Distribution Characteristics and Clinical Significance of Peripheral Blood Lymphocyte Subsets in Patients with Pituitary Neuroendocrine Tumors

Distribution Characteristics and Clinical Significance of Peripheral Blood Lymphocyte Subsets in Patients with Pituitary Neuroendocrine Tumors | 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 Distribution Characteristics and Clinical Significance of Peripheral Blood Lymphocyte Subsets in Patients with Pituitary Neuroendocrine Tumors Qi Liu, Yi Zhang, Kan Deng, Xinjie Bao, Ming Feng, Wei Lian, Bing Xing, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7720749/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 Objective To investigate the distribution characteristics of peripheral blood lymphocyte subsets in patients with Pituitary Neuroendocrine Tumors (PitNETs) based on the 2022 WHO classification and analyze their relationship with clinical features of the tumors. Methods A total of 230 patients with pathologically confirmed PitNETs admitted to the Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences between March and October 2023 were enrolled. Absolute counts and percentages of peripheral blood lymphocyte subsets (T cells, B cells, NK cells, and CD4+/CD8 + T cell ratio) were detected by flow cytometry and compared with reference values from healthy adults. Subgroup analyses were performed according to the 2022 WHO classification (PIT1, TPIT, SF1 lineages, etc.), clinical functional type, and Knosp grading. Results Compared with the normal population, PitNETs patients overall showed decreased absolute lymphocyte count and NK cell proportion, while B cell proportion, T cell proportion, and CD4+/CD8 + T cell ratio were increased (all P < 0.05). This trend was consistent across PIT1, TPIT, and SF1 lineage tumors. Subgroup analysis revealed that these alterations were most pronounced in non-functioning adenomas and growth hormone (GH) adenomas. Correlation analysis found that larger tumor volume (r = -0.114, P < 0.05) and higher Knosp grade (r = -0.046, P < 0.05) were associated with lower B cell proportion; whereas higher Knosp grade was associated with a higher CD4 + T cell proportion (r = 0.112, P < 0.05). Lymphocyte distribution was not significantly correlated with tumor recurrence. Conclusion Patients with PitNETs exhibit systemic immune dysfunction, characterized by abnormalities in both cellular immunity (decreased NK cells) and humoral immunity (increased B cells). This immune dysregulation is associated with tumor lineage, size, and invasiveness. Analysis of peripheral blood lymphocyte subsets may provide a new clinical perspective for assessing the immune status of PitNETs and exploring their potential for immunotherapy. Pituitary Neuroendocrine Tumors Lymphocyte Subsets Immune Cells NK Cells Introduction Pituitary adenoma (PA) is a common intracranial tumor originating from the anterior pituitary gland, with an incidence second only to gliomas and meningiomas, accounting for approximately 10–15% of primary intracranial tumors [1]. The 2017 World Health Organization (WHO) classification primarily categorized these tumors based on cell type, hormone production, and molecular characteristics [2, 3]. In 2022, the WHO introduced a significant update, officially renaming them Pituitary Neuroendocrine Tumors (PitNETs) to reflect the neuroendocrine nature of adenohypophyseal cells [4]. This new classification emphasizes precise diagnosis based on cell lineage (identified by transcription factors such as PIT1, TPIT, SF1), cell type, and hormone type, which more accurately reflects the tumor's biological essence [4]. PitNETs can cause complex neuroendocrine dysfunction. In recent years, the role of the tumor immune microenvironment (TIME) in tumorigenesis, progression, and treatment response has gained increasing attention [5]. However, research on the impact of PitNETs on systemic immune function remains limited. Peripheral blood lymphocyte subsets serve as an important window for assessing the body's overall immune status. This study aims to systematically examine the distribution of peripheral blood lymphocyte subsets in patients with different subtypes of PitNETs according to the latest 2022 WHO classification, and to explore its relationship with clinical-pathological features (such as size, invasiveness, recurrence), providing a theoretical basis for understanding the immunological characteristics of PitNETs and future immunotherapy strategies. Materials and Methods 1. Study Subjects A total of 230 patients with PitNETs hospitalized in the Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences between March and October 2023 were enrolled. Inclusion criteria (1) Postoperative pathological confirmation of PitNETs; (2) No prior radiotherapy or chemotherapy before surgery; (3) No history of autoimmune diseases, active infections, or other malignancies. Among them, there were 98 males and 132 females, aged 14–76 years (median age 44.5 years, mean age 45.2 ± 15.1 years). Clinical and Pathological Classification: By clinical function : 129 non-functioning adenomas, 51 GH adenomas, 34 PRL adenomas, 4 TSH adenomas, 7 ACTH adenomas, 1 gonadotroph adenoma, 4 plurihormonal / double-hormone cell adenomas. By 2022 WHO lineage : 84 PIT1-lineage, 39 TPIT-lineage, 71 SF1-lineage, 7 null cell tumors, 29 plurihormonal PitNETs. By Knosp grade : 37 grade 0, 40 grade I, 85 grade II, 51 grade III, 17 grade IV. Other characteristics : 68 invasive, 162 non-invasive; 28 recurrent, 202 primary. This study was approved by the hospital's ethics review committee, and all patients provided written informed consent. Normal control values were derived from the reference values for blood lymphocyte immunophenotyping of 102 healthy Chinese adults established by Zhu Li-hua et al. [6]. 2. Research Methods 2.1 Lymphocyte Subset Detection : 2 mL of peripheral venous blood was collected in EDTA anticoagulant tubes. Detection was performed using flow cytometry. Briefly: 100 µL of whole blood was incubated with a fluorescent antibody cocktail (targeting CD3, CD4, CD8, CD19, CD16/CD56, etc.) at room temperature in the dark for 15 minutes. Red blood cells were lysed using lysing reagent, followed by PBS washing and cell resuspension for acquisition. The percentages of lymphocyte subsets (T cells, helper/inducer T cells [CD4+], suppressor/cytotoxic T cells [CD8+], B cells, NK cells) were obtained, and absolute counts for each subset were calculated by combining these percentages with the absolute lymphocyte count from routine blood tests. 2.2 Pathological Evaluation : Tumor tissues from all patients were fixed in formalin, embedded in paraffin, and subjected to routine hematoxylin-eosin (HE) staining and immunohistochemical staining. Diagnosis and classification were made according to the 2022 WHO standards. 3. Statistical Analysis Statistical analysis was performed using SPSS 20.0 software. Measurement data are expressed as mean ± standard deviation (x̄ ± s). Comparisons between the patient group and normal reference values were conducted using independent samples t-test. The correlation between ranked variables (e.g., Knosp grade) and lymphocyte subsets was analyzed using Spearman's rank correlation. A P value < 0.05 was considered statistically significant. Results 1. Overall Lymphocyte Subset Characteristics in PitNETs Patients Compared with the normal population, PitNETs patients had significantly decreased absolute lymphocyte count and NK cell proportion, while B lymphocyte proportion, T lymphocyte proportion, and CD4+/CD8+ T cell ratio were significantly increased (all P < 0.05). (Table 1) 2. Comparison of Lymphocyte Subsets among PitNETs with Different WHO Lineages Subgroup analysis according to the 2022 WHO classification revealed that patients with PIT1-lineage, TPIT-lineage, SF1-lineage, and plurihormonal PitNETs all exhibited trends consistent with the overall findings: decreased absolute lymphocyte count and NK cell proportion, and increased B cell proportion, T cell proportion, and CD4+/CD8+ ratio (all P < 0.05). (Table 1) Table 1. Lymphocyte Subset Characteristics in PitNETs Patients with Different WHO Lineages Group No. Ly（/ul） NK（%） B（%） T（%） T4（%） T8（%） T4/T8 Normal 102 2479±594 20.0±9.6 10.0±3.9 68.0±9.7 38.0±6.7 28.0±8.3 1.51±0.55 PitNET 230 1972±544* 11.5±7.1* 14.5±5.1* 72.9±7.4* 42.7±7.7* 25.7±7.5* 1.87±0.83* PIT-1 84 2061±586* 9.9±5.7* 16.0±4.9* 73.2±6.5* 41.8±6.5* 26.9±7.8* 1.75±0.76* T-PIT 39 1858±600* 11.8±6.0* 12.1±4.3* 74.8±7.0* 43.7±8.3* 25.9±6.6 1.88±0.89* SF-1 71 1869±443* 14.0±9.0* 13.6±5.5* 71.1±8.6* 42.5±8.3* 24.4±7.7* 1.97±0.87* Pluri 29 1988±501* 9.8±4.9* 15.6±5.1* 73.5±6.1* 43.2±8.7* 25.7±7.0* 1.87±0.77* Null cell 7 2534±334 10.8±8.5* 13.7±2.9 74.8±9.19 46.8±7.9* 24.1±8.1 2.22±1.10 Ly（/ul）: lymphocyte count (cells/μL); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio; Pluri: Plurihormonal adenomas; *:P < 0.05, the difference was statistically significant. 