The Relationship Between Macrophages and Microvascular Density in the Microenvironment of Diffuse Large B-cell Lymphoma and Clinical Data

The Relationship Between Macrophages and Microvascular Density in the Microenvironment of Diffuse Large B-cell Lymphoma and Clinical Data | 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 The Relationship Between Macrophages and Microvascular Density in the Microenvironment of Diffuse Large B-cell Lymphoma and Clinical Data Sevdenur Ozduzgun Polat, Ozge Basaran Aydogdu, Merve Meryem Kiran, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6577179/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 Diffuse large B-cell lymphoma, NOS (DLBCL, NOS) is the most common subtype of non-Hodgkin lymphomas. About one-third of patients are resistant to standard treatment, highlighting the need for new therapies. Our study aimed to predict the prognosis of DLBCL, NOS patients by analyzing tumor-infiltrating macrophages and vascular structures, and to assist in identifying new therapeutic strategies targeting the tumor microenvironment. In our study, we retrospectively evaluated 122 excisional biopsy samples diagnosed with DLBCL, NOS from the archives of the pathology department of our hospital between 2006 and 2022. Patient data, including age, gender, localization, extranodal involvement, relapse status, Ann Arbor stage, and International Prognostic Index (IPI) score, were collected and recorded. Immunohistochemically, microvascular density (MVD) was assessed using CD31, and tumor-associated macrophages (TAMs) were evaluated with CD68 and CD163. The expression levels of CD68 and CD163 were significantly higher in cases with high IPI scores, advanced Ann Arbor stages, or extranodal disease (CD68; p = 0.028, p < 0.001, p = 0.002, CD163; p = 0.017, p = 0.002, p = 0.001). No statistically significant differences were observed between MVD and gender, extranodal disease, relapse status, IPI score, Ann Arbor stage, or the Hans algorithm. Furthermore, no statistically significant correlation was found between overall survival and the expression levels of CD68 and CD163, or MVD. Our findings suggest that (TAMs) and vascular structures, which are components of the tumor microenvironment, serve as prognostic factors in DLBCL, NOS. The tumor microenvironment is expected to have a high potential for combined or alternative therapeutic strategies to be added to classical chemotherapy. Diffuse large B-cell lymphoma microvascular density tumor-associated macrophages tumor microenvironment immunohistochemistry Figures Figure 1 1. Introduction Diffuse large B-cell lymphoma, NOS is the most common subtype of aggressive non-Hodgkin lymphoma. While standard treatment can be curative in many cases, 20–40% of patients experience relapse or are resistant to treatment [ 1 , 2 ]. This significantly reduces survival rates, and the disease continues to pose a challenging therapeutic problem. Although the prognostic significance of the International Prognostic Index (IPI) and Gene Expression Profiling (GEP) has been established in DLBCL, the fact that IPI only includes clinical parameters and does not account for the biological heterogeneity of the disease, as well as the high cost and limited applicability of GEP analysis in daily practice, necessitate the search for new methods. In this context, the analysis of TAMs and MVD, which are important components of the tumor microenvironment, may help in prognostic prediction for DLBCL patients. The components and function of the tumor microenvironment are among the most important factors for both tumor survival (immune escape) and antitumoral defense. TAMs play a pivotal role in the tumor microenvironment, influencing disease progression and response to treatment. TAMs are classified into two main subtypes based on their immunophenotype: M1 and M2. M1 TAMs are associated with inhibiting tumor growth, while M2 TAMs are linked to tumor progression [ 3 ]. Although the significance of TAMs in solid tumors has been well-established in recent years [ 4 , 5 ], their functions are believed to vary among different lymphoma types. While there are studies suggesting a prognostic role of TAMs in DLBCL, consensus on their importance has yet to be reached, and the issue remains controversial. Tumor growth is not solely dependent on the proliferation of tumor cells; it also requires the support of adjacent tissues. Neoangiogenesis in the tumor microenvironment is crucial. Angiogenesis allows tumor cells to reach blood vessels, thereby contributing to metastasis. It has been suggested that angiogenesis could serve as a prognostic marker for various types of tumors [ 6 ]. Diffuse large B-cell lymphoma is commonly encountered; however, due to the lack of treatment options for high-risk cases in terms of relapse and prognosis, further research in this area is warranted. The aim of our study is to assess the significance of TAMs in M1 and M2 subtypes and MVD in DLBCL, and to investigate the clinical implications of these findings. 2. Material and methods 2.1. Study population In this study, 161 excisional biopsy specimens diagnosed with DLBCL, NOS, excluding other types of large B-cell lymphoma, were retrospectively evaluated from our hospital between 2006 and 2022. In accordance with the 2022 World Health Organization Hematopoietic and Lymphoid Tissue Tumors, 5th Edition, all slides were re-evaluated and confirmed microscopically. Cases diagnosed by cytology, tru-cut biopsy, those with insufficient tissue, or those with technical artifacts were excluded, resulting in a final sample size of 122 cases for analysis. All cases were examined and data was recorded in terms of age, gender, localization, extranodal involvement, relapse and survival status, Ann Arbor stage, and IPI score. 2.2. Tissue microarrays preparation Tissue microarray (TMA) blocks were constructed using 4 mm diameter tissue cores that clearly represented the lymphoma area, without any artifacts or necrosis, from paraffin-embedded blocks. 2.3. Immunohistochemistry Paraffin sections, each 3 microns thick, were cut from tissues fixed in 10% Formalin. Following these preparations, the sections were processed for immunohistochemical staining. The procedures of baking, deparaffinization, and incubation were carried out using a BOND-MAX Fully Automated IHC and ISH Staining System. All slides were counterstained with hematoxylin, and the antibodies were incubated for 60 minutes. For secondary detection, a Leica HRP conjugated polymer detection kit (DS9800, New Castle, United Kingdom) was utilized. The polymer was applied for 12 minutes, followed by the addition of dab for 10 minutes and hematoxylin for 10 minutes. At each step, slides were washed with a washing solution, dehydrated, and subsequently mounted with Entellan. 