: alpha-1,6-mannosylglycoprotein 6-beta-N-acetylglucosaminyltransferase A Early diagnosis [52]
β1,3-N-acetylglucosaminyltransferase 6 Prognosis [53]
Hyaluronan Monitoring treatment response [54]

ADAMTS1—A disintegrin and metalloproteinase with thrombospondin motifs 1; BNC1—zinc finger protein basonuclin-1; BRCA2—Breast cancer susceptibility gene-2; EGFR—Epidermal growth factor receptor; ERBB2—Erb-b2 receptor tyrosine kinase 2; EVL—Ena/VASP-like; FAM64—Family with sequence similarity 64 member A; HOTAIR—HOX transcript antisense RNA; KDR—Kinase insert domain receptor; KRAS—Kirsten rat sarcoma viral oncogene homolog; LINC00460—Long intergenic non-protein coding RNA 460; LDLRAD3—Low density lipoprotein receptor class A domain containing 3; MALAT-1—Metastasis associated lung adenocarcinoma transcript 1; PVT1—Plasmacytoma variant translocation 1; RNU2-1—RNA U2 small nuclear 1; SNHG15—Small nucleolar RNA host gene 15; WASF-2—Wiskott–Aldrich syndrome protein family member 2. * Recently identified protein-based biomarkers for PDAC will be discussed in the subsequent section of this review. ** This has been previously extensively reviewed by Tesfaye et al. (2019).

1.1. PDAC: Risk Factors, Diagnosis, Staging and Treatment

PDAC typically appears as poorly defined masses with extensive fibrosis surrounding the tumour tissues known as desmoplasia. In PDAC, desmoplasia promotes the growth of the tumour and metastatic spread as well as inhibition of drug penetration and uptake [55]. The presence of extensive desmoplastic stroma consisting of pancreatic stellate cells, proliferating fibroblasts, inflammatory cells and macrophages, marrow-derived stem cells and various other growth factors (e.g., epidermal growth factor, fibroblast growth factor, transforming growth factor-β), all of which are suitable for nourishing and facilitating invasive behaviour of the cancer cells, making it a distinguishing feature for PDAC [56].
The development of PDAC is not de novo. It could originate from three types of precursor lesions which include mucinous cystic neoplasm, intraductal papillary mucinous neoplasm and pancreatic intraepithelial neoplasia (PanIN), with higher predominance [57]. Individuals with chronic pancreatitis, diabetes, a family history of PDAC, genetic disorders (e.g., Lynch syndrome and Peutz–Jeghers syndrome) as well as poor lifestyle habits are generally at higher risk of developing this disease [8].
At present, the gold standard for the diagnosis of PDAC is an endoscopic ultrasound (EUS)-guided fine-needle aspiration biopsy (FNAB) [58] followed by histopathological examination (HPE) of the tumour tissue for microscopic characterisation and assessment [59]. Further, EUS imaging supports the diagnosis of PDAC based on the visualization of tumour size (>2 cm), vascularity of the tumour mass (irregular arterial and/or absence of venous vasculature), irregular dilation of the pancreatic ducts, absence of cysts within the tumour mass and presence of lymphadenopathy [58]. Other imaging modalities used in the diagnosis of PDAC include non-invasive imaging such as magnetic resonance imaging (MRI) and multidetector computed tomography (CT) [60], and minimally invasive imaging such as endoscopic retrograde cholangiopancreatography [61].
Despite its wide use in clinics, these imaging techniques are not without limitations [62]. For example, EUS is not effective in differentiating malignant lesions from those of inflammatory masses, thus complicating treatment decisions particularly among the latter conditions [63]. Further, FNAB is generally not recommended for body and tail tumours of the pancreas (PDAC) during EUS procedures due to needle tract seeding, thus posing a theoretical risk of spread through the biopsy needle [64]. CT and MRI imaging, on the other hand, can easily overlook smaller lesions among patients with early-stage PDAC [65] as well as occult and unsuspected tumour metastasis. In the latter case though, the staging laparoscopy is used to supplement the limitations of non-invasive imaging techniques [66].
The designation of PDAC grading and staging are determined based on American Joint Committee Cancer (AJCC) Staging Manual [67][68] that take both the histopathological grading (G) and TNM scores into consideration [69][70]. The histopathological gradings (G1 to G3) are assigned based on the levels of glandular differentiation and pattern of tumour growth in the neoplastic pancreatic stroma on haematoxylin and eosin-stained tissue sections [69]. On the other hand, the TNM system is an expression of the anatomic extent of the primary tumour (T), presence or absence of regional lymph node metastasis (N), and the presence or absence of distant metastasis (M) while the numerical subsets of the TNM components (T0, T1, T2… M1), indicate the progressive extent of the malignancy.
Pancreaticoduodenectomy or best known as the Whipple procedure remains the standard surgical treatment of care [71], depending on the location of the PDAC. Unfortunately, since most PDAC cases are clinically presented at a very advanced stage, only less than 20% of patients qualify for surgical resection [72]. Nevertheless, the 5-year survival rate of patients with PDAC who had undergone successful surgical resection was reported to range between 15% and 40% and despite advances in surgical techniques, the rate of tumour recurrence remains as high as 80% [73]. On the other hand, patients diagnosed with locally advanced unresectable PDAC [72] are otherwise subjected to palliative therapy/care (e.g., chemoradiotherapy or stereotactic body radiotherapy) and other appropriate disease management practice [74].
Since 1997, gemcitabine, a drug that interferes with DNA synthesis, has been approved by the US Food and Drug Administration (FDA) and accepted as the first-line therapy for the alleviation of PanC symptoms in addition to exhibiting moderate improvement in patient survival rates [75]. Given its favourable effect, combination drug therapies consisting of gemcitabine and other cytotoxic agents such as paclitaxel and docetaxel are now under clinical trials for PDAC [76][77]. Aside from this, FOLFIRINOX, a multidrug containing fluorouracil, leucovorin, irinotecan, and oxaliplatin which was observed to improve the overall survival of PDAC patients [78] is also under clinical trial but intended for the treatment of metastasised PDAC [79]. Although both surgery and drug therapy persist as the standard treatment for PDAC, identification of novel biomarker(s) remains a necessity particularly in assessing the suitability of the selected treatment modalities, thus paving the way for personalised medicine and care of patients.