3. Comparison of Lymphocyte Subsets among PitNETs with Different Clinical Functional Classifications · Non-functioning adenomas and GH adenomas: Exhibited the most comprehensive alterations, including decreased absolute lymphocyte count, decreased NK cell proportion, and increased B cell proportion, T cell proportion, and CD4+/CD8+ ratio. · PRL adenomas: Showed decreased NK cell proportion and increased B and T cell proportions, but no significant differences in absolute lymphocyte count or CD4+/CD8+ ratio compared to the normal range. · ACTH adenomas: Exhibited decreased absolute lymphocyte count, decreased NK cell proportion, and increased B cell proportion, but no significant change in CD4+/CD8+ ratio. (Table 2) Table 2. Lymphocyte Subsets among PitNETs with Different Clinical Functional Classifications Group No. Ly（/ul） NK（%） B（%） T（%） T4（%） T8（%） T4/T8 NF 129 1942±502* 13.5±4.9* 12.9±8.1* 72.4±8.1* 43.5±8.27* 24.4±7.26* 2.00±0.91* GH 51 1923±490* 16.3±4.9* 9.3±5.1* 73.4±6.6* 42.6±7.3* 27.0±8.3 1.77±0.72* PRL 34 2341±623 15.0±4.4* 10.0±5.0* 74.1±6.0* 41.5±5.2* 27.1±5.8 1.63±0.53 ACTH 7 1519±443* 11.8±6.6* 13.5±5.7* 73.2±3.6* 38±6.7 30.1±8.0 1.45±0.85 Ly（/ul）: lymphocyte count (cells/μL); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio; NF: Nonfunctioning pituitary adenoma; *:P < 0.05, the difference was statistically significant. 4. Correlation between Lymphocyte Subsets and Clinical Features 1. Relationship with tumor size : Spearman correlation analysis showed that larger tumor volume was negatively correlated with a lower percentage of B lymphocytes (r = -0.114, P < 0.05). (Table 3) Table 3. Relationship between lymphocyte Subsets and tumor size Group No. Ly（/ul） NK（%） B（%） T（%） T4（%） T8（%） T4/T8 ＜10mm 30 1847±514 14.8±4.6 10.5±5.1 73.5±5.8 40.4±6.6 27.9±6.3 1.56±0.59 10~40mm 173 1970±523 14.5±5.0 11.7±7.5 72.7±7.4 43.0±7.9 25.1±7. 1.93±0.88 ＞40mm 27 2160±647 14.2±6.1 10.5±6.5 73.8±8.8 43.1±8.1 26.8±9.8 1.79±0.63 ｒ -0.029 -0.114* 0.087 -0.16 -0.036 -0.054 0.022 P 0.330 0.042 0.095 0.407 0.295 0.208 0.367 Ly（/ul）: lymphocyte count (cells/μL); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio; ｒ: Spearman's rank correlation coefficient; *:P < 0.05, the correlation was statistically significant. 2. Relationship with Knosp grade: A higher Knosp grade was correlated with a lower percentage of B lymphocytes (r = -0.046, P < 0.05) and a higher percentage of CD4+ T lymphocytes (r = 0.112, P < 0.05). (Table 4) Table 4. Relationship between lymphocyte Subsets and Knosp grade Group No. Ly（/ul） NK（%） B（%） T（%） T4（%） T8（%） T4/T8 0 37 1851±519 13.9±4.9 13.2±8.2 71.6±7.9 42.8±8.7 24.3±6.5 1.95±0.83 I 40 2133±682 13.3±5.8 12.0±8.6 71.6±7.9 40.5±4.7 26.6±7.3 1.69±0.71 II 85 1891±488 14.9±4.6 10.7±6.6 73.4±7.0 42.8±8.4 26.1±8.5 1.88±0.87 III 51 2003±521 14.2±4.9 11.9±6.0 73.8±6.4 44.1±6.7 25.5±6.2 1.91±084 IV 17 2175±465 15.4±6.5 9.8±5.9 73.7±9.7 42.9±10.0 25±8.2 1.93±0.88 ｒ 0.105 -0.046* -0.047 0.110* 0.112* 0.003 0.023 P 0.056 0.046 0.238 0.047 0.045 0.484 0.367 Ly（/ul）: lymphocyte count (cells/μL); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio; ｒ: Spearman's rank correlation coefficient; *:P < 0.05, the correlation was statistically significant. 3. Relationship with tumor recurrence: No significant difference in lymphocyte subset distribution was found between primary and recurrent patients. (Table 5) Table 5. Relationship between lymphocyte Subsets and tumor recurrence Group No. Ly（/ul） NK（%） B（%） T（%） T4（%） T8（%） T4/T8 Primary 220 1996±557 14.4±5.0 11.4±7.1 73.0±7.2 42.9±7.7 25.6±7.5 1.89±0.85 Recurrent 10 1799±399 15.2±6.0 12.0±7.1 71.9±9.2 41.3±7.6 26.1±7.3 1.71±0.67 P 0.055 0.223 0.441 0.175 0.339 0.961 0.131 Ly（/ul）: lymphocyte count (cells/μL); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio Discussion Pituitary Neuroendocrine Tumors (PitNETs) are common intracranial tumors whose occurrence, development, and immune escape are closely related to the body's immune function [1]. Studies have shown varying degrees of immune cell infiltration within the tumor immune microenvironment (TIME) of PitNETs, and this infiltration is closely associated with tumor invasiveness and recurrence [5]. T lymphocytes and NK cells are key effectors in anti-tumor immune responses, and the normal proportion and function of their subsets are prerequisites for effective immune function [7, 8]. However, current research mostly focuses on the local tumor microenvironment, with very few systematic studies on peripheral blood lymphocyte subsets in PitNETs patients [9]. Given that peripheral blood lymphocyte subsets are a window to assess the body's overall immune status, this study systematically analyzed the distribution characteristics of these subsets in patients with different subtypes of PitNETs. This study is the first to systematically analyze the distribution of peripheral blood lymphocyte subsets in PitNETs patients based on the new 2022 WHO classification (Pituitary Neuroendocrine Tumors) [4]. Our results indicate that PitNETs, as neuroendocrine tumors, indeed influence the systemic immune system through the complex neuro-endocrine-immune network [10–12]. The findings suggest that PitNETs patients exhibit systemic immune dysregulation characterized by decreased NK cells, increased B cells, and an elevated CD4+/CD8 + T cell ratio, which is associated with tumor lineage, size, and invasiveness. NK cells are key effector cells of innate immunity, and their decreased proportion may impair the body's immune surveillance capability against tumor cells [7, 13]. The increased B cell proportion and CD4+/CD8 + ratio may reflect a compensatory humoral immune and helper T-cell response to tumor antigens; however, this response might be insufficient or dysfunctional, failing to effectively control tumor growth [8]. This aligns with previous findings of scarce T lymphocyte infiltration but immune checkpoint molecule expression within PitNETs tissue, suggesting that tumors may achieve immune escape by creating an inhibitory microenvironment and causing peripheral immune disruption [14]. This study integrates immune phenotypes with the latest pathological classification. Although immune dysregulation exists across different PitNETs lineages, differences exist among specific subtypes [15, 16]. For instance, immune alterations were most significant in non-functioning adenomas and GH adenomas, which may be related to their specific hormone secretion profiles and microenvironment characteristics [17]. Some studies have indicated that GH adenomas have a higher degree of T-cell infiltration compared to other subtypes [14], and our results provide corroborating evidence from the perspective of peripheral blood. Our results suggest correlations between lymphocyte subsets and clinical features. The negative correlation between B cell proportion and both tumor volume and Knosp grade, and the positive correlation between CD4 + T cell proportion and Knosp grade, have potential clinical implications. On one hand, tumor progression and increased invasiveness might consume or inhibit B cell activation and proliferation [18]. On the other hand, highly invasive tumors might induce greater activation of CD4 + T cells or expansion of specific subsets (e.g., regulatory T cells, Tregs), thereby promoting immune tolerance [19]. This supports the view proposed by Ben-Shlomo that "the TME plays a dominant role in promoting pituitary tumor invasiveness and refractoriness" [20], suggesting that the systemic immune profile might reflect the activity level of the TME [9]. Furthermore, clinical studies have shown that Immune Checkpoint Inhibitors (ICIs) can achieve a disease control rate of