Correlation Between Circulating CD133+ Extracellular Vesicles and the Malignancy and Prognosis of Gliomas: A Retrospective Cohort Study

Correlation Between Circulating CD133+ Extracellular Vesicles and the Malignancy and Prognosis of Gliomas: A Retrospective Cohort Study | 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 Correlation Between Circulating CD133+ Extracellular Vesicles and the Malignancy and Prognosis of Gliomas: A Retrospective Cohort Study Jiaode Jiang, Feng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4347987/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 Purpose Gliomas are the most common malignant tumors in the central nervous system and have a poor prognosis. Circulating and plasma-derived extracellular vesicles (EVs) have been identified as effective biomarkers for the diagnosis and prognosis of gliomas, while Cluster of differentiation 133 (CD133) is closely associated with tumor aggressiveness, chemoresistance, and patient prognosis across various cancers. This study aims to evaluate the association between CD133 and malignancy, and prognosis of glioma patients. Methods A retrospective cohort study design was employed to compare plasma and plasma-derived CD133 + EVs and CD44 + EVs rates in 75 glioma patients and 38 healthy controls. Clinical and pathological parameters were compared using Mann-Whitney U tests or Kruskal-Wallis H tests about increased CD133 + rate. Additionally, quality of life, anxiety, and depression were assessed using the WHOQOL-BREF, Hamilton Anxiety Rating Scale (HAM-A), and Hamilton Depression Rating Scale (HDRS) to observe differences between CD133 high group and CD133 low group. The disease-free survival rate and overall survival rate were calculated using the Kaplan-Meier method, and the resulting curves were compared using log-rank tests. The impact of various clinical pathological features on survival was further assessed using a stepwise Cox proportional hazards regression model. Results Quantities of plasma CD44 and CD133 + EVs contents were 1.25 and 1.21 times those of healthy controls, respectively, yet only the quantity of CD133 + EVs was capable of differentiating glioma grades (P = 0.001). Stratifying glioma patients based on CD133 + EVs content revealed that the low rate group exhibited a significant survival advantage, with a mortality risk that was only 33.54% of the high rate group, which was statistically significant (P = 0.0124). Conclusion CD133 + EVs rate is a significant prognostic indicator in glioma patients, where lower rate is associated with better survival rates. These findings support the potential value of CD133 as a biomarker in the diagnosis and therapeutic monitoring of gliomas. gliomas CD133 biomarker extracellular vesicles Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Gliomas, as one of the most common malignant tumors in the central nervous system [1], generally have a poor prognosis, particularly in high-grade forms such as glioblastomas [2]. These tumors originate from glial cells that support neural functions, and they progress rapidly with limited therapeutic success [3]. The prognosis of glioma is related to various factors including the tumor's location, size, genetic mutations, and treatment response, yet currently, the biomarkers used to predict disease progression and patient survival are still limited [4]. Biomarkers play a crucial role in the diagnosis, prognosis assessment, and therapeutic monitoring of cancer [5]. Cluster of differentiation (CD133), also known as Prominin-1, is a cell surface protein widely recognized as a stem cell marker for tumor cells [6]. Initially discovered in the nervous system, it is also expressed in other tissues and organs. In normal tissues, CD133 is generally considered a marker of stem or progenitor cells, involved in maintaining tissue homeostasis and repair, and is seen as a cancer stem cell marker in various solid tumors [7–13]. In gliomas, the rate of CD133 + EVs is closely related to the tumor's aggressiveness, chemoresistance, and overall prognosis [14]. The presence of CD133 + EVs in the blood may reflect the tumor's biological characteristics, providing a non-invasive means to monitor tumor dynamics and predict disease progression [15]. Cluster of differentiation 44 (CD44) is another cell surface protein that is extensively found across various cell types and participates in numerous biological processes including cell adhesion, migration, signaling, and cell-cell interactions [16]. Its diverse structure and function make it a significant player in many physiological and pathological processes, influencing tumor cell adhesion, migration, and infiltration [17]. Through interactions with other proteins and signaling pathways, CD44 can regulate the tumor microenvironment, promoting tumor growth, metastasis, and resistance to therapy [18]. Despite the potential of CD133 and CD44 as biomarkers for glioma being initially explored, their specific roles and efficacy in clinical applications still require further validation. We employ a retrospective cohort design aimed at assessing the correlation between the rate of circulating CD133 and CD44 and the malignancy and prognosis of glioma patients. By comparing the levels of CD133 and CD44 biomarkers in 75 glioma patients with 38 healthy controls, we aim to reveal the potential value of these markers in disease monitoring and prognosis assessment. By analyzing the roles of these key biomarkers in depth, this study not only aids in understanding the biological behavior of gliomas but may also pave the way for new therapeutic strategies, providing patients with more personalized treatment options. 2. Materials and Methods 2.1 Study design This study is a retrospective cohort study designed to assess the rate of plasma and plasma-derived CD44 + EVs/CD133 + EVs in glioma patients and their correlation with the malignancy and prognosis of the disease The data were collected from January 1, 2020 to December 30, 2023 and included glioma patients and a healthy control group from The third Xiangya hospital of Central South University. A total of 156 samples were screened. Inclusion Criteria for Glioma Patients:(1) Confirmed diagnosis of glioma based on histopathological evaluation from a biopsy or surgical resection. (2) Patients must have medical records that include complete clinical history, treatment details, and follow-up data. (3) Imaging studies (MRI or CT scans) confirming the presence of glioma. (4) Patients must be capable of giving informed consent unless waived by the ethics committee. Exclusion Criteria for Glioma Patients: (1) Previous or concurrent participation in another clinical trial that could interfere with this study. (2) Patients who have received immunotherapy within the last six months. (3) Presence of metastatic disease from non-central nervous system cancers. (4) History of significant neurological disorders other than glioma, such as Alzheimer's disease or multiple sclerosis, which might confound the study outcomes. Inclusion Criteria for Healthy Controls: (1) Age and gender-matched to the patient group. (2) Confirmed through physical examination and medical history to be free of any chronic diseases, including neurological disorders. (3) No history of malignancies or significant psychiatric disorders. (4) Willing to provide informed consent for participation in the study. Exclusion Criteria for Healthy Controls: (1) Individuals with a history of significant neurological disorders or central nervous system trauma. (2) Presence of any acute or chronic infections at the time of enrollment. (3) Recent participation in any other clinical trials. This study adheres to the Declaration of Helsinki and has been approved by the Medical Ethics Committee. 