Lipid Markers of Breast Tissue for the Diagnosis of Regional Metastatic Lesion

Lipid Markers of Breast Tissue for the Diagnosis of Regional Metastatic Lesion | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lipid Markers of Breast Tissue for the Diagnosis of Regional Metastatic Lesion Vitaliy Chagovets, Alisa Tokareva, Natalia Starodubtseva, Vlada Kometova, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-396953/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The development of minimally invasive, non-traumatic and stable approaches for the diagnosis of metastatic lesions of regional lymph nodes upon breast cancer is of great urgency. Here we recorded lipid profiles of normal breast tissue and malignant tissue to reveal potential lipid markers of metastatic lesions of regional lymph nodes. Lipid identification was done using the Lipid Match package. The search for lipid markers was carried out using the Mann-Whitney test. Lipids for the construction of a diagnostic logistic regression were selected according to the Akaike information criterion. For normal breast tissue, a diagnostic model was obtained with the area under the curve (AUC) of 0.83; for tumor tissue, a model with AUC = 0.86 was obtained. The species PC 14:0_20:4, PE 18:1_20:1, PC P-16:0/20:4, PC P-16:0/20:4, PE P-16:0/22:4, SM d18:1/18 0, SM d18:1/22:0 were determined as markers for normal breast tissue. The species PC 18:2_22:6, PC O-18:0/20:2, SM d16:1/18:1, SM d22:0/20:2, SM d16:0/18:2 were determined as markers for tumor tissue. The high AUC values ​​for the developed diagnostic model indicate the potential significance of the revealed marker species for the diagnosis of breast cancer metastasis and indicate the need for further research in this direction. Cancer Biology Oncology Obstetrics & Gynecology mass spectrometry lipidomics breast cancer lymph node metastases lymph node. Figures Figure 1 Figure 2 Introduction For many years breast cancer (BC) remains the most frequent malignant tumor in women, with the highest mortality rate among cancers 1 . Surgery is the major approach to the treatment of BC patients. The number of surgical interventions has decreased over the past decades, both on the mammary gland and on the organs of regional metastasis: from complete lymphadenectomy to sentinel lymph node biopsy (SLNB). SLNB significantly reduced the number of both early and late postoperative complications. Nevertheless, the incidence of complications after SLNB remains rather high (up to 1/4 of cases) 2 . A complete abandonment of SLNB is proposed if no data on the metastatic process has been obtained at the stage of preoperative diagnosis. The standard method for preoperative assessment of axillary lymph nodes in BC patients is ultrasound (US). The sensitivity and specificity of detecting metastases in regional lymph nodes upon BC using US are on average 85% and 90%, respectively, and are determined by the level of the instrumental base and the competence of the operator 3 . Attempts to improve these numbers using tomography (MRI and PET) have not been successful. The sensitivity and specificity of diagnostics using MRI is on average 88% and 90%. However, MRI diagnostics is contraindicated in patients with allergies, pacemakers, and renal failure. Also, the accuracy of the analysis strongly depends on randomly occurring image artifacts, due to which the sensitivity can drop down to 60% 4 . The disadvantages of PET include low diagnostic sensitivity of metastases to the axillary nodes 5 . Of high importance is the search for biomarkers of metastases in regional lymph nodes based on clinical, instrumental and molecular data, in particular, the woman's age, size and histological subtype of the primary tumor, lymphovascular invasion, HER2 status, and other factors 6 , 7 . The most common approach to the search for molecular markers of a malignant process is mass spectrometry (MS) and NMR analysis of the metabolome and proteome of tumor tissues and blood plasma. Lipids are biologically active compounds that regulate a number of important cellular processes, incuding proliferation, apoptosis, and angiogenesis 8 . Differences in the lipid profile of a tissue allow the identification of benign and malignant processes in the tissue 9 – 15 . High performance liquid chromatography combined with mass spectrometry (HPLC-MS) is regarded to be the most informative method for lipid analysis 16 . The aim of this study was to study the possibility of using HPLC-MS analysis of the primary tumor and surrounding tissues for the diagnosis of metastasis to regional lymph nodes upon breast cancer. Results The groups of patients with metastases and without metastases to axillary lymph nodes did not differ significantly in terms of age, size and location of the tumor focus, HER2 status (Table 1 , Table 2 ). In the group without metastases, the nonspecific type (30.0%) and special histological variants of the tumor (35.0%) were statistically more frequent (p < 0.046). In the group with metastases, the most frequent histological variant was the mixed variant (41.7%). In the group of patients without metastases to regional lymph nodes, multifocal tumors with a high degree of malignancy (G3 = 55%) and proliferative activity (mean Ki67 level = 30.35%) were frequent. Table 1 Demographic and clinical data of patients. Characteristic Metastases absence Metastases presence P-value Age (years) 56 (10) 56 (11) 0.90 Size of tumor (sm) 2.1 (1.6; 2.9) 2.7 (2.1; 3.5) 0.07 Ki67 level 32.5 (15.8; 62.5) 22.0 (14.8; 32.8) 0.17 Table 2 Histological characteristic of tumor tissue Characteristic Metastases absence Metastases presence P-value Absolute number Percentages of the total number Absolute number Percentages of the total number Location (quadrant): • Top-outer • Bottom-outer • Top-inner • Bottom-inner • Center 7 2 7 3 1 35.0 10.0 35.0 15.0 5.0 11 5 2 3 3 45.8 20.8 8.4 12.5 2.5 0.23 Histological type of tumor: • No special • Lobular • Mixed type • Special types 6 2 3 7 30.0 10.0 15.0 35.0 1 5 10 8 4.2 20.8 41.7 33.3 0.046 Maligant level: • I • II • III 2 7 11 10.0 35.0 55.0 2 16 6 8.3 66.7 25.0 0.10 Multifocality: • presence • absence 5 15 25.0 75.0 2 22 8.4 91.6 0.28 Estrogen receptors: • presence • absence 16 4 80.0 20.0 23 1 95.8 4.2 0.24 Progesteron receptors: • presence • absence 14 6 70.0 30.0 20 4 83.3 16.7 0.49 HER2/neu: • presence • absence 1 19 5.0 95.0 1 23 4.2 95.8 1.00 The analysis of normal breast tissue revealed 6 compounds in the positive ion detection mode and 12 compounds in the negative ion detection mode which were the most significant for the potential diagnosis of metastases to regional lymph nodes. The analysis of breast tumor tissue revealed 4 compounds in the positive ion detection mode and 5 compounds in the negative ion detection mode which were the most significant for the potential diagnosis of metastases to regional lymph nodes. The best quality of diagnostics was demonstrated by the models obtained in the positive ion mode for normal tissues by the regression equation (Table 3 ) with the sensitivity of 81% and specificity of 78% and in the negative ion mode for