approximately 62% in refractory/metastatic PitNETs, particularly effective against ACTH and PRL adenomas [21, 22]. This provides a rationale for using peripheral blood immune markers as non-invasive biomarkers to screen potential patients who might benefit from ICI therapy [23]. As Nie et al. envisioned, identifying relevant immune markers is a key step in advancing PitNETs immunotherapy [24]. The study by Liu Weishuo et al. also reported elevated T-cell activation markers in the peripheral blood of pituitary adenoma patients, supporting our finding of increased CD4 + T cell proportion and collectively suggesting a state of persistent immune activation in PitNETs patients [25]. Immune checkpoint inhibitors (ICIs) have become an important exploratory direction for refractory, invasive, or metastatic PitNETs [26, 27, 22]. The basis for their application lies in findings that invasive and functional PitNETs (e.g., ACTH, GH adenomas) exhibit high PD-L1 expression [26, 21]. Preliminary clinical evidence confirms the potential of ICIs; for example, combination therapy with ipilimumab (anti-CTLA-4) and nivolumab (anti-PD-1) can induce significant tumor regression and hormone level reduction in metastatic corticotroph pituitary carcinoma [28]. However, efficacy is heterogeneous, indicating the need for reliable biomarkers to select the optimal patient population. This and previous studies suggest that peripheral blood lymphocyte subset analysis is a promising non-invasive biomarker. Elevated CD4 + T cell proportion and abnormal CD4+/CD8 + ratio may reflect a systemic state of activated T-cell immune checkpoint pathways in the tumor microenvironment, making such patients more likely to benefit from PD-1/PD-L1 inhibitors [9, 23]. Moreover, different PitNETs subtypes possess distinct immune cell infiltration patterns and peripheral immune profiles [26, 14]. Therefore, future immunotherapy strategies must move towards personalization, tailoring treatments based on the tumor's molecular subtype, PD-L1 expression status, and patient-specific peripheral immune characteristics to achieve precision medicine and improve treatment efficacy [29–33]. The limitations of this study include its single-center, cross-sectional design, lack of dynamic observation of immune indices before and after treatment, and absence of correlative analysis between intratumoral immune cell infiltration and peripheral blood indices. Future larger prospective studies are needed, along with in-depth analysis of functional lymphocyte subsets (e.g., Tregs, exhausted T cells), to more comprehensively reveal the immunological mechanisms of PitNETs. Conclusion In conclusion, this study demonstrates that patients with PitNETs exhibit characteristic disturbances in peripheral blood lymphocyte subsets, primarily manifesting as weakened cellular immunity and activated humoral immunity. This dysregulation is closely associated with the WHO lineage classification, size, and invasiveness of the tumor. Analysis of peripheral blood lymphocyte subsets helps understand the immunopathological process of PitNETs at a systemic level and may provide valuable reference for identifying patients with refractory or invasive PitNETs who might be suitable for immunotherapy in the future. Declarations Author Contribution Kan Deng, Xinjie Bao, Ming Feng, Wei Lian, Bing Xing, and Yong Yao were involved in patient diagnosis and treatment. Bing Xing and Yong Yao conceived and designed the study. Qi Liu was responsible for data curation, statistical analysis, and writing the original draft.All authors reviewed the manuscript. References Melmed S. Pituitary-tumor endocrinopathies. N Engl J Med. 2011;382(10):937-50. Lloyd RV, Osamura RY, Klöppel G, Rosai J, editors. WHO Classification of Tumours of Endocrine Organs. 4th ed. Lyon: IARC; 2017. Li C, Zhang Y. Interpretation of the 2017 WHO classification of pituitary tumors. Chin J Neurosurg. 2018;34(1):15. 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Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""},{"id":526963835,"identity":"b2c18131-e9cf-48f8-9d96-4a4582f19111","order_by":2,"name":"Kan Deng","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kan","middleName":"","lastName":"Deng","suffix":""},{"id":526963836,"identity":"0ae2f1b4-117d-4e58-9223-b69daccb88ef","order_by":3,"name":"Xinjie Bao","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xinjie","middleName":"","lastName":"Bao","suffix":""},{"id":526963837,"identity":"1e012f80-ae4c-4c1a-99f1-ea4234fe92a3","order_by":4,"name":"Ming Feng","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Feng","suffix":""},{"id":526963838,"identity":"2fcc4478-6849-4228-b869-bf6bccbc242d","order_by":5,"name":"Wei Lian","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Lian","suffix":""},{"id":526963839,"identity":"6e1dded8-2533-4a51-a820-72a148769f5f","order_by":6,"name":"Bing Xing","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Xing","suffix":""},{"id":526963840,"identity":"783dd708-c3db-4733-97b1-001914588f53","order_by":7,"name":"Yong Yao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBADORDBTJIWY9K1JDYQrcXg+OFjEh931KZvl8hO/FzAYJdHWMuZtDTJmWeO5+6ckbtZegZDcjFhLQdyzKR5247lbriRu42Zh+EA2IX4tZx/A9aSbkC8lhtgW2oSiNcieeNZsuXMtgOGO3vebpbmMUgmrIXvfPLBGx/b6uTN2XM3fuapsCOsReEAA4sEA8NhBgOIOwmpBwL5BgbmDwwMdUQpHgWjYBSMghEKAH1HP3TlsrIyAAAAAElFTkSuQmCC","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Yao","suffix":""}],"badges":[],"createdAt":"2025-09-26 10:23:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7720749/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7720749/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93263103,"identity":"9677ecae-d8ab-4d63-af69-f0fbc2208c0b","added_by":"auto","created_at":"2025-10-10 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18:58:07","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68478,"visible":true,"origin":"","legend":"","description":"","filename":"7d01a0ac41c34e84a984d4b8b2b658a41enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7720749/v1/e595c0da810eb47957a61783.xml"},{"id":93263104,"identity":"e161ecc8-d026-42e2-867c-33b105aec7e5","added_by":"auto","created_at":"2025-10-10 18:58:08","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71118,"visible":true,"origin":"","legend":"","description":"","filename":"7d01a0ac41c34e84a984d4b8b2b658a41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7720749/v1/4225111e2b67e25cb832ea59.xml"},{"id":93263105,"identity":"a33408fc-bdb1-403c-b5de-fa6be86001ff","added_by":"auto","created_at":"2025-10-10 18:58:08","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76678,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7720749/v1/04a7ceb3a80b905bd3c047e2.html"},{"id":106213605,"identity":"947ed953-07d8-4d94-81a9-d6875d3fed98","added_by":"auto","created_at":"2026-04-06 07:57:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":932375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7720749/v1/250e5a59-5697-47f2-b3d3-818682dff005.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distribution Characteristics and Clinical Significance of Peripheral Blood Lymphocyte Subsets in Patients with Pituitary Neuroendocrine Tumors","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePituitary adenoma (PA) is a common intracranial tumor originating from the anterior pituitary gland, with an incidence second only to gliomas and meningiomas, accounting for approximately 10\u0026ndash;15% of primary intracranial tumors [1]. The 2017 World Health Organization (WHO) classification primarily categorized these tumors based on cell type, hormone production, and molecular characteristics [2, 3]. In 2022, the WHO introduced a significant update, officially renaming them Pituitary Neuroendocrine Tumors (PitNETs) to reflect the neuroendocrine nature of adenohypophyseal cells [4]. This new classification emphasizes precise diagnosis based on cell lineage (identified by transcription factors such as PIT1, TPIT, SF1), cell type, and hormone type, which more accurately reflects the tumor's biological essence [4].