1.2. FDA-Approved Biomarkers for PDAC

The FDA plays an important role in the medical sector in which standards are established for the implementation of biomarkers into clinical practice [80]. These biomarkers could either function as routine diagnostic tests, for evaluation of prognosis or to monitor treatment response and tumour recurrence in patients. In 1981, researchers discovered the over-production of cancer antigen 19-9 (CA 19-9) in patients with PDAC, apart from those with colon carcinoma [81], which then later, was extensively studied for its potential use in the management of PDAC [82]. Currently, CA 19-9 is the most routinely and widely applied biomarker for PDAC that has been approved by the FDA [83][84]. The standard clinical threshold levels of CA 19-9 is at 37 U/mL [82] and patients with increased levels are at high risk of developing PDAC [81]. However, the utility of CA 19-9 in PDAC remains obscure owing to various interpretations of its applications in PDAC in the literature [81]. Although commonly used as a diagnostic marker [82], particularly in combination with imaging modalities such as MRI [85], the clinical utility of CA 19-9 better serves to provide information on prognosis and overall survival [82], monitor treatment responses [86], predict post-operative recurrence and prognosis [87], as well as to predict tumour stages and respectability in PDAC patients [88]. Nevertheless, elevated CA 19-9 levels can also be caused by biliary obstruction, endocrinal, gynaecological, hepatic, pulmonary, and spleen diseases [89] as well as other malignancies (e.g., colon, stomach, lung) [90][91]. Furthermore, individuals with Lewis antigen-negative phenotype (lack of Lewis glycosyltransferase) do not express CA 19-9 [92], thus undermining the use of CA19-9 as a diagnostic biomarker for PDAC in this cohort.
Another FDA-approved tumour marker that has been reported in various studies in the context of PDAC is the use of carcinoembryonic antigen (CEA). The increased production of CEA was initially detected, and subsequently implemented for use as biomarker for colorectal cancer in the 1960s [93]. However, it was later discovered that CEA levels were also elevated (>5 ng/mL) [94] in approximately 30–60% of patients with PanC [95] and significantly associated with poor prognosis and worse overall survival [96]. However, CEA alone is deemed unsuitable for screening for PDAC due to its lower diagnostic accuracy [97], but as a vital supplement to CA19-9 [82][95]. On a brighter note though, CEA can be potentially used as a diagnostic marker for Lewis antigen-negative individuals instead [95].