2.4. Markers CD68 (Clone 514H12, Mouse monoklonal clone, Leica Biosystems, ready to use), CD163 (Clone 10D6, Mouse monoklonal clone, Leica Biosystems, ready to use), CD31 (Clone JC70A, Mouse monoclonal clone, Leica Biosystems, ready to use), CD10 (Mouse monoclonal clone 56C6, Leica Biosystems, ready to use), BCL6 (Clone LN22, Mouse monoclonal clone, Leica Biosystems, ready to use) MUM1 (Clone EAU32, Mouse monoclonal clone, Leica Biosystems,). 2.5. Immunohistochemical and IPI score evaluation The preparations were simultaneously evaluated by two observers in a blinded manner for data analysis, using a Nikon Eclipse Ci-L model light microscope (x400/0.65). In each case, intratumoral MVD was assessed by counting the average number of vessels in eight high-power fields (x400) within the areas of lymphoma with the highest concentration [ 7 ]. Vessels, defined as endothelial cells or clusters of endothelial cells stained brown, with or without visible lumens, were considered positive (Fig. 1A, B). In our study, TAMs were assessed using CD68 and CD163 immunohistochemical stains (Fig. 1C, D). CD68 antibody is a pan-macrophage marker, while CD163 antibody is a marker for M2 macrophages [ 8 ]. Each case was initially examined at low magnifications (x40) and subsequently at higher magnifications (x400) to assess the general cellularity. Macrophages were identified by the brown cytoplasmic staining with CD68 and CD163 antibodies. The average ratio of CD68-positive or CD163-positive cells to total cells was determined and reported as a percentage (%). Lymph node tissue was used as a positive control. To minimize non-specific staining in cells other than macrophages, only those cells with morphological characteristics consistent with macrophages were included in the evaluation. CD10, BCL-6, and MUM-1 immunohistochemical stains were deemed positive if 30% or more of the lymphoma tissue in the TMA blocks showed staining [ 9 , 10 ]. Based on the results of these immunohistochemical markers, DLBCL was subclassified into Germinal Center (GC) and Activated B-Cell-like (ABC) subtypes following the Hans algorithm. Due to the sample size, the IPI score was divided into two groups, as performed by Garcia et al. and Moskowitz et al. Cases with scores of 0, 1, and 2 were classified as low risk, while those with scores of 3, 4, and 5 were classified as high risk [ 11 , 12 ]. 2.6. Ethics statement This study was approved by the Ethics Committee of Ankara City Hospital, 1st Ethics Committee (decision number 22-2538, dated 06/04/2022). The study was conducted in accordance with the Declaration of Helsinki, the Principles of Good Clinical Practice, and all applicable legal and regulatory requirements. 2.7. Statistical analysis Data from the 122 cases included in the study were transferred into a database, followed by error checking and data cleaning procedures. The normality of the distribution of continuous variables (e.g., age, staining percentage) was assessed using the Shapiro-Wilk test. It was found that all variables were skewed and did not follow a normal distribution. Therefore, the Mann-Whitney test was used to compare continuous variables (such as CD68 expression percentage, CD163 expression percentage, MVD, etc.) based on living status, Hans algorithm, IPI score, and Ann Arbor stage. Spearman’s rank correlation coefficient was calculated to examine the relationships between variables. Due to the non-normal distribution of our data, non-parametric Mann-Whitney U tests were applied. Statistical analyses and calculations were performed using IBM SPSS Statistics 22.0, with a p-value < 0.05 considered to indicate a statistically significant difference. 3. Result The age range of the cases included in the study was 12 to 90 years, with 44 (36.1%) females and 78 (63.9%) males. It was observed that 67 (54.9%) of the cases originated from lymph nodes, while 55 (45.1%) originated from extranodal tissues. Data on relapse, IPI score, and Ann Arbor stage were unavailable for 22 cases (18.1%). Among the 100 cases for which clinical data were available, relapse was observed in 43 cases (35.2%), while 57 cases (46.7%) did not experience relapse. Thirty-two cases (26.2%) were in early Ann Arbor stage, and 68 cases (55.7%) were in advanced stage. Additionally, 49 cases (40.1%) had a low IPI score, while 51 cases (41.8%) had a high IPI score. 3.1. Relationship of CD68 and CD163 expression with other markers and prognostic factors In the 122 cases evaluated, the expression percentages for CD68 ranged from a minimum of 5% to a maximum of 80%, with a median expression of 20% and an average of 22.9%. For CD163, expression percentages ranged from a minimum of 2% to a maximum of 80%, with a median expression of 20% and an average of 21.1%. Cases with a high IPI score or advanced Ann Arbor stage had significantly higher CD68 and CD163 expression percentages compared to those with low scores (CD68: p = 0.028, p < 0.001; CD163: p = 0.017, p = 0.002). Additionally, the CD68 and CD163 expression percentages in cases with extranodal disease were significantly higher compared to those without extranodal disease (CD68: p = 0.002, CD163: p = 0.001) (Table 1 ). No statistically significant difference was observed in the expression percentages of CD68 and CD163 in relation to gender, recurrence status, or overall survival (p > 0.05) (Table 1 ). Although not statistically significant, a trend towards decreased overall survival was observed with increasing expression levels of CD68 and CD163. According to Spearman correlation analysis, a moderate positive correlation was observed between the percentages of CD68 and CD163 (Rho = 0.465; p < 0.001). 3.2. Comparison of CD68 and CD163 expression percentages in germinal center b-cell-like and activated b-cell-like diffuse large b-cell lymphoma cases The origin cell type was retrospectively determined by immunohistochemistry using the Hans algorithm. Of the cases, 46 (37.7%) were classified as GCB type, and 76 (62.3%) were classified as ABC type. Statistically, no significant difference was found between the CD68 and CD163 expression percentages in GCB-DLBCL and ABC-DLBCL cases. However, higher CD68 and CD163 expression percentages were observed in ABC-DLBCL cases compared to GCB-DLBCL cases (Table 1 ). 