tumor tissues by the regression equation (Table 4 ) with the sensitivity of 79% and the specificity of 81% (Fig. 1 , Tables 3 and 4 ). The diagnostics of mammary gland by healthy tissue (Table 3 ) can be achieved with the predictive value of a positive result 78% and the predictive value of a negative result 80%. In the case of diagnostics by tumor tissue (Table 4 ), these corresponding values are 81% and 79%. The area under curve (AUC) values of 0.83 and 0.86 indicate a very good quality of the diagnostic models. Table 3 The analysis of multiple logistic regression for a diagnostic model built for the diagnosis of metastasis to regional lymph nodes based on the examination of normal tissue. The table contains information on the β coefficients and the probability of zero coefficient p. β p Free coefficient -1.2E1 (-1.9E1; -5.8E0) < 0.001 PC 14:0_20:4 9.3E-4 (5.0E-4; − 1.5E-3) < 0.001 PE 18:1_20:1 -2.0E-3 (-3.3E-3; -1.0E-4) < 0.001 PC P-16:0/20:4 2.2E-5 (2.5E-6; 4.5E-5) 0.04 PE P-16:0/22:4 -1.3E-4 (-2.1E-4; -7.5E-5) < 0.001 SM d18:1/18:0 1.1E-4 (5.7E-5; 1.8E-4) < 0.001 SM d18:1/22:0 8.2E-5 (4.5E-5; 1.3E-4) < 0.001 Table 4 The analysis of multiple logistic regression for a diagnostic model built for the diagnosis of lymph node metastasis based on the study of tumor tissue. The table contains information on the β coefficients and the probability of zero coefficient p Β p Free coefficient -3.0E0 (-6.1E0; -4.8E-1) 0.03 PC 18:2_22:6 -6.8E-4 (-1.1E-3; -3.9E-4) < 0.001 PC O-18:0/20:2 3.8E-4 (1.9E-4; 6.7E-4) 0.002 SM d16:1/18:1 -1.1E-3 (-2.0E-3; -4.8E-4) 0.006 SM d22:0/20:2 1.7E-3 (9.1E-4; 2.7E-3) < 0.001 SM d16:0/18:2 -2.7E-4 (-4.5E-4; -1.3E-4) < 0.001 Lipids identified as diagnostic markers of lymph node metastasis belong to the classes of phosphotidylcholine (PC 14:0_20:4, PC 18:2_22:6, PC P-16:0/20:4, plasmanyl-PC O-18:0/20:2), phosphotidylethanolamines (PE 18:1_20:1, PE P-16:0/22:4), ester lipids (PC P-16:0/20:4, PE P-16:0/22:4, PC O-18:0/20:2) and sphingomyelins (SM d18:1/18:0, SM d18:1/22:0, SM d18:1/22:0, SM d22:0/20:2, SM d16:0/18:2) (Fig. 2 ). From the above diagram, it can be seen that the level of sphingomyelins in normal and in tumor tissue alters due to the presence of metastases in the two opposite directions: the level grows in normal tissue and decreases in tumor tissue. In contrast, the level of essential lipids increases in the presence of metastases in both types of tissues. Discussion Sphingomyelins are involved in reactions that trigger the processes of apoptosis with the participation of sphingomyelases, which break down sphingolipids to ceramides. Ceramides induce the activation of protein phospholipase, which is responsible for the suppression of cell growth and cell division. 17 , 18 At the same time, the level of the SMPD3 gene, which is responsible for the expression of neutral sphingomyelase, is increased in tumor tissues compared with normal breast tissues 17 . In addition, in mice with a deactivated acid sphingomyelase gene which were injected with melanoma cells, metastasis was significantly less pronounced than in mice with a normal genome 19 . Roy et al. reported a lower level of sphingomyelins in metastatic bone cancer cells compared to primary neoplastic bone cancer cells 20 . Also Peng et al. in an article devoted to the comparison between the metabolomic profiles of two types of colon cancer cell lines reported a significantly higher level of sphingomyelins in the cancer cell line which was less prone to metastasis 21 . Essential phospholipids are known as lipid markers of neoplastic tissue damage 22 . A higher content of phosphotidylcholides and phosphotidylethanolamines with an ether bond was recorded for the cell lines with a high metastatic potential compared to the cell lines with a low metastatic potential 23 . Materials And Methods The current study included 44 patients with breast cancer treated in the National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of Russian Federation, Moscow. The exclusion criteria were neoadjuvant therapy and the presence of malignant neoplasms of other localization prior to the diagnosis of breast cancer. All experimental protocols and methods are approved by the Ethical Committee of the National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of Russian Federation, Moscow. All clinical investigations are conducted according to the principles expressed in the Declaration of Helsinki. All the patients signed informed consent. More than half of the patients (55%) had metastases to at least one lymph node. In patients with metastases to regional lymph nodes stage pT1N1M0 was diagnosed in 5 (21%) patients, stage pT2N1-3M0 was diagnosed in 18 (75%) patients, and stage pT3N3M0 was diagnosed in 1 (4%) patient. In the group of patients without metastases to regional lymph nodes, one half had the pT1N0M0 stage, and the other half had the pT2N0M0 stage. Two samples of breast tissue were collected from each patient: a tumor site and a site of normal tissue away from the tumor. Histological verification was performed for each sample. The analysis of lipid composition of the tissue was carried out by HPLC-MS according to the previously developed protocol 9 – 13 . The dried lipid extract was redissolved in acetonitrile / isopropanol mixture (1/1) and separated on a Dionex UltiMate 3000 chromatograph (Thermo Scientific, Germany) with detection on a Maxis Impact qTOF mass spectrometer (Bruker Daltonics, Germany) both in the positive and negative ion detection modes. To verify chemical identification, tandem MS analysis with a scanning window of 5 Da was additionally carried out. The resulting .d files were converted into ms2 files, which contained information on the ion fragmentation spectra at each time point (those .d files that contained tandem MS data were transformed), and MzXml, which contained full-MS data at each time point of chromatographic analysis. The free software msConvert (Proteowizard, 3.0.9987) was used for file conversion. The MxXml were then processed in MzMine to isolate the ion peaks and normalize them to the total ion current. Tandem MS files were used to identify lipids by means of LipidMatch scripts. Times and masses of ions from a table generated by MzMine were correlated with the tandem MS data of corresponding ions at a given time point. To evaluate the relevane of the ion fragmentation spectrum to the lipid fragmentation spectrum, a library of characteristic fragments included in the package 24 was used. Lipid nomenclature is consistent with LipidMaps 25 . Statistical analysis was done using scripts in the R language (3.3.3) in the RStudio (1.383 GNU) environment 26 , 27 . The clinical data of the patients and the histological characteristics of tissues related to the numerical characteristics were verified for normality using the Shapiro-Wilk test (p > 0.05). The presence of statistically significant differences for normally distributed variables was determined using the Student's t-test with an accepted critical value of p < 0.05. Values outside the normal distribution were tested by the nonparametric Mann-Whitney test for statistically significant