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. 2023 Jun 21;109(10):2934–2940. doi: 10.1097/JS9.0000000000000558

Inflammatory biomarkers and nanotechnology: new insights in pancreatic cancer early detection

Damiano Caputo a,b,*, Erica Quagliarini c, Alessandro Coppola d, Vincenzo La Vaccara b, Benedetta Marmiroli e, Barbara Sartori e, Giulio Caracciolo c, Daniela Pozzi c,*
PMCID: PMC10583897  PMID: 37352522

Abstract

Background:

Poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly due to the lack of effective early-stage detection strategies. Even though the link between inflammation and PDAC has been demonstrated and inflammatory biomarkers proved their efficacy in predicting several tumours, to date they have a role only in assessing PDAC prognosis. Recently, the studies of interactions between nanosystems and easily collectable biological fluids, alone or coupled with standard laboratory tests, have proven useful in facilitating PDAC diagnosis. Notably, tests based on magnetic levitation (MagLev) of biocoronated nanosystems have demonstrated high diagnostic accuracy in compliance with the criteria stated by WHO. Herein, the author developed a synergistic analysis that combines a user-friendly MagLev-based approach and common inflammatory biomarkers for discriminating PDAC subjects from healthy ones.

Materials and Methods:

Plasma samples from 24 PDAC subjects and 22 non-oncological patients have been collected and let to interact with graphene oxide nanosheets.

Biomolecular corona formed around graphene oxide nanosheets have been immersed in a Maglev platform to study the levitation profiles.

Inflammatory biomarkers such as neutrophil-to-lymphocyte ratio (NLR), derived-NLR (dNLR), and platelet to lymphocyte ratio have been calculated and combined with results obtained by the MagLev platform.

Results:

MagLev profiles resulted significantly different between non-oncological patients and PDAC and allowed to identify a MagLev fingerprint for PDAC. Four inflammatory markers were significantly higher in PDAC subjects: neutrophils (P=0.04), NLR (P=4.7 ×10−6), dNLR (P=2.7 ×10−5), and platelet to lymphocyte ratio (P=0.002). Lymphocytes were appreciably lower in PDACs (P=2.6 ×10−6).

Combining the MagLev fingerprint with dNLR and NLR returned global discrimination accuracy for PDAC of 95.7% and 91.3%, respectively.

Conclusions:

The multiplexed approach discriminated PDAC patients from healthy volunteers in up to 95% of cases. If further confirmed in larger-cohort studies, this approach may be used for PDAC detection.

Keywords: pancreatic cancer, pancreatic ductal adenocarcinoma, nanotechnology, early detection, inflammatory biomarkers, magnetic levitation

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Introduction

Highlights

  • Pancreatic ductal adenocarcinoma early detection strategies are urgently needed.

  • Inflammatory biomarkers proved their efficacy in several cancers but their utility in pancreatic ductal adenocarcinoma (PDAC) diagnosis needs deeper investigations.

  • tests based on magnetic levitation of nanosystems have demonstrated high diagnostic accuracy for PDAC

  • A multiplexed strategy based on the combination of inflammatory biomarkers and magnetic levitation technology discriminated PDAC from non-oncological subjects in up to 95% of the cases fulfilling the REASSURED WHO criteria.

Pancreatic ductal adenocarcinoma (PDAC) is a very lethal disease. The peculiar biological behaviour of this tumour, the lack of reliable biomarkers for its early detection, and the inability to arrange screening tools to be used on a large scale, account for the poor prognosis of PDAC.1,2