2. Proteomics-Based PDAC Research: Techniques, Samples, and Samples Processing

The advancements in proteomic technology in recent years has enabled an in-depth exploration at cellular and molecular levels for a better understanding of complex diseases such as cancer [98]. The wide range of proteomics tools and technologies allow systematic, comprehensive and/or targeted analyses of structure, function, expression, interactions, and modifications of proteins [99]. Recently, non-gel-based separation and detection proteomics techniques, particularly hyphenated technology, which are typically represented by an online combination of a separation technique and one or more spectroscopic detection techniques has fast gained popularity [100]. Amongst the hyphenated technologies, liquid chromatography tandem mass spectrometry (LC-MS/MS) has emerged as the most preferred methodology [101] for proteomics-based (cancer) research [102][103][104].
Further, quantitative proteomics has recently developed in its ability to generate reasonably accurate quantitation of the expression of proteins, especially in cancer research [105]. The coupling of mass spectrometry that uses ionisation techniques, paired with mass analysers with quantitative labelling strategies was reported to improve the detection and quantification of proteins [106]. Moreover, MS-based targeted proteomics methods such as multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) are now rising as promising tools for the validation of identified proteins in biomedical applications [107].
Proteomics technologies were likewise widely applied in the investigation of PDAC in pursuit of the identification of potential PDAC-associated proteins intended for diagnosis and staging, assessment of tumour resectability, prognosis and predicting responses to treatment in patients [108]. For this, various clinical specimens including pancreatic juice [109], tumour tissues [110] and cyst fluid [111], plasma or serum [112] and urine [113] have been previously extensively used.
In addition to this, PanC cell lines are a useful source and/or model for biomarker investigations. This is because cell lines can be easily obtained and allow the analysis of secreted proteins by means of culturing and harvesting the cells [92]. Different PanC cell lines (e.g., Capan-2, PANC-1) exhibit distinct phenotypes and genotypes that represent the different subclasses of PDAC (e.g., classical, quasi-mesenchymal, respectively) which when their proteins are profiled, may provide insights on the differential expressions of specific proteins associated with tumour growth [114], metastasis [115] as well as response to therapy [116]. For example, by comparing a primary cell line, BxPC-3, with a metastatic cell line, AsPC-1, researchers were able to detect the differentially expressed proteins involved in the metastasis of PDAC that may serve as potential biomarker(s), once validated [117].
Aside from this, the stromal compartment, which constitutes 80–85% of the tumour [118], consisting of the extracellular matrix [119] infiltrated by cancer-associated fibroblasts (CAF) [120], inflammatory cells [121], and immune cells (e.g., lymphocytes and macrophages) [122] is another choice of sample for biomarker investigations. Researchers were able to identify differentially regulated proteins in the stroma of PDAC by co-culturing the PanC cell lines together with the components of stroma such as CAF [123]. For example, the differentially regulated proteins expressed in the stromal compartment such as hemopexin were previously reported to regulate the progression of tumours, thus revealing association with lymph-node metastasis resulting in poor prognosis in patients [124]. Similarly in another study, Tao et al. reported that PDAC has an extensive stroma and abundant extracellular matrix that lacks vascularisation, resulting in hypoxia within the pancreatic tumour environment, thus revealing candidate biomarkers that may be useful in monitoring the treatment response in PDAC patients [125].
On the other hand, exosomes are extracellular vesicles produced by cells that are involved in cell communication [126]. The isolation and identification of cancer-specific proteins derived from exosomes may be developed into potential candidate biomarkers since the altered protein profiles of exosomes correlate with the pathogenesis of cancer [127]. For example, Melo et al. reported glypican-1 (GPC1), a cell surface proteoglycan that is specifically enriched in exosomes derived from cancer cells in PDAC patients by using MS techniques [128]. According to their research, GPC1 may serve as a potential biomarker for early stage PDAC. This shows that the analysis of exosomes can also be a useful target for potential clinical utility.
Glycosylation, a type of post-translational modification of proteins, is a hallmark of cancer [129]. As such, targeting the aberrations occurring during glycosylation either in terms of the levels of expressions of proteins or the glycans (glycome; e.g., truncated O-glycans, increased branching and fucosylation of N-glycans, upregulation of specific proteoglycans and galectins, and increased O-GlcNAcylation [130]) may be beneficial for the identification of diagnostic and/or therapeutic targets [131] as well as for aiding in treatment decisions for PDAC [132]. Along this line, mucin, a highly glycosylated protein, could also be a potential biomarker [133]. The levels of expression of mucin were previously reported to be altered in the course of development and progression of PDAC [134]. According to Wang et al., PDAC tissues expressed high levels of MUC1, MUC4, MUC5AC, MUC5B, MUC6, MUC13 and MUC16, all of which are involved in promoting the aggressive phenotype of this disease [134]. These findings further highlight the roles of aberrant protein glycosylation in the progression of PDAC.
The most critical step in a proteomics workflow is the optimal preparation and/or processing of samples, which in turn, ascertain the degree of sensitivity for detection of proteins in downstream application [135]. The complexity of the samples due to high biological variations and/or post-translational modifications remains a major limitation in proteomics study. Furthermore, samples such as serum and/or plasma contain significant amounts of proteins of higher abundance (e.g., albumin, immunoglobulins) that are involved in various yet important biological and physiological functionality, hence may not be useful as potential markers [136]. In turn, due to their predominant presence, these proteins are infamously known for masking away other proteins, particularly low molecular weight proteins that are present in lower abundance, but may have biomarker potential (e.g., small secreted proteins or peptide hormones) [137]. The complexity of serum or plasma is usually reduced via depletion of high-abundance proteins [138] or using sub-proteome-specific enrichment method [139]. While depletion workflows help to improve the detectability of low-abundance proteins, enrichment methods are focused on increasing the sensitivity of targeted sub-proteomes for detection [140]. On this note, Hashim et al. reviewed the use of lectins to enrich glycosylated proteins which are differentially expressed in cancer, as they might not be detected using conventional methods due to their low abundance [139]. Apart from this, immunoprecipitation is another method commonly used to enrich target proteins by using antibodies that bind to specific antigens resulting in immune complexes that are captured on solid phase support such as chromatography resin and magnetic beads [141].

