{"paper_id":"015b3f9a-49c1-4899-bc3d-e7263b9f12f9","body_text":"Breaking the mucin barrier: a new affinity chromatography-mass spectrometry approach to unveil potential cell markers and pathways altered in Pseudomyxoma peritonei | 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 Method Article Breaking the mucin barrier: a new affinity chromatography-mass spectrometry approach to unveil potential cell markers and pathways altered in Pseudomyxoma peritonei Antonio Romero-Ruiz, Melissa Granados-Rodríguez, Florina I. Bura, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3953334/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Pseudomyxoma peritonei (PMP) is a rare peritoneal mucinous carcinomatosis with unknown underlying molecular mechanisms. Cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy is the only therapeutic option; however, despite its use, recurrence with a fatal outcome is common. The lack of molecular characterisation in PMP and other mucinous tumours is mainly due to the physicochemical properties of mucin. Results This manuscript describes the first protocol capable of breaking the mucin barrier and isolating proteins from mucinous tumours. Thus, we present here the first proteome analysed in PMP and identified a distinct mucin isoform profile in soft compared to hard mucin tissues as well as key biological processes/pathways altered in mucinous tumours. Importantly, this protocol also allowed us to identify MUC13 as a potential tumour cell marker in PMP. Conclusions In summary, our results demonstrate that this protein isolation protocol from mucin will have a high impact, allowing the oncology research community to more rapidly advance in the knowledge of PMP and mucinous neoplasms, as well as develop new and effective therapeutic strategies. cancer mucin protein Pseudomyxoma peritonei MUC13 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Pseudomyxoma peritonei (PMP) is a rare, malignant disease defined by a progressive, abundant, and multifocal accumulation of mucinous tumour tissue within the peritoneal cavity, essentially without extraperitoneal growth nor distant metastases [ 1 ]. It has historically been considered a terminal condition for which debulking surgery or palliative treatments were deemed the only therapeutic options available to date. In recent years, some reference groups have published results showing benefits on patient survival when treating PMP with cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) [ 1 – 5 ]. However, recurrence of this condition is common despite this therapeutic effort, with subsequent progression and death occurring in the absence of an effective treatment. Diagnosing this entity is considered to be challenging, as the tumour can remain silent, simulate an episode of acute appendicitis, or be disguised by an abdominal disease. Besides, there are no specific tumour markers other than a biopsy. Therefore, the different nomenclatures and classifications of the disease have always been under debate. In this context, the most successful classification protocol is that established by the Peritoneal Surface Oncology Group International (PSOGI), which has recently been validated and includes three subtypes of PMP: i) low-grade mucinous carcinoma (LG-PMP), ii) high-grade mucinous carcinoma (HGPMP), and iii) PMP with the presence of signet ring cells (SRC-PMP) [ 3 – 7 ]. Acellular mucin was excluded from this classification. This protocol has been validated and improved by our group [ 8 ], including a proposal of a new classification within the high-grade subtype based on tumour marker Ki67 [ 9 ]. The fast development of personalized therapies for oncology patients has highlighted the importance of identifying distinct molecular profiles within the same histologic tumour type. In this context, the search for personalized therapeutic solutions has transformed the molecular classification of tumours into a critical step of clinical decisionmaking. Only a few molecular studies performed on cases of PMP have been published to date, which were all summarized by LundAndersen et al. in a recent narrative review [ 10 ]. Although some of the genes and proteins described in these studies were proposed as biomarkers, their usefulness is still limited and none of them were validated as a potential therapeutic target. Furthermore, to our knowledge, there are no studies describing the protein profile of this mucinous tumour or any other peritoneal mucinous carcinomatosis, most likely because the high concentration of glycoproteins, together with the inter-mucin physical and chemical junctions, interfere with any currently available protein isolation protocol [ 11 ]. Overall, mucins are a family of heavily glycosylated proteins, also known as glycoproteins, which are expressed in specialized epithelial cells of mucosal surfaces, including those of the respiratory, gastrointestinal, and urogenital tracts [ 12 , 13 ]. Mucins are classified into two subtypes: membrane-associated and secreted. Membrane-associated mucins act as sensors of the extracellular medium and are capable of activating intracellular signal transduction pathways when the mucous layer is disrupted. Secreted mucins constitute a physical barrier for epithelial cells against both microorganisms and insoluble materials and maintain the local molecular environment [ 14 ]. Under normal conditions, mucin production and degradation undergo a well-established metabolic turnover. However, in PMP, mucin is secreted and gradually deposited within the peritoneal cavity, where it become hard to break down and eliminate. Over time, mucin accumulates, causing the symptomatology associated with PMP, and can even surround and protect tumour cells against the host’s immune response or the effect of chemotherapeutic agents [ 12 ]. An important finding made in several studies is the presence within the mucus of non-mucin proteins, such as digestive enzymes, dietary proteins, immunoglobulins, albumins, and keratin, which directly interact with mucin proteins through strong, non-covalent interactions and enhance the viscosity of PMP secretions [ 12 , 15 , 16 ]. As in other rare diseases, both the low incidence of PMP (1–3 cases per one million people/year) coupled to the lack of molecular studies focused on this condition, hamper to attain a detailed description of its pathophysiology and thereby develop of efficient, novel diagnostic, prognostic, or therapeutic strategies [ 6 , 17 , 18 ]. To help solving this issue, this study provides a description of the first protocol available to isolate massive proteins from mucin sourced from soft and hard tumour samples, which will undoubtedly advance the research in this field, as it enabled us to readily identify a potential tumour cell marker. Methods Experimental Design and Statistical Rationale . Soft and hard mucin samples were obtained from patients diagnosed with PMP of appendiceal origin who were being treated in our center, as not all mucin PMP samples have a similar texture, compactness and hardness [ 11 ]. From a prospective cohort of 29 patients, we selected 18 low-grade (LG-PMP), including 15 soft mucin (SM) samples and 11 hard mucin samples (HM), and 11 high-grade (HG-PMP), including 8 SM and 11 HM tumour samples. It is important to take in account that soft and hard mucin samples were included for most of the patients. Additionally, we obtained 16 control tissue samples, nine of which corresponded to appendiceal samples acquired from a prophylactic appendectomy performed due to another medical condition and seven nontumorous colon tissue samples collected from PMP patients during a CRS with HIPEC procedure. All samples were immediately sent to the Anatomy Pathology Unit for histological examination by experienced pathologists to confirm the diagnosis. The remaining samples were then snap frozen and stored in the Biobank until requested for analysis. Importantly, all tissue samples were frozen within 30 minutes from resection. The demographic and histology findings of these examinations are detailed in Table 1 . The samples used in each experiment are indicated in the figure legends. All patients signed the corresponding informed consent form, and the study was approved by our local ethics committee (protocol code PI19/01603). Table 1 Demographic and histology results from PMP patients. Non tumoral Tumour Low grade High grade Patients, [n] 16 18 11 Soft mucin, [n] - 15 8 Hard mucin, [n] - 11 11 Age, [median (IQR)] 53 (20–86) 57 (27–79) 58 (23–74) Women, (%) 52,9 61,1 63,6 * Soft and hard mucin samples were included for most of the patients. IQR: interquartile range. All statistical analyses were performed using software Prism v.8.0 (GraphPad Software, La Jolla, CA, USA), except the PLS-DA analyses, which were performed with MetaboAnalyst v.5.0 (McGill University, Quebec, Canada). Volcano plots were generated using R language v.4.1.2. Regarding the proteomic results, Welch ttests were applied to assess the existence of statistical differences between groups. All data are presented as a mean ± standard error of the mean (SEM). Unless otherwise stated, a one-way analysis of variance (ANOVA) followed by posthoc Bonferroni tests were applied. P -values below 0.05 were considered significant. Asterisks (* P < 0.05, ** P < 0.01, *** P < 0.001) indicate statistically significant differences. Liquid chromatography and proteins isolation. Mucin samples (∼1 g) were dissected into small fragments (1–3 mm) and homogenized in 3.5–4.5 ml of binding buffer (20 mM Tris, 500 mM NaCl, 1 mM MnCl 2 , and 1 mM CaCl 2 ) using ultrasonic pulses. Following their centrifugation and filtration through a 0.22-µm filter, 3–4 ml of each sample were loaded into the Äkta Purifier (Cytiva, MA, USA), which was previously fitted with the Hitrap Con A 4B column (Cytiva, MA, USA). Glycoproteins, polysaccharides, and glycolipids were captured in the column and the rest of the sample was collected in 0.4 ml fractions corresponding to the maximum absorbance peaks. All collected fractions were then precipitated with four volumes of cold acetone, centrifuged, and the pellets were resuspended in 1.5 ml of binding buffer (20 mM NaH 2 PO 4 and 150 mM NaCl). All fractions were mixed in a single tube, centrifuged, and filtered. Next, 0.5ml of this filtered extract was loaded once again into the Äkta Purifier, which was previously fitted with the HiTrap Albumin and IgG Depletion column (Cytiva, MA, USA). As in the previous step, IgG and albumins were captured in the column and the rest of the sample was collected in 0.2ml fractions corresponding to the maximum absorbance peaks. These fractions were then precipitated with four volumes of cold acetone and some of them were used to perform a mass spectrometry analysis. The rest of the fractions were centrifuged, and the pellets were resuspended in 100 µl of DIGE lysis buffer (0.1 mM urea, 1 M thiourea, 30 mM Tris, and 4% (w/v) 3-[(3-cholamidopropyl)dimethylammonium]-1-propanesulfonate) for the Western Blot and ELISA measurements. SWATH-MS protein quantification. The proteomic analysis was performed in the proteomics facility of the SCSIE (University of Valencia). Purified protein extracts obtained by liquid chromatography were precipitated and dissolved in 100 µl of lysis buffer (EasyPep™ Mini MS Sample Prep Kit; Thermo Fisher Scientific). The samples were then quantified using the Qubit™ Protein Assay Kit (Thermo Fisher Scientific) and 20 µg of each protein extract were digested and cleaned using the EasyPep™ Mini MS Sample Prep Kit (Thermo Fisher Scientific) following the manufacturer’s instructions. Next, the samples were dried and resuspended with 2% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA) to a final concentration of 1 µg/µl. The samples were analyzed using a SWATH DIA approach for massive protein quantitation: Firstly, an equal amount of all samples from each group were pooled and subjected to a shotgun analysis to build a peptide spectral library. Each pooled sample was analyzed twice by data-dependent acquisition (DDA) nanoscale liquid chromatography, followed by tandem mass spectrometry (nano LCMS/MS) runs using LC system Ekspert™ nanoLC425 (Eksigent, Dublin, CA, USA) coupled to a Triple TOF® 6600+ (Sciex, Redwood City, CA, USA) mass spectrometer system. Next, 3 µl of each peptide mixture sample were loaded by the nanoLC425 system into a trap column (3 µm C18-CL, 350 µm x 0.5 mm; Eksigent) and desalted with 0.1% TFA at a flow of 5 µl/min for five minutes. The peptides were then eluted into an analytical column (3 µm C18-CL 120 Ᾰ, 0.075 x 150 mm; Eksigent) equilibrated in 5% ACN and 0.1% formic acid (FA). The peptide elution was carried out using a 60minute linear gradient of 7–40% buffer B (buffer A: 0.1% FA diluted in water; buffer B: 0.1% FA diluted in ACN) at a flow rate of 300 nl/min. Eluted peptides were ionized in a Optiflow < 1 uL Nano Source, applying 3.0 kV to the spray emitter at 175 ºC, and analyzed in a data-dependent mode. Survey MS1 scans were acquired from 350–1400 m/z for 250 ms. The quadrupole resolution was set to ‘LOW’ for MS2 experiments, which were acquired 100–1500 m/z for 25 ms in ‘high sensitivity’ mode. Following switch criteria were used: charge: 2 + to 4+; minimum intensity; 250 counts per second (cps). Up to 100 ions were selected for fragmentation after each survey scan. Dynamic exclusion was set to 15 s. The rolling collision energies equations were set for all ions as + 2 ions, according to the following equation: |CE|=(0.049)x(m/z)+(2). For quantitation the Triple TOF® was operated as above but in SWATH mode, in which a 0.050second TOF MS scan ranging between 350 m/z and 1250 m/z was performed. In addition, 0.080-s product ion scans in 100 variable windows ranging between 400 m/z and 1250 m/z were acquired throughout the experiment. Peptide and protein identifications were achieved using software Protein Pilot v5.0 (Sciex). The Paragon algorithm[ 19 ] was used to search the Swissprot_200601.fasta database with the following parameters: trypsin specificity, iodoacetamide (IAM) cysteine alkylation, and taxonomy restricted to Homo sapiens . The MS/MS spectra of the identified peptides were used to generate a spectral library for the SWATH peak extraction using the add-in for software PeakView v2.1 (Sciex) MS/MSALL with SWATH Acquisition MicroApp v2.0 (Sciex). Peptides with a confidence score ≥95%, as reported in the Protein Pilot database search, were included in the spectral library. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE[ 20 ] partner repository with the dataset identifier PXD037364. Prediction of protein interactions and pathway enrichment analysis. The differentially expressed proteins identified in the SWATH quantitative analysis were imported into the Metascape database[ 21 ], which was used to perform protein-protein interaction enrichment analyses and functional enrichment analysis. For protein-protein interaction enrichment analyses, Metascape applies a mature complex identification algorithm called Molecular Complex Detection (MCODE) to automatically extract protein complexes embedded in a large network[ 22 ]. For functional enrichment analysis, the number of minimal overlap and enrichment factor were 1.5 and p-value cut-off was 0.05. Western Blot. The protein concentration of each sample obtained by liquid chromatography was determined using the RC DC™ Protein Assay (Bio-Rad). The samples’ absorbance was read at a wavelength of 750 nm in a standard spectrophotometer (Beckman DU530). To summarize, 5 µg of the total protein sample were subjected to SDS-PAGE on 9% polyacrylamide gels, electro-transferred to polyvinylidene difluoride membranes (Millipore), and probed overnight at 4°C in the presence of the corresponding primary antibody (anti-MUC13 [1/500; Abcam]). Horseradish peroxidase-conjugated secondary antibody (anti-rabbit [1/5000; Abcam]) was used for protein detection and a chemiluminescence ECL Western Blotting Substrate (Thermo Fisher Scientific) was applied. Protein levels were normalized using the total line normalization (TLN) method, whereby the intensity value of the total protein present in each sample was calculated. Moreover, a densitometric analysis of the resulting protein bands was conducted using Image J. ELISA measurements. Protein extracts obtained by liquid chromatography were also used to determine the presence of MUC13 using a human commercial ELISA kit (MUC13 [MBS507589; MyBiosource] according to the manufacturer´s instructions. Results Adapted method to isolate proteins from mucin (AMIPROM) As we mentioned above, the molecular characterization of PMP is scarce and there are only a few papers showing some mucin isoforms (mainly MUC2 and MUC5AC) analysis by Western Blot [ 15 , 16 ]. Importantly, the outcome of these experiments were highly smeared band patterns due to the high content of mucin proteins and their interactions. Consequently, those protein extracts are unsuitable for high-throughput proteomic analyses. To solve this problem, we designed the first method specifically adapted to mucin samples to extract and isolate massive proteins with a high quality and purity for their use in proteomic analyses (Fig. 1 ). In detail, one gram of both soft and hard mucin samples (low and highgrade PMP), as well as control samples, were dissected into small fragments and homogenised using ultrasounds. Then, the samples were filtered and processed through two consecutive affinity chromatography steps. First, they were run through the chromatographer using a HiTrap Con A 4B column to capture glycoproteins, polysaccharides, and glycolipids specifically. The flowthroughs, corresponding to the maximum absorbance peaks identified in the two-dimensional plot chromatogram, were collected and precipitated to be used in the next step. The collected fractions were centrifuged