3.3. Microvascular density The microvascular density of the examined samples ranged from a minimum of 25 to a maximum of 454, with a median of 162 and an average of 169. Statistically, no significant differences were found between MVD and gender, extranodal disease, relaps disease, IPI score, Ann Arbor stage, gene expression, and overall survival (p > 0.05) (Table 1 ). Although not statistically significant, increased infiltration of TAMs was associated with a trend towards reduced overall survival. 4. Discussion In over half of DLBCL-NOS patients, first-line chemoimmunotherapy achieves remission, yet 20–40% experience relapse or resistance. Tumor microenvironment, including vascular structures and TAMs, may contribute to this variability. This study examined the relationship between vascular structures, TAMs, and clinical parameters to guide future DLBCL treatments. We found that higher proportions of CD68 and CD163 positive macrophages were significantly associated with higher IPI scores, advanced Ann Arbor stages, and extranodal disease. High CD68 or CD163 expression has been associated with poor prognostic factors such as high ECOG scores, multiple extranodal involvements, advanced Ann Arbor stages, and high serum LDH levels [ 13 ]. A meta-analysis found M2 TAMs significantly associated with advanced disease stage (p = 0.003) but not IPI scores (p = 0.138) [ 14 ]. This study found statistically significant associations between high CD68 or CD163 expression and high IPI scores (3–5), advanced stages (III-IV), and extranodal involvement (CD68: p = 0.028, p < 0.001, p = 0.002; CD163: p = 0.017, p = 0.002, p = 0.001). A meta-analysis of 23 studies with 2992 DLBCL patients linked high-density M2 TAMs (CD163 positive macrophages) with poor overall survival (p = 0.005) [ 14 ]. Several studies in the literature have similarly found that an increase in CD163 (+) macrophages is associated with a shorter overall and progression-free survival [ 13 , 15 , 16 ]. However, several published studies observed no relationship between CD163 and progression-free survival [ 17 , 18 ]. In our study, although not statistically significant, an increase in CD163 expression levels was associated with a decrease in overall survival (p > 0.05). Several studies linked high CD68 expression to shorter survival [ 13 , 16 ], while others reported longer progression-free and overall survival [ 15 , 17 ]. In other studies, CD68 + TAMs have shown an inverse relationship with overall and progression-free survival, but it was not statistically significant [ 14 , 18 ]. In this study, higher CD68 expression correlated with decreased survival but was not statistically significant (p > 0.05). According to the gene expression profile-based classification in DLBCL cases reported in the literature, the ABC DLBCL subgroup has worse overall survival compared to the GCB DLBCL subgroup [ 20 , 21 ]. In the studies conducted to reveal the microenvironmental differences of these two subtypes, CD68 and CD163 expression was examined and it was noted that there was more CD68 and CD163 expression in the ABC type [ 21 ]. In our study, CD68 and CD163 expression percentages were higher in ABC-DLBCL compared to GCB-DLBCL, although not statistically significant. This aligns with ABC-DLBCL’s known worse prognosis [ 22 ], suggesting that increased macrophage expression may indicate more aggressive disease characteristics. The clinical prognostic significance of CD68 and CD163 (+) macrophages remains controversial due to patient populations, the absence of a specific threshold value, and differences in macrophage subtypes. Therefore, there is a need for larger studies evaluating TAMs to determine the prognosis of the disease and for future development of targeted therapies. The angiogenic processes in lymphomas are complex. While MVD has been extensively studied in various tumors over the years, it has only recently gained attention in lymphomas, where it has been found to be heterogeneous across different lymphoma subtypes [ 23 – 25 ]. Due to the diversity observed in the literature, microvascular density continues to be an area of ongoing investigation in various lymphoma types. This diversity observed in the literature may be due to the use of different immunohistochemical markers such as CD31, CD34, and vWF. Studies have reported conflicting relationships between MVD and clinical outcomes in DLBCL. While some studies have linked high MVD with poor prognosis [ 25 , 26 ], our study found no significant association between MVD and GMC-DLBCL, ABC-DLBCL, IPI scores, extranodal disease, or disease stage. In our study population, 45.1% of the cases were diagnosed as extranodal diseases involving tissues other than lymph nodes. Since excisional biopsy specimens were evaluated in our study, the proportion of cases localized to extranodal sites may be higher compared to the existing literature. Moreover, considering that the vascular architecture of various extranodal tissues can differ significantly, variations in MVD may be expected [ 27 ]. Therefore, potential differences in MVD may not have been clearly demonstrated. In conclusion, CD68 and CD163(+) macrophages and MVD play roles in DLBCL prognosis and tumor aggressiveness. Limitations include the lack of specificity of the markers used for M1 and M2 TAMs, the small sample size, and tumor heterogeneity. Further studies with larger, diverse cohorts are needed to better understand these relationships. 5. Conclusion This study underscores the prognostic significance of TAMs and microvascular characteristics in DLBCL, highlighting their potential roles in influencing clinical outcomes. Higher CD68 and CD163 expression levels were significantly associated with poor prognostic factors, such as advanced disease stage, higher IPI scores, and extranodal involvement, suggesting their relevance in disease progression and aggressiveness. MVD did not show significant correlations with clinical parameters in this study, reflecting the heterogeneity of vascular contributions to lymphoma progression and prognosis. Variability in marker specificity and study findings highlights the complexity of the tumor microenvironment in DLBCL. Future research with larger, diverse cohorts and advanced methodologies, including spatial and functional analyses, is essential to refine our understanding of TAMs, vascular structures, and their interplay with clinical outcomes. These insights could inform the development of targeted therapies and improve prognostic assessments for DLBCL patients. Declarations FUNDING This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. CONFLICT OF INTEREST The authors declare that they have no conflict of interest. ETHICAL APPROVAL This study was approved by the Ethics Committee of Ankara Bilkent City Hospital (Approval number: 22-2538, Date: 06/04/2022). INFORMED CONSENT Informed consent was obtained from all individual participants included in the study. CONSENT FOR PUBLICATION Consent for publication was obtained from all individual participants included in the study. 