differences with an accepted critical value of p < 0.05. To assess the differences in factorial histological characteristics of tissues in patients with and without metastasis, the Pearson chi-square criterion of agreement was used with the accepted critical value p < 0.05. The identified lipids were tested for significant differences in the level in the presence and absence of metastases separately for tumor tissues and for tissues of normal mammary gland by the nonparametric Mann-Whitney test with the accepted critical value p < 0.05. Categorical data were described using the absolute number and percentages of the total number of patients in the group. Quantitative normally distributed data were described using the arithmetic mean value (M) and standard deviation (SD) as M ± SD. Quantitative data with a distribution other than normal were presented as the median (Me) and quartiles Q1 and Q3 as Me (Q1; Q3). Lipids with levels that statistically significantly changed in the group were used to create a diagnostic model based on logistic regression. The optimization of logistic regression was carried out by the stepwise addition of variables and verification of the Akaike information criterion 28 . Lipid levels in tissues were used as variables. The diagnosis for the presence / absence of metastases was used as response variables. To assess the quality of a potential diagnostic model based on logistic regression, N logistic regressions were constructed based on N different samples containing (N – 1) object, followed by a test on an object not participating in the construction of regression, where N is the number of all objects in a pair of clinical groups. Sensitivity and specificity were evaluated as the number of true positives / total number of patients with metastases and the number of true negative results / total number of patients without metastases, respectively. The predictive value of positive and negative results was evaluated as the number of true positives / the number of positives and true negative results / the number of negative results. Conclusion Metastatic lesions of regional lymph nodes are associated with alterations in the lipid composition of tumor and normal breast tissues. Moreover, the alterations in the level of sphingomyelin differ for normal and tumor tissue, which may indicate a disturbance in the metabolic pathways associated with apoptosis. Thus, there exists a potential possibility of using the lipid profile of normal and tumor breast tissues in order to predict metastatic lesions of regional lymph nodes in breast cancer patients. Declarations Author Contributions: Conceptualization, V.V.C., N.L.S., V.V.R., and V.E.F.; data curation, M.V.R., A.O.T., V.V.K. and K.C.; formal analysis, M.V.R., A.O.T., and K.C.; investigation, V.V.C., N.L.S., V.V.K.; methodology, V.V.C., N.L.S., A.O.T. and M.V.R.; project administration, V.E.F., and V.V.R.; resources, V.V.R., V.V.K. and V.E.F.; software, V.V.C., A.O.T., and K.C.; supervision, V.V.C., N.L.S. and V.E.F.; validation, N.L.S., A.O.T. and K.C.; writing—original draft, V.V.C., A.O.T., and N.L.S.; writing—review and editing, K.C., V.V.R., V.V.K. and V.E.F.. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by RFBR (No. 19-515-55021 China_a), the National Natural Science Foundation of China (NSFC) (Nos. 21765001, 81961138016) and the Science and Technology Planning Project at the Ministry of Science and Technology of Jiangxi Province, China (No. 20192AEI91006). Authors thank the Laboratory for the collection and storage of biological material (Biobank) for providing tissue samples. Conflicts of Interest: The authors declare no conflict of interest. References American Cancer Society. Global Cancer. Facts and Figures . (Atlanta: American Cancer Society, 2019). doi:10.1787/health_glance_eur-2018-graph47-en. Lucci, A. et al. Surgical complications associated with sentinel lymph node dissection (SLND) plus axillary lymph node dissection compared with SLND alone in the American College of Surgeons Oncology Group trial Z0011. J. Clin. Oncol. 25 , 3657–3663 (2007). Sukhikh, G. T. & Sencha, A. N. Multiparametric ultrasound diagnosis of breast diseases . (Springer, 2018). doi:10.1007/978-3-319-75034-7. Zhou, M. et al. Differential diagnosis between metastatic and non-metastatic lymph nodes using DW-MRI: a meta-analysis of diagnostic accuracy studies. J. Cancer Res. Clin. Oncol. 141 , 1119–1130 (2015). Ulaner, G. A. PET/CT for patients with breast cancer: Where is the clinical impact? Am. J. Roentgenol. 213 , 254–265 (2019). Voogd, A. C. et al. The risk of nodal metastases in breast cancer patients with clinically negative lymph nodes: A population-based analysis. Breast Cancer Res. Treat. 62 , 63–69 (2000). Viale, G. et al. Predicting the status of axillary sentinel lymph nodes in 4351 patients with invasive breast carcinoma treated in a single institution. Cancer 103 , 492–500 (2005). Bandu, R., Mok, H. J. & Kim, K. P. Phospholipids as cancer biomarkers: mass spectrometry-based analysis. Mass Spectrom. Rev. 47 , 1–32 (2016). Adamyan, L. V. et al. Direct Mass Spectrometry Differentiation of Ectopic and Eutopic Endometrium in Patients with Endometriosis. J. Minim. Invasive Gynecol. 25 , 426–433 (2018). Chagovets, V. V. et al. Endometriosis foci differentiation by rapid lipid profiling using tissue spray ionization and high resolution mass spectrometry. Sci. Rep. 7 , 1–10 (2017). Tokareva, A. O. et al. Feature selection for OPLS discriminant analysis of cancer tissue lipidomics data. J. Mass Spectrom. 55 , 0–2 (2020). Sukhikh, G. et al. Combination of low-temperature electrosurgical unit and extractive electrospray ionization mass spectrometry for molecular profiling and classification of tissues. Molecules 24 , e2957 (2019). Chagovets, V. et al. A Comparison of Tissue Spray and Lipid Extract Direct Injection Electrospray Ionization Mass Spectrometry for the Differentiation of Eutopic and Ectopic Endometrial Tissues. J. Am. Soc. Mass Spectrom. 29 , 323–330 (2018). Chagovets, V. V. et al. Validation of breast cancer margins by tissue spray mass spectrometry. Int. J. Mol. Sci. 21 , 1–11 (2020). Chagovets, V. et al. Relative quantitation of phosphatidylcholines with interfered masses of protonated and sodiated molecules by tandem and Fourier-transform ion cyclotron resonance mass spectrometry. Eur. J. Mass Spectrom. 25 , 259–264 (2019). Han, X. Lipidomics for studying metabolism. Nat. Rev. Endocrinol. 12 , 668–679 (2016). Shamseddine, A. A., Airola, M. V. & Hannun, Y. A. Roles and regulation of neutral sphingomyelinase-2 in cellular and pathological processes. Adv. Biol. Regul. 