and filtered once again and subsequently run a second time through the chromatographer using a HiTrap Albumin and IgG Depletion column to capture albumins and immunoglobulin G (IgG) specifically. As in the previous step, the flowthrough fractions, corresponding to the maximum absorbance peaks identified in the chromatogram, were collected and precipitated in acetone to be used for the mass spectrometry (MS) analysis. Finally, all protein samples were precipitated, quantified, and properly prepared (see Methods section) to perform a nanoliquid chromatography coupled to tandem mass spectrometry (nanoLC/MSMS) using a sequential window acquisition of all theoretical mass spectra (SWATH-MS) data-independent acquisition (DIA) approach for massive protein quantification. Proteomic profile in Pseudomyxoma peritonei Soft and hard (low and high-grade PMP) mucin samples processed by AMIPROM were used to determine the proteomic profile of PMP to identify intracellular pathways altered in PMP, as well as potential tumoral cell markers, through the application of quantitative proteomics. Healthy samples from an appendectomy carried out for an unrelated medical condition or of normal colon tissue were used as controls, given the lack of proper non-tumoral mucin samples (Table 1 ). Nano-LC/MS-MS equipped with SWATH for label-free quantitative proteomics enabled us to create the first proteome profile described in PMP. Considering all these proteins, a partial least squares-discriminant analysis (PLS-DA) revealed a clear discrimination pattern between the proteomic profile of the soft mucin, hard mucin, and control tissue samples (Fig. 2 A). Furthermore, using a log2fold change difference > 1 and a p -value < 0.05 to determine differentially expressed proteins compared with the control tissues, we identified 93 upregulated and 243 downregulated proteins in the soft mucin samples and 86 upregulated and 27 downregulated proteins in the hard mucin samples (Fig. 2 B). Considering that mucins are the main proteins that characterize this entity, we then proceeded to identify the different mucin isoforms detected in the analyzed PMP subtypes, detecting a differential pattern of mucin isoforms between the soft and hard mucin samples (Fig. 2 C). Specifically, we detected MUC2, MUC5AC, MUC5B, MUC6, and MUC13 in the soft mucin samples compared to the control tissues, with MUC2, MUC5AC, MUC5B, and MUC6 being significantly upregulated in these samples (Fig. 2 C; left graph) and MUC1, MUC2, MUC4, MUC5AC, MUC5B, and MUC13 in the hard mucin samples, with MUC2, MUC5AC, and MUC13 being significantly upregulated in these samples (Fig. 2 C; right graph). MUC2, despite being depleted by the glycoprotein affinity column, was the most expressed mucin isoform in all cases. We then performed an analysis to compare low and highgrade PMP soft mucin samples, which revealed that PLS-DA could perfectly segregate both groups and clearly distinguish them from the control samples (Fig. 2 D). Taking account only differentially expressed proteins compared with the control tissue samples, volcano plots showed 72 upregulated and 220 downregulated proteins in the lowgrade soft mucin samples (Fig. 2 E-left panel) and 19 upregulated and 380 downregulated proteins in the highgrade soft mucin samples (Fig. 2 E-right panel). As we mentioned above, although we found a different pattern of mucin isoforms between soft and hard mucin samples, no significant differences were found between the mucin isoforms identified in the low and high-grade soft mucin samples (Fig. 2 F). In the same line, the PLS-DA performed with low and high-grade hard mucin samples did reveal a clear discrimination pattern between the whole proteomic profile of these samples and that of the control tissues (Fig. 2 G). Of note, as illustrated by the volcano plots (Fig. 2 H), the number of differentially expressed proteins was substantially higher for soft mucin than for hard mucin compared with the control tissues. Furthermore, MUC5AC and MUC13 expression levels were significantly upregulated in low-grade but not high-grade hard mucin samples compared with control tissues. Nevertheless, there were no statistically differences in their expression between low- and high-grade hard mucin samples (Fig. 2 I). Finally, we analysed the extracts captured in the two columns used during the protocol to evaluate how many proteins were depleted along with glycoproteins, albumins and immunoglobulins. To do this, we run an electrophoresis on the extracts from the columns and the main bands were excised and analysed by mass spectrometry (see Supplemental Methods). The results are shown in Figure S1 , Table S1 and Supplemental Excel 1. The results were analysed using a cut-off of the 1% spectral count value of the most abundant protein identified for each column. Briefly, a total of 85 proteins were identified in the extracts captured on the HiTrap Con A 4B colum, of which 82 were secreted proteins (including glycoproteins), 15 were cell membrane proteins and 7 were cytosolic proteins. It is important to note that most of the cell membrane and cytosolic proteins are also considered secreted proteins. For the HiTrap Albumin and IgG Depletion column, a total of 16 proteins were identified, all of which were secreted proteins (including albumin and immunoglobulins) and 7 of which were cell membrane proteins. In addition, 10 out of 15 (66.7%) of the identified cell membrane proteins captured in the HiTrap Con A 4B column and all (100%) of the identified cell membrane proteins captured in the HiTrap Albumin and IgG Depletion column were immunoglobulins, which were one of the targets to be depleted. Soft and hard mucin tissues are highly similar at functional level To explore the functional relevance of soft and hard mucin PMP tissues, all differentially expressed proteins found in SM and HM compared to control tissues were analysed using the Metascape database. In this sense, we found an 84.2% of unique proteins in SM compared to controls and 53.1% in HM samples compared to controls, with only a small fraction of proteins shared between both comparisons, as illustrated by the Circos plot (Fig. 3 A). Interestingly, although most of the differentially expressed proteins were different between both comparisons, they shared a high number of enriched pathways and processes (Fig. 3 B-C). Indeed, from the top 100 enriched terms included in the heatmap (where the colour scale represents statistical significance), most of them were significantly altered in both comparisons (pattern 1), with the terms “Golgi lumen” and “Extracellular vesicles in the crosstalk of cardiac cells” enriched exclusively in the comparison HM vs. control (pattern 3) and the terms “Cellular aldehyde metabolic process”, “Biological oxidations”, “Negative regulation of cell migration”, among others, enriched exclusively in the comparison SM vs. control (pattern 2) (Fig. 3 C). In general, an elevated number of enriched processes were related with extracellular matrix (including “extracellular matrix organization”, “Naba matrisome associated”, “Naba core associated”, “collagen binding”, “focal adhesion”, etc.), regulation of cytoskeleton (including “actomyosin structure organization”, cortical cytoskeleton organization”, “structural constituent of cytoskeleton”, etc), metabolism (including “Glycolysis/Gluconeogenesis”, “Carbon metabolism”, “metabolism of carbohydrates”, “pyruvate metabolism and Citric Acid (TCA) cycle”, etc), and signalling pathways highly related with cancer (including “VEGFA VEGFR2 signalling”, “EPH-Ephrin signalling”, “signalling by Rho GTPases”, “regulation of MAPK cascade”, etc). To facilitate the understanding of pathways/processes that are shared between the two comparisons, additional visualizations were developed. Thus, an enrichment network visualization including the results from both comparisons confirmed the same results as the heatmap, showing an overlap of biological processes by both soft and hard mucin tissues, as these proteins probably are likely to capture different parts of the same biological processes (Fig. 3 D). Furthermore, a protein-protein interaction (PPI) network was also generated to elucidate common/selective functional clusters. In this sense, 9 different clusters were identified based on the MCODE algorithm, which most of them were shared between both comparisons and only cluster 9 (related to regulation of Insulin-like growth factor transport and uptake) was specifically enriched in SM vs. CTRL. Cluster 6 (related to signalling by ROBO receptors and metabolism of amino acids) was also mainly enriched in SM vs. CTRL (Fig. 3 E and Table S2 ). Low and high-grade soft mucin tissues share a high number of biological processes/pathways Next, we wanted to explore the functional relevance of low and high-grade SM PMP tissues. Therefore, we compared all differentially expressed proteins found in LG and HG SM compared to control tissues and in LG compared to HG using the Metascape database. In this case, the Circos plot showed an elevated number of differentially altered proteins shared between the three comparisons, with only 28.4% of unique proteins in LG vs. CTRL, 40.6% of unique proteins in HG vs. CTRL and 20.6% of unique proteins in LG vs. HG (Fig. 4 A). Interestingly, 94% of the proteins in the LG vs. HG comparison were common to the HG vs. CTRL protein list, suggesting that most of these proteins are specifically altered in HG. In terms of functional enrichment, an increased number of biological processes/pathways were found to be altered in both LG vs. CTRL and HG vs. CTRL, which was corroborated by comparing all differentially altered proteins in LG vs. HG, showing a high number of common enriched terms between LG and HG (grey colour; pattern 2) (Fig. 4 B-C). Among these common terms, we found processes and pathways related to protein regulation (e.g. “unfolded protein binding”, “protein homodimerization activity”, “positive regulation of protein localization”, regulation of protein stability”, “negative regulation of protein polymerization”, etc), metabolism (e.g. “biological oxidations”, “cellular aldehyde metabolic process”, “generation of precursor metabolites and energy”, “pyruvate metabolism and citric acid (TCA) cycle”, etc.), and extracellular matrix (e.g. “glycosaminoglycan binding”, “extracellular matrix structural constituent”, “cell-cell junction”, “collagen-containing extracellular matrix”, etc.). In addition, we found some enriched terms that were differentially altered between LG and HG (pattern 1), which could be used to understand the differences between these two grades of the disease. Some of these terms were related to the regulation of the cytoskeleton (e.g. “establishment or maintenance of cell polarity”, “cortical cytoskeleton organization”, “cytoskeleton-dependent cytokinesis”, “endocytosis”, “regulation of vesicle-mediated transport”, “regulation of actin-filament organization”, etc), signalling pathways highly related with cancer (e.g. “G13 signalling pathway”, “nuclear receptors meta pathway”, VEGFA VEGFR2 signalling”, and “gene and protein expression by JAK-STAT signalling after Interleukin-12 stimulation”), metabolism (e.g. “glycolysis/gluconeogenesis”, “monocarboxylic acid metabolic process”, “small molecule catabolic process”, “isomerase activity”, “peptidase activity”, etc.), and other important extracellular matrix-related pathways such as “Proteoglycans in cancer”. The pathway “amino sugar and nucleotide sugar metabolism” was also found to be altered in LG vs. HG and in HG vs. CTRL, but not in LG vs. HG, suggesting that this pathway might be specifically altered in HG-PMP samples (pattern 3; Fig. 4 C). Consistent with these results, the enrichment network visualization showed the same overlapping pattern, with most of the enriched terms shared between HG vs. CTRL (red) and LG vs. CTRL (blue), and only a few of them also shared with the LG vs. HG comparison (green) (Fig. 4 D). Furthermore, the PPI network revealed 7 different functional clusters, all of them related to the cytoskeleton, signalling pathways and metabolism, and all shared between the three protein lists (Fig. 4 E; Table S3). Identification of functional enrichment terms specifically associated with LG or HG hard mucin tissues We then examined the functional relevance in LG and HG HM tissues as we did above with SM tissues. Circos plot analysis showed a higher number of differentially altered proteins in the LG vs. HG comparison in HM compared to the number of differentially altered proteins found in SM (Fig. 5 A and 4 A). In general, the number of shared proteins between the three protein lists in HM tissues was smaller than that observed in SM tissues, with a higher number of unique proteins (48% of unique proteins in LG vs. CTRL, 61.3% in HG vs. CTRL and 36.9% in LG vs. HG) (Fig. 5 A). The functional enrichment derived from these protein lists generated a heatmap with five different patterns according to the distribution of the enriched terms. Thus, patterns 1 and 4 (grey colour) show all common enriched terms found between LG and HG, but differentially altered compared to control tissues. This is also illustrated in the Circos plot (Fig. 5 B-C). Pattern 2 includes all enriched terms that are able to discriminate LG from HG HM tissues. Among these terms we found processes/pathways related to metabolism (e.g. “amino sugar and nucleotide sugar metabolism”, “small molecule catabolic process”, “glycolysis/gluconeogenesis”, etc.), cellular homeostasis and detoxification (e.g. “detoxification of reactive oxygen species”, “cellular detoxification”, “programmed cell death” and immune response (e.g. “acute-phase response”, “innate immune response”, “complement and coagulation cascades”, etc). Additionally, patterns 3 and 5 revealed differentially enriched terms that are specifically associated with LG or HG HM-PMP tissues. For example, pattern 3 included important cellular activities such as “peptidase activity”, “lyase activity”, “hydrolase activity”, and also other processes such as “organic acid binding”, “insulin-like growth factor binding” and “protein-folding chaperone binding”, which might be specifically associated with LG HM tissues, since they were altered in LG vs. HG and LG vs. CTRL, but not in HG vs. CTRL. In the same line, pattern 5 included important metabolic processes such as “pyruvate metabolism” and “cellular aldehyde metabolic process” and others such as “protein tetramerization” and “intramolecular phosphotransferase activity” that might be associated with HG HM tissues (Fig. 5 C). As before, to better understand the pathways/processes that are shared between the three comparisons, we generated the enrichment network visualization and the PPI network. In the enrichment network we found that most of the enriched terms were shared by the three protein lists, as they mainly represented patterns 1 and 2 from the heatmap. Nevertheless, processes such as “vesicle-mediated transport” and “programmed cell death” were mainly associated with LG vs. CTRL and LG vs. HG, suggesting that these processes were mainly altered in LG-PMP tissues (Fig. 5 D). Furthermore, the PPI network revealed 12 different functional clusters, all of them related to the proteasome, collagens, mRNA processing, complement activation and secretory granule lumen. However, although no cluster was found to be specific to any protein list, cluster 2, 8 and 10 were mainly enriched in HG vs. CTRL and LG vs. HG protein lists (Fig. 5 E; Table S4). Validation of MUC13 alteration in soft and hard mucin samples of Pseudomyxoma peritonei As we mentioned before, mucin isoforms are the main entity that characterize this pathology. Interestingly, we observed a differential mucin isoform pattern between SM and HM tissues, with MUC13 being the only membrane-associated mucin found altered (Fig. 2 ), which make him a potential candidate to be considered a cell tumour marker or a cell therapeutic target. For these reasons, MUC13 was quantified by Western Blot and an enzyme-linked immunosorbent assay (ELISA) in soft and hard mucin samples obtained from patients with PMP (Fig. 6 ). In the Western Blot analysis (Fig. 6 A), MUC13 showed a significant higher expression in LG and HG SM and in LG HM tissues compared to control tissues, and also was found overexpressed in LG and HG SM compared to HM. Additionally, MUC13 was quantified by an ELISA in a larger cohort of PMP samples, where was found overexpressed in LG SM samples compared to control tissues (Fig. 6 B). Importantly, MUC13 was not found in the depleted extracts (Supplemental Excel 1). Discussion The aberrant production of mucin, the most common phenotype of PMP, has impeded the isolation of proteins from mucus-producing cells because its structure is intrinsically designed to avoid invasion by external agents, such as microorganisms or insoluble material [ 14 ]. In this study, we describe the first protocol designed to isolate proteins in this context and present the first proteomic profile of PMP. Bioinformatic analyses have validated these pioneering data, and a new potential tumoral cell marker for PMP has been identified. The main limitation in improving the pathophysiology of rare diseases and finding therapeutic solutions to these conditions is the availability of samples. However, in the specific case of PMP, the lack of detailed and efficient protein isolation protocols allowing to conduct functional studies is even more limiting. In this sense, all molecular studies published in relation to PMP are based on histological data and expression analyses of genes already described in other types of cancer, mainly colon and appendiceal neoplasms. Tumour-related genes KRAS , GNAS , FAT4 , TGFBR1 , TP53 , and SMAD3/4 may be mutated in this context, with KRAS and GNAS being the genes most frequently mutated and most