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Rom J Morphol Embryol 52(3 Suppl):1091–1096 PMID: 22119815​ Tables Table 1 The relationship between the average expression percentages of CD68 and CD163, and the average microvascular density, with clinical parameters Type of finding n (%) CD68 Comparison result CD163 Comparison result MVD Comparison result Gender (n = 122) Female 44 (%36) 20,45 p = 0.26 21,23 p = 0.679 166,89 p = 0.302 Male 78 (%64) 24,29 21,1 173,36 Extranodal Disease (n = 100) Absent 49 (%49) 17,61 p = 0.002 16,12 p = 0.001 161,46 p = 0.477 Present 51 (%51) 27,35 27,18 170,78 Relaps disease (n = 100) Absent 57 (%57) 21,16 p = 0.108 18,72 p = 0.105 159,86 p = 0.459 Present 43 (%43) 24,47 25,79 174,95 Ann Arbor Stage (n = 100) Early 32 (%32) 14,62 p < 0.001 13,63 p = 0.002 161,84 p = 0.645 Advanced 68 (%68) 26,32 25,59 168,37 IPI score (n = 100) Low 49 (%49) 19,31 p = 0.028 17,82 p = 0.017 154,71 p = 0.121 High 51 (%51) 25,73 25,55 177,14 Gene expression (n = 122) GCB 46 (37,7) 19,5 p = 0.21 20,48 p = 0.347 174,49 p = 0.558 ABC 76 (62,2) 24,97 21,55 168,62 Survival Status Dead 61 (50) 24.46 p = 0.353 22.8 p = 0.173 173.53 p = 0.827 Alive 61 (50) 21.36 19.49 168.45 Additional Declarations No competing interests reported. Supplementary Files KopyaCD68CD163istatistikSON32.xlsx 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-6577179","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453496932,"identity":"d53c71de-8bf8-4059-932f-3641cd959cb9","order_by":0,"name":"Sevdenur Ozduzgun Polat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYLACHjDJfAxMsbETr4UtjYEhAUgxE6+FxwyshYGQFvkG5sMf3rbV2W243fPtwccf2+T5mBkYP3zMwa3F4ABbguHcNrbkDXfObjeckXDbsI2ZgVly5jY8Whh4DJJ523iSDW7kbpPmSbjNCNTCxsyLR4t8A4/BYd42CaCWnGcgLfYEtTAc4DFs5m0zsANqYQNpSSSoxeAwWzLjnHMJCZI30swkZ6TdTm5jZmzG6xf59ubDH96U1dnz3Uh+JvHB5rbt/Pbmgx8+4nMYNBYSGxBCjA3YFGIAe6JUjYJRMApGwcgEAFp+ShvLnsrEAAAAAElFTkSuQmCC","orcid":"","institution":"Ankara Bilkent City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Sevdenur","middleName":"Ozduzgun","lastName":"Polat","suffix":""},{"id":453496933,"identity":"b62e6d8a-c331-452c-9970-0f3242d802c1","order_by":1,"name":"Ozge Basaran Aydogdu","email":"","orcid":"","institution":"Ankara Bilkent City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ozge","middleName":"Basaran","lastName":"Aydogdu","suffix":""},{"id":453496934,"identity":"97886c40-5a6c-487a-b029-7f8f8aaeca60","order_by":2,"name":"Merve Meryem Kiran","email":"","orcid":"","institution":"Ankara Bilkent City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Merve","middleName":"Meryem","lastName":"Kiran","suffix":""},{"id":453496935,"identity":"06a8a1d4-aecb-4ab3-90f1-2974d2efe048","order_by":3,"name":"Funda Ceran","email":"","orcid":"","institution":"Ankara Bilkent City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Funda","middleName":"","lastName":"Ceran","suffix":""},{"id":453496936,"identity":"4c516b55-794e-4771-b0dd-0c480d01f59f","order_by":4,"name":"Ferda Can","email":"","orcid":"","institution":"Ankara Bilkent City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ferda","middleName":"","lastName":"Can","suffix":""},{"id":453496937,"identity":"453b6dd2-6684-445d-ac39-2fa5be4c7428","order_by":5,"name":"Gulten Korkmaz","email":"","orcid":"","institution":"Ankara Bilkent City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gulten","middleName":"","lastName":"Korkmaz","suffix":""},{"id":453496938,"identity":"72ee16d7-755c-4924-b7b7-630eb2616121","order_by":6,"name":"Gulsum Ozet","email":"","orcid":"","institution":"Ankara Yıldırım Beyazıt University","correspondingAuthor":false,"prefix":"","firstName":"Gulsum","middleName":"","lastName":"Ozet","suffix":""},{"id":453496939,"identity":"22667186-65ad-4707-a866-df00800d3b3b","order_by":7,"name":"Aydan Kilicarslan","email":"","orcid":"","institution":"Ankara Yıldırım Beyazıt University","correspondingAuthor":false,"prefix":"","firstName":"Aydan","middleName":"","lastName":"Kilicarslan","suffix":""}],"badges":[],"createdAt":"2025-05-02 09:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6577179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6577179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82601834,"identity":"b610ec67-7a03-4ce6-bd70-0405f1cb3e26","added_by":"auto","created_at":"2025-05-13 09:45:04","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1380198,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical evaluation. A, B: An example of CD31 expression evaluation used to determine microvascular density (x400). C: An example of CD68 expression evaluation used to identify M1 and M2 tumor-associated macrophages (x400). D: An example of CD163 expression evaluation used to identify M2 tumor-associated macrophages (x400).\u003c/p\u003e","description":"","filename":"figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6577179/v1/cf80ab382c15b0548e75df77.jpeg"},{"id":83056274,"identity":"3bd5c8c4-8e92-42aa-929b-7af063e9162d","added_by":"auto","created_at":"2025-05-19 13:47:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2258681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6577179/v1/e70b3510-1f35-4fa6-bd93-68bfac4cbc3e.pdf"},{"id":82601833,"identity":"79da9593-6a9d-4c8e-9c03-7d49a61276df","added_by":"auto","created_at":"2025-05-13 09:45:04","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21389,"visible":true,"origin":"","legend":"","description":"","filename":"KopyaCD68CD163istatistikSON32.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6577179/v1/03bb84cbf68fe8ce4ab0824d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Relationship Between Macrophages and Microvascular Density in the Microenvironment of Diffuse Large B-cell Lymphoma and Clinical Data\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiffuse large B-cell lymphoma, NOS is the most common subtype of aggressive non-Hodgkin lymphoma. While standard treatment can be curative in many cases, 20\u0026ndash;40% of patients experience relapse or are resistant to treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This significantly reduces survival rates, and the disease continues to pose a challenging therapeutic problem. Although the prognostic significance of the International Prognostic Index (IPI) and Gene Expression Profiling (GEP) has been established in DLBCL, the fact that IPI only includes clinical parameters and does not account for the biological heterogeneity of the disease, as well as the high cost and limited applicability of GEP analysis in daily practice, necessitate the search for new methods. In this context, the analysis of TAMs and MVD, which are important components of the tumor microenvironment, may help in prognostic prediction for DLBCL patients.