57 , 24–41 (2015). Revill, K. et al. Genome-wide methylation analysis and epigenetic unmasking identify tumor suppressor genes in hepatocellular carcinoma. Gastroenterology 145 , 1–22 (2013). Carpinteiro, A. et al. Regulation of hematogenous tumor metastasis by acid sphingomyelinase. EMBO Mol. Med. 7 , 714–734 (2015). Roy, J., Dibaeinia, P., Fan, T. M., Sinha, S. & Das, A. Global analysis of osteosarcoma lipidomes reveal altered lipid profiles in metastatic versus nonmetastatic cells. J. Lipid Res. 60 , 375–387 (2019). Peng, W. et al. LC-MS/MS metabolome analysis detects the changes in the lipid metabolic profiles of dMMR and pMMR cells. Oncol. Rep. 40 , 1026–1034 (2018). Smith, R. E. et al. A reliable biomarker derived from plasmalogens to evaluate malignancy and metastatic capacity of human cancers. Lipids 43 , 79–89 (2008). Fallani, A., Mannori, G. & Ruggieri, S. Composition of ether‐linked sub‐classes of glycerophospholipids in clones with a different metastatic potential isolated from a murine fibrosarcoma line (T3 cells). Int. J. Cancer 62 , 230–232 (1995). Koelmel, J. P. et al. LipidMatch: An automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC Bioinformatics 18 , 1–11 (2017). Sud, M. et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35 , 527–532 (2007). R Development Core Team. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2019). R team. R Studio: Integrated Development for R. (2016). Akaike, H. Information Theory and an Extension of the Maximum Likelihood Principle. Sel. Pap. Hirotugu Akaike 199–213 (1998) doi:10.1007/978-1-4612-1694-0_15. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-396953","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":20392568,"identity":"3fa41fc2-114b-4069-83a6-ce7fd0229c77","order_by":0,"name":"Vitaliy Chagovets","email":"","orcid":"","institution":"National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. 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Kulakov of the Ministry of Healthcare of Russian Federation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Valeriy","middleName":"","lastName":"Rodionov","suffix":""},{"id":20392575,"identity":"354c3f2d-b00a-45a4-aafd-ae8d461dfa3a","order_by":7,"name":"Vladimir Frankevich","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDAC5gNAAojZGHgYHwCZPHwEtbAlwLUwG4C0sBGtBaRYAixASIfBMd6HnwvO2CT2sZ89Vvk1x06GjYH54aMbeLWwG0vPuJGW2MaTl3Zbdlsy0GFsxsY5eLRIzm9jkOb5cNiYTYLH7LbkNmagFh42abxa2tiYf/N8+A/WUiy5rZ6wFn42NjZpnhsH5EBaGD9uO0ycFmueM8lybDw5xtKM247zsDET8AtQB/NtnmN2PPLtZww//txWbc/P3vzwMT4tKICZB0wSqxwEGH+QonoUjIJRMApGDAAAZ9M61Pjoe2AAAAAASUVORK5CYII=","orcid":"","institution":"National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of Russian Federation","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Vladimir","middleName":"","lastName":"Frankevich","suffix":""}],"badges":[],"createdAt":"2021-04-06 07:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-396953/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-396953/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":7836130,"identity":"a9c438fe-e003-4c7f-80bc-6b931f4b8877","added_by":"auto","created_at":"2021-04-09 13:48:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19198,"visible":true,"origin":"","legend":"ROC curves constructed to analyze the predictive ability of diagnostic models of metastasis to regional lymph nodes based on the study of normal tissues (blue line, black line) and tumor tissues (red line, green line) analyzed in the positive ion detection mode (blue line, red line) and in the negative ion detection mode (black line, green line) based on logistic regression using the relative intensities of the selected biomarker compounds as variables.","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-396953/v1/a1f3279815b100fac3b4d742.png"},{"id":7836131,"identity":"6166012e-24fd-4987-af4a-beab79c17967","added_by":"auto","created_at":"2021-04-09 13:48:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55804,"visible":true,"origin":"","legend":"Box plot for diagnostic lipid markers, obtained by Mann-Whitney test and Akaike information criteria (in arbitrary units). The diagram shows Q1–1.5 * IQR, Q1, Me, Q3, Q3 + 1.5 * IQR and outcomes.","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-396953/v1/0e531ed865d5260620a6ee6e.jpg"},{"id":13685083,"identity":"afb9b4b2-cf62-4e2b-83fd-d05969655d4c","added_by":"auto","created_at":"2021-09-17 12:10:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":379197,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-396953/v1/19145b2e-0738-4aca-9a90-91a52edf6607.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eLipid Markers of Breast Tissue for the Diagnosis of Regional Metastatic Lesion\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eFor many years breast cancer (BC) remains the most frequent malignant tumor in women, with the highest mortality rate among cancers \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Surgery is the major approach to the treatment of BC patients. The number of surgical interventions has decreased over the past decades, both on the mammary gland and on the organs of regional metastasis: from complete lymphadenectomy to sentinel lymph node biopsy (SLNB). SLNB significantly reduced the number of both early and late postoperative complications. Nevertheless, the incidence of complications after SLNB remains rather high (up to 1/4 of cases) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. A complete abandonment of SLNB is proposed if no data on the metastatic process has been obtained at the stage of preoperative diagnosis.\u003c/p\u003e \u003cp\u003eThe standard method for preoperative assessment of axillary lymph nodes in BC patients is ultrasound (US). The sensitivity and specificity of detecting metastases in regional lymph nodes upon BC using US are on average 85% and 90%, respectively, and are determined by the level of the instrumental base and the competence of the operator \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Attempts to improve these numbers using tomography (MRI and PET) have not been successful. The sensitivity and specificity of diagnostics using MRI is on average 88% and 90%. However, MRI diagnostics is contraindicated in patients with allergies, pacemakers, and renal failure. Also, the accuracy of the analysis strongly depends on randomly occurring image artifacts, due to which the sensitivity can drop down to 60% \u003csup\u003e4\u003c/sup\u003e. The disadvantages of PET include low diagnostic sensitivity of metastases to the axillary nodes \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Of high importance is the search for biomarkers of metastases in regional lymph nodes based on clinical, instrumental and molecular data, in particular, the woman's age, size and histological subtype of the primary tumor, lymphovascular invasion, HER2 status, and other factors \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe most common approach to the search for molecular markers of a malignant process is mass spectrometry (MS) and NMR analysis of the metabolome and proteome of tumor tissues and blood plasma. Lipids are biologically active compounds that regulate a number of important cellular processes, incuding proliferation, apoptosis, and angiogenesis \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Differences in the lipid profile of a tissue allow the identification of benign and malignant processes in the tissue \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. High performance liquid chromatography combined with mass spectrometry (HPLC-MS) is regarded to be the most informative method for lipid analysis \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe aim of this study was to study the possibility of using HPLC-MS analysis of the primary tumor and surrounding tissues for the diagnosis of metastasis to regional lymph nodes upon breast cancer.