studied ones in PMP [ 10 ]. However, there is still significant controversy in this regard because of the lack of information about the functional implications of these transcriptional alterations [ 23 – 25 ]. Nevertheless, apart from a few studies on the expression and mutation of certain tumour-related genes, no analyses have been published that focus on the actual protein component involved in these tumours. To resolve this handicap and to isolate proteins from PMP tumour samples, we designed the AMIPROM protocol relying on affinity liquid chromatography based on the depletion of mucusforming glycoproteins, IgG, and albumins. In addition, AMIPROM made it possible for us to develop the first PMP protein library, which enabled the conduct of a differential expression analysis using mass spectrometry DIA that revealed more than 300 deregulated proteins. Thus, for the first time, this protocol breaks the mucin barrier in PMP and allows access to the protein fraction in this rare tumour type, opening new perspectives for other more prevalent mucinous tumours, such as mucinous colorectal cancer (CRC), which accounts for 10% − 20% of CRC patients [ 26 ]. Moreover, this protocol is a reliable option for searching for potential tumour cell membrane markers, as only secreted proteins were depleted along with the most abundant glycoproteins, albumin and immunoglobulins. The current diagnostic and prognostic methods used in PMP are based on histological data [ 6 ]. Developing a classification method based on molecular characterization is mandatory in this scenario. The first PMP protein profile described in this work shows great capacity to classify tumour and control samples of both soft and hard mucin, but it also enables making a distinction between lowgrade and highgrade SM and HM PMP samples. On the other hand, we wanted to elucidate the mechanisms (biological processes and signalling pathways) underlying this pathology. To do this, we used Metascape database to do a functional enrichment and PPI networks. We paid special attention to all the altered proteins and regulatory networks that were uniquely enriched in the different groups of samples. Interestingly, the results derived from the analysis of the SM vs. CTRL and HM vs. CTRL comparisons (without distinction of histological grade) showed that although most of the altered proteins were different in each comparison, the terms associated to these alterations were very similar, suggesting that these proteins are probably different parts of the same biological processes and therefore that SM and HM tissues are similar at the functional but not at the structural level. These results are complementary to the one published by Pillai et al, where they showed that soft, semi-hard and hard mucin samples had different textures and hardness, probably due to cell content, hydration, glucose, proteins, lipids, thiols and mucin distribution [ 11 ]. Additionally, the enriched biological processes/pathways altered in these comparisons (mainly related to extracellular matrix, regulation of cytoskeleton, metabolism and signalling pathways highly related with cancer) confirmed the occurrence of tissue adaptation to promote the progression of a malignant tumour [ 27 ]. Next, we wanted to elucidate the biological processes that may discern between LG and HG in SM and HM tissues. In this sense, we found that there are a higher number of altered proteins shared between LG and HG in SM than in HM samples. In line with this, while in SM there are a high number of common processes between LG and HG, in HM we were able to identify processes/pathways that could specifically help to understand and better distinguish between LG and HG-PMP. Thus, important cellular activities (e.g.“peptidase activity”, “lyase activity”, “hydrolase activity”, etc.) were specifically associated with LG HM tissues and metabolic processes such as “pyruvate metabolism” and “cellular aldehyde metabolic process” and other processes were specifically associated with HG HM tissues. In keeping with this notion, those cellular activities as well as tumour-associated metabolic deregulation has been described at various carcinogenesis stages, and it is now evident that these alterations encompass all stages of the cellmetabolite interaction, increasing the tumour ability to acquire nutrients, determining how the nutrients are preferentially assigned to specific metabolic pathways that contribute to altering the cell cycle and modifying cell differentiation [ 27 , 28 ]. Furthermore, we found specific enriched terms related to extracellular matrix, cytoskeleton, metabolism and immune system, among others, that were differentially altered between LG and HG in SM and HM and could help to better understand this pathology, although we couldn’t specifically associate them with LG or HG. The fact that PMP is a tumour associated with advanced age often implies the prior existence of alterations in the extracellular matrix and the immune system of the patients affected by this condition. In this context, detailed descriptions have been published on how cellular and molecular changes in non-cancerous cells during ageing may contribute to a tumourpermissive microenvironment. These changes encompass biophysical alterations in the extracellular matrix, changes in secreted factors, and changes in the immune system [ 29 ], which certainly warrant further investigation in PMP. Finally, our results revealed a different mucin isoform profile between SM and HM, with MUC13 being the only membrane-associated mucin found to be altered. The individual quantification of MUC13 in a larger number of samples confirmed its aberrant synthesis in PMP tissues. MUC13 is a transmembrane protein expressed in mucin-producing epithelial cells whose main function is to activate an inflammatory response when an external agent damages the mucosal layer [ 30 ]. There is currently no data on the expression of MUC13 in serous or mesothelial cells. All this information makes it a new membrane protein target for detecting the presence of residual tumoral cells after CRS-HIPEC treatment 29 . In sum, in this work we have broken the mucin barrier by developing AMIPROM, the first protocol available to isolate proteins from mucin, which has been the main obstacle to molecular studies of this rare cancer. In addition, we provide the first proteomic profile of PMP described to date, providing novel information to characterise and identify the pathways altered in this tumour. Furthermore, our differential protein expression analysis followed by bioinformatic approaches has, for the first time, revealed the biological and molecular processes involved in PMP genesis and identified a potential tumour cell marker. Overall, we believe that our study provides essential, original information to facilitate rapid advances in the knowledge of PMP pathogenesis, which is undoubtedly the first step towards the development of new and effective therapeutic tools to treat this disease. Abbreviations AMIPROM Adapted method to isolate proteins from mucin CRC Colorectal cancer CRS Cytoreductive surgery DIA Data-independent acquisition GO Gene Ontology HG-PMP Highgrade PMP HIPEC Hyperthermic intraperitoneal chemotherapy HM Hard mucin IGs Immunoglobulins LG-PMP Lowgrade PMP MS Mass spectrometry PLS-DA Partial least squares-discriminant analysis PMP Pseudomyxoma peritonei PPI Protein-protein interaction PSOGI Peritoneal Surface Oncology Group International SM Soft mucin SWAH-MS Sequential window acquisition of all theoretical mass spectra Declarations Ethics approval and consent to participate All techniques used in this study were applied in accordance with the ethical standards of the Helsinki Declaration and the World Medical Association, with the approval of the University of Córdoba/IMIBIC and the research ethics committee of Córdoba (CETICO, Comité de Ética de la Investigación de Córdoba ), and can be implemented within the HURS and the IMIBIC (protocol code PI19/01603, version 1, dated 13 March 2019). Written informed consent was obtained from each patient. Consent for publication Not applicable. Availability of data and materials The datasets used and analysed in this study are available upon reasonable request from the corresponding authors ( [email protected] and [email protected] ). Competing interests The authors declare no competing interests in relation to this study. Funding This work was supported by: i) Ref. PI22/01213 co-financed by the ISCIII Sub-directorate general for evaluation and research promotion ( Subdirección General de Evaluación y Fomento de la Investigación ) and the European Regional Development Fund (ERDF), ii) Ref. ProteoRed-0000141 (ISCIII) and iii) Ref. PRYES223170ARJO funded by “Asociación Española Contra el Cáncer – AECC” in the call “Ayudas a Proyectos Estratégicos 2022”. Authors’ contributions : ARR: Conception, data curation, formal analysis, funding acquisition, research, methodology, project administration, resources, supervision, visualisation, writing the original draft, and reviewing and editing the final draft. MGR: Research, methodology, validation, visualisation, writing the original draft, and reviewing and editing the final draft. FIB: Research, methodology, validation, visualisation, writing the original draft, and reviewing and editing the final draft. FVM: Methodology, formal analysis, research, and reviewing and editing the final draft. BRA: Methodology, formal analysis, validation, and reviewing and editing the final draft. AML: Methodology, formal analysis, validation, and reviewing and editing the final draft. LRO: Methodology, formal analysis, validation, resources, and reviewing and editing the final draft. ROS: Formal analysis, validation, resources, and reviewing and editing the final draft. MTM: Methodology, formal analysis, research, and reviewing and editing the final draft. AMS: Methodology, formal analysis, research, and reviewing and editing the final draft. JC: Formal analysis, methodology, writing the original draft, and reviewing and editing the final draft. JAC: Formal analysis, validation, and reviewing and editing the final draft. CMD: Formal analysis, methodology, writing the original draft, and reviewing and editing the final draft. MCVB: Conception, data curation, formal analysis, funding acquisition, research, methodology, supervision, visualisation, writing the original draft, and reviewing and editing the final draft. AAS: Conception, data curation, formal analysis, funding acquisition, research, methodology, project administration, resources, supervision, visualisation, writing the original draft, and reviewing and editing the final draft. Acknowledgements: We acknowledge Ángela Casado-Adam and Juan Manuel Sánchez-Hidalgo for their support during surgeries. Additionally, we acknowledge Luz Valero and Manuel M. Sanchez-del Pino from SCSIE Proteomics Facility at the University of Valencia (a member of Proteored, PRB3), and Eduardo Chicano-Gálvez and Ángela Peralbo-Molina from IMIBIC Mass Spectrometry and Molecular Imaging Unit (IMSMI) at the Maimonides Biomedical Research Institute of Cordoba (IMIBIC). References Sommariva A, Tonello M, Rigotto G, Lazzari N, Pilati P, Calabrò ML. Novel Perspectives in Pseudomyxoma Peritonei Treatment. Cancers (Basel) [Internet]. 2021;13:5965. Available from: https://www.mdpi.com/2072-6694/13/23/5965/htm . Arjona-Sánchez Á, Muñoz-Casares FC, Rufián-Peña S, Díaz-Nieto R, Casado-Adam Á, Rubio-Pérez MJ et al. Pseudomyxoma peritonei treated by cytoreductive surgery and hyperthermic intraperitoneal chemotherapy: results from a single centre. Clin Transl Oncol [Internet]. 2011;13:261–7. Available from: http://link.springer.com/ 10.1007/s12094-011-0651-8 . Delhorme J-B, Severac F, Averous G, Glehen O, Passot G, Bakrin N et al. Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for pseudomyxoma peritonei of appendicular and extra-appendicular origin. Br J Surg [Internet]. 2018;105:668–76. http://doi.wiley.com/10.1002/bjs.10716 . Arjona-Sánchez Á, Muñoz-Casares FC, Casado-Adam Á, Sánchez-Hidalgo JM, Teran MDA, Orti-Rodriguez R et al. Outcome of Patients with Aggressive Pseudomyxoma Peritonei Treated by Cytoreductive Surgery and Intraperitoneal Chemotherapy. World J Surg [Internet]. 2013;37:1263–70. Available from: http://link.springer.com/ 10.1007/s00268-013-2000-2 . Choudry HA, Mavanur A, O’Malley ME, Zeh HJ, Guo ZS, Bartlett DL. Chronic Anti-inflammatory Drug Therapy Inhibits Gel-Forming Mucin Production in a Murine Xenograft Model of Human Pseudomyxoma Peritonei. Ann Surg Oncol [Internet]. 2012;19:1402–9. Available from: http://www.springerlink.com/index/ 10.1245/s10434-012-2242-5 . Carr NJ, Cecil TD, Mohamed F, Sobin LH, Sugarbaker PH, González-Moreno S et al. A Consensus for Classification and Pathologic Reporting of Pseudomyxoma Peritonei and Associated Appendiceal Neoplasia: The Results of the Peritoneal Surface Oncology Group International (PSOGI) Modified Delphi Process. Am J Surg Pathol; 2016. p. 14–26. Available from: http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage &an=00000478-201601000-00004. Baratti D, Kusamura S, Milione M, Bruno F, Guaglio M, Deraco M. Validation of the Recent PSOGI Pathological Classification of Pseudomyxoma Peritonei in a Single-Center Series of 265 Patients Treated by Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy. Ann Surg Oncol [Internet]. 2017;25:1–10. http://dx.doi.org/10.1245/s10434-017-6252-1 . Rufián-Andujar B, Valenzuela-Molina F, Rufián-Peña S, Casado-Adam Á, Sánchez-Hidalgo JM, Rodríguez-Ortiz L et al. From the Ronnett to the PSOGI Classification System for Pseudomyxoma Peritonei: A Validation Study. Ann Surg Oncol [Internet]. 2021;33:1–9. https://doi.org/10.1245/s10434-020-09560-w . Arjona-Sánchez Á, Martínez-López A, Valenzuela-Molina F, Rufián-Andujar B, Rufián-Peña S, Casado-Adam Á et al. A Proposal for Modification of the PSOGI Classification According to the Ki-67 Proliferation Index in Pseudomyxoma Peritonei. Ann Surg Oncol [Internet]. 2021;1–11. Available from: https://link.springer.com/article/ 10.1245/s10434-021-10372-9 . Lund-Andersen C, Torgunrud A, Fleten KG, Flatmark K. Omics analyses in peritoneal metastasis—utility in the management of peritoneal metastases from colorectal cancer and pseudomyxoma peritonei: a narrative review. J Gastrointest Oncol [Internet]. 2020;12:S191–203. Available from: https://jgo.amegroups.com/article/view/43080/html . Pillai K, Akhter J, Mekkawy A, Chua TC, Morris DL. Physical and chemical characteristics of mucin secreted by pseudomyxoma peritonei (PMP). Int J Med Sci [Internet]. 2017;14:18–28. Available from: http://www.medsci.org/v14p0018.htm . Amini A, Masoumi-Moghaddam S, Ehteda A, Morris DL. Secreted mucins in pseudomyxoma peritonei: pathophysiological significance and potential therapeutic prospects. Orphanet J Rare Dis. 2014;9:71–1. Jr. JRG. Mucin Genes and the Proteins They Encode: Structure, Diversity, and Regulation. Am J Resp Cell Mol. 1992;7:557–64. Hollingsworth MA, Swanson BJ. Mucins in cancer: protection and control of the cell surface. Nat Rev Cancer. 2004;4:45–60. Mall AS, Chirwa N, Govender D, Lotz Z, Tyler M, Rodrigues J et al. MUC2, MUC5AC and MUC5B in the mucus of a patient with pseudomyxoma peritonei: biochemical and immunohistochemical study. Pathol Int [Internet]. 2007;57:537–47. http://doi.wiley.com/10.1111/j.1440-1827.2007.02137.x . Meldrum OW, Yakubov GE, Bonilla MR, Deshmukh O, McGuckin MA, Gidley MJ. Mucin gel assembly is controlled by a collective action of non-mucin proteins, disulfide bridges, Ca2+-mediated links, and hydrogen bonding. Sci Rep-uk. 2018;8:5802. Dayal S, Taflampas P, Riss S, Chandrakumaran K, Cecil TD, Mohamed F et al. Complete cytoreduction for pseudomyxoma peritonei is optimal but maximal tumor debulking may be beneficial in patients in whom complete tumor removal cannot be achieved. Dis Colon Rectum [Internet]. 2013;56:1366–72. Available from: https://insights.ovid.com/crossref?an=00003453-201312000-00008 . Smeenk RM, van Velthuysen MLF, Verwaal VJ, Zoetmulder FAN. Appendiceal neoplasms and pseudomyxoma peritonei: a population based study. Eur J Surg Oncol [Internet]. 2008;34:196–201. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0748798307001709 . Shilov IV, Seymour SL, Patel AA, Loboda A, Tang WH, Keating SP, et al. The Paragon Algorithm, a Next Generation Search Engine That Uses Sequence Temperature Values and Feature Probabilities to Identify Peptides from Tandem Mass Spectra*. Mol Cell Proteom. 2007;6:1638–55. Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S, Kamatchinathan S, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2021;50:D543–52. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 2003;4:2. Singhi AD, Davison JM, Choudry HA, Pingpank JF, Ahrendt SA, Holtzman MP, et al. GNAS is frequently mutated in both low-grade and high-grade disseminated appendiceal mucinous neoplasms but does not affect survival. Hum Pathol. 2014;45:1737–43. Shetty S, Thomas P, Ramanan B, Sharma P, Govindarajan V, Loggie B. Kras mutations and p53 overexpression in pseudomyxoma peritonei: association with phenotype and prognosis. J Surg Res. 2013;180:97–103. Pietrantonio F, Perrone F, Mennitto A, Gleeson EM, Milione M, Tamborini E, et al. Toward the molecular dissection of peritoneal pseudomyxoma. Ann Oncol. 2016;27:2097–103. Luo C, Cen S, Ding G, Wu W. Mucinous colorectal adenocarcinoma: clinical pathology and treatment options. Cancer Commun [Internet]. 2019;39:1–13. https://doi.org/10.1186/s40880-019-0361-0 . Pavlova NN, Thompson CB. The Emerging Hallmarks of Cancer Metabolism. Cell Metab. 2016;23:27–47. Kos J, Proteases. Role and Function in Cancer. Int J Mol Sci. 2022;23:4632. Fane M, Weeraratna AT. How the ageing microenvironment influences tumour progression. Nat Rev Cancer. 