\u003c/p\u003e \u003cp\u003eThe components and function of the tumor microenvironment are among the most important factors for both tumor survival (immune escape) and antitumoral defense. TAMs play a pivotal role in the tumor microenvironment, influencing disease progression and response to treatment. TAMs are classified into two main subtypes based on their immunophenotype: M1 and M2. M1 TAMs are associated with inhibiting tumor growth, while M2 TAMs are linked to tumor progression [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although the significance of TAMs in solid tumors has been well-established in recent years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], their functions are believed to vary among different lymphoma types. While there are studies suggesting a prognostic role of TAMs in DLBCL, consensus on their importance has yet to be reached, and the issue remains controversial.\u003c/p\u003e \u003cp\u003eTumor growth is not solely dependent on the proliferation of tumor cells; it also requires the support of adjacent tissues. Neoangiogenesis in the tumor microenvironment is crucial. Angiogenesis allows tumor cells to reach blood vessels, thereby contributing to metastasis. It has been suggested that angiogenesis could serve as a prognostic marker for various types of tumors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiffuse large B-cell lymphoma is commonly encountered; however, due to the lack of treatment options for high-risk cases in terms of relapse and prognosis, further research in this area is warranted. The aim of our study is to assess the significance of TAMs in M1 and M2 subtypes and MVD in DLBCL, and to investigate the clinical implications of these findings.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population\u003c/h2\u003e \u003cp\u003eIn this study, 161 excisional biopsy specimens diagnosed with DLBCL, NOS, excluding other types of large B-cell lymphoma, were retrospectively evaluated from our hospital between 2006 and 2022. In accordance with the 2022 World Health Organization Hematopoietic and Lymphoid Tissue Tumors, 5th Edition, all slides were re-evaluated and confirmed microscopically. Cases diagnosed by cytology, tru-cut biopsy, those with insufficient tissue, or those with technical artifacts were excluded, resulting in a final sample size of 122 cases for analysis. All cases were examined and data was recorded in terms of age, gender, localization, extranodal involvement, relapse and survival status, Ann Arbor stage, and IPI score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Tissue microarrays preparation\u003c/h2\u003e \u003cp\u003eTissue microarray (TMA) blocks were constructed using 4 mm diameter tissue cores that clearly represented the lymphoma area, without any artifacts or necrosis, from paraffin-embedded blocks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Immunohistochemistry\u003c/h2\u003e \u003cp\u003eParaffin sections, each 3 microns thick, were cut from tissues fixed in 10% Formalin. Following these preparations, the sections were processed for immunohistochemical staining. The procedures of baking, deparaffinization, and incubation were carried out using a BOND-MAX Fully Automated IHC and ISH Staining System.\u003c/p\u003e \u003cp\u003eAll slides were counterstained with hematoxylin, and the antibodies were incubated for 60 minutes. For secondary detection, a Leica HRP conjugated polymer detection kit (DS9800, New Castle, United Kingdom) was utilized. The polymer was applied for 12 minutes, followed by the addition of dab for 10 minutes and hematoxylin for 10 minutes. At each step, slides were washed with a washing solution, dehydrated, and subsequently mounted with Entellan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Markers\u003c/h2\u003e \u003cp\u003eCD68 (Clone 514H12, Mouse monoklonal clone, Leica Biosystems, ready to use), CD163 (Clone 10D6, Mouse monoklonal clone, Leica Biosystems, ready to use), CD31 (Clone JC70A, Mouse monoclonal clone, Leica Biosystems, ready to use), CD10 (Mouse monoclonal clone 56C6, Leica Biosystems, ready to use), BCL6 (Clone LN22, Mouse monoclonal clone, Leica Biosystems, ready to use) MUM1 (Clone EAU32, Mouse monoclonal clone, Leica Biosystems,).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Immunohistochemical and IPI score evaluation\u003c/h2\u003e \u003cp\u003eThe preparations were simultaneously evaluated by two observers in a blinded manner for data analysis, using a Nikon Eclipse Ci-L model light microscope (x400/0.65).\u003c/p\u003e \u003cp\u003eIn each case, intratumoral MVD was assessed by counting the average number of vessels in eight high-power fields (x400) within the areas of lymphoma with the highest concentration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Vessels, defined as endothelial cells or clusters of endothelial cells stained brown, with or without visible lumens, were considered positive (Fig.\u0026nbsp;1A, B).\u003c/p\u003e \u003cp\u003eIn our study, TAMs were assessed using CD68 and CD163 immunohistochemical stains (Fig.\u0026nbsp;1C, D). CD68 antibody is a pan-macrophage marker, while CD163 antibody is a marker for M2 macrophages [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Each case was initially examined at low magnifications (x40) and subsequently at higher magnifications (x400) to assess the general cellularity. Macrophages were identified by the brown cytoplasmic staining with CD68 and CD163 antibodies. The average ratio of CD68-positive or CD163-positive cells to total cells was determined and reported as a percentage (%). Lymph node tissue was used as a positive control. To minimize non-specific staining in cells other than macrophages, only those cells with morphological characteristics consistent with macrophages were included in the evaluation.