2.2 Data Collection and Definitions Clinical data were collected from the patients' medical records, including baseline information (such as age, gender, tumor type, grade, resection status, and treatment history) and follow-up data (including survival time and recurrence status). Tumor size was recorded as the maximum dimension for each sample. The tumors were staged using the 2002 edition of the UICC (International Union Against Cancer) TNM system (New York, USA), and graded according to the criteria outlined by Edmondson and Steiner. Survival time was calculated from the date of diagnosis to the date of death or last follow-up, allowing for the analysis of survival rates. Recurrence was monitored through regular clinical evaluations and imaging studies, with recurrence being defined as the return of tumor activity as evidenced by radiologic assessment or clinical symptoms. Clinical and pathological characteristics of the 75 glioma patients are listed in Table 1 . Additionally, 38 healthy adults were collected as negative controls. 2.3 Samples Collection Upon admission for treatment, fasting blood samples were collected and separated into at least 1ml of plasma, which was then aliquoted into two portions for storage [20]. One portion of plasma was used for analysis using the Enzyme-Linked Immunosorbent Assay (ELISA), and the other portion was used for the isolation and analysis of EVs. 2.4 CD133 and CD44 Content Detection The concentrations of CD133 (Human Prominin-1 / CD133 ELISA Kit, Assay Genie) and CD44 (Human CD44 ELISA Kit, Abcam) in plasma were measured. All procedures were carried out according to the manufacturer’s instructions. 2.5 The Characteristics and CD44/CD133 rate of EVs The quantity and distribution of EVs were analyzed using a NanoSight LM10 (Malvern PANalytical). To analyze the rate of circulating CD133 + EVs and CD44 + EVs, flow cytometry was employed with specific antibodies targeting CD133 and CD44 to quantify the positive rate of circulating vesicles in the plasma. 2.6 Quality of Life, Anxiety, and Depression Scores Three months after the initial consultation, patients were divided into two groups based on the positive rate of CD133 + EVs in plasma: high CD133 + EVs group and low CD133 EVs group. The patients' quality of life, anxiety, and depression levels were assessed using standardized evaluation tools. World Health Organization Quality of Life-BREF (WHOQOL-BREF), covers physical health, psychological health, social relationships, and environment. Each item is rated on a scale from 1 to 5, where higher scores generally indicate better quality of life. The scores for each domain are then converted to a 0-100 scale, where a higher score signifies better quality of life. Hamilton Anxiety Rating Scale (HAM-A) rates the severity of a patient's anxiety. It consists of 14 items, each defined by a series of symptoms, and measures both psychic anxiety (mental agitation and psychological distress) and somatic anxiety (physical complaints related to anxiety). Each item is scored on a scale of 0 (not present) to 4 (severe), with a total score range of 0–56. A total score of 17 or less indicates mild severity, 18–24 mild to moderate severity and 25–30 moderate to severe anxiety. Scores above 30 suggest severe anxiety. Hamilton Depression Rating Scale (HDRS) assesses the severity of depression in individuals already diagnosed with the disorder. It includes 21 items that evaluate depressive symptoms such as mood, guilt, suicide ideation, insomnia, agitation, anxiety, weight loss, and somatic symptoms. Items are rated on a scale of 0 (not present) to 4 (extreme symptoms), though some are scored only 0–2. The total score can range from 0 to 52. Scores up to 7 are considered to be normal, 8–13 mild depression, 14–18 mild to moderate depression, 19–22 moderate to severe depression, and over 23 indicate severe depression[21,22]. All scores were collected three months after initial consultation. 2.7 Statistical Analysis Statistical analysis and graphical representation were performed using SPSS (SPSS Inc., Chicago, Illinois). Clinical and pathological parameters were compared with increased CD133 + EVs using Mann-Whitney U tests or Kruskal-Wallis H tests. Disease-free survival rate and overall survival rate were calculated using the Kaplan-Meier method, and the resulting curves were compared using log-rank tests. A stepwise Cox proportional hazards regression model was used to evaluate the impact of various clinical and pathological features on survival. P-value < 0.05 was considered statistically significant. 3. Results 3.1 Baseline Characteristicss This study, conducted from January 1, 2020 to December 30, 2023, initially included 132 glioma patients and 59 healthy controls. After excluding patients who did not meet the study criteria, the preliminary analysis involved 112 glioma patients and 38 healthy controls. However, follow-up data could not be obtained for 37 patients; therefore, the study ultimately included 38 healthy controls and 75 glioma patients (Fig. 1 ). 3.2 Biomarker and Demographic Characteristics The study ultimately included 75 pathologically confirmed glioma patients and 38 healthy adults without any neurological diseases, all of whom met the inclusion and exclusion criteria. Glioma patients exhibited significantly higher rates of CD133 positivity (average 52.58 ± 20.87 vs. 43.49 ± 10.84, p = 0.003) and higher levels of CD133 + rate (454.03 ± 266.22 vs. 333.63 ± 172.29, p = 0.005). The rate of CD133 + EVs and CD44 + EVs were significantly elevated in glioma patients compared to the healthy controls, suggesting their roles in the oncogenesis and progression of the tumor. There were no significant differences in age and gender between the two groups(Table 1 ). 