2020;20:89–106. Williams SJ, Wreschner DH, Tran M, Eyre HJ, Sutherland GR, McGuckin MA. MUC13, a Novel Human Cell Surface Mucin Expressed by Epithelial and Hemopoietic Cells*. Journal of Biological Chemistry [Internet]. 2001;276:18327–36. http://dx.doi.org/10.1074/jbc.M008850200 . Additional Declarations No competing interests reported. Supplementary Files Supplementalexcel1.xlsx Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Mar, 2024 Reviews received at journal 10 Mar, 2024 Reviewers agreed at journal 23 Feb, 2024 Reviewers agreed at journal 20 Feb, 2024 Reviewers invited by journal 15 Feb, 2024 Editor assigned by journal 14 Feb, 2024 Submission checks completed at journal 14 Feb, 2024 First submitted to journal 13 Feb, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3953334\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Method Article\",\"associatedPublications\":[],\"authors\":[{\"id\":272919712,\"identity\":\"4036837c-52e8-4243-b8fa-db0822293058\",\"order_by\":0,\"name\":\"Antonio Romero-Ruiz\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACA/nHB8AMfuK1MKQlgBmSDcRryTGAMA4Qq8Wc4YDZh497Dssb30h++IChoo6wFsvGhuSZM54dNtx2I83YgOHMYSIcdpjhMDPPgcOM227ksEkwthHhPINjjM0gLfabZ4C0/CPCYQZnmJlBWhI3SIC0NDAT1mI5g42ZccaB9OQZZ54ZGyQcI8Iv5hL8nxk+HLC27W8HhtiHGiIchgoSSNUwCkbBKBgFowA7AACY2DlMF1PzAAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"University of Córdoba\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Antonio\",\"middleName\":\"\",\"lastName\":\"Romero-Ruiz\",\"suffix\":\"\"},{\"id\":272919713,\"identity\":\"476fcedd-8953-4345-886e-4ab8417e145f\",\"order_by\":1,\"name\":\"Melissa Granados-Rodríguez\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Córdoba\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Melissa\",\"middleName\":\"\",\"lastName\":\"Granados-Rodríguez\",\"suffix\":\"\"},{\"id\":272919714,\"identity\":\"456b89b7-cc25-4354-912d-2dd78c5bc0f8\",\"order_by\":2,\"name\":\"Florina I. Bura\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Córdoba\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Florina\",\"middleName\":\"I.\",\"lastName\":\"Bura\",\"suffix\":\"\"},{\"id\":272919715,\"identity\":\"2db5411e-08e7-465e-953b-98ac544eecc9\",\"order_by\":3,\"name\":\"Francisca Valenzuela-Molina\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Reina Sofía University Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Francisca\",\"middleName\":\"\",\"lastName\":\"Valenzuela-Molina\",\"suffix\":\"\"},{\"id\":272919716,\"identity\":\"3b497e10-fcc9-4865-9225-292412a896f3\",\"order_by\":4,\"name\":\"Blanca Rufián-Andújar\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Reina Sofía University Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Blanca\",\"middleName\":\"\",\"lastName\":\"Rufián-Andújar\",\"suffix\":\"\"},{\"id\":272919717,\"identity\":\"6584e294-fea9-4c0d-86e8-efedfbbeee36\",\"order_by\":5,\"name\":\"Ana Martínez-López\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Reina Sofía University Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ana\",\"middleName\":\"\",\"lastName\":\"Martínez-López\",\"suffix\":\"\"},{\"id\":272919718,\"identity\":\"1e2edacd-a2ab-4127-aede-f44c2418b7bf\",\"order_by\":6,\"name\":\"Lidia Rodríguez-Ortiz\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Reina Sofía University Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lidia\",\"middleName\":\"\",\"lastName\":\"Rodríguez-Ortiz\",\"suffix\":\"\"},{\"id\":272919719,\"identity\":\"d1f9943f-fa13-4630-ad8a-0d7795b08acc\",\"order_by\":7,\"name\":\"Rosa Ortega-Salas\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Reina Sofía University Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rosa\",\"middleName\":\"\",\"lastName\":\"Ortega-Salas\",\"suffix\":\"\"},{\"id\":272919720,\"identity\":\"795ea5a3-5af6-4ad5-9923-db2442d408bc\",\"order_by\":8,\"name\":\"María Torres-Martínez\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Córdoba\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"María\",\"middleName\":\"\",\"lastName\":\"Torres-Martínez\",\"suffix\":\"\"},{\"id\":272919721,\"identity\":\"72216d99-0f64-44c3-a106-7c5a7caabebd\",\"order_by\":9,\"name\":\"Ana Moreno-Serrano\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Córdoba\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ana\",\"middleName\":\"\",\"lastName\":\"Moreno-Serrano\",\"suffix\":\"\"},{\"id\":272919722,\"identity\":\"193cd446-10c5-408e-9a5b-a1adb0ec58b5\",\"order_by\":10,\"name\":\"Justo Castaño\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Córdoba\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Justo\",\"middleName\":\"\",\"lastName\":\"Castaño\",\"suffix\":\"\"},{\"id\":272919723,\"identity\":\"913d615f-3d4f-4f40-ba2c-dcdd8f86e24e\",\"order_by\":11,\"name\":\"Carmen Michán\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Córdoba\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Carmen\",\"middleName\":\"\",\"lastName\":\"Michán\",\"suffix\":\"\"},{\"id\":272919724,\"identity\":\"2b98b1f4-b772-42b1-8ff3-57e050d8d16b\",\"order_by\":12,\"name\":\"José Alhama\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Córdoba\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"José\",\"middleName\":\"\",\"lastName\":\"Alhama\",\"suffix\":\"\"},{\"id\":272919725,\"identity\":\"9dd9e04f-dba2-49cd-9ea8-4c40d89da32c\",\"order_by\":13,\"name\":\"Mari C. Vázquez-Borrego\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Córdoba\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mari\",\"middleName\":\"C.\",\"lastName\":\"Vázquez-Borrego\",\"suffix\":\"\"},{\"id\":272919726,\"identity\":\"f32b60f9-2936-4f83-8894-d6a4bd19b7a6\",\"order_by\":14,\"name\":\"Álvaro Arjona-Sánchez\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Reina Sofía University Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Álvaro\",\"middleName\":\"\",\"lastName\":\"Arjona-Sánchez\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-02-13 10:48:10\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3953334/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3953334/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":51199737,\"identity\":\"ae0fbed0-ebbd-4343-b8ab-4ce6c98d2236\",\"added_by\":\"auto\",\"created_at\":\"2024-02-15 20:28:45\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":826906,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRepresentation of the adapted method to isolate proteins from mucin.\\u003c/strong\\u003e Adapted method to isolate proteins from mucin (AMIPROM): soft and hard mucin samples (low and high-grade) are dissected into small pieces and homogenised with ultrasounds. The homogenate is then centrifugated, and the supernatant is collected and filtered. Next, the homogenate is used in a liquid chromatography (LC) run fitted with two different columns to eliminate glycoproteins, IgGs, and albumin from the sample. The protein extract derived from LC is used to perform a mass spectrometry analysis using an unbiased targeted proteomic approach with SWATH-MS.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3953334/v1/02dc2c96fac94af934b256da.png\"},{\"id\":51199735,\"identity\":\"50c2461f-7902-476b-a763-8821ab4347e2\",\"added_by\":\"auto\",\"created_at\":\"2024-02-15 20:28:44\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":850644,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eProteome analysis of soft and hard (low- and high-grade) mucin samples from Pseudomyxoma peritonei (PMP) compared to control samples. (A)\\u003c/strong\\u003e Partial least squares-discriminant analysis (PLS-DA) of the proteome profile between soft (left; n=14) and hard (right; n=15) mucin samples and control tissues (n=10). \\u003cstrong\\u003e(B)\\u003c/strong\\u003e Volcano plots showing Log2 Fold Change expression vs –log10 (p-value) of differentially expressed proteins with a p-value \\u0026lt; 0.05 and an absolute Log2 Fold Change \\u0026gt; 1 in the same set of samples. The green color indicates upregulated proteins and red color indicates downregulated proteins. \\u003cstrong\\u003e(C)\\u003c/strong\\u003e Protein expression levels of mucin isoforms identified in soft (green bars; SM) and hard (blue bars; HM) mucin samples of PMP compared to control tissue (set at 100%; dashed line). \\u003cstrong\\u003e(D,G)\\u003c/strong\\u003e PLS-DA analysis of the proteome profile of low- and high-grade SM (D) and HM (G) samples compared with the control tissues. \\u003cstrong\\u003e(E,H)\\u003c/strong\\u003e Volcano plots showing Log2 Fold Change expression vs –log10 (p-value) of differentially expressed proteins with a p-value \\u0026lt; 0.05 and an absolute Log2 Fold Change \\u0026gt; 1 in low- (left panel) and high-grade (right panel) SM (E) and HM (H) samples compared with control tissues. \\u003cstrong\\u003e(F,I)\\u003c/strong\\u003eProtein expression levels of mucin isoforms identified in low- (light bars) and high grade (dark bars) SM (F) and HM (I) samples compared to control tissue (set at 100%; dashed line). * p\\u0026lt;0.05 and, ** p\\u0026lt;0.01, *** p\\u0026lt;0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3953334/v1/854879fe42211778b7987106.png\"},{\"id\":51199738,\"identity\":\"195e8c31-930e-48e9-8532-9337ae9c1495\",\"added_by\":\"auto\",\"created_at\":\"2024-02-15 20:28:45\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2878612,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVisualization of functional enrichment meta-analysis based on two protein lists (SM vs. CTRL and HM vs. CTRL). (A) \\u003c/strong\\u003eCircos plot visualisation of the overlaps among protein lists (SM vs. CTRL and HM vs. CTRL). Each candidate protein is assigned to one spot on the arc of the corresponding protein list(s). Proteins shared among both lists are linked through purple curves. \\u003cstrong\\u003e(B)\\u003c/strong\\u003e Circos plot visualisation with blue curves connecting those candidate proteins that have different identities but share an enriched pathway/process, i.e. they represent the functional overlap between protein lists. \\u003cstrong\\u003e(C)\\u003c/strong\\u003e Heatmap showing the top 100 enrichment clusters, one row per cluster, using a discrete color scale to represent statistical significance. Grey color indicates the lack of enrichment for that term in the corresponding gene list, light yellow color indicates boundary between significance and insignificance, deep yellow color indicates a high degree of significance. \\u003cstrong\\u003e(D)\\u003c/strong\\u003e Enrichment network visualization for results from the two protein lists, where nodes are represented by pie charts indicating their associations with each input list. Cluster labels were added manually. Color code represents the identities of protein lists, where blue indicates SM vs. CTRL and red indicates HM vs. CTRL. \\u003cstrong\\u003e(E)\\u003c/strong\\u003e Visualization of PPI network and MCODE components identified from the combined list of proteins, where each node represents a protein with a pie chart encoding its origin. Color codes for pie sectors represent a protein list.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3953334/v1/41025bdcfb3518566922c4d5.png\"},{\"id\":51199736,\"identity\":\"a7f737eb-2853-4e46-9e3a-7e7e85dd6b1b\",\"added_by\":\"auto\",\"created_at\":\"2024-02-15 20:28:44\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3240025,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVisualization of functional enrichment meta-analysis based on three protein lists (LG vs. CTRL, HG vs. CTRL and LG vs. HG) in soft mucin samples compared to control tissues. (A) \\u003c/strong\\u003eCircos plot visualisation of the overlaps among protein lists. Each candidate protein is assigned to one spot on the arc of the corresponding protein list(s). Proteins shared among both lists are linked through purple curves. \\u003cstrong\\u003e(B)\\u003c/strong\\u003e Circos plot visualisation with blue curves connecting those candidate proteins that have different identities but share an enriched pathway/process, i.e. they represent the functional overlap between protein lists. \\u003cstrong\\u003e(C)\\u003c/strong\\u003e Heatmap showing the top 100 enrichment clusters, one row per cluster, using a discrete color scale to represent statistical significance. Grey color indicates the lack of enrichment for that term in the corresponding gene list, light yellow color indicates boundary between significance and insignificance, deep yellow color indicates a high degree of significance. \\u003cstrong\\u003e(D)\\u003c/strong\\u003e Enrichment network visualization for results from the three protein lists, where nodes are represented by pie charts indicating their associations with each input list. Cluster labels were added manually. Color code represents the identities of protein lists, where blue indicates LM vs. CTRL, red indicates HG vs. CTRL and green indicates LG vs. HG. \\u003cstrong\\u003e(E)\\u003c/strong\\u003e Visualization of PPI network and MCODE components identified from the combined list of proteins, where each node represents a protein with a pie chart encoding its origin. Color codes for pie sectors represent a protein list.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3953334/v1/97cfb45043e114982ec6ba84.png\"},{\"id\":51199742,\"identity\":\"c66c84be-47a5-4058-9241-99aa59b94855\",\"added_by\":\"auto\",\"created_at\":\"2024-02-15 20:28:45\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3095998,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVisualization of functional enrichment meta-analysis based on three protein lists (LG vs. CTRL, HG vs. CTRL and LG vs. HG) in hard mucin samples compared to control tissues. (A) \\u003c/strong\\u003eCircos plot visualisation of the overlaps among protein lists. Each candidate protein is assigned to one spot on the arc of the corresponding protein list(s). Proteins shared among both lists are linked through purple curves. \\u003cstrong\\u003e(B)\\u003c/strong\\u003e Circos plot visualisation with blue curves connecting those candidate proteins that have different identities but share an enriched pathway/process, i.e. they represent the functional overlap between protein lists. \\u003cstrong\\u003e(C)\\u003c/strong\\u003e Heatmap showing the top 100 enrichment clusters, one row per cluster, using a discrete color scale to represent statistical significance. Grey color indicates the lack of enrichment for that term in the corresponding gene list, light yellow color indicates boundary between significance and insignificance, deep yellow color indicates a high degree of significance. \\u003cstrong\\u003e(D)\\u003c/strong\\u003e Enrichment network visualization for results from the three protein lists, where nodes are represented by pie charts indicating their associations with each input list. Cluster labels were added manually. Color code represents the identities of protein lists, where blue indicates LM vs. CTRL, red indicates HG vs. CTRL and green indicates LG vs. HG. \\u003cstrong\\u003e(E)\\u003c/strong\\u003e Visualization of PPI network and MCODE components identified from the combined list of proteins, where each node represents a protein with a pie chart encoding its origin. Color codes for pie sectors represent a protein list.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3953334/v1/8e18bbc6b0bca62e7a940b2d.png\"},{\"id\":51199741,\"identity\":\"0d78699b-d270-43b2-93f4-a7bf7dcc5978\",\"added_by\":\"auto\",\"created_at\":\"2024-02-15 20:28:45\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":385991,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eValidation of MUC13 expression levels in PMP. (A) \\u003c/strong\\u003eProtein expression levels of MUC13 in soft (SM – green bars) and hard (HM – blue bars) mucin [low (LG-PMP; n=4) and high-grade (HG-PMP; n=4)] compared to control tissues (n=4; no tumoral appendix) evaluated by Western Blot. The arbitrary densitometric unit (ADU) for each protein was normalized by the Total Protein Normalization (TPN) value. \\u003cstrong\\u003e(B)\\u003c/strong\\u003e Ample cohort validation of MUC13 by ELISA quantitation in soft (SM) and hard mucin (HM) samples [low (LG-PMP) and high-grade (HG-PMP)] compared to control tissues (n=16) (number of PMP samples analysed is indicated in the bars of the graph). One way ANOVA analysis was carried out with multiple comparisons (LG and HG-PMP vs Control). * p\\u0026lt;0.05 and *** p\\u0026lt;0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3953334/v1/9e88434ad0df47acddc245d7.png\"},{\"id\":51199876,\"identity\":\"ffb60466-3812-47b6-8bc2-02571da4c0f2\",\"added_by\":\"auto\",\"created_at\":\"2024-02-15 20:36:46\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3572882,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3953334/v1/953e0b0e-501e-43da-9f22-609e4385b1e6.pdf\"},{\"id\":51199739,\"identity\":\"d28ea3b1-277b-4131-a75e-f1d714d6697a\",\"added_by\":\"auto\",\"created_at\":\"2024-02-15 20:28:45\",\"extension\":\"xlsx\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":19727,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementalexcel1.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3953334/v1/7bfcd68cd1588b57597ab89f.xlsx\"},{\"id\":51199743,\"identity\":\"09547ce2-1919-4674-83c8-c490a1fb9d45\",\"added_by\":\"auto\",\"created_at\":\"2024-02-15 20:28:45\",\"extension\":\"docx\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":3530629,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarymaterial.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3953334/v1/c4cdd28b383e857c22eb8c35.