\u003c/p\u003e \u003cp\u003eCD10, BCL-6, and MUM-1 immunohistochemical stains were deemed positive if 30% or more of the lymphoma tissue in the TMA blocks showed staining [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Based on the results of these immunohistochemical markers, DLBCL was subclassified into Germinal Center (GC) and Activated B-Cell-like (ABC) subtypes following the Hans algorithm.\u003c/p\u003e \u003cp\u003eDue to the sample size, the IPI score was divided into two groups, as performed by Garcia et al. and Moskowitz et al. Cases with scores of 0, 1, and 2 were classified as low risk, while those with scores of 3, 4, and 5 were classified as high risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Ethics statement\u003c/h2\u003e \u003cp\u003e This study was approved by the Ethics Committee of Ankara City Hospital, 1st Ethics Committee (decision number 22-2538, dated 06/04/2022). The study was conducted in accordance with the Declaration of Helsinki, the Principles of Good Clinical Practice, and all applicable legal and regulatory requirements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eData from the 122 cases included in the study were transferred into a database, followed by error checking and data cleaning procedures. The normality of the distribution of continuous variables (e.g., age, staining percentage) was assessed using the Shapiro-Wilk test. It was found that all variables were skewed and did not follow a normal distribution. Therefore, the Mann-Whitney test was used to compare continuous variables (such as CD68 expression percentage, CD163 expression percentage, MVD, etc.) based on living status, Hans algorithm, IPI score, and Ann Arbor stage. Spearman\u0026rsquo;s rank correlation coefficient was calculated to examine the relationships between variables. Due to the non-normal distribution of our data, non-parametric Mann-Whitney U tests were applied. Statistical analyses and calculations were performed using IBM SPSS Statistics 22.0, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered to indicate a statistically significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003eThe age range of the cases included in the study was 12 to 90 years, with 44 (36.1%) females and 78 (63.9%) males. It was observed that 67 (54.9%) of the cases originated from lymph nodes, while 55 (45.1%) originated from extranodal tissues.\u003c/p\u003e \u003cp\u003eData on relapse, IPI score, and Ann Arbor stage were unavailable for 22 cases (18.1%). Among the 100 cases for which clinical data were available, relapse was observed in 43 cases (35.2%), while 57 cases (46.7%) did not experience relapse. Thirty-two cases (26.2%) were in early Ann Arbor stage, and 68 cases (55.7%) were in advanced stage. Additionally, 49 cases (40.1%) had a low IPI score, while 51 cases (41.8%) had a high IPI score.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Relationship of CD68 and CD163 expression with other markers and prognostic factors\u003c/h2\u003e \u003cp\u003eIn the 122 cases evaluated, the expression percentages for CD68 ranged from a minimum of 5% to a maximum of 80%, with a median expression of 20% and an average of 22.9%. For CD163, expression percentages ranged from a minimum of 2% to a maximum of 80%, with a median expression of 20% and an average of 21.1%.\u003c/p\u003e \u003cp\u003eCases with a high IPI score or advanced Ann Arbor stage had significantly higher CD68 and CD163 expression percentages compared to those with low scores (CD68: p\u0026thinsp;=\u0026thinsp;0.028, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; CD163: p\u0026thinsp;=\u0026thinsp;0.017, p\u0026thinsp;=\u0026thinsp;0.002). Additionally, the CD68 and CD163 expression percentages in cases with extranodal disease were significantly higher compared to those without extranodal disease (CD68: p\u0026thinsp;=\u0026thinsp;0.002, CD163: p\u0026thinsp;=\u0026thinsp;0.001) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNo statistically significant difference was observed in the expression percentages of CD68 and CD163 in relation to gender, recurrence status, or overall survival (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although not statistically significant, a trend towards decreased overall survival was observed with increasing expression levels of CD68 and CD163.\u003c/p\u003e \u003cp\u003eAccording to Spearman correlation analysis, a moderate positive correlation was observed between the percentages of CD68 and CD163 (Rho\u0026thinsp;=\u0026thinsp;0.465; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2. Comparison of CD68 and CD163 expression percentages in germinal center b-cell-like and activated b-cell-like diffuse large b-cell lymphoma cases\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe origin cell type was retrospectively determined by immunohistochemistry using the Hans algorithm. Of the cases, 46 (37.7%) were classified as GCB type, and 76 (62.3%) were classified as ABC type.\u003c/p\u003e \u003cp\u003eStatistically, no significant difference was found between the CD68 and CD163 expression percentages in GCB-DLBCL and ABC-DLBCL cases. However, higher CD68 and CD163 expression percentages were observed in ABC-DLBCL cases compared to GCB-DLBCL cases (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Microvascular density\u003c/h2\u003e \u003cp\u003eThe microvascular density of the examined samples ranged from a minimum of 25 to a maximum of 454, with a median of 162 and an average of 169.\u003c/p\u003e \u003cp\u003eStatistically, no significant differences were found between MVD and gender, extranodal disease, relaps disease, IPI score, Ann Arbor stage, gene expression, and overall survival (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although not statistically significant, increased infiltration of TAMs was associated with a trend towards reduced overall survival.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn over half of DLBCL-NOS patients, first-line chemoimmunotherapy achieves remission, yet 20\u0026ndash;40% experience relapse or resistance. Tumor microenvironment, including vascular structures and TAMs, may contribute to this variability. This study examined the relationship between vascular structures, TAMs, and clinical parameters to guide future DLBCL treatments. We found that higher proportions of CD68 and CD163 positive macrophages were significantly associated with higher IPI scores, advanced Ann Arbor stages, and extranodal disease.