3.3 EVs amount and sizes were dysregulated in glioma cases compared with healthy controls The average size of EVs in glioma patients was 100 nm, slightly smaller than that observed in the control group, which averaged 120 nm (Fig. 2 A, p = 0.0002). The average number of EVs per milliliter of plasma in glioma patients was significantly higher compared to healthy controls, which was approximately four times the amount observed in healthy controls (Fig. 2 B, p < 0.0001). Table 1 Comparison of Biomarker and Demographic Characteristics Between Glioma Patients and Healthy Control Characteristics Glioma Patients Healthy Group P-value Age (years) 48.37 ± 16.75 48.39 ± 10.80 0.993 Gender (Male/Female) 30/45 18/20 0.463 BMI (kg/m²) 24.90 ± 4.12 25.59 ± 2.29 0.308 Smoking Status (Smoker/Non-smoker) 43/32 24/14 0.554 CD133 + Rate 52.58 ± 20.87 43.49 ± 10.84 0.003 CD133+ 332.79 ± 217.76 333.63 ± 172.29 0.099 CD44 + Rate 46.68 ± 26.66 39.75 ± 8.98 0.044 CD44 410.68 ± 234.52 332.61 ± 153.93 0.036 Glucose Level (mg/dL) 88.20 ± 16.73 95.68 ± 26.27 0.071 Blood Pressure (Systolic, mmHg) 121.43 ± 36.12 119.48 ± 17.12 0.736 Blood Pressure (Diastolic, mmHg) 74.66 ± 21.42 72.58 ± 17.12 0.578 Table 2 CD133 + rate was different among glioma of different stages. Characteristics Tumor Stage P-value I–II III–IV Evs amount 11706.40 ± 6371.38 11711.32 ± 6206.90 0.997 Evs size 100.83 ± 19.72 97.17 ± 21.39 0.536 CD133 + rate 38.09 ± 13.04 66.09 ± 18.02 < 0.001 CD44 + rate 54.03 ± 22.09 43.77 ± 27.49 0.077 3.4 Associations between CD133 + EVs and various clinical pathological characteristics in glioma patients CD133 + EVs rate (high or low), along with their associated p-values, demonstrating statistical significance. Age, gender, tumor size, location, and the extent of resection were not associated with CD133 + EVs rate. Significant differences in CD133 + EVs rate were observed between cases with and without tumor recurrence, as well as across different tumor grades and stages. Lower CD133 + EVs often indicates a better prognosis for patients, underscoring its potential role as a prognostic biomarker in gliomas (Table 2 ). In contrast, the distribution of CD44 + EVs levels showed no significant statistical differences across different characteristics, suggesting its potential limitations as a biomarker in gliomas (Table 3 ). Table 3 Association Between Increased CD133 + EVs and Clinical Pathological Characteristics in Glioma Patients Characteristics Cases(n = 75) CD133 P-value High Low Age(years) 75 37 38 0.568 ≤ 40 25 14 11 > 40 50 23 27 Gender 1.000 Male 30 15 15 Female 45 22 23 Tumor Size(cm) 1.000 ≤ 6 25 13 12 > 6 50 25 25 Tumor Location 0.814 Frontal 14 8 6 Parietal 16 6 10 Temporal 18 10 8 Occipital 13 6 7 Others 14 7 7 Extent of Surgical Resection 0.541 Partial 45 24 21 Total 30 13 17 Recurrence < 0.001 Yes 55 34 21 No 20 3 17 Tumor Grade < 0.001 well 32 6 26 moderate 18 9 9 poor 25 22 3 Tumor Stage < 0.001 I–II 35 3 32 III–IV 40 34 6 3.5 Relationship Between CD133 + EVs and Overall Survival in Glioblastoma Patients Most glioblastoma patients experience disease recurrence and die after surgical resection. We evaluated the association between increased CD133 + EVs and disease-free survival rates. Kaplan-Meier analysis showed that patients with increased CD133 + EVs had significantly shorter disease-free survival times compared to those with low rate (Fig. 2 ). 3.6 Analysis of CD133 + rate and Its Impact on Survival Rates Post-Surgery During the one-year and three-year follow-up periods for gliomas patients, our study focused on the correlation between CD133 protein + EVs rate and patient survival times. Among 75 patients followed for one year, we observed 22 events (such as death). Analysis using the Cox proportional hazards model revealed that patients in the low CD133 + EVs rate group (compared to the high group) exhibited a significantly reduced risk of death (p < 0.05). During the three-year follow-up period for gliomas patients, out of 75 patients, we observed 69 deaths. Further analysis using the Cox proportional hazards model indicated that the low CD133 + EVs group had a significantly reduced risk of death (p < 0.05) compared to the high group. Low rate of CD133 is a significant prognostic indicator for extended survival times in gliomas patients. This significance in the model was statistically confirmed through likelihood ratio tests (p < 0.05), Wald tests (p < 0.01), and score (log-rank) tests (p < 0.01) (Table 4 ). Table 4 Relationship Between CD133 + rate and Survival Rate CD133 + Rate Cases(n = 75) 1 year After Operation P-value 3 years After Operation P-value Survival Death Survival Death High 37 21 16 < 0.05 1 36 < 0.05 low 38 32 6 5 33 3.7 Cox Regression Analysis of Glioblastoma Patients CD133 + EVs rate significantly affects the prognosis of patients with GBM. The hazard ratio (HR) for CD133 was 0.302, with a 95% confidence interval (CI) ranging from 0.118 to 0.772, and a p-value = 0.0124. Other variables such as age, gender, tumor size, extent of surgical resection, recurrence status, and CD44 + rate did not reach statistical significance in affecting survival rates. The low rate of CD133 + is associated with a significantly better survival rate, highlighting its importance as a potential biomarker in the treatment and prognosis assessment of glioma. Table 5 Cox Regression Analysis of Glioblastoma Patients Characteristics B-value SE Wald HR (95%CI) P-value Age (years) 0.314 1.369 0.43 1.369 (0.535,3.501) 0.512 Gender 0.168 0.443 0.14 0.845 (0.355, 2.015) 0.704 Tumor Size (cm) 0.608 0.509 1.43 1.837 (0.678,4.981) 0.232 Extent of Surgical Resection 0.429 0.458 0.88 1.535 (0.626,3.766) 0.349 Recurrence 0.252 0.509 0.25 1.287 (0.475,3.488) 0.620 CD133 1.198 0.479 6.25 0.302 (0.118,0.772) 0.012* CD44 0.279 0.428 0.43 0.756 (0.327,1.75) 0.514 3.8 Correlation Between CD133 + EVs Positivity and Quality of Life in Glioma Patients Three months after being included in the study, a comparative analysis of CD133 + rate levels and the patients' anxiety, depression, and quality of life scores revealed a significant correlation between CD133 + rate and both depression (Figuren 4 B, P < 0.001) and quality of life (Fig. 4 C, P < 0.001). Higher CD133 + rate was associated with higher depression scores and lower quality of life scores. CD133 + rate did not appear to play a significant role in anxiety scores (Fig. 4 A, p = 0.7526). 