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Breaking the mucin barrier: a new affinity chromatography-mass spectrometry approach to unveil potential cell markers and pathways altered in Pseudomyxoma peritonei\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003ePseudomyxoma peritonei (PMP) is a rare, malignant disease defined by a progressive, abundant, and multifocal accumulation of mucinous tumour tissue within the peritoneal cavity, essentially without extraperitoneal growth nor distant metastases [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. It has historically been considered a terminal condition for which debulking surgery or palliative treatments were deemed the only therapeutic options available to date. In recent years, some reference groups have published results showing benefits on patient survival when treating PMP with cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) [\\u003cspan additionalcitationids=\\\"CR2 CR3 CR4\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. However, recurrence of this condition is common despite this therapeutic effort, with subsequent progression and death occurring in the absence of an effective treatment.\\u003c/p\\u003e \\u003cp\\u003eDiagnosing this entity is considered to be challenging, as the tumour can remain silent, simulate an episode of acute appendicitis, or be disguised by an abdominal disease. Besides, there are no specific tumour markers other than a biopsy. Therefore, the different nomenclatures and classifications of the disease have always been under debate. In this context, the most successful classification protocol is that established by the Peritoneal Surface Oncology Group International (PSOGI), which has recently been validated and includes three subtypes of PMP: i) low-grade mucinous carcinoma (LG-PMP), ii) high-grade mucinous carcinoma (HGPMP), and iii) PMP with the presence of signet ring cells (SRC-PMP) [\\u003cspan additionalcitationids=\\\"CR4 CR5 CR6\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Acellular mucin was excluded from this classification. This protocol has been validated and improved by our group [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], including a proposal of a new classification within the high-grade subtype based on tumour marker Ki67 [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe fast development of personalized therapies for oncology patients has highlighted the importance of identifying distinct molecular profiles within the same histologic tumour type. In this context, the search for personalized therapeutic solutions has transformed the molecular classification of tumours into a critical step of clinical decisionmaking. Only a few molecular studies performed on cases of PMP have been published to date, which were all summarized by LundAndersen et al. in a recent narrative review [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Although some of the genes and proteins described in these studies were proposed as biomarkers, their usefulness is still limited and none of them were validated as a potential therapeutic target. Furthermore, to our knowledge, there are no studies describing the protein profile of this mucinous tumour or any other peritoneal mucinous carcinomatosis, most likely because the high concentration of glycoproteins, together with the inter-mucin physical and chemical junctions, interfere with any currently available protein isolation protocol [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOverall, mucins are a family of heavily glycosylated proteins, also known as glycoproteins, which are expressed in specialized epithelial cells of mucosal surfaces, including those of the respiratory, gastrointestinal, and urogenital tracts [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Mucins are classified into two subtypes: membrane-associated and secreted. Membrane-associated mucins act as sensors of the extracellular medium and are capable of activating intracellular signal transduction pathways when the mucous layer is disrupted. Secreted mucins constitute a physical barrier for epithelial cells against both microorganisms and insoluble materials and maintain the local molecular environment [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Under normal conditions, mucin production and degradation undergo a well-established metabolic turnover. However, in PMP, mucin is secreted and gradually deposited within the peritoneal cavity, where it become hard to break down and eliminate. Over time, mucin accumulates, causing the symptomatology associated with PMP, and can even surround and protect tumour cells against the host\\u0026rsquo;s immune response or the effect of chemotherapeutic agents [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. An important finding made in several studies is the presence within the mucus of non-mucin proteins, such as digestive enzymes, dietary proteins, immunoglobulins, albumins, and keratin, which directly interact with mucin proteins through strong, non-covalent interactions and enhance the viscosity of PMP secretions [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAs in other rare diseases, both the low incidence of PMP (1\\u0026ndash;3 cases per one million people/year) coupled to the lack of molecular studies focused on this condition, hamper to attain a detailed description of its pathophysiology and thereby develop of efficient, novel diagnostic, prognostic, or therapeutic strategies [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. To help solving this issue, this study provides a description of the first protocol available to isolate massive proteins from mucin sourced from soft and hard tumour samples, which will undoubtedly advance the research in this field, as it enabled us to readily identify a potential tumour cell marker.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e \\u003cem\\u003eExperimental Design and Statistical Rationale\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eSoft and hard mucin samples were obtained from patients diagnosed with PMP of appendiceal origin who were being treated in our center, as not all mucin PMP samples have a similar texture, compactness and hardness [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. From a prospective cohort of 29 patients, we selected 18 low-grade (LG-PMP), including 15 soft mucin (SM) samples and 11 hard mucin samples (HM), and 11 high-grade (HG-PMP), including 8 SM and 11 HM tumour samples. It is important to take in account that soft and hard mucin samples were included for most of the patients. Additionally, we obtained 16 control tissue samples, nine of which corresponded to appendiceal samples acquired from a prophylactic appendectomy performed due to another medical condition and seven nontumorous colon tissue samples collected from PMP patients during a CRS with HIPEC procedure. All samples were immediately sent to the Anatomy Pathology Unit for histological examination by experienced pathologists to confirm the diagnosis. The remaining samples were then snap frozen and stored in the Biobank until requested for analysis. Importantly, all tissue samples were frozen within 30 minutes from resection. The demographic and histology findings of these examinations are detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. The samples used in each experiment are indicated in the figure legends. All patients signed the corresponding informed consent form, and the study was approved by our local ethics committee (protocol code PI19/01603).\\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\\u003eDemographic and histology results from PMP patients.\\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=\\\"left\\\" 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\\u003e\\u003cem\\u003eNon tumoral\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eTumour\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLow grade\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHigh grade\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ePatients, [n]\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eSoft mucin, [n]\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eHard mucin, [n]\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAge, [median (IQR)]\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e53 (20\\u0026ndash;86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57 (27\\u0026ndash;79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e58 (23\\u0026ndash;74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eWomen, (%)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e52,9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e61,1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e63,6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e* Soft and hard mucin samples were included for most of the patients. IQR: interquartile range.\\u003c/p\\u003e \\u003cp\\u003eAll statistical analyses were performed using software Prism v.8.0 (GraphPad Software, La Jolla, CA, USA), except the PLS-DA analyses, which were performed with MetaboAnalyst v.5.0 (McGill University, Quebec, Canada). Volcano plots were generated using R language v.4.1.2. Regarding the proteomic results, Welch ttests were applied to assess the existence of statistical differences between groups. All data are presented as a mean \\u0026plusmn; standard error of the mean (SEM). Unless otherwise stated, a one-way analysis of variance (ANOVA) followed by posthoc Bonferroni tests were applied. \\u003cem\\u003eP\\u003c/em\\u003e-values below 0.05 were considered significant. Asterisks (* \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, ** \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, *** \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) indicate statistically significant differences.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eLiquid chromatography and proteins isolation.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eMucin samples (\\u0026sim;1 g) were dissected into small fragments (1\\u0026ndash;3 mm) and homogenized in 3.5\\u0026ndash;4.5 ml of binding buffer (20 mM Tris, 500 mM NaCl, 1 mM MnCl\\u003csub\\u003e2\\u003c/sub\\u003e, and 1 mM CaCl\\u003csub\\u003e2\\u003c/sub\\u003e) using ultrasonic pulses. Following their centrifugation and filtration through a 0.22-\\u0026micro;m filter, 3\\u0026ndash;4 ml of each sample were loaded into the \\u0026Auml;kta Purifier (Cytiva, MA, USA), which was previously fitted with the Hitrap Con A 4B column (Cytiva, MA, USA). Glycoproteins, polysaccharides, and glycolipids were captured in the column and the rest of the sample was collected in 0.4 ml fractions corresponding to the maximum absorbance peaks. All collected fractions were then precipitated with four volumes of cold acetone, centrifuged, and the pellets were resuspended in 1.5 ml of binding buffer (20 mM NaH\\u003csub\\u003e2\\u003c/sub\\u003ePO\\u003csub\\u003e4\\u003c/sub\\u003e and 150 mM NaCl). All fractions were mixed in a single tube, centrifuged, and filtered. Next, 0.5ml of this filtered extract was loaded once again into the \\u0026Auml;kta Purifier, which was previously fitted with the HiTrap Albumin and IgG Depletion column (Cytiva, MA, USA). As in the previous step, IgG and albumins were captured in the column and the rest of the sample was collected in 0.2ml fractions corresponding to the maximum absorbance peaks. These fractions were then precipitated with four volumes of cold acetone and some of them were used to perform a mass spectrometry analysis. The rest of the fractions were centrifuged, and the pellets were resuspended in 100 \\u0026micro;l of DIGE lysis buffer (0.1 mM urea, 1 M thiourea, 30 mM Tris, and 4% (w/v) 3-[(3-cholamidopropyl)dimethylammonium]-1-propanesulfonate) for the Western Blot and ELISA measurements.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eSWATH-MS protein quantification.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe proteomic analysis was performed in the proteomics facility of the SCSIE (University of Valencia). Purified protein extracts obtained by liquid chromatography were precipitated and dissolved in 100 \\u0026micro;l of lysis buffer (EasyPep\\u0026trade; Mini MS Sample Prep Kit; Thermo Fisher Scientific). The samples were then quantified using the Qubit\\u0026trade; Protein Assay Kit (Thermo Fisher Scientific) and 20 \\u0026micro;g of each protein extract were digested and cleaned using the EasyPep\\u0026trade; Mini MS Sample Prep Kit (Thermo Fisher Scientific) following the manufacturer\\u0026rsquo;s instructions. Next, the samples were dried and resuspended with 2% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA) to a final concentration of 1 \\u0026micro;g/\\u0026micro;l.\\u003c/p\\u003e \\u003cp\\u003eThe samples were analyzed using a SWATH DIA approach for massive protein quantitation:\\u003c/p\\u003e \\u003cp\\u003eFirstly, an equal amount of all samples from each group were pooled and subjected to a shotgun analysis to build a peptide spectral library. Each pooled sample was analyzed twice by data-dependent acquisition (DDA) nanoscale liquid chromatography, followed by tandem mass spectrometry (nano LCMS/MS) runs using LC system Ekspert\\u0026trade; nanoLC425 (Eksigent, Dublin, CA, USA) coupled to a Triple TOF\\u0026reg; 6600+ (Sciex, Redwood City, CA, USA) mass spectrometer system. Next, 3 \\u0026micro;l of each peptide mixture sample were loaded by the nanoLC425 system into a trap column (3 \\u0026micro;m C18-CL, 350 \\u0026micro;m x 0.5 mm; Eksigent) and desalted with 0.1% TFA at a flow of 5 \\u0026micro;l/min for five minutes. The peptides were then eluted into an analytical column (3 \\u0026micro;m C18-CL 120 Ᾰ, 0.075 x 150 mm; Eksigent) equilibrated in 5% ACN and 0.1% formic acid (FA). The peptide elution was carried out using a 60minute linear gradient of 7\\u0026ndash;40% buffer B (buffer A: 0.1% FA diluted in water; buffer B: 0.1% FA diluted in ACN) at a flow rate of 300 nl/min.\\u003c/p\\u003e \\u003cp\\u003eEluted peptides were ionized in a Optiflow\\u0026thinsp;\\u0026lt;\\u0026thinsp;1 uL Nano Source, applying 3.0 kV to the spray emitter at 175 \\u0026ordm;C, and analyzed in a data-dependent mode. Survey MS1 scans were acquired from 350\\u0026ndash;1400 m/z for 250 ms. The quadrupole resolution was set to \\u0026lsquo;LOW\\u0026rsquo; for MS2 experiments, which were acquired 100\\u0026ndash;1500 m/z for 25 ms in \\u0026lsquo;high sensitivity\\u0026rsquo; mode. Following switch criteria were used: charge: 2\\u0026thinsp;+\\u0026thinsp;to 4+; minimum intensity; 250 counts per second (cps). Up to 100 ions were selected for fragmentation after each survey scan. Dynamic exclusion was set to 15 s. The rolling collision energies equations were set for all ions as +\\u0026thinsp;2 ions, according to the following equation: |CE|=(0.049)x(m/z)+(2).\\u003c/p\\u003e \\u003cp\\u003eFor quantitation the Triple TOF\\u0026reg; was operated as above but in SWATH mode, in which a 0.050second TOF MS scan ranging between 350 m/z and 1250 m/z was performed. In addition, 0.080-s product ion scans in 100 variable windows ranging between 400 m/z and 1250 m/z were acquired throughout the experiment.\\u003c/p\\u003e \\u003cp\\u003ePeptide and protein identifications were achieved using software Protein Pilot v5.0 (Sciex). The Paragon algorithm[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e] was used to search the Swissprot_200601.fasta database with the following parameters: trypsin specificity, iodoacetamide (IAM) cysteine alkylation, and taxonomy restricted to \\u003cem\\u003eHomo sapiens\\u003c/em\\u003e. The MS/MS spectra of the identified peptides were used to generate a spectral library for the SWATH peak extraction using the add-in for software PeakView v2.1 (Sciex) MS/MSALL with SWATH Acquisition MicroApp v2.0 (Sciex). Peptides with a confidence score \\u0026ge;95%, as reported in the Protein Pilot database search, were included in the spectral library.\\u003c/p\\u003e \\u003cp\\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e] partner repository with the dataset identifier PXD037364.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003ePrediction of protein interactions and pathway enrichment analysis.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe differentially expressed proteins identified in the SWATH quantitative analysis were imported into the Metascape database[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e], which was used to perform protein-protein interaction enrichment analyses and functional enrichment analysis. For protein-protein interaction enrichment analyses, Metascape applies a mature complex identification algorithm called Molecular Complex Detection (MCODE) to automatically extract protein complexes embedded in a large network[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. For functional enrichment analysis, the number of minimal overlap and enrichment factor were 1.5 and p-value cut-off was 0.05.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eWestern Blot.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe protein concentration of each sample obtained by liquid chromatography was determined using the RC DC\\u0026trade; Protein Assay (Bio-Rad). The samples\\u0026rsquo; absorbance was read at a wavelength of 750 nm in a standard spectrophotometer (Beckman DU530). To summarize, 5 \\u0026micro;g of the total protein sample were subjected to SDS-PAGE on 9% polyacrylamide gels, electro-transferred to polyvinylidene difluoride membranes (Millipore), and probed overnight at 4\\u0026deg;C in the presence of the corresponding primary antibody (anti-MUC13 [1/500; Abcam]). Horseradish peroxidase-conjugated secondary antibody (anti-rabbit [1/5000; Abcam]) was used for protein detection and a chemiluminescence ECL Western Blotting Substrate (Thermo Fisher Scientific) was applied. Protein levels were normalized using the total line normalization (TLN) method, whereby the intensity value of the total protein present in each sample was calculated. Moreover, a densitometric analysis of the resulting protein bands was conducted using Image J.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eELISA measurements.