Even if better results in the fight against this dreadful tumour in its more advanced stages have been achieved with multimodal treatment strategies that combine surgery, chemotherapy, and radiotherapy,3 a turning point in the optimization of PDAC prognosis can be reached with the development of strategies able to detect the tumour in its early stage.4 Ca 19.9, recognized as the main oncological marker of pancreatic cancer, although has recently been shown to be able to predict unfavourable prognostic conditions such as lymph node involvement,5 is still not recommended by the Food and Drug Administration (FDA) for diagnostic use in pancreatic adenocarcinoma management. In the recent past, many efforts have been done looking for reliable biomarkers for PDAC early diagnosis. However, despite being promising, most of them failed to meet many of the criteria that the WHO requires for their use in clinical practice,6 as those of cheapness and reproducibility.4 To fill this gap, our research team has developed tests based on the use of nanoparticles (NPs) that meet the WHO requisites and are useful in distinguishing between blood samples derived from oncological and healthy subjects.7,8 Nonetheless, tests based on the use of NPs proved their efficacy also in distinguishing between different stages of PDAC.9 A typical nanoparticle-enabled blood test is based on direct characterization of the shell of biomolecules that forms around NPs when immersed in biological media (e.g. plasma).10,11 This shell, usually referred to as protein corona (PC), is personalized for each individual.12 Thus, it appears clear that the characterization of personalized PCs may be a powerful strategy for the detection of potential biomarkers that are typically present at low concentrations to be recognized by conventional blood tests. Currently, PC characterization methods involve protein isolation from NP surface and subsequent analysis through proteomic techniques such as sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and mass spectrometry coupled with liquid chromatography.13,14 However, the diagnostic results obtained from these techniques may suffer from the inter-operator variability caused by the numerous steps required for PC isolation.

To go behind this limitation, indirect characterization methods, that bypass the corona isolation step, emerged as effective tools for protein biomarker detection.15,16 Among these techniques, magnetic levitation (MagLev) is a powerful approach for distinguishing between healthy and oncological protein patterns.17 MagLev is a cheap, manageable, and reproducible technique that employs a high-intensity magnetic field to levitate diamagnetic objects according to their densities.18 Recent improvements in our diagnostic studies have seen in MagLev a useful tool for detecting PDAC patients with very high sensitivity and accuracy.19

The MagLev diagnostic analysis is based on a step-by-step workflow that can be summarized as follows: (i) incubation; (ii) injection; (iii) magnetic levitation; (iv) acquisition; and (v) processing (hereafter referred to as IIMAP analysis) (Fig. 1). Briefly, diamagnetic PC-NPs obtained by incubation between NPs and human plasma (HP) are injected in a cuvette containing a paramagnetic solution and subjected to the magnetic field. Thus, levitating protein patterns are followed over time by a camera, and the derived image sequencing is analyzed by dedicated software. Recently, the diagnostic ability for PDAC has been improved by combing the PC analysis with non-specific laboratory data such as haemoglobin or acute phase protein levels.20,21 This novel approach demonstrated high discrimination between PDAC and healthy subjects in up to 90% of cases, thus appropriate for PDAC diagnosis.

Figure 1.

Figure 1

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Schematic representation of the step-by-step procedure for the indirect characterization of personalized protein corona (PC) of nanoparticles (NPs) by using magnetic levitation (MagLev) technology. The workflow includes five working steps, that are (i) incubation between nanoparticles and human plasma derived from healthy and oncological subjects; (ii) injection of the obtained PC-NPs in the Maglev cuvette filled with a paramagnetic solution; (iii) magnetic levitation of the diamagnetic samples upon high intense magnetic field; (iv) acquisition of the image sequencing related to the levitating protein bands; (v) processing of the images by a dedicated software; (IIMAP analysis). NOP, non-oncological patient; PDAC, pancreatic ductal adenocarcinoma.

Taking up the same multiplexed strategy, considering the reported association between inflammation and PDAC22 and that inflammatory markers such as white blood cells (WBC), neutrophil-to-lymphocyte ratio (NLR), derived-NLR (d-NLR), platelet to lymphocyte ratio (PLR), etc., have long been considered as useful markers for early cancer detection,23,24 herein we aimed to evaluate whether the combination of IMAPP results with inflammatory markers could represent a valid tool to identify PDAC patients with good sensitivity and specificity.