4. Discussion The grave prognosis of high-grade gliomas is attributed to their invasiveness, high recurrence rates, and resistance to treatment. Gliomas is an extremely malignant diseases, often resulting in treatment failure and a short survival period (approximately 15 months) [23]. The significant molecular heterogeneity of this tumor means that even patients with the same clinical grade may experience vastly different disease progressions and treatment responses [24]. This heterogeneity intensifies the urgent need to identify effective prognostic markers and therapeutic targets. CD133, a pentaspan transmembrane glycoprotein, has long been considered a marker of tumor stem cells in various solid tumors [25]. In malignant tumors, CD133 + tumor cells exhibit strong recurrence and chemoresistance [26]. This is likely due to the critical role of CD133 + cells in maintaining the tumor stem cell pool, thereby promoting the continuous growth and spread of the tumor. Early study found that high rate of CD133 is associated with increased recurrence rates and significantly reduced overall survival rates in colorectal cancer patients [27]. A similar pattern has been observed in non-small cell lung cancer [7], where patients with high CD133 + rate had significantly shorter survival times than those with low. rate. These studies suggest that CD133 may promote tumor aggressiveness by supporting the self-renewal of tumor cells and their chemoresistance. Further research also indicates that CD133 + rate is not only related to tumor aggressiveness but may also influence the tumor microenvironment, thereby indirectly promoting tumor growth and metastasis [28,29]. For instance, a study on pancreatic cancer [9] discovered that CD133 is associated with increased vascular density around tumors, possibly through the regulation of angiogenic factors. This potential link with angiogenesis provides a new perspective on the role of CD133 in tumor biology. Given these functions, it is plausible that high rate of CD133 is associated with disease progression and poor treatment response, which may directly impact patients' quality of life [30]. In this study, by analyzing CD133 + rate through plasma and EVs, we have identified results consistent with previous literature, highlighting the role of CD133 as a negative prognostic factor in GBM. Moreover, this study uniquely reveals the correlation between CD133 positivity rates and patients' quality of life. Patients in the low CD133 + rate group showed significantly higher quality of life scores compared to those in the high group. This statistically significant difference suggests a potential correlation between CD133 + rate and quality of life, where differences in quality-of-life scores may reflect variations in disease control, symptom management, and overall well-being among patients with different CD133 + EVs rate. Monitoring CD133 in plasma and EVs can serve not only as a critical tool for GBM diagnosis and prognosis but also helps assess patient quality of life, thereby guiding more personalized treatment strategies. This non-invasive biomarker monitoring could improve disease management and ultimately enhance overall patient outcomes. Monitoring CD133 may help identify high-risk patient groups who might need more intensive monitoring and treatment strategies. For patients with high CD133 + EVs rate, there may be a need to intensify management of depression and declines in quality of life during treatment to enhance their quality of life and overall effectiveness of treatment. Due to its retrospective cohort design, causal relationships cannot be established. The relatively small sample size may limit the statistical power of the results. Future research should validate these findings in a larger patient cohort and consider including more biomarkers to enhance the predictive capability of the model. 5. Conclusions The study of CD133 not only reveals its multifaceted roles in tumor biology but also highlights its potential value in improving patient quality of life and enhancing treatment outcomes. As our understanding of this marker deepens, future therapeutic strategies could become more targeted, aiming to reduce the aggressiveness of GBM and improve patient survival quality. Abbreviations GBM glioblastomas EVs Extracellular vesicles CD133 Cluster of differentiation 133 CD44 Cluster of differentiation 44 WHOQOL-BREF World Health Organization Quality of Life-BREF HAM-A Hamilton Anxiety Rating Scale HDRS Hamilton Depression Rating Scale ELISA Enzyme-Linked Immunosorbent Assay HR Hazard Ratio CI Confidence Interval. Declarations Conflicts of Interest: The authors declare no conflicts of interest. Funding: This research was funded by Natural Science Foundation of Hunan Province, grant number No. S2024JJMSXM2846. Author Contribution J.J. submitted ethics approval, collected the data, and started the original draft of the manuscript. F.L. was the supervising investigator; he prepared the original elements of the protocol and supervised the data collection. All authors have read and agreed to the published version of the manuscript. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy and ethical restrictions. References Materljan E, Materljan B, Sepèi J, Tuškan-Mohar L, Zamolo G. Epidemiology of Central Nervous System Tumors in Labin Area, Croatia, 1974-200. Croat Med J . Ohka F, Natsume A, Wakabayashi T. Current Trends in Targeted Therapies for Glioblastoma Multiforme. Neurology Research International . 2012;2012:1-13. doi:10.1155/2012/878425 Lathia JD, Mack SC, Mulkearns-Hubert EE, Valentim CLL, Rich JN. Cancer stem cells in glioblastoma. Chen XY, Pan DL, Xu JH, et al. Serum Inflammatory Biomarkers Contribute to the Prognosis Prediction in High-Grade Glioma. Front Oncol . 2022;11:754920. doi:10.3389/fonc.2021.754920 Das S, Dey MK, Devireddy R, Gartia MR. Biomarkers in Cancer Detection, Diagnosis, and Prognosis. Sensors . 2023;24(1):37. doi:10.3390/s24010037 Pleskač P, Fargeas CA, Veselska R, Corbeil D, Skoda J. Emerging roles of prominin-1 (CD133) in the dynamics of plasma membrane architecture and cell signaling pathways in health and disease. Cell Mol Biol Lett . 2024;29(1):41. doi:10.1186/s11658-024-00554-0 Wang S, Xu ZY, Wang LF, Su W. CD133+ cancer stem cells in lung cancer. Front Biosci (Landmark Ed). 2013 Jan 1;18(2):447-53. doi: 10.2741/4113. Richardson GD, Robson CN, Lang SH, Neal DE, Maitland NJ, Collins AT. CD133, a novel marker for human prostatic epithelial stem cells. Journal of Cell Science . 2004;117(16):3539-3545. doi:10.1242/jcs.01222 Immervoll H, Hoem D, Sakariassen PØ, Steffensen OJ, Molven A. Expression of the “stem cell marker” CD133 in pancreas and pancreatic ductal adenocarcinomas. BMC Cancer . 2008;8(1):48. doi:10.1186/1471-2407-8-48 Suetsugu A, Nagaki M, Aoki H, Motohashi T, Kunisada T, Moriwaki H. Characterization of CD133+ hepatocellular carcinoma cells as cancer stem/progenitor cells. Biochemical and Biophysical Research Communications . 2006;351(4):820-824. doi:10.1016/j.bbrc.2006.10.128 Karbanová J, Missol-Kolka E, Fonseca AV, et al. The Stem Cell Marker CD133 (Prominin-1) Is Expressed in Various Human Glandular Epithelia. J Histochem Cytochem . 2008;56(11):977-993. doi:10.1369/jhc.2008.951897 Klein WM, Wu BP, Zhao S, Wu H, Klein-Szanto AJP, Tahan SR. Increased expression of stem cell markers in malignant melanoma. Modern Pathology . 2007;20(1):102-107. doi:10.1038/modpathol.3800720 Florek M, Haase M, Marzesco AM, et al. Prominin-1/CD133, a neural and hematopoietic stem cell marker, is expressed in adult human differentiated cells and certain types of kidney cancer. Cell Tissue Res . 