\u003c/p\u003e "},{"header":"Results","content":" \u003cp\u003eThe groups of patients with metastases and without metastases to axillary lymph nodes did not differ significantly in terms of age, size and location of the tumor focus, HER2 status (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the group without metastases, the nonspecific type (30.0%) and special histological variants of the tumor (35.0%) were statistically more frequent (p\u0026thinsp;\u0026lt;\u0026thinsp;0.046). In the group with metastases, the most frequent histological variant was the mixed variant (41.7%). In the group of patients without metastases to regional lymph nodes, multifocal tumors with a high degree of malignancy (G3\u0026thinsp;=\u0026thinsp;55%) and proliferative activity (mean Ki67 level\u0026thinsp;=\u0026thinsp;30.35%) were frequent.\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 clinical data of patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetastases absence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetastases presence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize of tumor (sm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1 (1.6; 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 (2.1; 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.5 (15.8; 62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0 (14.8; 32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHistological characteristic of tumor tissue\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMetastases absence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMetastases presence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsolute number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentages of the total number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsolute number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentages of the total number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation (quadrant):\u003c/p\u003e \u003cp\u003e\u0026bull; Top-outer\u003c/p\u003e \u003cp\u003e\u0026bull; Bottom-outer\u003c/p\u003e \u003cp\u003e\u0026bull; Top-inner\u003c/p\u003e \u003cp\u003e\u0026bull; Bottom-inner\u003c/p\u003e \u003cp\u003e\u0026bull; Center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003cp\u003e10.0\u003c/p\u003e \u003cp\u003e35.0\u003c/p\u003e \u003cp\u003e15.0\u003c/p\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003cp\u003e20.8\u003c/p\u003e \u003cp\u003e8.4\u003c/p\u003e \u003cp\u003e12.5\u003c/p\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological type of tumor:\u003c/p\u003e \u003cp\u003e\u0026bull; No special\u003c/p\u003e \u003cp\u003e\u0026bull; Lobular\u003c/p\u003e \u003cp\u003e\u0026bull; Mixed type\u003c/p\u003e \u003cp\u003e\u0026bull; Special types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003cp\u003e10.0\u003c/p\u003e \u003cp\u003e15.0\u003c/p\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003cp\u003e20.8\u003c/p\u003e \u003cp\u003e41.7\u003c/p\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaligant level:\u003c/p\u003e \u003cp\u003e\u0026bull; I\u003c/p\u003e \u003cp\u003e\u0026bull; II\u003c/p\u003e \u003cp\u003e\u0026bull; III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003cp\u003e35.0\u003c/p\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003cp\u003e66.7\u003c/p\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultifocality:\u003c/p\u003e \u003cp\u003e\u0026bull; presence\u003c/p\u003e \u003cp\u003e\u0026bull; absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstrogen receptors:\u003c/p\u003e \u003cp\u003e\u0026bull; presence\u003c/p\u003e \u003cp\u003e\u0026bull; absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.8\u003c/p\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgesteron receptors:\u003c/p\u003e \u003cp\u003e\u0026bull; presence\u003c/p\u003e \u003cp\u003e\u0026bull; absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2/neu:\u003c/p\u003e \u003cp\u003e\u0026bull; presence\u003c/p\u003e \u003cp\u003e\u0026bull; absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003cp\u003e95.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\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\u003eThe analysis of normal breast tissue revealed 6 compounds in the positive ion detection mode and 12 compounds in the negative ion detection mode which were the most significant for the potential diagnosis of metastases to regional lymph nodes. The analysis of breast tumor tissue revealed 4 compounds in the positive ion detection mode and 5 compounds in the negative ion detection mode which were the most significant for the potential diagnosis of metastases to regional lymph nodes.\u003c/p\u003e \u003cp\u003eThe best quality of diagnostics was demonstrated by the models obtained in the positive ion mode for normal tissues by the regression equation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) with the sensitivity of 81% and specificity of 78% and in the negative ion mode for tumor tissues by the regression equation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) with the sensitivity of 79% and the specificity of 81% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The diagnostics of mammary gland by healthy tissue (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) can be achieved with the predictive value of a positive result 78% and the predictive value of a negative result 80%. In the case of diagnostics by tumor tissue (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), these corresponding values are 81% and 79%. The area under curve (AUC) values of 0.83 and 0.86 indicate a very good quality of the diagnostic models.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe analysis of multiple logistic regression for a diagnostic model built for the diagnosis of metastasis to regional lymph nodes based on the examination of normal tissue. The table contains information on the β coefficients and the probability of zero coefficient p.