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003e Protein extracts obtained by liquid chromatography were also used to determine the presence of MUC13 using a human commercial ELISA kit (MUC13 [MBS507589; MyBiosource] according to the manufacturer\\u0026acute;s instructions.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAdapted method to isolate proteins from mucin (AMIPROM)\\u003c/h2\\u003e \\u003cp\\u003eAs we mentioned above, the molecular characterization of PMP is scarce and there are only a few papers showing some mucin isoforms (mainly MUC2 and MUC5AC) analysis by Western Blot [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Importantly, the outcome of these experiments were highly smeared band patterns due to the high content of mucin proteins and their interactions. Consequently, those protein extracts are unsuitable for high-throughput proteomic analyses.\\u003c/p\\u003e \\u003cp\\u003eTo solve this problem, we designed the first method specifically adapted to mucin samples to extract and isolate massive proteins with a high quality and purity for their use in proteomic analyses (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). In detail, one gram of both soft and hard mucin samples (low and highgrade PMP), as well as control samples, were dissected into small fragments and homogenised using ultrasounds. Then, the samples were filtered and processed through two consecutive affinity chromatography steps. First, they were run through the chromatographer using a HiTrap Con A 4B column to capture glycoproteins, polysaccharides, and glycolipids specifically. The flowthroughs, corresponding to the maximum absorbance peaks identified in the two-dimensional plot chromatogram, were collected and precipitated to be used in the next step. The collected fractions were centrifuged and filtered once again and subsequently run a second time through the chromatographer using a HiTrap Albumin and IgG Depletion column to capture albumins and immunoglobulin G (IgG) specifically. As in the previous step, the flowthrough fractions, corresponding to the maximum absorbance peaks identified in the chromatogram, were collected and precipitated in acetone to be used for the mass spectrometry (MS) analysis.\\u003c/p\\u003e \\u003cp\\u003eFinally, all protein samples were precipitated, quantified, and properly prepared (see \\u003cspan refid=\\\"Sec2\\\" class=\\\"InternalRef\\\"\\u003eMethods\\u003c/span\\u003e section) to perform a nanoliquid chromatography coupled to tandem mass spectrometry (nanoLC/MSMS) using a sequential window acquisition of all theoretical mass spectra (SWATH-MS) data-independent acquisition (DIA) approach for massive protein quantification.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eProteomic profile in Pseudomyxoma peritonei\\u003c/h2\\u003e \\u003cp\\u003eSoft and hard (low and high-grade PMP) mucin samples processed by \\u003cem\\u003eAMIPROM\\u003c/em\\u003e were used to determine the proteomic profile of PMP to identify intracellular pathways altered in PMP, as well as potential tumoral cell markers, through the application of quantitative proteomics. Healthy samples from an appendectomy carried out for an unrelated medical condition or of normal colon tissue were used as controls, given the lack of proper non-tumoral mucin samples (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eNano-LC/MS-MS equipped with SWATH for label-free quantitative proteomics enabled us to create the first proteome profile described in PMP. Considering all these proteins, a partial least squares-discriminant analysis (PLS-DA) revealed a clear discrimination pattern between the proteomic profile of the soft mucin, hard mucin, and control tissue samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). Furthermore, using a log2fold change difference\\u0026thinsp;\\u0026gt;\\u0026thinsp;1 and a \\u003cem\\u003ep\\u003c/em\\u003e-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 to determine differentially expressed proteins compared with the control tissues, we identified 93 upregulated and 243 downregulated proteins in the soft mucin samples and 86 upregulated and 27 downregulated proteins in the hard mucin samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB). Considering that mucins are the main proteins that characterize this entity, we then proceeded to identify the different mucin isoforms detected in the analyzed PMP subtypes, detecting a differential pattern of mucin isoforms between the soft and hard mucin samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC). Specifically, we detected MUC2, MUC5AC, MUC5B, MUC6, and MUC13 in the soft mucin samples compared to the control tissues, with MUC2, MUC5AC, MUC5B, and MUC6 being significantly upregulated in these samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC; left graph) and MUC1, MUC2, MUC4, MUC5AC, MUC5B, and MUC13 in the hard mucin samples, with MUC2, MUC5AC, and MUC13 being significantly upregulated in these samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC; right graph). MUC2, despite being depleted by the glycoprotein affinity column, was the most expressed mucin isoform in all cases.\\u003c/p\\u003e \\u003cp\\u003eWe then performed an analysis to compare low and highgrade PMP soft mucin samples, which revealed that PLS-DA could perfectly segregate both groups and clearly distinguish them from the control samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD). Taking account only differentially expressed proteins compared with the control tissue samples, volcano plots showed 72 upregulated and 220 downregulated proteins in the lowgrade soft mucin samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE-left panel) and 19 upregulated and 380 downregulated proteins in the highgrade soft mucin samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE-right panel). As we mentioned above, although we found a different pattern of mucin isoforms between soft and hard mucin samples, no significant differences were found between the mucin isoforms identified in the low and high-grade soft mucin samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eF). In the same line, the PLS-DA performed with low and high-grade hard mucin samples did reveal a clear discrimination pattern between the whole proteomic profile of these samples and that of the control tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eG). Of note, as illustrated by the volcano plots (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eH), the number of differentially expressed proteins was substantially higher for soft mucin than for hard mucin compared with the control tissues. Furthermore, MUC5AC and MUC13 expression levels were significantly upregulated in low-grade but not high-grade hard mucin samples compared with control tissues. Nevertheless, there were no statistically differences in their expression between low- and high-grade hard mucin samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eI).\\u003c/p\\u003e \\u003cp\\u003eFinally, we analysed the extracts captured in the two columns used during the protocol to evaluate how many proteins were depleted along with glycoproteins, albumins and immunoglobulins. To do this, we run an electrophoresis on the extracts from the columns and the main bands were excised and analysed by mass spectrometry (see Supplemental Methods). The results are shown in Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e, Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e and Supplemental Excel 1. The results were analysed using a cut-off of the 1% spectral count value of the most abundant protein identified for each column. Briefly, a total of 85 proteins were identified in the extracts captured on the HiTrap Con A 4B colum, of which 82 were secreted proteins (including glycoproteins), 15 were cell membrane proteins and 7 were cytosolic proteins. It is important to note that most of the cell membrane and cytosolic proteins are also considered secreted proteins. For the HiTrap Albumin and IgG Depletion column, a total of 16 proteins were identified, all of which were secreted proteins (including albumin and immunoglobulins) and 7 of which were cell membrane proteins. In addition, 10 out of 15 (66.7%) of the identified cell membrane proteins captured in the HiTrap Con A 4B column and all (100%) of the identified cell membrane proteins captured in the HiTrap Albumin and IgG Depletion column were immunoglobulins, which were one of the targets to be depleted.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSoft and hard mucin tissues are highly similar at functional level\\u003c/h2\\u003e \\u003cp\\u003eTo explore the functional relevance of soft and hard mucin PMP tissues, all differentially expressed proteins found in SM and HM compared to control tissues were analysed using the Metascape database. In this sense, we found an 84.2% of unique proteins in SM compared to controls and 53.1% in HM samples compared to controls, with only a small fraction of proteins shared between both comparisons, as illustrated by the Circos plot (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). Interestingly, although most of the differentially expressed proteins were different between both comparisons, they shared a high number of enriched pathways and processes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB-C). Indeed, from the top 100 enriched terms included in the heatmap (where the colour scale represents statistical significance), most of them were significantly altered in both comparisons (pattern 1), with the terms \\u0026ldquo;Golgi lumen\\u0026rdquo; and \\u0026ldquo;Extracellular vesicles in the crosstalk of cardiac cells\\u0026rdquo; enriched exclusively in the comparison HM vs. control (pattern 3) and the terms \\u0026ldquo;Cellular aldehyde metabolic process\\u0026rdquo;, \\u0026ldquo;Biological oxidations\\u0026rdquo;, \\u0026ldquo;Negative regulation of cell migration\\u0026rdquo;, among others, enriched exclusively in the comparison SM vs. control (pattern 2) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). In general, an elevated number of enriched processes were related with extracellular matrix (including \\u0026ldquo;extracellular matrix organization\\u0026rdquo;, \\u0026ldquo;Naba matrisome associated\\u0026rdquo;, \\u0026ldquo;Naba core associated\\u0026rdquo;, \\u0026ldquo;collagen binding\\u0026rdquo;, \\u0026ldquo;focal adhesion\\u0026rdquo;, etc.), regulation of cytoskeleton (including \\u0026ldquo;actomyosin structure organization\\u0026rdquo;, cortical cytoskeleton organization\\u0026rdquo;, \\u0026ldquo;structural constituent of cytoskeleton\\u0026rdquo;, etc), metabolism (including \\u0026ldquo;Glycolysis/Gluconeogenesis\\u0026rdquo;, \\u0026ldquo;Carbon metabolism\\u0026rdquo;, \\u0026ldquo;metabolism of carbohydrates\\u0026rdquo;, \\u0026ldquo;pyruvate metabolism and Citric Acid (TCA) cycle\\u0026rdquo;, etc), and signalling pathways highly related with cancer (including \\u0026ldquo;VEGFA VEGFR2 signalling\\u0026rdquo;, \\u0026ldquo;EPH-Ephrin signalling\\u0026rdquo;, \\u0026ldquo;signalling by Rho GTPases\\u0026rdquo;, \\u0026ldquo;regulation of MAPK cascade\\u0026rdquo;, etc).\\u003c/p\\u003e \\u003cp\\u003eTo facilitate the understanding of pathways/processes that are shared between the two comparisons, additional visualizations were developed. Thus, an enrichment network visualization including the results from both comparisons confirmed the same results as the heatmap, showing an overlap of biological processes by both soft and hard mucin tissues, as these proteins probably are likely to capture different parts of the same biological processes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). Furthermore, a protein-protein interaction (PPI) network was also generated to elucidate common/selective functional clusters. In this sense, 9 different clusters were identified based on the MCODE algorithm, which most of them were shared between both comparisons and only cluster 9 (related to regulation of Insulin-like growth factor transport and uptake) was specifically enriched in SM vs. CTRL. Cluster 6 (related to signalling by ROBO receptors and metabolism of amino acids) was also mainly enriched in SM vs. CTRL (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLow and high-grade soft mucin tissues share a high number of biological processes/pathways\\u003c/h2\\u003e \\u003cp\\u003eNext, we wanted to explore the functional relevance of low and high-grade SM PMP tissues. Therefore, we compared all differentially expressed proteins found in LG and HG SM compared to control tissues and in LG compared to HG using the Metascape database. In this case, the Circos plot showed an elevated number of differentially altered proteins shared between the three comparisons, with only 28.4% of unique proteins in LG vs. CTRL, 40.6% of unique proteins in HG vs. CTRL and 20.6% of unique proteins in LG vs. HG (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). Interestingly, 94% of the proteins in the LG vs. HG comparison were common to the HG vs. CTRL protein list, suggesting that most of these proteins are specifically altered in HG. In terms of functional enrichment, an increased number of biological processes/pathways were found to be altered in both LG vs. CTRL and HG vs. CTRL, which was corroborated by comparing all differentially altered proteins in LG vs. HG, showing a high number of common enriched terms between LG and HG (grey colour; pattern 2) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB-C). Among these common terms, we found processes and pathways related to protein regulation (e.g. \\u0026ldquo;unfolded protein binding\\u0026rdquo;, \\u0026ldquo;protein homodimerization activity\\u0026rdquo;, \\u0026ldquo;positive regulation of protein localization\\u0026rdquo;, regulation of protein stability\\u0026rdquo;, \\u0026ldquo;negative regulation of protein polymerization\\u0026rdquo;, etc), metabolism (e.g. \\u0026ldquo;biological oxidations\\u0026rdquo;, \\u0026ldquo;cellular aldehyde metabolic process\\u0026rdquo;, \\u0026ldquo;generation of precursor metabolites and energy\\u0026rdquo;, \\u0026ldquo;pyruvate metabolism and citric acid (TCA) cycle\\u0026rdquo;, etc.), and extracellular matrix (e.g. \\u0026ldquo;glycosaminoglycan binding\\u0026rdquo;, \\u0026ldquo;extracellular matrix structural constituent\\u0026rdquo;, \\u0026ldquo;cell-cell junction\\u0026rdquo;, \\u0026ldquo;collagen-containing extracellular matrix\\u0026rdquo;, etc.). In addition, we found some enriched terms that were differentially altered between LG and HG (pattern 1), which could be used to understand the differences between these two grades of the disease. Some of these terms were related to the regulation of the cytoskeleton (e.g. \\u0026ldquo;establishment or maintenance of cell polarity\\u0026rdquo;, \\u0026ldquo;cortical cytoskeleton organization\\u0026rdquo;, \\u0026ldquo;cytoskeleton-dependent cytokinesis\\u0026rdquo;, \\u0026ldquo;endocytosis\\u0026rdquo;, \\u0026ldquo;regulation of vesicle-mediated transport\\u0026rdquo;, \\u0026ldquo;regulation of actin-filament organization\\u0026rdquo;, etc), signalling pathways highly related with cancer (e.g. \\u0026ldquo;G13 signalling pathway\\u0026rdquo;, \\u0026ldquo;nuclear receptors meta pathway\\u0026rdquo;, VEGFA VEGFR2 signalling\\u0026rdquo;, and \\u0026ldquo;gene and protein expression by JAK-STAT signalling after Interleukin-12 stimulation\\u0026rdquo;), metabolism (e.g. \\u0026ldquo;glycolysis/gluconeogenesis\\u0026rdquo;, \\u0026ldquo;monocarboxylic acid metabolic process\\u0026rdquo;, \\u0026ldquo;small molecule catabolic process\\u0026rdquo;, \\u0026ldquo;isomerase activity\\u0026rdquo;, \\u0026ldquo;peptidase activity\\u0026rdquo;, etc.), and other important extracellular matrix-related pathways such as \\u0026ldquo;Proteoglycans in cancer\\u0026rdquo;. The pathway \\u0026ldquo;amino sugar and nucleotide sugar metabolism\\u0026rdquo; was also found to be altered in LG vs. HG and in HG vs. CTRL, but not in LG vs. HG, suggesting that this pathway might be specifically altered in HG-PMP samples (pattern 3; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC). Consistent with these results, the enrichment network visualization showed the same overlapping pattern, with most of the enriched terms shared between HG vs. CTRL (red) and LG vs. CTRL (blue), and only a few of them also shared with the LG vs. HG comparison (green) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD). Furthermore, the PPI network revealed 7 different functional clusters, all of them related to the cytoskeleton, signalling pathways and metabolism, and all shared between the three protein lists (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE; Table S3).