Materials and methods

The case series included 24 PDAC patients and 22 non-oncological patients (NOP). The inclusion criteria for the study were age older than 18 years, adequate renal function (creatinine < 1.5 mg/dl, blood urea nitrogen < 1.5 times the upper limit), previous personal medical history negative for malignancy, renal, liver, or blood disorders, no previous chemotherapy or radiotherapy, absence of uncontrolled infections, and obtained written informed consent. All patients in the oncology group had a cyto-histological diagnosis of PDAC. Healthy controls have been identified among patients admitted to the Surgery Center of the Campus Bio-Medico University Hospital Foundation of Rome for benign surgical diseases. In PDAC group, none of the patients had recent or previous history of pancreatitis. In both the two groups, plasma samples of all the patients that met the inclusion criteria have been collected before any treatments. Data regarding medical history and clinical-instrumental work-up have been assessed for each patient; data regarding NLR, dNLR, PLR have been calculated according to what reported in25

Blood storage

Blood samples have been collected and stored as previously reported.7 The Ethical Committee of the ….. approved this study (Prot. 10/12 ComEt ….).

Graphene oxide (GO)

A water solution of GO nanosheets has been purchased by Graphenea (San Sebastian). GO was further diluted with ultrapure water to have a concentration equal to 0.25 mg/ml and sonicated for 2 min with Vibra cell sonicator VC505 (Sonics and Materials, Newton) as described elsewhere.14

Protein-coronated GO complexes

Protein-coronated GO complexes were prepared by both static (i.e. bulk mixing) and dynamic (i.e. microfluidic mixing) incubation. A recurring question in the scientific community concerns the reproducibility of experimental data obtained from different laboratories. To this end, a relevant contribution could be provided using microfluidic devices. For this reason, we decided to evaluate whether the preparation of the complexes by microfluidic mixing produced protein corona different from those produced by bulk mixing. For bulk mixing experiments 20 µl of HP derived from 22 NOP and 24 PDAC-affected subjects were added to a 50 µl GO solution. For each sample, 30 µl of distilled water was added to reach a final volume of 100 µl, needed for the next experiments. The HP-GO mixtures were incubated by bulk mixing for 1 h at 37°C to produce protein-coronated GO complexes. For the microfluidic mixing condition, a commercial cross-shaped microfluidic device was used (Fluidic 394, microfluidic ChipShop GmbH) which featured square cross-section channels of 200 µm width. The central channel length after the cross was 80 mm. The mixing was obtained through hydrodynamic focusing, a well-known technique to achieve fast mixing inserting HP in the central channel and GO in the lateral ones. The solutions were injected into the microfluidic device with three syringe pumps (Harvard Apparatus). The ratio between the lateral flow rates and the central one was kept constant at 19:1, giving a central HP stream around 10 µm wide. The total flow rate was set at 35 μl/min, thus the residence time of the solution inside the channels was 55 s.16 A suitable amount of the mixed solution was collected at the end of the microfluidic device for subsequent analysis.

One-dimensional sodium dodecyl sulphate-polyacrylamide gel electrophoresis

One hundred microlitres of GO-HP samples (see section “Protein-coronated GO complexes”) prepared both by bulk and microfluidic mixing was centrifuged at 18 620 RCF for 20 min at 4°C, then the supernatant was removed, and the pellet was washed in 200 µl of ultrapure water. To fully remove the unbound proteins, the centrifugation was repeated three times. Then, the last pellets composed of GO-PC complexes were suspended in 20 µl of Laemmli loading buffer 1×, boiled at 100°C for 10 min, and centrifuged at 18 620 RCF for 15 min at 4°C. Finally, 10 µl of supernatants containing the proteins were collected and loaded on a stain-free gradient polyacrylamide gel (4–20% TGX precast gels, Bio-Rad) and run at 150vV for about 100 min. Gel images were obtained with a ChemiDocTM imaging system (Bio-Rad) and processed by ImageLab Software and custom Matlab (MathWorks) scripts.

MagLev platform and IMAPP analysis

The MagLev platform is composed of N42-grade neodymium coaxial square permanent magnets of 2.5 cm length, 2.5 cm width, and 5.0 cm height (Magnet4less). The north poles of the two magnets face each other with a distance of 2.8 cm, to generate a highly intense magnetic field (0.5 T). A plastic cuvette of 4 ml capacity and 2.5 cm height was filled with a paramagnetic aqueous solution of Dysprosium (III) nitrate hydrate (Sigma–Aldrich, Inc. Merk KGaA) with a concentration of 50 mg/ml, as reported elsewhere.19 The MagLev system can levitate a diamagnetic object in a paramagnetic solution according to its density. After GO-HP incubation, 100 ul of diamagnetic GO-PC complexes were injected with a syringe at the bottom of the cuvette. When the sample volume reached the surface, the cuvette was inserted between the two magnets. More details can be found in the section entitled “Magnetic Levitation” and in Figure S1 in the Supplementary Materials, Supplemental Digital Content 1, http://links.lww.com/JS9/A725.