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.2E1 (-1.9E1; -5.8E0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC 14:0_20:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3E-4 (5.0E-4; \u0026minus;\u0026thinsp;1.5E-3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE 18:1_20:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.0E-3 (-3.3E-3; -1.0E-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC P-16:0/20:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2E-5 (2.5E-6; 4.5E-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE P-16:0/22:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.3E-4 (-2.1E-4; -7.5E-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM d18:1/18:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1E-4 (5.7E-5; 1.8E-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM d18:1/22:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2E-5 (4.5E-5; 1.3E-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe analysis of multiple logistic regression for a diagnostic model built for the diagnosis of lymph node metastasis based on the study of tumor tissue. The table contains information on the β coefficients and the probability of zero coefficient p\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΒ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.0E0 (-6.1E0; -4.8E-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC 18:2_22:6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.8E-4 (-1.1E-3; -3.9E-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC O-18:0/20:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8E-4 (1.9E-4; 6.7E-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM d16:1/18:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.1E-3 (-2.0E-3; -4.8E-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM d22:0/20:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7E-3 (9.1E-4; 2.7E-3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM d16:0/18:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.7E-4 (-4.5E-4; -1.3E-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eLipids identified as diagnostic markers of lymph node metastasis belong to the classes of phosphotidylcholine (PC 14:0_20:4, PC 18:2_22:6, PC P-16:0/20:4, plasmanyl-PC O-18:0/20:2), phosphotidylethanolamines (PE 18:1_20:1, PE P-16:0/22:4), ester lipids (PC P-16:0/20:4, PE P-16:0/22:4, PC O-18:0/20:2) and sphingomyelins (SM d18:1/18:0, SM d18:1/22:0, SM d18:1/22:0, SM d22:0/20:2, SM d16:0/18:2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom the above diagram, it can be seen that the level of sphingomyelins in normal and in tumor tissue alters due to the presence of metastases in the two opposite directions: the level grows in normal tissue and decreases in tumor tissue. In contrast, the level of essential lipids increases in the presence of metastases in both types of tissues.\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eSphingomyelins are involved in reactions that trigger the processes of apoptosis with the participation of sphingomyelases, which break down sphingolipids to ceramides. Ceramides induce the activation of protein phospholipase, which is responsible for the suppression of cell growth and cell division. \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e At the same time, the level of the SMPD3 gene, which is responsible for the expression of neutral sphingomyelase, is increased in tumor tissues compared with normal breast tissues \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In addition, in mice with a deactivated acid sphingomyelase gene which were injected with melanoma cells, metastasis was significantly less pronounced than in mice with a normal genome \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Roy et al. reported a lower level of sphingomyelins in metastatic bone cancer cells compared to primary neoplastic bone cancer cells \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Also Peng et al. in an article devoted to the comparison between the metabolomic profiles of two types of colon cancer cell lines reported a significantly higher level of sphingomyelins in the cancer cell line which was less prone to metastasis \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEssential phospholipids are known as lipid markers of neoplastic tissue damage \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. A higher content of phosphotidylcholides and phosphotidylethanolamines with an ether bond was recorded for the cell lines with a high metastatic potential compared to the cell lines with a low metastatic potential \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cp\u003eThe current study included 44 patients with breast cancer treated in the National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of Russian Federation, Moscow. The exclusion criteria were neoadjuvant therapy and the presence of malignant neoplasms of other localization prior to the diagnosis of breast cancer. All experimental protocols and methods are approved by the Ethical Committee of the National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of Russian Federation, Moscow. All clinical investigations are conducted according to the principles expressed in the Declaration of Helsinki. All the patients signed informed consent.\u003c/p\u003e \u003cp\u003eMore than half of the patients (55%) had metastases to at least one lymph node. In patients with metastases to regional lymph nodes stage pT1N1M0 was diagnosed in 5 (21%) patients, stage pT2N1-3M0 was diagnosed in 18 (75%) patients, and stage pT3N3M0 was diagnosed in 1 (4%) patient. In the group of patients without metastases to regional lymph nodes, one half had the pT1N0M0 stage, and the other half had the pT2N0M0 stage.\u003c/p\u003e \u003cp\u003eTwo samples of breast tissue were collected from each patient: a tumor site and a site of normal tissue away from the tumor. Histological verification was performed for each sample. The analysis of lipid composition of the tissue was carried out by HPLC-MS according to the previously developed protocol \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The dried lipid extract was redissolved in acetonitrile / isopropanol mixture (1/1) and separated on a Dionex UltiMate 3000 chromatograph (Thermo Scientific, Germany) with detection on a Maxis Impact qTOF mass spectrometer (Bruker Daltonics, Germany) both in the positive and negative ion detection modes. To verify chemical identification, tandem MS analysis with a scanning window of 5 Da was additionally carried out.\u003c/p\u003e \u003cp\u003eThe resulting .d files were converted into ms2 files, which contained information on the ion fragmentation spectra at each time point (those .d files that contained tandem MS data were transformed), and MzXml, which contained full-MS data at each time point of chromatographic analysis. The free software msConvert (Proteowizard, 3.0.9987) was used for file conversion. The MxXml were then processed in MzMine to isolate the ion peaks and normalize them to the total ion current. Tandem MS files were used to identify lipids by means of LipidMatch scripts. Times and masses of ions from a table generated by MzMine were correlated with the tandem MS data of corresponding ions at a given time point. To evaluate the relevane of the ion fragmentation spectrum to the lipid fragmentation spectrum, a library of characteristic fragments included in the package \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e was used. Lipid nomenclature is consistent with LipidMaps \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStatistical analysis was done using scripts in the R language (3.3.3) in the RStudio (1.383 GNU) environment \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The clinical data of the patients and the histological characteristics of tissues related to the numerical characteristics were verified for normality using the Shapiro-Wilk test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The presence of statistically significant differences for normally