\u003c/p\u003e\u003cp\u003ePitNETs can cause complex neuroendocrine dysfunction. In recent years, the role of the tumor immune microenvironment (TIME) in tumorigenesis, progression, and treatment response has gained increasing attention [5]. However, research on the impact of PitNETs on systemic immune function remains limited. Peripheral blood lymphocyte subsets serve as an important window for assessing the body's overall immune status. This study aims to systematically examine the distribution of peripheral blood lymphocyte subsets in patients with different subtypes of PitNETs according to the latest 2022 WHO classification, and to explore its relationship with clinical-pathological features (such as size, invasiveness, recurrence), providing a theoretical basis for understanding the immunological characteristics of PitNETs and future immunotherapy strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003e1. Study Subjects\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 230 patients with PitNETs hospitalized in the Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences between March and October 2023 were enrolled.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003cp\u003e(1) Postoperative pathological confirmation of PitNETs; (2) No prior radiotherapy or chemotherapy before surgery; (3) No history of autoimmune diseases, active infections, or other malignancies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAmong them, there were 98 males and 132 females, aged 14\u0026ndash;76 years (median age 44.5 years, mean age 45.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1 years).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eClinical and Pathological Classification:\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBy clinical function\u003c/b\u003e: 129 non-functioning adenomas, 51 GH adenomas, 34 PRL adenomas, 4 TSH adenomas, 7 ACTH adenomas, 1 gonadotroph adenoma, 4 plurihormonal / double-hormone cell adenomas.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBy 2022 WHO lineage\u003c/b\u003e: 84 PIT1-lineage, 39 TPIT-lineage, 71 SF1-lineage, 7 null cell tumors, 29 plurihormonal PitNETs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBy Knosp grade\u003c/b\u003e: 37 grade 0, 40 grade I, 85 grade II, 51 grade III, 17 grade IV.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eOther characteristics\u003c/b\u003e: 68 invasive, 162 non-invasive; 28 recurrent, 202 primary.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e This study was approved by the hospital's ethics review committee, and all patients provided written informed consent. Normal control values were derived from the reference values for blood lymphocyte immunophenotyping of 102 healthy Chinese adults established by Zhu Li-hua et al. [6].\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2. Research Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.1 Lymphocyte Subset Detection\u003c/b\u003e: 2 mL of peripheral venous blood was collected in EDTA anticoagulant tubes. Detection was performed using flow cytometry. Briefly: 100 \u0026micro;L of whole blood was incubated with a fluorescent antibody cocktail (targeting CD3, CD4, CD8, CD19, CD16/CD56, etc.) at room temperature in the dark for 15 minutes. Red blood cells were lysed using lysing reagent, followed by PBS washing and cell resuspension for acquisition. The percentages of lymphocyte subsets (T cells, helper/inducer T cells [CD4+], suppressor/cytotoxic T cells [CD8+], B cells, NK cells) were obtained, and absolute counts for each subset were calculated by combining these percentages with the absolute lymphocyte count from routine blood tests.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.2 Pathological Evaluation\u003c/b\u003e: Tumor tissues from all patients were fixed in formalin, embedded in paraffin, and subjected to routine hematoxylin-eosin (HE) staining and immunohistochemical staining. Diagnosis and classification were made according to the 2022 WHO standards.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3. Statistical Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStatistical analysis was performed using SPSS 20.0 software. Measurement data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s). Comparisons between the patient group and normal reference values were conducted using independent samples t-test. The correlation between ranked variables (e.g., Knosp grade) and lymphocyte subsets was analyzed using Spearman's rank correlation. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. \u0026nbsp; Overall Lymphocyte Subset Characteristics in PitNETs Patients\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Compared with the normal population, PitNETs patients had significantly decreased absolute lymphocyte count and NK cell proportion, while B lymphocyte proportion, T lymphocyte proportion, and CD4+/CD8+ T cell ratio were significantly increased (all P \u0026lt; 0.05). (Table 1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. \u0026nbsp; Comparison of Lymphocyte Subsets among PitNETs with Different WHO Lineages\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Subgroup analysis according to the 2022 WHO classification revealed that patients with PIT1-lineage, TPIT-lineage, SF1-lineage, and plurihormonal PitNETs all exhibited trends consistent with the overall findings: decreased absolute lymphocyte count and NK cell proportion, and increased B cell proportion, T cell proportion, and CD4+/CD8+ ratio (all P \u0026lt; 0.05). (Table 1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Lymphocyte Subset Characteristics in PitNETs Patients with Different WHO Lineages\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.88836%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.90112%;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9187%;\"\u003e\n \u003cp\u003eLy（/ul）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8022%;\"\u003e\n \u003cp\u003eNK（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6427%;\"\u003e\n \u003cp\u003eB（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8022%;\"\u003e\n \u003cp\u003eT（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6427%;\"\u003e\n \u003cp\u003eT4（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6427%;\"\u003e\n \u003cp\u003eT8（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7592%;\"\u003e\n \u003cp\u003eT4/T8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.88836%;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.90112%;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9187%;\"\u003e\n \u003cp\u003e2479\u0026plusmn;594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e20.0\u0026plusmn;9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e10.0\u0026plusmn;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e68.0\u0026plusmn;9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e38.0\u0026plusmn;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e28.0\u0026plusmn;8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7592%;\"\u003e\n \u003cp\u003e1.51\u0026plusmn;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.88836%;\"\u003e\n \u003cp\u003ePitNET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.90112%;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9187%;\"\u003e\n \u003cp\u003e1972\u0026plusmn;544*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e11.5\u0026plusmn;7.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e14.5\u0026plusmn;5.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e72.9\u0026plusmn;7.4*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e42.7\u0026plusmn;7.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e25.7\u0026plusmn;7.5*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7592%;\"\u003e\n \u003cp\u003e1.87\u0026plusmn;0.83*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.88836%;\"\u003e\n \u003cp\u003ePIT-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.90112%;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9187%;\"\u003e\n \u003cp\u003e2061\u0026plusmn;586*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e9.9\u0026plusmn;5.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e16.0\u0026plusmn;4.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e73.2\u0026plusmn;6.5*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e41.8\u0026plusmn;6.5*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e26.9\u0026plusmn;7.8*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7592%;\"\u003e\n \u003cp\u003e1.75\u0026plusmn;0.76*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.88836%;\"\u003e\n \u003cp\u003eT-PIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.90112%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9187%;\"\u003e\n \u003cp\u003e1858\u0026plusmn;600*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e11.8\u0026plusmn;6.