Statistical analysis

A multivariate analysis was performed to classify healthy and PDAC-affected subjects. Briefly, the starting position obtained by the IMAPP analysis performed on all the samples was coupled to the levels of the seven inflammatory markers (i.e. WBCs, NLR, d-NLR, PLR). Linear discriminant analysis (LDA) was carried out to evaluate the classification ability of the test in terms of global accuracy. Statistical data analysis was performed with Matlab (MathWorks, Version R2022a) software.

Results

The IMAPP analysis was performed on 46 HP samples derived from 22 NOPs and 24 PDAC patients. Their demographic and clinical characteristics are reported in Table S1, Supplemental Digital Content 1, http://links.lww.com/JS9/A725.

As a first step, we investigated whether PCs composition changed by varying the incubation conditions between GO and HP samples (i.e. static or dynamic). 1D SDS-PAGE was performed to compare the PCs profile of GO-HP samples produced by bulk and microfluidic mixing. In Figure S2, Supplemental Digital Content 1, http://links.lww.com/JS9/A725 we showed the overlapped intensity protein profiles derived from GO incubation with HP derived from three different subjects respectively from bulk and microfluidic incubation conditions. Since protein profiles were almost superimposable between the two conditions, we used bulk mixing samples for the subsequent experiments.

Among NOPs, 13 (54%) patients had undergone laparoscopic cholecystectomy for cholelithiasis, 7 patients (29%) had undergone inguinal hernia repair, one (4%) to left hemicolectomy for diverticular disease and one (4%) has undergone upper limb lipoma excision. Among the healthy donors, the median age was 64.5 years (interquartile range: 46–74); 13 (60%) subjects were female and 9 (40%) were male. Pancreatic cancer patients had a median age of 73 years (interquartile range: 64–79). Thirteen (54%) patients were female and 11 (46%) were male.

In the healthy group, the median value of WBC was 6 ×109/l (interquartile range: 5–7); the median value of platelets was 247 ×103/ml (208.5–284.5): the median value of NLR was 1.65 (interquartile range: 1.3–2.02); the median value of dNLR was 1.3 (interquartile range: 1.05–1.5): the median value of PLR was 100.55 (interquartile range: 85.07–134). In the PDAC group, the median value of WBCs was 7 ×109/l (interquartile range: 5–8); the median value of platelets was 221 ×103/ml (153–282); the median value of NLR was 3.1 (interquartile range: 2.2–4.5); the median value of dNLR was 2.2 (interquartile range: 1.6–2.6); the median value of PLR was 140.63 (interquartile range: 100.22–229.25).

First, 46 HP samples derived from 22 NOPs and 24 PDAC patients were incubated with GO nanosheets to generate 46 PC-GO complexes. The MagLev profiles were found to be significantly different between NOP and PDAC subjects and allowed us to identify the height reached by PC-GO samples as soon as subjected to the magnetic field as a MagLev fingerprint for PDAC (Fig. 2, panel a). In Fig. 2 we also show the seven inflammatory markers distributions (panel b-h). Four out of 7 were significantly higher in PDAC subjects, that is neutrophils (p value= 0,041), NLR (p value= 4.7 ×10−6), dNLR (p value= 2.7 ×10−5), and PLR (p value= 0.0016). On the other hand, the blood level of lymphocytes was appreciably lower in PDAC patients (p value= 2.6 ×10−6). No substantial change was found for WBCs and Platelets.

Figure 2.

Figure 2

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Distributions of the Maglev starting position (A), WBCs (B), neutrophils (C), lymphocytes (D), neutrophil-to-lymphocyte ratio (NLR) (E), derived-NLR (d-NLR) (F), platelets (G) and platelets to lymphocytes ratio (PLR) (H) related to 22 non-oncological patients (NOP) (grey box plot) and 24 PDAC (orange box plot) subjects. P values is determined by Student’s t-test and asterisks mean as follows: *P<0.05; **P<0.001. NP, nanoparticles; PC, protein corona; PDAC, pancreatic ductal adenocarcinoma; WBC, white blood cell.