\u003c/p\u003e \u003cp\u003eHigh CD68 or CD163 expression has been associated with poor prognostic factors such as high ECOG scores, multiple extranodal involvements, advanced Ann Arbor stages, and high serum LDH levels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A meta-analysis found M2 TAMs significantly associated with advanced disease stage (p\u0026thinsp;=\u0026thinsp;0.003) but not IPI scores (p\u0026thinsp;=\u0026thinsp;0.138) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This study found statistically significant associations between high CD68 or CD163 expression and high IPI scores (3\u0026ndash;5), advanced stages (III-IV), and extranodal involvement (CD68: p\u0026thinsp;=\u0026thinsp;0.028, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.002; CD163: p\u0026thinsp;=\u0026thinsp;0.017, p\u0026thinsp;=\u0026thinsp;0.002, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eA meta-analysis of 23 studies with 2992 DLBCL patients linked high-density M2 TAMs (CD163 positive macrophages) with poor overall survival (p\u0026thinsp;=\u0026thinsp;0.005) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Several studies in the literature have similarly found that an increase in CD163 (+) macrophages is associated with a shorter overall and progression-free survival [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, several published studies observed no relationship between CD163 and progression-free survival [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our study, although not statistically significant, an increase in CD163 expression levels was associated with a decrease in overall survival (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eSeveral studies linked high CD68 expression to shorter survival [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], while others reported longer progression-free and overall survival [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In other studies, CD68\u0026thinsp;+\u0026thinsp;TAMs have shown an inverse relationship with overall and progression-free survival, but it was not statistically significant [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, higher CD68 expression correlated with decreased survival but was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAccording to the gene expression profile-based classification in DLBCL cases reported in the literature, the ABC DLBCL subgroup has worse overall survival compared to the GCB DLBCL subgroup [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the studies conducted to reveal the microenvironmental differences of these two subtypes, CD68 and CD163 expression was examined and it was noted that there was more CD68 and CD163 expression in the ABC type [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our study, CD68 and CD163 expression percentages were higher in ABC-DLBCL compared to GCB-DLBCL, although not statistically significant. This aligns with ABC-DLBCL\u0026rsquo;s known worse prognosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], suggesting that increased macrophage expression may indicate more aggressive disease characteristics.\u003c/p\u003e \u003cp\u003eThe clinical prognostic significance of CD68 and CD163 (+) macrophages remains controversial due to patient populations, the absence of a specific threshold value, and differences in macrophage subtypes. Therefore, there is a need for larger studies evaluating TAMs to determine the prognosis of the disease and for future development of targeted therapies.\u003c/p\u003e \u003cp\u003eThe angiogenic processes in lymphomas are complex. While MVD has been extensively studied in various tumors over the years, it has only recently gained attention in lymphomas, where it has been found to be heterogeneous across different lymphoma subtypes [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Due to the diversity observed in the literature, microvascular density continues to be an area of ongoing investigation in various lymphoma types. This diversity observed in the literature may be due to the use of different immunohistochemical markers such as CD31, CD34, and vWF.\u003c/p\u003e \u003cp\u003eStudies have reported conflicting relationships between MVD and clinical outcomes in DLBCL. While some studies have linked high MVD with poor prognosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], our study found no significant association between MVD and GMC-DLBCL, ABC-DLBCL, IPI scores, extranodal disease, or disease stage.\u003c/p\u003e \u003cp\u003eIn our study population, 45.1% of the cases were diagnosed as extranodal diseases involving tissues other than lymph nodes. Since excisional biopsy specimens were evaluated in our study, the proportion of cases localized to extranodal sites may be higher compared to the existing literature. Moreover, considering that the vascular architecture of various extranodal tissues can differ significantly, variations in MVD may be expected [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, potential differences in MVD may not have been clearly demonstrated.\u003c/p\u003e \u003cp\u003eIn conclusion, CD68 and CD163(+) macrophages and MVD play roles in DLBCL prognosis and tumor aggressiveness. Limitations include the lack of specificity of the markers used for M1 and M2 TAMs, the small sample size, and tumor heterogeneity. Further studies with larger, diverse cohorts are needed to better understand these relationships.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study underscores the prognostic significance of TAMs and microvascular characteristics in DLBCL, highlighting their potential roles in influencing clinical outcomes. Higher CD68 and CD163 expression levels were significantly associated with poor prognostic factors, such as advanced disease stage, higher IPI scores, and extranodal involvement, suggesting their relevance in disease progression and aggressiveness.\u003c/p\u003e \u003cp\u003eMVD did not show significant correlations with clinical parameters in this study, reflecting the heterogeneity of vascular contributions to lymphoma progression and prognosis. Variability in marker specificity and study findings highlights the complexity of the tumor microenvironment in DLBCL.\u003c/p\u003e \u003cp\u003eFuture research with larger, diverse cohorts and advanced methodologies, including spatial and functional analyses, is essential to refine our understanding of TAMs, vascular structures, and their interplay with clinical outcomes. These insights could inform the development of targeted therapies and improve prognostic assessments for DLBCL patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL APPROVAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Ankara Bilkent City Hospital (Approval number: 22-2538, Date: 06/04/2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINFORMED CONSENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTilly H, Gomes da Silva M, Vitolo U, Jack A, Meignan M, Lopez-Guillermo A et al (2015) Diffuse large B-cell lymphoma (DLBCL): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. 