distributed variables was determined using the Student's t-test with an accepted critical value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Values outside the normal distribution were tested by the nonparametric Mann-Whitney test for statistically significant differences with an accepted critical value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To assess the differences in factorial histological characteristics of tissues in patients with and without metastasis, the Pearson chi-square criterion of agreement was used with the accepted critical value p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The identified lipids were tested for significant differences in the level in the presence and absence of metastases separately for tumor tissues and for tissues of normal mammary gland by the nonparametric Mann-Whitney test with the accepted critical value p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eCategorical data were described using the absolute number and percentages of the total number of patients in the group. Quantitative normally distributed data were described using the arithmetic mean value (M) and standard deviation (SD) as M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Quantitative data with a distribution other than normal were presented as the median (Me) and quartiles Q1 and Q3 as Me (Q1; Q3).\u003c/p\u003e \u003cp\u003eLipids with levels that statistically significantly changed in the group were used to create a diagnostic model based on logistic regression. The optimization of logistic regression was carried out by the stepwise addition of variables and verification of the Akaike information criterion \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Lipid levels in tissues were used as variables. The diagnosis for the presence / absence of metastases was used as response variables. To assess the quality of a potential diagnostic model based on logistic regression, N logistic regressions were constructed based on N different samples containing (N \u0026ndash; 1) object, followed by a test on an object not participating in the construction of regression, where N is the number of all objects in a pair of clinical groups. Sensitivity and specificity were evaluated as the number of true positives / total number of patients with metastases and the number of true negative results / total number of patients without metastases, respectively. The predictive value of positive and negative results was evaluated as the number of true positives / the number of positives and true negative results / the number of negative results.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eMetastatic lesions of regional lymph nodes are associated with alterations in the lipid composition of tumor and normal breast tissues. Moreover, the alterations in the level of sphingomyelin differ for normal and tumor tissue, which may indicate a disturbance in the metabolic pathways associated with apoptosis. Thus, there exists a potential possibility of using the lipid profile of normal and tumor breast tissues in order to predict metastatic lesions of regional lymph nodes in breast cancer patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions: \u003c/strong\u003eConceptualization, V.V.C., N.L.S., V.V.R., and V.E.F.; data curation, M.V.R., A.O.T., V.V.K. and K.C.; formal analysis, M.V.R., A.O.T., and K.C.; investigation, V.V.C., N.L.S., V.V.K.; methodology, V.V.C., N.L.S., A.O.T. and M.V.R.; project administration, V.E.F., and V.V.R.; resources, V.V.R., V.V.K. and V.E.F.; software, V.V.C., A.O.T., and K.C.; supervision, V.V.C., N.L.S. and V.E.F.; validation, N.L.S., A.O.T. and K.C.; writing\u0026mdash;original draft, V.V.C., A.O.T., and N.L.S.; writing\u0026mdash;review and editing, K.C., V.V.R., V.V.K. and V.E.F.. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by RFBR (No. 19-515-55021 China_a), the National Natural Science Foundation of China (NSFC) (Nos. 21765001, 81961138016) and the Science and Technology Planning Project at the Ministry of Science and Technology of Jiangxi Province, China (No. 20192AEI91006). Authors thank the Laboratory for the collection and storage of biological material (Biobank) for providing tissue samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest: \u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican Cancer Society. \u003cem\u003eGlobal Cancer. Facts and Figures\u003c/em\u003e. (Atlanta: American Cancer Society, 2019). doi:10.1787/health_glance_eur-2018-graph47-en.\u003c/li\u003e\n\u003cli\u003eLucci, A. \u003cem\u003eet al.\u003c/em\u003e Surgical complications associated with sentinel lymph node dissection (SLND) plus axillary lymph node dissection compared with SLND alone in the American College of Surgeons Oncology Group trial Z0011. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 3657\u0026ndash;3663 (2007).\u003c/li\u003e\n\u003cli\u003eSukhikh, G. T. \u0026amp; Sencha, A. N. \u003cem\u003eMultiparametric ultrasound diagnosis of breast diseases\u003c/em\u003e. (Springer, 2018). doi:10.1007/978-3-319-75034-7.\u003c/li\u003e\n\u003cli\u003eZhou, M. \u003cem\u003eet al.\u003c/em\u003e Differential diagnosis between metastatic and non-metastatic lymph nodes using DW-MRI: a meta-analysis of diagnostic accuracy studies. \u003cem\u003eJ. Cancer Res. Clin. Oncol.\u003c/em\u003e \u003cstrong\u003e141\u003c/strong\u003e, 1119\u0026ndash;1130 (2015).\u003c/li\u003e\n\u003cli\u003eUlaner, G. A. PET/CT for patients with breast cancer: Where is the clinical impact? \u003cem\u003eAm. J. Roentgenol.\u003c/em\u003e \u003cstrong\u003e213\u003c/strong\u003e, 254\u0026ndash;265 (2019).\u003c/li\u003e\n\u003cli\u003eVoogd, A. C. \u003cem\u003eet al.\u003c/em\u003e The risk of nodal metastases in breast cancer patients with clinically negative lymph nodes: A population-based analysis. \u003cem\u003eBreast Cancer Res. Treat.\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 63\u0026ndash;69 (2000).\u003c/li\u003e\n\u003cli\u003eViale, G. \u003cem\u003eet al.\u003c/em\u003e Predicting the status of axillary sentinel lymph nodes in 4351 patients with invasive breast carcinoma treated in a single institution. \u003cem\u003eCancer\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 492\u0026ndash;500 (2005).\u003c/li\u003e\n\u003cli\u003eBandu, R., Mok, H. J. \u0026amp; Kim, K. P. Phospholipids as cancer biomarkers: mass spectrometry-based analysis. \u003cem\u003eMass Spectrom. Rev.\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 1\u0026ndash;32 (2016).\u003c/li\u003e\n\u003cli\u003eAdamyan, L. V. \u003cem\u003eet al.\u003c/em\u003e Direct Mass Spectrometry Differentiation of Ectopic and Eutopic Endometrium in Patients with Endometriosis. \u003cem\u003eJ. Minim. Invasive Gynecol.\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 426\u0026ndash;433 (2018).\u003c/li\u003e\n\u003cli\u003eChagovets, V. V. \u003cem\u003eet al.\u003c/em\u003e Endometriosis foci differentiation by rapid lipid profiling using tissue spray ionization and high resolution mass spectrometry. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 1\u0026ndash;10 (2017).