Next, we combined the starting position with each of the inflammatory markers by performing a LDA for all the possible couplings (Figure S3 in Supplementary Materials, Supplemental Digital Content 1, http://links.lww.com/JS9/A725). The best discrimination between PDAC patients and NOPs was achieved when the MagLev starting position was combined with dNLR (Fig. 3, panel a) and NLR (Fig. 3, panel b) levels. The two combinations returned a global discrimination accuracy of 95.7% and 91.3%, respectively.

Figure 3.

Figure 3

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Scatter plot of the MagLev pattern’s starting position with, respectively, derived neutrophils to lymphocytes ratio (dNLR) (A) and NLR (B) levels, for a training set of 22 NOP (grey dots) and 24 PDAC samples (empty orange dots). The black line is the output of a linear discriminant analysis (LDA). By LDA computation, the resulting test’s parameters read global accuracy= 95.7% for dNLR, and 91.3% for NLR. The respective distributions of dNLR and NLR levels compared between healthy (grey box blot) and PDAC-affected (orange box plot) subjects are depicted in the inset of panel A and B, respectively. P values is determined by Student’s t-test and asterisks mean as follows: *P<0.05; **P<0.001. MagLev, magnetic levitation; NOP, non-oncological patient; PDAC, pancreatic ductal adenocarcinoma.

As recently it was demonstrated that NLR and PLR had higher diagnostic values in male gastric cancer patients,26 we asked whether a similar effect could be found in PDAC patients. However, when data were sex-disaggregated no relevant change in the test’s detection ability was found (Figure S4, Supplemental Digital Content 1, http://links.lww.com/JS9/A725). Finally, we tried to determine whether the sensibility and specificity of the test could be affected by PDAC stages. We classified PDAC patients into three subgroups, namely T1, T2+T3, and T4. In Figure S5, Supplemental Digital Content 1, http://links.lww.com/JS9/A725 and Table S2, Supplemental Digital Content 1, http://links.lww.com/JS9/A725 respectively, the ROC curves and the corresponding Area Under the Curve values show the best discriminations obtained, that is by coupling MagLev starting position with NLR and dNLR levels according to each tumour stage subgroups and compared with the whole PDAC group. This analysis did not show any significant changes in the test sensibility.

Discussion

The link between local and systemic inflammation and pancreatic adenocarcinoma tumorigenesis has been previously reported.27 The present study confirms that inflammatory status is significantly different between NOPs and PDAC patients. Mostly, a significant increase of neutrophils and a significant reduction of lymphocytes in the group of PDAC patients with consequent significant differences in NLR, dNLR, and PLR in the two analyzed groups have been found. These data, as the fact that we found that between NLR and PLR the first one proved to be more reliable, are in line with those that other Authors have previously reported in the literature. Indeed, as NLR reflects the systemic host inflammatory response against the tumour,28 demonstrating that in PDAC patients NLR reflects the response to chemotherapy more than PLR, Yuan Gao also underlined that the increase of NLR (due to an increase of neutrophils and/or a decrease of lymphocytes), is due to the fact that the tumour produces hematopoietic cytokines that stimulate neutrophils, while cytotoxic lymphocytes are impaired by arginase, nitric oxide, reactive oxygen species produced by the stimulated neutrophils.29

Even though Min et al.23 demonstrated that NLR is related to the diameter of pancreatic tumours, and furthermore, given that high NLR values have been significantly associated with the presence of more advanced PDACs30 and that recently some Authors31–33 reported that differences in NLR values are able to predict within subjects affected by Intraductal papillary mucinous neoplasm and pancreatic mucinous cystic neoplasms, those who will develop invasive tumours; however, it remains hard to hypothesize that the alterations of WBC count (and therefore of the inflammatory markers linked to them) can be used alone in the early diagnosis of pancreatic cancer among the general population.24

Indeed, the alterations of the inflammatory markers could certainly alert the physicians, but being non-specific, by themselves they would not be enough to justify the execution of invasive and expensive second-level tests (e.g. ultrasound-endoscopy and pancreatic MRI) for early detection of PDAC.