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eIdentification of functional enrichment terms specifically associated with LG or HG hard mucin tissues\\u003c/h2\\u003e \\u003cp\\u003eWe then examined the functional relevance in LG and HG HM tissues as we did above with SM tissues. Circos plot analysis showed a higher number of differentially altered proteins in the LG vs. HG comparison in HM compared to the number of differentially altered proteins found in SM (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA and \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). In general, the number of shared proteins between the three protein lists in HM tissues was smaller than that observed in SM tissues, with a higher number of unique proteins (48% of unique proteins in LG vs. CTRL, 61.3% in HG vs. CTRL and 36.9% in LG vs. HG) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). The functional enrichment derived from these protein lists generated a heatmap with five different patterns according to the distribution of the enriched terms. Thus, patterns 1 and 4 (grey colour) show all common enriched terms found between LG and HG, but differentially altered compared to control tissues. This is also illustrated in the Circos plot (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB-C). Pattern 2 includes all enriched terms that are able to discriminate LG from HG HM tissues. Among these terms we found processes/pathways related to metabolism (e.g. \\u0026ldquo;amino sugar and nucleotide sugar metabolism\\u0026rdquo;, \\u0026ldquo;small molecule catabolic process\\u0026rdquo;, \\u0026ldquo;glycolysis/gluconeogenesis\\u0026rdquo;, etc.), cellular homeostasis and detoxification (e.g. \\u0026ldquo;detoxification of reactive oxygen species\\u0026rdquo;, \\u0026ldquo;cellular detoxification\\u0026rdquo;, \\u0026ldquo;programmed cell death\\u0026rdquo; and immune response (e.g. \\u0026ldquo;acute-phase response\\u0026rdquo;, \\u0026ldquo;innate immune response\\u0026rdquo;, \\u0026ldquo;complement and coagulation cascades\\u0026rdquo;, etc). Additionally, patterns 3 and 5 revealed differentially enriched terms that are specifically associated with LG or HG HM-PMP tissues. For example, pattern 3 included important cellular activities such as \\u0026ldquo;peptidase activity\\u0026rdquo;, \\u0026ldquo;lyase activity\\u0026rdquo;, \\u0026ldquo;hydrolase activity\\u0026rdquo;, and also other processes such as \\u0026ldquo;organic acid binding\\u0026rdquo;, \\u0026ldquo;insulin-like growth factor binding\\u0026rdquo; and \\u0026ldquo;protein-folding chaperone binding\\u0026rdquo;, which might be specifically associated with LG HM tissues, since they were altered in LG vs. HG and LG vs. CTRL, but not in HG vs. CTRL. In the same line, pattern 5 included important metabolic processes such as \\u0026ldquo;pyruvate metabolism\\u0026rdquo; and \\u0026ldquo;cellular aldehyde metabolic process\\u0026rdquo; and others such as \\u0026ldquo;protein tetramerization\\u0026rdquo; and \\u0026ldquo;intramolecular phosphotransferase activity\\u0026rdquo; that might be associated with HG HM tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC).\\u003c/p\\u003e \\u003cp\\u003eAs before, to better understand the pathways/processes that are shared between the three comparisons, we generated the enrichment network visualization and the PPI network. In the enrichment network we found that most of the enriched terms were shared by the three protein lists, as they mainly represented patterns 1 and 2 from the heatmap. Nevertheless, processes such as \\u0026ldquo;vesicle-mediated transport\\u0026rdquo; and \\u0026ldquo;programmed cell death\\u0026rdquo; were mainly associated with LG vs. CTRL and LG vs. HG, suggesting that these processes were mainly altered in LG-PMP tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD). Furthermore, the PPI network revealed 12 different functional clusters, all of them related to the proteasome, collagens, mRNA processing, complement activation and secretory granule lumen. However, although no cluster was found to be specific to any protein list, cluster 2, 8 and 10 were mainly enriched in HG vs. CTRL and LG vs. HG protein lists (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eE; Table S4).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eValidation of MUC13 alteration in soft and hard mucin samples of Pseudomyxoma peritonei\\u003c/h2\\u003e \\u003cp\\u003eAs we mentioned before, mucin isoforms are the main entity that characterize this pathology. Interestingly, we observed a differential mucin isoform pattern between SM and HM tissues, with MUC13 being the only membrane-associated mucin found altered (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), which make him a potential candidate to be considered a cell tumour marker or a cell therapeutic target. For these reasons, MUC13 was quantified by Western Blot and an enzyme-linked immunosorbent assay (ELISA) in soft and hard mucin samples obtained from patients with PMP (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). In the Western Blot analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA), MUC13 showed a significant higher expression in LG and HG SM and in LG HM tissues compared to control tissues, and also was found overexpressed in LG and HG SM compared to HM. Additionally, MUC13 was quantified by an ELISA in a larger cohort of PMP samples, where was found overexpressed in LG SM samples compared to control tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB). Importantly, MUC13 was not found in the depleted extracts (Supplemental Excel 1).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe aberrant production of mucin, the most common phenotype of PMP, has impeded the isolation of proteins from mucus-producing cells because its structure is intrinsically designed to avoid invasion by external agents, such as microorganisms or insoluble material [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. In this study, we describe the first protocol designed to isolate proteins in this context and present the first proteomic profile of PMP. Bioinformatic analyses have validated these pioneering data, and a new potential tumoral cell marker for PMP has been identified.\\u003c/p\\u003e \\u003cp\\u003eThe main limitation in improving the pathophysiology of rare diseases and finding therapeutic solutions to these conditions is the availability of samples. However, in the specific case of PMP, the lack of detailed and efficient protein isolation protocols allowing to conduct functional studies is even more limiting. In this sense, all molecular studies published in relation to PMP are based on histological data and expression analyses of genes already described in other types of cancer, mainly colon and appendiceal neoplasms. Tumour-related genes \\u003cem\\u003eKRAS\\u003c/em\\u003e, \\u003cem\\u003eGNAS\\u003c/em\\u003e, \\u003cem\\u003eFAT4\\u003c/em\\u003e, \\u003cem\\u003eTGFBR1\\u003c/em\\u003e, \\u003cem\\u003eTP53\\u003c/em\\u003e, and \\u003cem\\u003eSMAD3/4\\u003c/em\\u003e may be mutated in this context, with \\u003cem\\u003eKRAS\\u003c/em\\u003e and \\u003cem\\u003eGNAS\\u003c/em\\u003e being the genes most frequently mutated and most studied ones in PMP [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. However, there is still significant controversy in this regard because of the lack of information about the functional implications of these transcriptional alterations [\\u003cspan additionalcitationids=\\\"CR24\\\" citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Nevertheless, apart from a few studies on the expression and mutation of certain tumour-related genes, no analyses have been published that focus on the actual protein component involved in these tumours.\\u003c/p\\u003e \\u003cp\\u003eTo resolve this handicap and to isolate proteins from PMP tumour samples, we designed the AMIPROM protocol relying on affinity liquid chromatography based on the depletion of mucusforming glycoproteins, IgG, and albumins. In addition, AMIPROM made it possible for us to develop the first PMP protein library, which enabled the conduct of a differential expression analysis using mass spectrometry DIA that revealed more than 300 deregulated proteins. Thus, for the first time, this protocol breaks the mucin barrier in PMP and allows access to the protein fraction in this rare tumour type, opening new perspectives for other more prevalent mucinous tumours, such as mucinous colorectal cancer (CRC), which accounts for 10% \\u0026minus;\\u0026thinsp;20% of CRC patients [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Moreover, this protocol is a reliable option for searching for potential tumour cell membrane markers, as only secreted proteins were depleted along with the most abundant glycoproteins, albumin and immunoglobulins.\\u003c/p\\u003e \\u003cp\\u003eThe current diagnostic and prognostic methods used in PMP are based on histological data [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Developing a classification method based on molecular characterization is mandatory in this scenario. The first PMP protein profile described in this work shows great capacity to classify tumour and control samples of both soft and hard mucin, but it also enables making a distinction between lowgrade and highgrade SM and HM PMP samples. On the other hand, we wanted to elucidate the mechanisms (biological processes and signalling pathways) underlying this pathology. To do this, we used Metascape database to do a functional enrichment and PPI networks. We paid special attention to all the altered proteins and regulatory networks that were uniquely enriched in the different groups of samples. Interestingly, the results derived from the analysis of the SM vs. CTRL and HM vs. CTRL comparisons (without distinction of histological grade) showed that although most of the altered proteins were different in each comparison, the terms associated to these alterations were very similar, suggesting that these proteins are probably different parts of the same biological processes and therefore that SM and HM tissues are similar at the functional but not at the structural level. These results are complementary to the one published by Pillai et al, where they showed that soft, semi-hard and hard mucin samples had different textures and hardness, probably due to cell content, hydration, glucose, proteins, lipids, thiols and mucin distribution [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Additionally, the enriched biological processes/pathways altered in these comparisons (mainly related to extracellular matrix, regulation of cytoskeleton, metabolism and signalling pathways highly related with cancer) confirmed the occurrence of tissue adaptation to promote the progression of a malignant tumour [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eNext, we wanted to elucidate the biological processes that may discern between LG and HG in SM and HM tissues. In this sense, we found that there are a higher number of altered proteins shared between LG and HG in SM than in HM samples. In line with this, while in SM there are a high number of common processes between LG and HG, in HM we were able to identify processes/pathways that could specifically help to understand and better distinguish between LG and HG-PMP. Thus, important cellular activities (e.g.\\u0026ldquo;peptidase activity\\u0026rdquo;, \\u0026ldquo;lyase activity\\u0026rdquo;, \\u0026ldquo;hydrolase activity\\u0026rdquo;, etc.) were specifically associated with LG HM tissues and metabolic processes such as \\u0026ldquo;pyruvate metabolism\\u0026rdquo; and \\u0026ldquo;cellular aldehyde metabolic process\\u0026rdquo; and other processes were specifically associated with HG HM tissues. In keeping with this notion, those cellular activities as well as tumour-associated metabolic deregulation has been described at various carcinogenesis stages, and it is now evident that these alterations encompass all stages of the cellmetabolite interaction, increasing the tumour ability to acquire nutrients, determining how the nutrients are preferentially assigned to specific metabolic pathways that contribute to altering the cell cycle and modifying cell differentiation [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Furthermore, we found specific enriched terms related to extracellular matrix, cytoskeleton, metabolism and immune system, among others, that were differentially altered between LG and HG in SM and HM and could help to better understand this pathology, although we couldn\\u0026rsquo;t specifically associate them with LG or HG. The fact that PMP is a tumour associated with advanced age often implies the prior existence of alterations in the extracellular matrix and the immune system of the patients affected by this condition. In this context, detailed descriptions have been published on how cellular and molecular changes in non-cancerous cells during ageing may contribute to a tumourpermissive microenvironment. These changes encompass biophysical alterations in the extracellular matrix, changes in secreted factors, and changes in the immune system [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], which certainly warrant further investigation in PMP.\\u003c/p\\u003e \\u003cp\\u003eFinally, our results revealed a different mucin isoform profile between SM and HM, with MUC13 being the only membrane-associated mucin found to be altered. The individual quantification of MUC13 in a larger number of samples confirmed its aberrant synthesis in PMP tissues. MUC13 is a transmembrane protein expressed in mucin-producing epithelial cells whose main function is to activate an inflammatory response when an external agent damages the mucosal layer [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. There is currently no data on the expression of MUC13 in serous or mesothelial cells. All this information makes it a new membrane protein target for detecting the presence of residual tumoral cells after CRS-HIPEC treatment \\u003csup\\u003e29\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIn sum, in this work we have broken the mucin barrier by developing AMIPROM, the first protocol available to isolate proteins from mucin, which has been the main obstacle to molecular studies of this rare cancer. In addition, we provide the first proteomic profile of PMP described to date, providing novel information to characterise and identify the pathways altered in this tumour. Furthermore, our differential protein expression analysis followed by bioinformatic approaches has, for the first time, revealed the biological and molecular processes involved in PMP genesis and identified a potential tumour cell marker. Overall, we believe that our study provides essential, original information to facilitate rapid advances in the knowledge of PMP pathogenesis, which is undoubtedly the first step towards the development of new and effective therapeutic tools to treat this disease.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAMIPROM\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eAdapted method to isolate proteins from mucin\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCRC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eColorectal cancer\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCRS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eCytoreductive surgery\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eDIA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eData-independent acquisition\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eGO\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eGene Ontology\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eHG-PMP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eHighgrade PMP\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eHIPEC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eHyperthermic intraperitoneal chemotherapy\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eHM\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eHard mucin\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eIGs\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eImmunoglobulins\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eLG-PMP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eLowgrade PMP\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eMS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eMass spectrometry\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePLS-DA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ePartial least squares-discriminant analysis\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePMP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ePseudomyxoma peritonei\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePPI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eProtein-protein interaction\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePSOGI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ePeritoneal Surface Oncology Group International\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSM\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSoft mucin\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSWAH-MS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSequential window acquisition of all theoretical mass spectra\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAll techniques used in this study were applied in accordance with the ethical standards of the Helsinki Declaration and the World Medical Association, with the approval of the University of C\\u0026oacute;rdoba/IMIBIC and the research ethics committee of C\\u0026oacute;rdoba (CETICO, \\u003cem\\u003eComit\\u0026eacute; de \\u0026Eacute;tica de la Investigaci\\u0026oacute;n de C\\u0026oacute;rdoba\\u003c/em\\u003e), and can be implemented within the HURS and the IMIBIC (protocol code PI19/01603, version\\u0026nbsp;1, dated 13\\u0026nbsp;March 2019). Written informed consent was obtained from each patient.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and analysed in this study are available upon reasonable request from the corresponding authors (b72rorua@uco.es and carmen.vazquez@imibic.org).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests in relation to this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by: i) Ref. PI22/01213 co-financed by the ISCIII Sub-directorate general for evaluation and research promotion (\\u003cem\\u003eSubdirecci\\u0026oacute;n General de Evaluaci\\u0026oacute;n y Fomento de la Investigaci\\u0026oacute;n\\u003c/em\\u003e) and the European Regional Development Fund (ERDF), ii) Ref.\\u0026nbsp;ProteoRed-0000141 (ISCIII) and iii) Ref. PRYES223170ARJO funded by \\u0026ldquo;Asociaci\\u0026oacute;n Espa\\u0026ntilde;ola Contra el C\\u0026aacute;ncer \\u0026ndash; AECC\\u0026rdquo; in the call \\u0026ldquo;Ayudas a Proyectos Estrat\\u0026eacute;gicos 2022\\u0026rdquo;.