2005;319(1):15-26. doi:10.1007/s00441-004-1018-z Zhang M, Song T, Yang L, et al. Nestin and CD133: valuable stem cell-specific markers for determining clinical outcome of glioma patients. J Exp Clin Cancer Res . 2008;27(1):85. doi:10.1186/1756-9966-27-85 Brocco D, Simeone P, Buca D, et al. Blood Circulating CD133+ Extracellular Vesicles Predict Clinical Outcomes in Patients with Metastatic Colorectal Cancer. Cancers . 2022;14(5):1357. doi:10.3390/cancers14051357 Sherman L, Sleeman J, Herrlich P, Ponta H. Hyaluronate receptors: key players in growth, differentiation, migration and tumor progression. Current Opinion in Cell Biology . 1994;6(5):726-733. doi:10.1016/0955-0674(94)90100-7 Jordan AR, Racine RR, Hennig MJP, Lokeshwar VB. The Role of CD44 in Disease Pathophysiology and Targeted Treatment. Front Immunol . 2015;6. doi:10.3389/fimmu.2015.00182 Chen C, Zhao S, Karnad A, Freeman JW. The biology and role of CD44 in cancer progression: therapeutic implications. J Hematol Oncol . 2018;11(1):64. doi:10.1186/s13045-018-0605-5 Brown DV, Filiz G, Daniel PM, et al. Expression of CD133 and CD44 in glioblastoma stem cells correlates with cell proliferation, phenotype stability and intra-tumor heterogeneity. Harrison JK, ed. PLoS ONE . 2017;12(2):e0172791. doi:10.1371/journal.pone.0172791 Tuck MK, Chan DW, Chia D, et al. Standard Operating Procedures for Serum and Plasma Collection: Early Detection Research Network Consensus Statement Standard Operating Procedure Integration Working Group . J Proteome Res . 2009;8(1):113-117. doi:10.1021/pr800545q Koshy B, Gopal Das C, Rajashekarachar Y, Bharathi D, Hosur S. A cross-sectional comparative study on the assessment of quality of life in psychiatric patients under remission treated with monotherapy and polypharmacy. Indian J Psychiatry . 2017;59(3):333. doi:10.4103/psychiatry.IndianJPsychiatry_126_16 Moriya RM, Urbano MR, Vargas HO, et al. Digital mental health interventions for anxiety and mood disorders patients: A 24-week follow-up. Clinical eHealth . 2023;6:114-120. doi:10.1016/j.ceh.2023.09.002 Thakkar JP, Dolecek TA, Horbinski C, et al. Epidemiologic and Molecular Prognostic Review of Glioblastoma. Cancer Epidemiology, Biomarkers & Prevention . 2014;23(10):1985-1996. doi:10.1158/1055-9965.EPI-14-0275 Chen R, Smith-Cohn M, Cohen AL, Colman H. Glioma Subclassifications and Their Clinical Significance. Neurotherapeutics . 2017;14(2):284-297. doi:10.1007/s13311-017-0519-x Brugnoli F, Grassilli S, Al-Qassab Y, Capitani S, Bertagnolo V. CD133 in Breast Cancer Cells: More than a Stem Cell Marker. Journal of Oncology . 2019;2019:1-8. doi:10.1155/2019/7512632 Liu G, Yuan X, Zeng Z, et al. Analysis of gene expression and chemoresistance of CD133+ cancer stem cells in glioblastoma. Mol Cancer . 2006;5(1):67. doi:10.1186/1476-4598-5-67 Park YY, An CH, Oh ST, Chang ED, Lee J. Expression of CD133 is associated with poor prognosis in stage II colorectal carcinoma. Medicine . 2019;98(32):e16709. doi:10.1097/MD.0000000000016709 Li Z. CD133: a stem cell biomarker and beyond. Published online 2013. Moreno-Londoño AP, Robles-Flores M. Functional Roles of CD133: More than Stemness Associated Factor Regulated by the Microenvironment. Stem Cell Rev and Rep . 2024;20(1):25-51. doi:10.1007/s12015-023-10647-6 Liu B lin, Liu S juan, Baskys A, et al. Platinum sensitivity and CD133 expression as risk and prognostic predictors of central nervous system metastases in patients with epithelial ovarian cancer. BMC Cancer . 2014;14(1):829. doi:10.1186/1471-2407-14-829 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4347987","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299279450,"identity":"26f63e09-9862-4b8f-92c0-8ee64cc3eec3","order_by":0,"name":"Jiaode Jiang","email":"","orcid":"","institution":"The third Xiangya hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jiaode","middleName":"","lastName":"Jiang","suffix":""},{"id":299279453,"identity":"f816a1f6-f21f-4930-a741-c7775591ccef","order_by":1,"name":"Feng Liu","email":"data:image/png;base64,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","orcid":"","institution":"The third Xiangya hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-04-30 09:42:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4347987/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4347987/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56101015,"identity":"cd6b3660-500c-489e-8b59-4353381f8009","added_by":"auto","created_at":"2024-05-08 14:37:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87363,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design and overview of the patient inclusion and analytical cohorts.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4347987/v1/63c716c329734a9e9e331a65.png"},{"id":56101017,"identity":"b514ec5c-8361-4439-95d7-52c6d70d65a8","added_by":"auto","created_at":"2024-05-08 14:37:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81449,"visible":true,"origin":"","legend":"\u003cp\u003eEVs amount and sizes were dysregulated in glioma cases compared with healthy controls\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4347987/v1/389858f8cf1540083a12b4f3.png"},{"id":56101779,"identity":"400c184a-afec-45a9-89d7-189744c7a94e","added_by":"auto","created_at":"2024-05-08 14:45:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39811,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship Between CD133+ EVs rate and Overall Survival in Glioblastoma Patients\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4347987/v1/700e58c8562f8817243c34e4.png"},{"id":56101013,"identity":"6c8f25b6-1b09-4276-9a29-8715ec6ac59d","added_by":"auto","created_at":"2024-05-08 14:37:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54028,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Between CD133+EVs Positivity and Quality of Life in Glioma Patients\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4347987/v1/f2b29dd22cd70bd39e6c1a7e.png"},{"id":56334626,"identity":"bc16fc68-1810-4331-bdfa-b9e77ed714e2","added_by":"auto","created_at":"2024-05-12 15:32:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1090324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4347987/v1/699024c1-517d-4a62-811f-434ffbce2ea5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Between Circulating CD133+ Extracellular Vesicles and the Malignancy and Prognosis of Gliomas: A Retrospective Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGliomas, as one of the most common malignant tumors in the central nervous system [1], generally have a poor prognosis, particularly in high-grade forms such as glioblastomas [2]. These tumors originate from glial cells that support neural functions, and they progress rapidly with limited therapeutic success [3]. The prognosis of glioma is related to various factors including the tumor's location, size, genetic mutations, and treatment response, yet currently, the biomarkers used to predict disease progression and patient survival are still limited [4].