The results of our study would seem to lead to a step forward in solving this problem. Mainly, the fact that the combination of MagLev technology with inflammatory markers turns into a test able to discriminate pancreatic cancer from non-cancer patients with very high accuracy, could represent the missing piece in the process of the diagnosis of PDAC in its early stage.

In the present experience, identifying the fingerprint of the PDAC, MagLev technology confirmed its efficacy in distinguishing oncological from non-oncological patients.

Nevertheless, the best diagnostic accuracy of the test returned when the fingerprint, identified during the MagLev experiments, has been combined with dNLR. These data confirm what already reported by Liu et al.34 in 2018 that showed how dNLR is able to distinguish early-stage PDAC from healthy controls.

In other words, if further confirmed on larger series we could envision the use of this multiplexed strategy in high-risk patients that show inflammatory markers alterations. The positivity of this cheap, user-friendly, and reproducible coupled analysis may represent the basis for second levels exams.

We are aware that this study is not without limitations; the main one is represented by the small sample size. However, it is our opinion that, as happened in other studies focused on this topic and with the same limitation, it could represent a proof of concept of the validity of this approach becoming a useful basis for future studies. Furthermore, we cannot be sure that, despite the use of very selective inclusion and exclusion criteria, potential selection biases cannot be present especially in the control group.

Conclusions

Early diagnosis of pancreatic ductal adenocarcinoma is crucial. In recent years, nanotechnologies are proving to be useful, alone or in association with common laboratory tests, in satisfying the aforementioned need. Notably, the use of the technology of magnetic levitation of nanoparticles (MagLev) is able to identify a signature of the presence of pancreatic cancer in more than 90% of cases. Furthermore, the combination of the identified fingerprint with inflammatory markers such as dNLR and NLR allowed to develop a multiplexed diagnostic strategy to be used in PDAC with a diagnostic accuracy of about 95%. Nevertheless, this strategy totally fulfills all WHO REASSURED criteria as those of reproducibility, cheapness, and non-invasiveness.

Ethical approval

This work was approved by the Ethical Committee of the University Campus Bio-Medico di Roma (Prot. 10/12 ComEt CBM and further amendments.

Source of funding

The research leading to these results has received funding from AIRC under IG 2020-ID.24521 project–P.I. Pozzi Daniela.

Author contribution

Conceptualization: D.C., E.Q., G.C., D.P.; Data curation: A.C., V.L.V.; Formal analysis: E.Q.; Funding acquisition: D.P.; Investigation: D.C., E.Q., G.C., D.P.; Methodology: D.C., E.Q., G.C.; Project administration: D.P.; Resources: D.P.; Software: E.Q.; Supervision: D.C., G.C., D.P.; Validation: D.P.; Visualization: E.Q.; Roles/writing—original draft: D.C., G.C, E.Q.; Writing—review and editing: D.C., E.Q., A.C., V.L.V.

Conflicts of interest disclosure

The authors declare that they have no competing interests.

Guarantor

Damiano Caputo and Daniela Pozzi.

Data statement

The data that support the findings of this study are available on request from the corresponding authors, [D.C., D.P.] in respect of the in force’s Ethical and Legal regulations.

Footnotes

D.C., E.Q. contributed equally to this work.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Supplemental Digital Content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal’s website, www.lww.com/international-journal-of-surgery.

Published online 21 June 2023

Contributor Information

Damiano Caputo, Email: d.caputo@policlinicocampus.it.

Erica Quagliarini, Email: erica.quagliarini@uniroma1.it.

Alessandro Coppola, Email: coppola.chirurgia@gmail.com.

Vincenzo La Vaccara, Email: v.lavaccara@policlinicocampus.it.

Benedetta Marmiroli, Email: benedetta.marmiroli@elettra.eu.

Barbara Sartori, Email: barbara.sartori@elettra.eu.

Giulio Caracciolo, Email: giulio.caracciolo@uniroma1.it.

Daniela Pozzi, Email: daniela.pozzi@uniroma1.it.

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