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e12.1\u0026plusmn;4.3*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e74.8\u0026plusmn;7.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e43.7\u0026plusmn;8.3*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e25.9\u0026plusmn;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7592%;\"\u003e\n \u003cp\u003e1.88\u0026plusmn;0.89*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.88836%;\"\u003e\n \u003cp\u003eSF-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.90112%;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9187%;\"\u003e\n \u003cp\u003e1869\u0026plusmn;443*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e14.0\u0026plusmn;9.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e13.6\u0026plusmn;5.5*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e71.1\u0026plusmn;8.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e42.5\u0026plusmn;8.3*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e24.4\u0026plusmn;7.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7592%;\"\u003e\n \u003cp\u003e1.97\u0026plusmn;0.87*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.88836%;\"\u003e\n \u003cp\u003ePluri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.90112%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9187%;\"\u003e\n \u003cp\u003e1988\u0026plusmn;501*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e9.8\u0026plusmn;4.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e15.6\u0026plusmn;5.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e73.5\u0026plusmn;6.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e43.2\u0026plusmn;8.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e25.7\u0026plusmn;7.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7592%;\"\u003e\n \u003cp\u003e1.87\u0026plusmn;0.77*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.88836%;\"\u003e\n \u003cp\u003eNull cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.90112%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9187%;\"\u003e\n \u003cp\u003e2534\u0026plusmn;334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e10.8\u0026plusmn;8.5*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e13.7\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8022%;\"\u003e\n \u003cp\u003e74.8\u0026plusmn;9.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e46.8\u0026plusmn;7.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6427%;\"\u003e\n \u003cp\u003e24.1\u0026plusmn;8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7592%;\"\u003e\n \u003cp\u003e2.22\u0026plusmn;1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLy（/ul）: lymphocyte count (cells/\u0026mu;L); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio; Pluri: Plurihormonal adenomas; *:P \u0026lt; 0.05, the difference was statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Comparison of Lymphocyte Subsets among PitNETs with Different Clinical Functional Classifications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eNon-functioning adenomas and GH adenomas:\u003c/strong\u003e Exhibited the most comprehensive alterations, including decreased absolute lymphocyte count, decreased NK cell proportion, and increased B cell proportion, T cell proportion, and CD4+/CD8+ ratio.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003ePRL adenomas:\u003c/strong\u003e Showed decreased NK cell proportion and increased B and T cell proportions, but no significant differences in absolute lymphocyte count or CD4+/CD8+ ratio compared to the normal range.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eACTH adenomas:\u003c/strong\u003e Exhibited decreased absolute lymphocyte count, decreased NK cell proportion, and increased B cell proportion, but no significant change in CD4+/CD8+ ratio. (Table 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Lymphocyte Subsets among PitNETs with Different Clinical Functional Classifications\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7037%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.81481%;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5926%;\"\u003e\n \u003cp\u003eLy（/ul）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5926%;\"\u003e\n \u003cp\u003eNK（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2593%;\"\u003e\n \u003cp\u003eB（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2593%;\"\u003e\n \u003cp\u003eT（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5926%;\"\u003e\n \u003cp\u003eT4（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5926%;\"\u003e\n \u003cp\u003eT8（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5926%;\"\u003e\n \u003cp\u003eT4/T8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7037%;\"\u003e\n \u003cp\u003eNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.81481%;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e1942\u0026plusmn;502*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e13.5\u0026plusmn;4.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2593%;\"\u003e\n \u003cp\u003e12.9\u0026plusmn;8.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2593%;\"\u003e\n \u003cp\u003e72.4\u0026plusmn;8.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e43.5\u0026plusmn;8.27*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e24.4\u0026plusmn;7.26*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e2.00\u0026plusmn;0.91*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7037%;\"\u003e\n \u003cp\u003eGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.81481%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e1923\u0026plusmn;490*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e16.3\u0026plusmn;4.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2593%;\"\u003e\n \u003cp\u003e9.3\u0026plusmn;5.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2593%;\"\u003e\n \u003cp\u003e73.4\u0026plusmn;6.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e42.6\u0026plusmn;7.3*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e27.0\u0026plusmn;8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e1.77\u0026plusmn;0.72*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7037%;\"\u003e\n \u003cp\u003ePRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.81481%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e2341\u0026plusmn;623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e15.0\u0026plusmn;4.4*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2593%;\"\u003e\n \u003cp\u003e10.0\u0026plusmn;5.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2593%;\"\u003e\n \u003cp\u003e74.1\u0026plusmn;6.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e41.5\u0026plusmn;5.2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e27.1\u0026plusmn;5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e1.63\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7037%;\"\u003e\n \u003cp\u003eACTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.81481%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e1519\u0026plusmn;443*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e11.8\u0026plusmn;6.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2593%;\"\u003e\n \u003cp\u003e13.5\u0026plusmn;5.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2593%;\"\u003e\n \u003cp\u003e73.2\u0026plusmn;3.