\u003c/li\u003e\n\u003cli\u003eTokareva, A. O. \u003cem\u003eet al.\u003c/em\u003e Feature selection for OPLS discriminant analysis of cancer tissue lipidomics data. \u003cem\u003eJ. Mass Spectrom.\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 0\u0026ndash;2 (2020).\u003c/li\u003e\n\u003cli\u003eSukhikh, G. \u003cem\u003eet al.\u003c/em\u003e Combination of low-temperature electrosurgical unit and extractive electrospray ionization mass spectrometry for molecular profiling and classification of tissues. \u003cem\u003eMolecules\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, e2957 (2019).\u003c/li\u003e\n\u003cli\u003eChagovets, V. \u003cem\u003eet al.\u003c/em\u003e A Comparison of Tissue Spray and Lipid Extract Direct Injection Electrospray Ionization Mass Spectrometry for the Differentiation of Eutopic and Ectopic Endometrial Tissues. \u003cem\u003eJ. Am. Soc. Mass Spectrom.\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 323\u0026ndash;330 (2018).\u003c/li\u003e\n\u003cli\u003eChagovets, V. V. \u003cem\u003eet al.\u003c/em\u003e Validation of breast cancer margins by tissue spray mass spectrometry. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 1\u0026ndash;11 (2020).\u003c/li\u003e\n\u003cli\u003eChagovets, V. \u003cem\u003eet al.\u003c/em\u003e Relative quantitation of phosphatidylcholines with interfered masses of protonated and sodiated molecules by tandem and Fourier-transform ion cyclotron resonance mass spectrometry. \u003cem\u003eEur. J. Mass Spectrom.\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 259\u0026ndash;264 (2019).\u003c/li\u003e\n\u003cli\u003eHan, X. Lipidomics for studying metabolism. \u003cem\u003eNat. Rev. Endocrinol.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 668\u0026ndash;679 (2016).\u003c/li\u003e\n\u003cli\u003eShamseddine, A. A., Airola, M. V. \u0026amp; Hannun, Y. A. Roles and regulation of neutral sphingomyelinase-2 in cellular and pathological processes. \u003cem\u003eAdv. Biol. Regul.\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 24\u0026ndash;41 (2015).\u003c/li\u003e\n\u003cli\u003eRevill, K. \u003cem\u003eet al.\u003c/em\u003e Genome-wide methylation analysis and epigenetic unmasking identify tumor suppressor genes in hepatocellular carcinoma. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cstrong\u003e145\u003c/strong\u003e, 1\u0026ndash;22 (2013).\u003c/li\u003e\n\u003cli\u003eCarpinteiro, A. \u003cem\u003eet al.\u003c/em\u003e Regulation of hematogenous tumor metastasis by acid sphingomyelinase. \u003cem\u003eEMBO Mol. Med.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 714\u0026ndash;734 (2015).\u003c/li\u003e\n\u003cli\u003eRoy, J., Dibaeinia, P., Fan, T. M., Sinha, S. \u0026amp; Das, A. Global analysis of osteosarcoma lipidomes reveal altered lipid profiles in metastatic versus nonmetastatic cells. \u003cem\u003eJ. Lipid Res.\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 375\u0026ndash;387 (2019).\u003c/li\u003e\n\u003cli\u003ePeng, W. \u003cem\u003eet al.\u003c/em\u003e LC-MS/MS metabolome analysis detects the changes in the lipid metabolic profiles of dMMR and pMMR cells. \u003cem\u003eOncol. Rep.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 1026\u0026ndash;1034 (2018).\u003c/li\u003e\n\u003cli\u003eSmith, R. E. \u003cem\u003eet al.\u003c/em\u003e A reliable biomarker derived from plasmalogens to evaluate malignancy and metastatic capacity of human cancers. \u003cem\u003eLipids\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 79\u0026ndash;89 (2008).\u003c/li\u003e\n\u003cli\u003eFallani, A., Mannori, G. \u0026amp; Ruggieri, S. Composition of ether‐linked sub‐classes of glycerophospholipids in clones with a different metastatic potential isolated from a murine fibrosarcoma line (T3 cells). \u003cem\u003eInt. J. Cancer\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 230\u0026ndash;232 (1995).\u003c/li\u003e\n\u003cli\u003eKoelmel, J. P. \u003cem\u003eet al.\u003c/em\u003e LipidMatch: An automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1\u0026ndash;11 (2017).\u003c/li\u003e\n\u003cli\u003eSud, M. \u003cem\u003eet al.\u003c/em\u003e LMSD: LIPID MAPS structure database. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 527\u0026ndash;532 (2007).\u003c/li\u003e\n\u003cli\u003eR Development Core Team. A Language and Environment for Statistical Computing. \u003cem\u003eR Foundation for Statistical Computing\u003c/em\u003e (2019).\u003c/li\u003e\n\u003cli\u003eR team. R Studio: Integrated Development for R. (2016).\u003c/li\u003e\n\u003cli\u003eAkaike, H. Information Theory and an Extension of the Maximum Likelihood Principle. \u003cem\u003eSel. Pap. Hirotugu Akaike\u003c/em\u003e 199\u0026ndash;213 (1998) doi:10.1007/978-1-4612-1694-0_15.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mass spectrometry, lipidomics, breast cancer, lymph node metastases, lymph node.","lastPublishedDoi":"10.21203/rs.3.rs-396953/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-396953/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe development of minimally invasive, non-traumatic and stable approaches for the diagnosis of metastatic lesions of regional lymph nodes upon breast cancer is of great urgency. Here we recorded lipid profiles of normal breast tissue and malignant tissue to reveal potential lipid markers of metastatic lesions of regional lymph nodes. Lipid identification was done using the Lipid Match package. The search for lipid markers was carried out using the Mann-Whitney test. Lipids for the construction of a diagnostic logistic regression were selected according to the Akaike information criterion. For normal breast tissue, a diagnostic model was obtained with the area under the curve (AUC) of 0.83; for tumor tissue, a model with AUC\u0026thinsp;=\u0026thinsp;0.86 was obtained. The species PC 14:0_20:4, PE 18:1_20:1, PC P-16:0/20:4, PC P-16:0/20:4, PE P-16:0/22:4, SM d18:1/18 0, SM d18:1/22:0 were determined as markers for normal breast tissue. The species PC 18:2_22:6, PC O-18:0/20:2, SM d16:1/18:1, SM d22:0/20:2, SM d16:0/18:2 were determined as markers for tumor tissue. The high AUC values ​​for the developed diagnostic model indicate the potential significance of the revealed marker species for the diagnosis of breast cancer metastasis and indicate the need for further research in this direction.\u003c/p\u003e","manuscriptTitle":"Lipid Markers of Breast Tissue for the Diagnosis of Regional Metastatic Lesion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-09 13:48:10","doi":"10.21203/rs.3.rs-396953/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1480317f-05f7-41e7-af8d-329f09a167a5","owner":[],"postedDate":"April 9th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":3523487,"name":"Cancer Biology"},{"id":3523488,"name":"Oncology"},{"id":3523489,"name":"Obstetrics \u0026 Gynecology"}],"tags":[],"updatedAt":"2021-05-12T04:14:07+00:00","versionOfRecord":[],"versionCreatedAt":"2021-04-09 13:48:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-396953","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-396953","identity":"rs-396953","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}