\u003c/p\u003e \u003cp\u003eBiomarkers play a crucial role in the diagnosis, prognosis assessment, and therapeutic monitoring of cancer [5]. Cluster of differentiation (CD133), also known as Prominin-1, is a cell surface protein widely recognized as a stem cell marker for tumor cells [6]. Initially discovered in the nervous system, it is also expressed in other tissues and organs. In normal tissues, CD133 is generally considered a marker of stem or progenitor cells, involved in maintaining tissue homeostasis and repair, and is seen as a cancer stem cell marker in various solid tumors [7\u0026ndash;13]. In gliomas, the rate of CD133\u0026thinsp;+\u0026thinsp;EVs is closely related to the tumor's aggressiveness, chemoresistance, and overall prognosis [14]. The presence of CD133\u0026thinsp;+\u0026thinsp;EVs in the blood may reflect the tumor's biological characteristics, providing a non-invasive means to monitor tumor dynamics and predict disease progression [15]. Cluster of differentiation 44 (CD44) is another cell surface protein that is extensively found across various cell types and participates in numerous biological processes including cell adhesion, migration, signaling, and cell-cell interactions [16]. Its diverse structure and function make it a significant player in many physiological and pathological processes, influencing tumor cell adhesion, migration, and infiltration [17]. Through interactions with other proteins and signaling pathways, CD44 can regulate the tumor microenvironment, promoting tumor growth, metastasis, and resistance to therapy [18].\u003c/p\u003e \u003cp\u003eDespite the potential of CD133 and CD44 as biomarkers for glioma being initially explored, their specific roles and efficacy in clinical applications still require further validation. We employ a retrospective cohort design aimed at assessing the correlation between the rate of circulating CD133 and CD44 and the malignancy and prognosis of glioma patients. By comparing the levels of CD133 and CD44 biomarkers in 75 glioma patients with 38 healthy controls, we aim to reveal the potential value of these markers in disease monitoring and prognosis assessment.\u003c/p\u003e \u003cp\u003eBy analyzing the roles of these key biomarkers in depth, this study not only aids in understanding the biological behavior of gliomas but may also pave the way for new therapeutic strategies, providing patients with more personalized treatment options.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThis study is a retrospective cohort study designed to assess the rate of plasma and plasma-derived CD44\u0026thinsp;+\u0026thinsp;EVs/CD133\u0026thinsp;+\u0026thinsp;EVs in glioma patients and their correlation with the malignancy and prognosis of the disease The data were collected from January 1, 2020 to December 30, 2023 and included glioma patients and a healthy control group from The third Xiangya hospital of Central South University. A total of 156 samples were screened. Inclusion Criteria for Glioma Patients:(1) Confirmed diagnosis of glioma based on histopathological evaluation from a biopsy or surgical resection. (2) Patients must have medical records that include complete clinical history, treatment details, and follow-up data. (3) Imaging studies (MRI or CT scans) confirming the presence of glioma. (4) Patients must be capable of giving informed consent unless waived by the ethics committee. Exclusion Criteria for Glioma Patients: (1) Previous or concurrent participation in another clinical trial that could interfere with this study. (2) Patients who have received immunotherapy within the last six months. (3) Presence of metastatic disease from non-central nervous system cancers. (4) History of significant neurological disorders other than glioma, such as Alzheimer's disease or multiple sclerosis, which might confound the study outcomes. Inclusion Criteria for Healthy Controls: (1) Age and gender-matched to the patient group. (2) Confirmed through physical examination and medical history to be free of any chronic diseases, including neurological disorders. (3) No history of malignancies or significant psychiatric disorders. (4) Willing to provide informed consent for participation in the study. Exclusion Criteria for Healthy Controls: (1) Individuals with a history of significant neurological disorders or central nervous system trauma. (2) Presence of any acute or chronic infections at the time of enrollment. (3) Recent participation in any other clinical trials. This study adheres to the Declaration of Helsinki and has been approved by the Medical Ethics Committee.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection and Definitions\u003c/h2\u003e \u003cp\u003eClinical data were collected from the patients' medical records, including baseline information (such as age, gender, tumor type, grade, resection status, and treatment history) and follow-up data (including survival time and recurrence status). Tumor size was recorded as the maximum dimension for each sample. The tumors were staged using the 2002 edition of the UICC (International Union Against Cancer) TNM system (New York, USA), and graded according to the criteria outlined by Edmondson and Steiner. Survival time was calculated from the date of diagnosis to the date of death or last follow-up, allowing for the analysis of survival rates. Recurrence was monitored through regular clinical evaluations and imaging studies, with recurrence being defined as the return of tumor activity as evidenced by radiologic assessment or clinical symptoms. Clinical and pathological characteristics of the 75 glioma patients are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additionally, 38 healthy adults were collected as negative controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Samples Collection\u003c/h2\u003e \u003cp\u003eUpon admission for treatment, fasting blood samples were collected and separated into at least 1ml of plasma, which was then aliquoted into two portions for storage [20]. One portion of plasma was used for analysis using the Enzyme-Linked Immunosorbent Assay (ELISA), and the other portion was used for the isolation and analysis of EVs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 CD133 and CD44 Content Detection\u003c/h2\u003e \u003cp\u003eThe concentrations of CD133 (Human Prominin-1 / CD133 ELISA Kit, Assay Genie) and CD44 (Human CD44 ELISA Kit, Abcam) in plasma were measured. All procedures were carried out according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 The Characteristics and CD44/CD133 rate of EVs\u003c/h2\u003e \u003cp\u003eThe quantity and distribution of EVs were analyzed using a NanoSight LM10 (Malvern PANalytical). To analyze the rate of circulating CD133\u0026thinsp;+\u0026thinsp;EVs and CD44\u0026thinsp;+\u0026thinsp;EVs, flow cytometry was employed with specific antibodies targeting CD133 and CD44 to quantify the