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e38\u0026plusmn;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e30.1\u0026plusmn;8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5926%;\"\u003e\n \u003cp\u003e1.45\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLy（/ul）: lymphocyte count (cells/\u0026mu;L); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio; NF: Nonfunctioning pituitary adenoma; *:P \u0026lt; 0.05, the difference was statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Correlation between Lymphocyte Subsets and Clinical Features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eRelationship with tumor size\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Spearman correlation analysis showed that larger tumor volume was negatively correlated with a lower percentage of B lymphocytes (r = -0.114, P \u0026lt; 0.05). (Table 3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Relationship between lymphocyte Subsets and tumor size\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"597\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9195%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87919%;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4161%;\"\u003e\n \u003cp\u003eLy（/ul）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0738%;\"\u003e\n \u003cp\u003eNK（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0738%;\"\u003e\n \u003cp\u003eB（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0738%;\"\u003e\n \u003cp\u003eT（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0738%;\"\u003e\n \u003cp\u003eT4（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0738%;\"\u003e\n \u003cp\u003eT8（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4161%;\"\u003e\n \u003cp\u003eT4/T8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9195%;\"\u003e\n \u003cp\u003e＜10mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87919%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e1847\u0026plusmn;514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e14.8\u0026plusmn;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e10.5\u0026plusmn;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e73.5\u0026plusmn;5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e40.4\u0026plusmn;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e27.9\u0026plusmn;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e1.56\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9195%;\"\u003e\n \u003cp\u003e10~40mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87919%;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e1970\u0026plusmn;523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e14.5\u0026plusmn;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e11.7\u0026plusmn;7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e72.7\u0026plusmn;7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e43.0\u0026plusmn;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e25.1\u0026plusmn;7.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e1.93\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9195%;\"\u003e\n \u003cp\u003e＞40mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87919%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e2160\u0026plusmn;647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e14.2\u0026plusmn;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e10.5\u0026plusmn;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e73.8\u0026plusmn;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e43.1\u0026plusmn;8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e26.8\u0026plusmn;9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e1.79\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9195%;\"\u003e\n \u003cp\u003eｒ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87919%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e-0.114*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9195%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87919%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0738%;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4161%;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLy（/ul）: lymphocyte count (cells/\u0026mu;L); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio; ｒ: Spearman\u0026apos;s rank correlation coefficient; *:P \u0026lt; 0.05, the correlation was statistically significant.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eRelationship with Knosp grade:\u003c/strong\u003e A higher Knosp grade was correlated with a lower percentage of B lymphocytes (r = -0.046, P \u0026lt; 0.05) and a higher percentage of CD4+ T lymphocytes (r = 0.112, P \u0026lt; 0.05). (Table 4)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Relationship between lymphocyte Subsets and Knosp grade\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.15751%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.22711%;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8205%;\"\u003e\n \u003cp\u003eLy（/ul）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eNK（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eB（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eT（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8205%;\"\u003e\n \u003cp\u003eT4（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eT8（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8205%;\"\u003e\n \u003cp\u003eT4/T8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.15751%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.22711%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e1851\u0026plusmn;519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e13.9\u0026plusmn;4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e13.2\u0026plusmn;8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e71.6\u0026plusmn;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e42.8\u0026plusmn;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e24.3\u0026plusmn;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e1.95\u0026plusmn;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.15751%;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.22711%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e2133\u0026plusmn;682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e13.3\u0026plusmn;5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e12.0\u0026plusmn;8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e71.6\u0026plusmn;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e40.5\u0026plusmn;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e26.6\u0026plusmn;7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e1.69\u0026plusmn;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.15751%;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.22711%;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e1891\u0026plusmn;488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e14.9\u0026plusmn;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e10.7\u0026plusmn;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e73.4\u0026plusmn;7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e42.8\u0026plusmn;8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e26.1\u0026plusmn;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e1.88\u0026plusmn;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.15751%;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.22711%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e2003\u0026plusmn;521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e14.2\u0026plusmn;4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e11.9\u0026plusmn;6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e73.8\u0026plusmn;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e44.1\u0026plusmn;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e25.5\u0026plusmn;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e1.91\u0026plusmn;084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.15751%;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.22711%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e2175\u0026plusmn;465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e15.4\u0026plusmn;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e9.8\u0026plusmn;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e73.7\u0026plusmn;9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e42.9\u0026plusmn;10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e25\u0026plusmn;8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e1.93\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.15751%;\"\u003e\n \u003cp\u003eｒ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.22711%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e-0.046*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.110*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e0.112*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.15751%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.22711%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8205%;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLy（/ul）: lymphocyte count (cells/\u0026mu;L); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio; ｒ: Spearman\u0026apos;s rank correlation coefficient; *:P \u0026lt; 0.05, the correlation was statistically significant.