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e:\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eARR: Conception, data curation, formal analysis, funding acquisition, research, methodology, project administration, resources, supervision, visualisation, writing the original draft, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eMGR: Research, methodology, validation, visualisation, writing the original draft, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eFIB: Research, methodology, validation, visualisation, writing the original draft, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eFVM: Methodology, formal analysis, research, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eBRA: Methodology, formal analysis, validation, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eAML: Methodology, formal analysis, validation, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eLRO: Methodology, formal analysis, validation, resources, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eROS: Formal analysis, validation, resources, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eMTM: Methodology, formal analysis, research, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eAMS: Methodology, formal analysis, research, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eJC: Formal analysis, methodology, writing the original draft, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eJAC: Formal analysis, validation, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eCMD: Formal analysis, methodology, writing the original draft, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eMCVB: Conception, data curation, formal analysis, funding acquisition, research, methodology, supervision, visualisation, writing the original draft, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003eAAS: Conception, data curation, formal analysis, funding acquisition, research, methodology, project administration, resources, supervision, visualisation, writing the original draft, and reviewing and editing the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe acknowledge \\u0026Aacute;ngela Casado-Adam and Juan Manuel S\\u0026aacute;nchez-Hidalgo for their support during surgeries. Additionally, we acknowledge Luz Valero and Manuel M. Sanchez-del Pino from SCSIE Proteomics Facility at the University of Valencia (a member of Proteored, PRB3), and \\u0026nbsp;Eduardo Chicano-G\\u0026aacute;lvez and \\u0026Aacute;ngela Peralbo-Molina from IMIBIC Mass Spectrometry and Molecular Imaging Unit (IMSMI) at the Maimonides Biomedical Research Institute of Cordoba (IMIBIC).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eSommariva A, Tonello M, Rigotto G, Lazzari N, Pilati P, Calabr\\u0026ograve; ML. Novel Perspectives in Pseudomyxoma Peritonei Treatment. Cancers (Basel) [Internet]. 2021;13:5965. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.mdpi.com/2072-6694/13/23/5965/htm\\u003c/span\\u003e\\u003cspan address=\\\"https://www.mdpi.com/2072-6694/13/23/5965/htm\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eArjona-S\\u0026aacute;nchez \\u0026Aacute;, Mu\\u0026ntilde;oz-Casares FC, Rufi\\u0026aacute;n-Pe\\u0026ntilde;a S, D\\u0026iacute;az-Nieto R, Casado-Adam \\u0026Aacute;, Rubio-P\\u0026eacute;rez MJ et al. Pseudomyxoma peritonei treated by cytoreductive surgery and hyperthermic intraperitoneal chemotherapy: results from a single centre. Clin Transl Oncol [Internet]. 2011;13:261\\u0026ndash;7. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://link.springer.com/\\u003c/span\\u003e\\u003cspan address=\\\"http://link.springer.com/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s12094-011-0651-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s12094-011-0651-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDelhorme J-B, Severac F, Averous G, Glehen O, Passot G, Bakrin N et al. Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for pseudomyxoma peritonei of appendicular and extra-appendicular origin. Br J Surg [Internet]. 2018;105:668\\u0026ndash;76. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://doi.wiley.com/10.1002/bjs.10716\\u003c/span\\u003e\\u003cspan address=\\\"http://doi.wiley.com/10.1002/bjs.10716\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eArjona-S\\u0026aacute;nchez \\u0026Aacute;, Mu\\u0026ntilde;oz-Casares FC, Casado-Adam \\u0026Aacute;, S\\u0026aacute;nchez-Hidalgo JM, Teran MDA, Orti-Rodriguez R et al. Outcome of Patients with Aggressive Pseudomyxoma Peritonei Treated by Cytoreductive Surgery and Intraperitoneal Chemotherapy. World J Surg [Internet]. 2013;37:1263\\u0026ndash;70. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://link.springer.com/\\u003c/span\\u003e\\u003cspan address=\\\"http://link.springer.com/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s00268-013-2000-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00268-013-2000-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChoudry HA, Mavanur A, O\\u0026rsquo;Malley ME, Zeh HJ, Guo ZS, Bartlett DL. Chronic Anti-inflammatory Drug Therapy Inhibits Gel-Forming Mucin Production in a Murine Xenograft Model of Human Pseudomyxoma Peritonei. Ann Surg Oncol [Internet]. 2012;19:1402\\u0026ndash;9. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.springerlink.com/index/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.springerlink.com/index/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1245/s10434-012-2242-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1245/s10434-012-2242-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCarr NJ, Cecil TD, Mohamed F, Sobin LH, Sugarbaker PH, Gonz\\u0026aacute;lez-Moreno S et al. A Consensus for Classification and Pathologic Reporting of Pseudomyxoma Peritonei and Associated Appendiceal Neoplasia: The Results of the Peritoneal Surface Oncology Group International (PSOGI) Modified Delphi Process. Am J Surg Pathol; 2016. p. 14\\u0026ndash;26. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage\\u003c/span\\u003e\\u003cspan address=\\\"http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u0026amp;an=00000478-201601000-00004.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBaratti D, Kusamura S, Milione M, Bruno F, Guaglio M, Deraco M. Validation of the Recent PSOGI Pathological Classification of Pseudomyxoma Peritonei in a Single-Center Series of 265 Patients Treated by Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy. Ann Surg Oncol [Internet]. 2017;25:1\\u0026ndash;10. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://dx.doi.org/10.1245/s10434-017-6252-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1245/s10434-017-6252-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRufi\\u0026aacute;n-Andujar B, Valenzuela-Molina F, Rufi\\u0026aacute;n-Pe\\u0026ntilde;a S, Casado-Adam \\u0026Aacute;, S\\u0026aacute;nchez-Hidalgo JM, Rodr\\u0026iacute;guez-Ortiz L et al. From the Ronnett to the PSOGI Classification System for Pseudomyxoma Peritonei: A Validation Study. Ann Surg Oncol [Internet]. 2021;33:1\\u0026ndash;9. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1245/s10434-020-09560-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1245/s10434-020-09560-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eArjona-S\\u0026aacute;nchez \\u0026Aacute;, Mart\\u0026iacute;nez-L\\u0026oacute;pez A, Valenzuela-Molina F, Rufi\\u0026aacute;n-Andujar B, Rufi\\u0026aacute;n-Pe\\u0026ntilde;a S, Casado-Adam \\u0026Aacute; et al. A Proposal for Modification of the PSOGI Classification According to the Ki-67 Proliferation Index in Pseudomyxoma Peritonei. Ann Surg Oncol [Internet]. 2021;1\\u0026ndash;11. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://link.springer.com/article/\\u003c/span\\u003e\\u003cspan address=\\\"https://link.springer.com/article/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1245/s10434-021-10372-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1245/s10434-021-10372-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLund-Andersen C, Torgunrud A, Fleten KG, Flatmark K. Omics analyses in peritoneal metastasis\\u0026mdash;utility in the management of peritoneal metastases from colorectal cancer and pseudomyxoma peritonei: a narrative review. J Gastrointest Oncol [Internet]. 2020;12:S191\\u0026ndash;203. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://jgo.amegroups.com/article/view/43080/html\\u003c/span\\u003e\\u003cspan address=\\\"https://jgo.amegroups.com/article/view/43080/html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePillai K, Akhter J, Mekkawy A, Chua TC, Morris DL. Physical and chemical characteristics of mucin secreted by pseudomyxoma peritonei (PMP). Int J Med Sci [Internet]. 2017;14:18\\u0026ndash;28. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.medsci.org/v14p0018.htm\\u003c/span\\u003e\\u003cspan address=\\\"http://www.medsci.org/v14p0018.htm\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAmini A, Masoumi-Moghaddam S, Ehteda A, Morris DL. Secreted mucins in pseudomyxoma peritonei: pathophysiological significance and potential therapeutic prospects. Orphanet J Rare Dis. 2014;9:71\\u0026ndash;1.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJr. JRG. Mucin Genes and the Proteins They Encode: Structure, Diversity, and Regulation. Am J Resp Cell Mol. 1992;7:557\\u0026ndash;64.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHollingsworth MA, Swanson BJ. Mucins in cancer: protection and control of the cell surface. Nat Rev Cancer. 2004;4:45\\u0026ndash;60.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMall AS, Chirwa N, Govender D, Lotz Z, Tyler M, Rodrigues J et al. MUC2, MUC5AC and MUC5B in the mucus of a patient with pseudomyxoma peritonei: biochemical and immunohistochemical study. Pathol Int [Internet]. 2007;57:537\\u0026ndash;47. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://doi.wiley.com/10.1111/j.1440-1827.2007.02137.x\\u003c/span\\u003e\\u003cspan address=\\\"http://doi.wiley.com/10.1111/j.1440-1827.2007.02137.x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMeldrum OW, Yakubov GE, Bonilla MR, Deshmukh O, McGuckin MA, Gidley MJ. Mucin gel assembly is controlled by a collective action of non-mucin proteins, disulfide bridges, Ca2+-mediated links, and hydrogen bonding. Sci Rep-uk. 2018;8:5802.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDayal S, Taflampas P, Riss S, Chandrakumaran K, Cecil TD, Mohamed F et al. Complete cytoreduction for pseudomyxoma peritonei is optimal but maximal tumor debulking may be beneficial in patients in whom complete tumor removal cannot be achieved. Dis Colon Rectum [Internet]. 2013;56:1366\\u0026ndash;72. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://insights.ovid.com/crossref?an=00003453-201312000-00008\\u003c/span\\u003e\\u003cspan address=\\\"https://insights.ovid.com/crossref?an=00003453-201312000-00008\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSmeenk RM, van Velthuysen MLF, Verwaal VJ, Zoetmulder FAN. Appendiceal neoplasms and pseudomyxoma peritonei: a population based study. Eur J Surg Oncol [Internet]. 2008;34:196\\u0026ndash;201. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S0748798307001709\\u003c/span\\u003e\\u003cspan address=\\\"https://linkinghub.elsevier.com/retrieve/pii/S0748798307001709\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShilov IV, Seymour SL, Patel AA, Loboda A, Tang WH, Keating SP, et al. The Paragon Algorithm, a Next Generation Search Engine That Uses Sequence Temperature Values and Feature Probabilities to Identify Peptides from Tandem Mass Spectra*. Mol Cell Proteom. 2007;6:1638\\u0026ndash;55.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePerez-Riverol Y, Bai J, Bandla C, Garc\\u0026iacute;a-Seisdedos D, Hewapathirana S, Kamatchinathan S, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2021;50:D543\\u0026ndash;52.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 2003;4:2.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSinghi AD, Davison JM, Choudry HA, Pingpank JF, Ahrendt SA, Holtzman MP, et al. GNAS is frequently mutated in both low-grade and high-grade disseminated appendiceal mucinous neoplasms but does not affect survival. Hum Pathol. 2014;45:1737\\u0026ndash;43.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShetty S, Thomas P, Ramanan B, Sharma P, Govindarajan V, Loggie B. Kras mutations and p53 overexpression in pseudomyxoma peritonei: association with phenotype and prognosis. J Surg Res. 2013;180:97\\u0026ndash;103.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePietrantonio F, Perrone F, Mennitto A, Gleeson EM, Milione M, Tamborini E, et al. Toward the molecular dissection of peritoneal pseudomyxoma. Ann Oncol. 2016;27:2097\\u0026ndash;103.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLuo C, Cen S, Ding G, Wu W. Mucinous colorectal adenocarcinoma: clinical pathology and treatment options. Cancer Commun [Internet]. 2019;39:1\\u0026ndash;13. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s40880-019-0361-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s40880-019-0361-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePavlova NN, Thompson CB. The Emerging Hallmarks of Cancer Metabolism. Cell Metab. 2016;23:27\\u0026ndash;47.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKos J, Proteases. Role and Function in Cancer. Int J Mol Sci. 2022;23:4632.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFane M, Weeraratna AT. How the ageing microenvironment influences tumour progression. Nat Rev Cancer. 2020;20:89\\u0026ndash;106.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWilliams SJ, Wreschner DH, Tran M, Eyre HJ, Sutherland GR, McGuckin MA. MUC13, a Novel Human Cell Surface Mucin Expressed by Epithelial and Hemopoietic Cells*. Journal of Biological Chemistry [Internet]. 2001;276:18327\\u0026ndash;36. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://dx.doi.org/10.1074/jbc.M008850200\\u003c/span\\u003e\\u003cspan address=\\\"10.1074/jbc.M008850200\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\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\":\"info@researchsquare.com\",\"identity\":\"biological-procedures-online\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bpro\",\"sideBox\":\"Learn more about [Biological Procedures Online](http://biologicalproceduresonline.biomedcentral.com/)\",\"snPcode\":\"12575\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12575/3\",\"title\":\"Biological Procedures Online\",\"twitterHandle\":\"@MedicalEvidence\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"cancer, mucin, protein, Pseudomyxoma peritonei, MUC13\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3953334/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3953334/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003ePseudomyxoma peritonei (PMP) is a rare peritoneal mucinous carcinomatosis with unknown underlying molecular mechanisms. Cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy is the only therapeutic option; however, despite its use, recurrence with a fatal outcome is common. The lack of molecular characterisation in PMP and other mucinous tumours is mainly due to the physicochemical properties of mucin.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eThis manuscript describes the first protocol capable of breaking the mucin barrier and isolating proteins from mucinous tumours. Thus, we present here the first proteome analysed in PMP and identified a distinct mucin isoform profile in soft compared to hard mucin tissues as well as key biological processes/pathways altered in mucinous tumours. Importantly, this protocol also allowed us to identify MUC13 as a potential tumour cell marker in PMP.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eIn summary, our results demonstrate that this protein isolation protocol from mucin will have a high impact, allowing the oncology research community to more rapidly advance in the knowledge of PMP and mucinous neoplasms, as well as develop new and effective therapeutic strategies.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Breaking the mucin barrier: a new affinity chromatography-mass spectrometry approach to unveil potential cell markers and pathways altered in Pseudomyxoma peritonei\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-02-15 20:28:39\",\"doi\":\"10.21203/rs.3.rs-3953334/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-03-18T19:27:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-03-10T11:41:49+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"c3b02a34-605c-4da1-afae-8007d8e69328\",\"date\":\"2024-02-23T10:56:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"0e83c6bc-b001-4ac5-ae5a-c804d8e1e88f\",\"date\":\"2024-02-20T23:58:12+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-02-15T17:06:42+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-02-14T07:54:44+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-02-14T07:54:44+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Biological Procedures Online\",\"date\":\"2024-02-13T10:45:47+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"biological-procedures-online\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bpro\",\"sideBox\":\"Learn more about [Biological Procedures Online](http://biologicalproceduresonline.biomedcentral.com/)\",\"snPcode\":\"12575\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12575/3\",\"title\":\"Biological Procedures Online\",\"twitterHandle\":\"@MedicalEvidence\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"24f6f7b1-0a4e-4f0b-be00-cd0fd30d9e80\",\"owner\":[],\"postedDate\":\"February 15th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-04-25T20:07:44+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-02-15 20:28:39\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3953334\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3953334\",\"identity\":\"rs-3953334\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}