positive rate of circulating vesicles in the plasma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Quality of Life, Anxiety, and Depression Scores\u003c/h2\u003e \u003cp\u003eThree months after the initial consultation, patients were divided into two groups based on the positive rate of CD133\u0026thinsp;+\u0026thinsp;EVs in plasma: high CD133\u0026thinsp;+\u0026thinsp;EVs group and low CD133 EVs group. The patients' quality of life, anxiety, and depression levels were assessed using standardized evaluation tools. World Health Organization Quality of Life-BREF (WHOQOL-BREF), covers physical health, psychological health, social relationships, and environment. Each item is rated on a scale from 1 to 5, where higher scores generally indicate better quality of life. The scores for each domain are then converted to a 0-100 scale, where a higher score signifies better quality of life. Hamilton Anxiety Rating Scale (HAM-A) rates the severity of a patient's anxiety. It consists of 14 items, each defined by a series of symptoms, and measures both psychic anxiety (mental agitation and psychological distress) and somatic anxiety (physical complaints related to anxiety). Each item is scored on a scale of 0 (not present) to 4 (severe), with a total score range of 0\u0026ndash;56. A total score of 17 or less indicates mild severity, 18\u0026ndash;24 mild to moderate severity and 25\u0026ndash;30 moderate to severe anxiety. Scores above 30 suggest severe anxiety. Hamilton Depression Rating Scale (HDRS) assesses the severity of depression in individuals already diagnosed with the disorder. It includes 21 items that evaluate depressive symptoms such as mood, guilt, suicide ideation, insomnia, agitation, anxiety, weight loss, and somatic symptoms. Items are rated on a scale of 0 (not present) to 4 (extreme symptoms), though some are scored only 0\u0026ndash;2. The total score can range from 0 to 52. Scores up to 7 are considered to be normal, 8\u0026ndash;13 mild depression, 14\u0026ndash;18 mild to moderate depression, 19\u0026ndash;22 moderate to severe depression, and over 23 indicate severe depression[21,22]. All scores were collected three months after initial consultation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis and graphical representation were performed using SPSS (SPSS Inc., Chicago, Illinois). Clinical and pathological parameters were compared with increased CD133\u0026thinsp;+\u0026thinsp;EVs using Mann-Whitney U tests or Kruskal-Wallis H tests. Disease-free survival rate and overall survival rate were calculated using the Kaplan-Meier method, and the resulting curves were compared using log-rank tests. A stepwise Cox proportional hazards regression model was used to evaluate the impact of various clinical and pathological features on survival. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristicss\u003c/h2\u003e \u003cp\u003eThis study, conducted from January 1, 2020 to December 30, 2023, initially included 132 glioma patients and 59 healthy controls. After excluding patients who did not meet the study criteria, the preliminary analysis involved 112 glioma patients and 38 healthy controls. However, follow-up data could not be obtained for 37 patients; therefore, the study ultimately included 38 healthy controls and 75 glioma patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Biomarker and Demographic Characteristics\u003c/h2\u003e \u003cp\u003eThe study ultimately included 75 pathologically confirmed glioma patients and 38 healthy adults without any neurological diseases, all of whom met the inclusion and exclusion criteria. Glioma patients exhibited significantly higher rates of CD133 positivity (average 52.58\u0026thinsp;\u0026plusmn;\u0026thinsp;20.87 vs. 43.49\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84, p\u0026thinsp;=\u0026thinsp;0.003) and higher levels of CD133\u0026thinsp;+\u0026thinsp;rate (454.03\u0026thinsp;\u0026plusmn;\u0026thinsp;266.22 vs. 333.63\u0026thinsp;\u0026plusmn;\u0026thinsp;172.29, p\u0026thinsp;=\u0026thinsp;0.005). The rate of CD133\u0026thinsp;+\u0026thinsp;EVs and CD44\u0026thinsp;+\u0026thinsp;EVs were significantly elevated in glioma patients compared to the healthy controls, suggesting their roles in the oncogenesis and progression of the tumor. There were no significant differences in age and gender between the two groups(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 EVs amount and sizes were dysregulated in glioma cases compared with healthy controls\u003c/h2\u003e \u003cp\u003eThe average size of EVs in glioma patients was 100 nm, slightly smaller than that observed in the control group, which averaged 120 nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, p\u0026thinsp;=\u0026thinsp;0.0002). The average number of EVs per milliliter of plasma in glioma patients was significantly higher compared to healthy controls, which was approximately four times the amount observed in healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Biomarker and Demographic Characteristics Between Glioma Patients and Healthy Control\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlioma Patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.37\u0026thinsp;\u0026plusmn;\u0026thinsp;16.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.39\u0026thinsp;\u0026plusmn;\u0026thinsp;10.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30/45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18/20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.90\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Status (Smoker/Non-smoker)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43/32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.58\u0026thinsp;\u0026plusmn;\u0026thinsp;20.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.49\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD133+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332.79\u0026thinsp;\u0026plusmn;\u0026thinsp;217.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.63\u0026thinsp;\u0026plusmn;\u0026thinsp;172.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD44\u0026thinsp;+\u0026thinsp;Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.68\u0026thinsp;\u0026plusmn;\u0026thinsp;26.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e410.68\u0026thinsp;\u0026plusmn;\u0026thinsp;234.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332.61\u0026thinsp;\u0026plusmn;\u0026thinsp;153.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose Level (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.20\u0026thinsp;\u0026plusmn;\u0026thinsp;16.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.68\u0026thinsp;\u0026plusmn;\u0026thinsp;26.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Pressure (Systolic, mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.43\u0026thinsp;\u0026plusmn;\u0026thinsp;36.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.48\u0026thinsp;\u0026plusmn;\u0026thinsp;17.