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eRelationship with tumor recurrence:\u003c/strong\u003e No significant difference in lymphocyte subset distribution was found between primary and recurrent patients. (Table 5)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Relationship between lymphocyte Subsets and tumor recurrence\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.145%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.64885%;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8244%;\"\u003e\n \u003cp\u003eLy（/ul）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6031%;\"\u003e\n \u003cp\u003eNK（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6031%;\"\u003e\n \u003cp\u003eB（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6031%;\"\u003e\n \u003cp\u003eT（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6031%;\"\u003e\n \u003cp\u003eT4（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6031%;\"\u003e\n \u003cp\u003eT8（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3664%;\"\u003e\n \u003cp\u003eT4/T8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.145%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.64885%;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8244%;\"\u003e\n \u003cp\u003e1996\u0026plusmn;557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e14.4\u0026plusmn;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e11.4\u0026plusmn;7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e73.0\u0026plusmn;7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e42.9\u0026plusmn;7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e25.6\u0026plusmn;7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3664%;\"\u003e\n \u003cp\u003e1.89\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.145%;\"\u003e\n \u003cp\u003eRecurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.64885%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.8244%;\"\u003e\n \u003cp\u003e1799\u0026plusmn;399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e15.2\u0026plusmn;6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e12.0\u0026plusmn;7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e71.9\u0026plusmn;9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e41.3\u0026plusmn;7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e26.1\u0026plusmn;7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3664%;\"\u003e\n \u003cp\u003e1.71\u0026plusmn;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.145%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.64885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8244%;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6031%;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3664%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLy（/ul）: lymphocyte count (cells/\u0026mu;L); NK（%）:NK cell percentage (%); B（%）:B lymphocyte percentage (%); T（%）: T lymphocyte percentage (%); T4（%）: T4 lymphocyte percentage (%); T8（%）: T8 lymphocyte percentage (%); T4/T8: T4/T8 ratio\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePituitary Neuroendocrine Tumors (PitNETs) are common intracranial tumors whose occurrence, development, and immune escape are closely related to the body's immune function [1]. Studies have shown varying degrees of immune cell infiltration within the tumor immune microenvironment (TIME) of PitNETs, and this infiltration is closely associated with tumor invasiveness and recurrence [5]. T lymphocytes and NK cells are key effectors in anti-tumor immune responses, and the normal proportion and function of their subsets are prerequisites for effective immune function [7, 8]. However, current research mostly focuses on the local tumor microenvironment, with very few systematic studies on peripheral blood lymphocyte subsets in PitNETs patients [9]. Given that peripheral blood lymphocyte subsets are a window to assess the body's overall immune status, this study systematically analyzed the distribution characteristics of these subsets in patients with different subtypes of PitNETs.\u003c/p\u003e\u003cp\u003eThis study is the first to systematically analyze the distribution of peripheral blood lymphocyte subsets in PitNETs patients based on the new 2022 WHO classification (Pituitary Neuroendocrine Tumors) [4]. Our results indicate that PitNETs, as neuroendocrine tumors, indeed influence the systemic immune system through the complex neuro-endocrine-immune network [10\u0026ndash;12]. The findings suggest that PitNETs patients exhibit systemic immune dysregulation characterized by decreased NK cells, increased B cells, and an elevated CD4+/CD8\u0026thinsp;+\u0026thinsp;T cell ratio, which is associated with tumor lineage, size, and invasiveness. NK cells are key effector cells of innate immunity, and their decreased proportion may impair the body's immune surveillance capability against tumor cells [7, 13]. The increased B cell proportion and CD4+/CD8\u0026thinsp;+\u0026thinsp;ratio may reflect a compensatory humoral immune and helper T-cell response to tumor antigens; however, this response might be insufficient or dysfunctional, failing to effectively control tumor growth [8]. This aligns with previous findings of scarce T lymphocyte infiltration but immune checkpoint molecule expression within PitNETs tissue, suggesting that tumors may achieve immune escape by creating an inhibitory microenvironment and causing peripheral immune disruption [14].\u003c/p\u003e\u003cp\u003eThis study integrates immune phenotypes with the latest pathological classification. Although immune dysregulation exists across different PitNETs lineages, differences exist among specific subtypes [15, 16]. For instance, immune alterations were most significant in non-functioning adenomas and GH adenomas, which may be related to their specific hormone secretion profiles and microenvironment characteristics [17]. Some studies have indicated that GH adenomas have a higher degree of T-cell infiltration compared to other subtypes [14], and our results provide corroborating evidence from the perspective of peripheral blood.\u003c/p\u003e\u003cp\u003eOur results suggest correlations between lymphocyte subsets and clinical features. The negative correlation between B cell proportion and both tumor volume and Knosp grade, and the positive correlation between CD4\u0026thinsp;+\u0026thinsp;T cell proportion and Knosp grade, have potential clinical implications. On one hand, tumor progression and increased invasiveness might consume or inhibit B cell activation and proliferation [18]. On the other hand, highly invasive tumors might induce greater activation of CD4\u0026thinsp;+\u0026thinsp;T cells or expansion of specific subsets (e.g., regulatory T cells, Tregs), thereby promoting immune tolerance [19]. This supports the view proposed by Ben-Shlomo that \"the TME plays a dominant role in promoting pituitary tumor invasiveness and refractoriness\" [20], suggesting that the systemic immune profile might reflect the activity level of the TME [9]. Furthermore, clinical studies have shown that Immune Checkpoint Inhibitors (ICIs) can achieve a disease control rate of approximately 62% in refractory/metastatic PitNETs, particularly effective against ACTH and PRL adenomas [21, 22]. This provides a rationale for using peripheral blood immune markers as non-invasive biomarkers to screen potential patients who might benefit from ICI therapy [23]. As Nie et al. envisioned, identifying relevant immune markers is a key step in advancing PitNETs immunotherapy [24]. The study by Liu Weishuo et al. also reported elevated T-cell activation markers in the peripheral blood of pituitary adenoma patients, supporting our finding of increased CD4\u0026thinsp;+\u0026thinsp;T cell proportion and collectively suggesting a state of persistent immune activation in PitNETs patients [25].