3. Biomarker Investigations

An ideal biomarker should be able to reliably characterise a particular disease status and/or outcome, hence, shall be disease and/or condition specific (100% specificity) and highly sensitive (100% sensitivity) (e.g., everyone with and without cancer shall test positive and negative, respectively, for the biomarker) [142]. On the same note, a reliable biomarker should also meet other criteria including being robust and well-validated thus allowing effective use and implementation in clinical routines [143]. With this, the subsequent section discusses the protein signatures identified as potential biomarkers in assorted biological matrices for various clinical applications of PDAC using proteomics technologies in recent years.

Biomarkers for Early Detection and/or Diagnosis of PDAC

Early detection and reliable diagnosis for PDAC is paramount. This is because it would not only result in the identification of eligible patients for surgical resection but consequently, improve the overall survival of patients [17]. In line with this, Guo et al. investigated concanavalin A enriched N-glycosylated proteins from the pre-and post-operative serum of patients with PDAC using offline liquid chromatography (LC) coupled to a matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-ToF-MS) [144]. Together, the investigation led to the identification of dysbindin to be significantly correlated with the size and differentiation of the tumour. The overexpression of dysbindin in pre-operative sera was thought to promote the phosphorylation of PI3/Akt signalling pathway [145], which in turn stimulates the proliferation of the cancer cells. In addition, the subsequent validation set assessed via ELISA further confirmed the high levels of specificity (73.9%) and sensitivity (82.3%) of dysbindin for PDAC, and also demonstrated improved diagnostic performance compared to CA 19-9. Interestingly, a study by Fang et al. reported an overexpression of dysbindin in patients with PDAC to correlate with the size of tumour and histological differentiation, thus suggesting its role-play in the prognostic measure of patients [145]. Likewise, another study by Zhu et al. showed that dysbindin promotes metastasis of PDAC through the activation of NF-κB/MDM2 signalling pathway, further indicating that this protein additionally assumes the role of prognostic predictor [146]. Based on these studies, it can be postulated that dysbindin plays more than one role in the pathogenesis of PDAC, hence the use of this protein for (any) clinical utility must first be carefully clarified.
In a similar source of samples and enrichment methods but using tandem mass tags (TMT) labelling and LC-MS/MS, Sogawa et al. had otherwise reported increased expression of glycosylated 4b-binding protein α-chain (C4BPA) and polymeric immunoglobulin receptor (PIGR) in the pre-operative serum of PDAC patients than in post-operative patients [147]. However, upon validation using ELISA, only the elevated levels of C4BPA which was due to the host immune responses against the tumour, remained consistently significantly high in patients with PDAC when compared to those with pancreatitis and healthy controls. To further assess the ability of C4BPA as a specific biomarker for PDAC, the researchers also compared the serum levels of C4BPA in comparison with CA 19-9 in other types of gastroenterological malignancies (e.g., biliary tract cancer). Interestingly, they found that the AUC values of C4BPA were much higher than CA 19-9, suggesting this protein could indeed be a specific biomarker for PDAC. However, studies have also shown that this protein is additionally highly expressed in other cancers such as ovarian cancer [148] and breast cancer [149].