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Rom J Morphol Embryol 52(3 Suppl):1091\u0026ndash;1096 PMID: 22119815​\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\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\u003eThe relationship between the average expression percentages of CD68 and CD163, and the average microvascular density, with clinical parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of finding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD68\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComparison result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCD163\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eComparison result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMVD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eComparison result\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (n\u0026thinsp;=\u0026thinsp;122)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (%36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e166,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (%64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e173,36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtranodal Disease (n\u0026thinsp;=\u0026thinsp;100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbsent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (%49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e161,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (%51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e170,78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelaps disease (n\u0026thinsp;=\u0026thinsp;100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbsent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (%57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e159,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (%43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e174,95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnn Arbor Stage (n\u0026thinsp;=\u0026thinsp;100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (%32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e161,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdvanced\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (%68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e168,37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIPI score (n\u0026thinsp;=\u0026thinsp;100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (%49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e154,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (%51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e177,14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGene expression\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;122)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGCB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (37,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e174,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eABC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (62,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e168,62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurvival 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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diffuse large B-cell lymphoma, microvascular density, tumor-associated macrophages, tumor microenvironment, immunohistochemistry","lastPublishedDoi":"10.21203/rs.3.rs-6577179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6577179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiffuse large B-cell lymphoma, NOS (DLBCL, NOS) is the most common subtype of non-Hodgkin lymphomas. About one-third of patients are resistant to standard treatment, highlighting the need for new therapies. Our study aimed to predict the prognosis of DLBCL, NOS patients by analyzing tumor-infiltrating macrophages and vascular structures, and to assist in identifying new therapeutic strategies targeting the tumor microenvironment.\u003c/p\u003e \u003cp\u003eIn our study, we retrospectively evaluated 122 excisional biopsy samples diagnosed with DLBCL, NOS from the archives of the pathology department of our hospital between 2006 and 2022. Patient data, including age, gender, localization, extranodal involvement, relapse status, Ann Arbor stage, and International Prognostic Index (IPI) score, were collected and recorded. Immunohistochemically, microvascular density (MVD) was assessed using CD31, and tumor-associated macrophages (TAMs) were evaluated with CD68 and CD163.\u003c/p\u003e \u003cp\u003eThe expression levels of CD68 and CD163 were significantly higher in cases with high IPI scores, advanced Ann Arbor stages, or extranodal disease (CD68; p\u0026thinsp;=\u0026thinsp;0.028, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.002, CD163; p\u0026thinsp;=\u0026thinsp;0.017, p\u0026thinsp;=\u0026thinsp;0.002, p\u0026thinsp;=\u0026thinsp;0.001). No statistically significant differences were observed between MVD and gender, extranodal disease, relapse status, IPI score, Ann Arbor stage, or the Hans algorithm. Furthermore, no statistically significant correlation was found between overall survival and the expression levels of CD68 and CD163, or MVD.\u003c/p\u003e \u003cp\u003eOur findings suggest that (TAMs) and vascular structures, which are components of the tumor microenvironment, serve as prognostic factors in DLBCL, NOS. The tumor microenvironment is expected to have a high potential for combined or alternative therapeutic strategies to be added to classical chemotherapy.\u003c/p\u003e","manuscriptTitle":"The Relationship Between Macrophages and Microvascular Density in the Microenvironment of Diffuse Large B-cell Lymphoma and Clinical Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 09:44:59","doi":"10.21203/rs.3.rs-6577179/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":"aa87e3eb-954c-46e1-8320-c06089966146","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T13:38:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 09:44:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6577179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6577179","identity":"rs-6577179","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}