\u003c/p\u003e\u003cp\u003eImmune checkpoint inhibitors (ICIs) have become an important exploratory direction for refractory, invasive, or metastatic PitNETs [26, 27, 22]. The basis for their application lies in findings that invasive and functional PitNETs (e.g., ACTH, GH adenomas) exhibit high PD-L1 expression [26, 21]. Preliminary clinical evidence confirms the potential of ICIs; for example, combination therapy with ipilimumab (anti-CTLA-4) and nivolumab (anti-PD-1) can induce significant tumor regression and hormone level reduction in metastatic corticotroph pituitary carcinoma [28]. However, efficacy is heterogeneous, indicating the need for reliable biomarkers to select the optimal patient population. This and previous studies suggest that peripheral blood lymphocyte subset analysis is a promising non-invasive biomarker. Elevated CD4\u0026thinsp;+\u0026thinsp;T cell proportion and abnormal CD4+/CD8\u0026thinsp;+\u0026thinsp;ratio may reflect a systemic state of activated T-cell immune checkpoint pathways in the tumor microenvironment, making such patients more likely to benefit from PD-1/PD-L1 inhibitors [9, 23]. Moreover, different PitNETs subtypes possess distinct immune cell infiltration patterns and peripheral immune profiles [26, 14]. Therefore, future immunotherapy strategies must move towards personalization, tailoring treatments based on the tumor's molecular subtype, PD-L1 expression status, and patient-specific peripheral immune characteristics to achieve precision medicine and improve treatment efficacy [29\u0026ndash;33].\u003c/p\u003e\u003cp\u003eThe limitations of this study include its single-center, cross-sectional design, lack of dynamic observation of immune indices before and after treatment, and absence of correlative analysis between intratumoral immune cell infiltration and peripheral blood indices. Future larger prospective studies are needed, along with in-depth analysis of functional lymphocyte subsets (e.g., Tregs, exhausted T cells), to more comprehensively reveal the immunological mechanisms of PitNETs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study demonstrates that patients with PitNETs exhibit characteristic disturbances in peripheral blood lymphocyte subsets, primarily manifesting as weakened cellular immunity and activated humoral immunity. This dysregulation is closely associated with the WHO lineage classification, size, and invasiveness of the tumor. Analysis of peripheral blood lymphocyte subsets helps understand the immunopathological process of PitNETs at a systemic level and may provide valuable reference for identifying patients with refractory or invasive PitNETs who might be suitable for immunotherapy in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKan Deng, Xinjie Bao, Ming Feng, Wei Lian, Bing Xing, and Yong Yao were involved in patient diagnosis and treatment. Bing Xing and Yong Yao conceived and designed the study. Qi Liu was responsible for data curation, statistical analysis, and writing the original draft.All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMelmed S. Pituitary-tumor endocrinopathies. N Engl J Med. 2011;382(10):937-50.\u003c/li\u003e\n\u003cli\u003eLloyd RV, Osamura RY, Kl\u0026ouml;ppel G, Rosai J, editors. 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Journal of Clinical Neurosurgery. 2018;15(4): 289-292.\u003c/li\u003e\n\u003cli\u003eLin AL, Jiwani Z, Wang XF, Sloan L, Raza S, Wu CC, et al. Immunohistochemical Analysis of PD-L1 Expression in Pituitary Neuroendocrine Tumors and Its Correlation with Tumor Invasiveness. J Clin Endocrinol Metab. 2020;105(8):dgaa305.\u003c/li\u003e\n\u003cli\u003eLin AL, Jiwani Z, Wang XF, Sloan L, Raza S, Wu CC, et al. Programmed Death Ligand 1 Expression in Pituitary Neuroendocrine Tumors. J Clin Endocrinol Metab. 2020;105(8):dgaa305.\u003c/li\u003e\n\u003cli\u003eLin AL, Jiwani Z, Worsech J, Tabar V, Aghi MK, post KD, et al. Successful treatment of pituitary carcinoma with combined ipilimumab and nivolumab: a case report. J Immunother Cancer. 2021;9(5):e002126.\u003c/li\u003e\n\u003cli\u003eMayorga A, Varshney M, Hinojosa-Amaya JM, Cuenca D, Cote DJ, Blevins LS, et al. Exploring the Genomic Landscape of Aggressive Pituitary Tumors for Targeted and Immunotherapy. Pituitary. 2024;27(2):135-147.\u003c/li\u003e\n\u003cli\u003eZhou K, Chen Z, Zhang J, Wang R. Harnessing the immune system against pituitary neuroendocrine tumors: current insights and future directions. Biochim Biophys Acta Rev Cancer. 2024;1879(2):189069.\u003c/li\u003e\n\u003cli\u003eDutta P, Limumpornpetch P, Adams C, Korbonits M. Novel therapeutic targets and personalized medicine in pituitary tumors. Curr Opin Endocrinol Diabetes Obes. 2023;30(4):183-191.\u003c/li\u003e\n\u003cli\u003eMcCormack A, Dekkers OM, Petersenn S, Popovic V, Trouillas J, Raverot G, et al. European Society of Endocrinology clinical practice guidelines on the management of aggressive pituitary tumors. Eur J Endocrinol. 2022;187(1):G1-G33.\u003c/li\u003e\n\u003cli\u003eAsa SL, Mete O, Perry A, Osamura RY. Overview of the 2022 WHO Classification of Pituitary Tumors. Endocr Pathol. 2022;33(1):6-26.\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":"Pituitary Neuroendocrine Tumors, Lymphocyte Subsets, Immune Cells, NK Cells","lastPublishedDoi":"10.21203/rs.3.rs-7720749/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7720749/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo investigate the distribution characteristics of peripheral blood lymphocyte subsets in patients with Pituitary Neuroendocrine Tumors (PitNETs) based on the 2022 WHO classification and analyze their relationship with clinical features of the tumors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 230 patients with pathologically confirmed PitNETs admitted to the Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences between March and October 2023 were enrolled. Absolute counts and percentages of peripheral blood lymphocyte subsets (T cells, B cells, NK cells, and CD4+/CD8\u0026thinsp;+\u0026thinsp;T cell ratio) were detected by flow cytometry and compared with reference values from healthy adults. Subgroup analyses were performed according to the 2022 WHO classification (PIT1, TPIT, SF1 lineages, etc.), clinical functional type, and Knosp grading.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCompared with the normal population, PitNETs patients overall showed decreased absolute lymphocyte count and NK cell proportion, while B cell proportion, T cell proportion, and CD4+/CD8\u0026thinsp;+\u0026thinsp;T cell ratio were increased (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This trend was consistent across PIT1, TPIT, and SF1 lineage tumors. Subgroup analysis revealed that these alterations were most pronounced in non-functioning adenomas and growth hormone (GH) adenomas. Correlation analysis found that larger tumor volume (r = -0.114, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and higher Knosp grade (r = -0.046, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were associated with lower B cell proportion; whereas higher Knosp grade was associated with a higher CD4\u0026thinsp;+\u0026thinsp;T cell proportion (r\u0026thinsp;=\u0026thinsp;0.112, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Lymphocyte distribution was not significantly correlated with tumor recurrence.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePatients with PitNETs exhibit systemic immune dysfunction, characterized by abnormalities in both cellular immunity (decreased NK cells) and humoral immunity (increased B cells). This immune dysregulation is associated with tumor lineage, size, and invasiveness. Analysis of peripheral blood lymphocyte subsets may provide a new clinical perspective for assessing the immune status of PitNETs and exploring their potential for immunotherapy.\u003c/p\u003e","manuscriptTitle":"Distribution Characteristics and Clinical Significance of Peripheral Blood Lymphocyte Subsets in Patients with Pituitary Neuroendocrine Tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 18:58:03","doi":"10.21203/rs.3.rs-7720749/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":"da883f86-07e2-41b6-9345-58af7b6ada06","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T07:56:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 18:58:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7720749","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7720749","identity":"rs-7720749","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}