Nevertheless, four years later, Sogawa and colleagues then applied ELISA by using lens culinaris agglutinin (LCA)-lectin that binds specifically to fucose, to measure the levels of fucosylated (Fuc-) C4BPA in the serum of pre-operative PDAC patients, chronic pancreatitis patients and healthy controls [150]. In various validation sets with different sample groups, Fuc-C4BPA was found to be upregulated in pre-operative PDAC patients. Moreover, in comparison to total C4BPA, CA 19-9 and CEA, Fuc-C4BPA showed higher AUC values for discriminating PDAC patients from other groups of subjects, thus counter-suggesting it as a potential diagnostic biomarker instead. Furthermore, the upregulation of Fuc-C4BPA is not reported in other types of cancers to date. Intriguingly, the researchers also discovered that Fuc-C4BPA was able to predict lymph node metastasis. This is particularly useful as the prediction of metastasis would aid in the treatment decision for PDAC patients [151].
Earlier, Kim et al. had reprogrammed PDAC cells into induced pluripotent stem cell-like lines to study the development of the disease by isolating epithelial cells and then inducing reprogramming factors such as Oct4, Sox2, Klf4, and c-Myc [152]. The normal-phase (NP)-LC-MS/MS analysis of the iPSC-like lines revealed 43 differentially regulated proteins that were associated with transforming growth factor-β and integrin signalling involved in the development of PDAC. Four years later, the same group of researchers focused on just three proteins namely, matrix metallopeptidase 2, matrix metallopeptidase 10 and thrombospondin-2 (THBS2) for validation using ELISA [153]. Nevertheless, the results only substantiated the previous works by Kim et al. for elevated levels of plasma THBS2 in patients with PDAC compared to patients with benign pancreatic disease and healthy controls [152]. Further, when THBS2 was assessed for accuracy in combination with CA 19-9, together they yielded a specificity of 98% and sensitivity of 87% for the diagnosis of PDAC. However, in recent research, Le Large et al. found that the plasma THBS2 levels of PDAC and distal cholangiocarcinoma (dCCA) patients were significantly higher than in patients with benign pancreas diseases and healthy controls [154]. Although the clinical symptoms of PDAC and dCCA are relatively similar, these diseases have distinct entities and require specific biomarkers for discrimination [155]. In view of this, the specificity of THBS2 as a biomarker for PDAC remains to be determined.
Years ago, Nakamura et al. identified 260 genes that were upregulated in PDAC [156]. Almost two decades later, as a follow-up study, Yoneyama et al. had, in turn, explored the combinatory potential of antibody- (reverse-phase protein array (RPPA)) and LC-MS/MS-based proteomics in their quest to identify new PDAC diagnostic biomarkers, by focusing on only 130 encoded proteins having known functions and available commercial antibodies [157]. Based on the RPPA-based biomarker screening, the researchers then chose only 23 proteins for validation by MRM-MS. Of these, significantly different reciprocal levels of insulin-like growth factor-binding protein 2 and 3 (IGFBP2 and IGFBP3, respectively) were observed in the plasma of patients with early stage PDAC compared to control subjects. In agreement with this finding, Baxter had previously explained that these proteins consisting of Arg-Gly-Asp motif bind to integrins, thus resulting in the stimulation of cancer cell proliferation [158]. Moreover, it was revealed that the combinatory assessment of IGFBP2 and IGFBP3 with CA 19-9 effectively discriminates early-stage PDAC patients from healthy controls by recording an AUC value of 0.9 [157]. Contradictorily though, IGFBP2 too was previously reported to induce epithelial to mesenchymal transition, which is involved in metastasis of PDAC, suggesting its prognostic role in PDAC patients [159].

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