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Pressure (Diastolic, mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.66\u0026thinsp;\u0026plusmn;\u0026thinsp;21.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.58\u0026thinsp;\u0026plusmn;\u0026thinsp;17.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;rate was different among glioma of different stages.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTumor Stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u0026ndash;II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIII\u0026ndash;IV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvs amount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11706.40\u0026thinsp;\u0026plusmn;\u0026thinsp;6371.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11711.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6206.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvs size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e100.83\u0026thinsp;\u0026plusmn;\u0026thinsp;19.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e97.17\u0026thinsp;\u0026plusmn;\u0026thinsp;21.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e38.09\u0026thinsp;\u0026plusmn;\u0026thinsp;13.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e66.09\u0026thinsp;\u0026plusmn;\u0026thinsp;18.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD44\u0026thinsp;+\u0026thinsp;rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e54.03\u0026thinsp;\u0026plusmn;\u0026thinsp;22.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e43.77\u0026thinsp;\u0026plusmn;\u0026thinsp;27.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Associations between CD133\u0026thinsp;+\u0026thinsp;EVs and various clinical pathological characteristics in glioma patients\u003c/h2\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;EVs rate (high or low), along with their associated p-values, demonstrating statistical significance. Age, gender, tumor size, location, and the extent of resection were not associated with CD133\u0026thinsp;+\u0026thinsp;EVs rate. Significant differences in CD133\u0026thinsp;+\u0026thinsp;EVs rate were observed between cases with and without tumor recurrence, as well as across different tumor grades and stages. Lower CD133\u0026thinsp;+\u0026thinsp;EVs often indicates a better prognosis for patients, underscoring its potential role as a prognostic biomarker in gliomas (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, the distribution of CD44\u0026thinsp;+\u0026thinsp;EVs levels showed no significant statistical differences across different characteristics, suggesting its potential limitations as a biomarker in gliomas (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Between Increased CD133\u0026thinsp;+\u0026thinsp;EVs and Clinical Pathological Characteristics in Glioma Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases(n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCD133\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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 \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Size(cm)\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 \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Location\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 \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParietal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccipital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExtent of Surgical Resection\u003c/p\u003e \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 \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrence\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Grade\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Stage\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026ndash;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u0026ndash;IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Relationship Between CD133\u0026thinsp;+\u0026thinsp;EVs and Overall Survival in Glioblastoma Patients\u003c/h2\u003e \u003cp\u003eMost glioblastoma patients experience disease recurrence and die after surgical resection. We evaluated the association between increased CD133\u0026thinsp;+\u0026thinsp;EVs and disease-free survival rates. Kaplan-Meier analysis showed that patients with increased CD133\u0026thinsp;+\u0026thinsp;EVs had significantly shorter disease-free survival times compared to those with low rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Analysis of CD133\u0026thinsp;+\u0026thinsp;rate and Its Impact on Survival Rates Post-Surgery\u003c/h2\u003e \u003cp\u003eDuring the one-year and three-year follow-up periods for gliomas patients, our study focused on the correlation between CD133 protein\u0026thinsp;+\u0026thinsp;EVs rate and patient survival times. Among 75 patients followed for one year, we observed 22 events (such as death). Analysis using the Cox proportional hazards model revealed that patients in the low CD133\u0026thinsp;+\u0026thinsp;EVs rate group (compared to the high group) exhibited a significantly reduced risk of death (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). During the three-year follow-up period for gliomas patients, out of 75 patients, we observed 69 deaths. Further analysis using the Cox proportional hazards model indicated that the low CD133\u0026thinsp;+\u0026thinsp;EVs group had a significantly reduced risk of death (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the high group. Low rate of CD133 is a significant prognostic indicator for extended survival times in gliomas patients. This significance in the model was statistically confirmed through likelihood ratio tests (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Wald tests (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and score (log-rank) tests (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship Between CD133\u0026thinsp;+\u0026thinsp;rate and Survival Rate\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases(n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1 year After Operation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3 years After Operation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Cox Regression Analysis of Glioblastoma Patients\u003c/h2\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;EVs rate significantly affects the prognosis of patients with GBM. The hazard ratio (HR) for CD133 was 0.302, with a 95% confidence interval (CI) ranging from 0.118 to 0.772, and a p-value\u0026thinsp;=\u0026thinsp;0.0124. Other variables such as age, gender, tumor size, extent of surgical resection, recurrence status, and CD44\u0026thinsp;+\u0026thinsp;rate did not reach statistical significance in affecting survival rates. The low rate of CD133\u0026thinsp;+\u0026thinsp;is associated with a significantly better survival rate, highlighting its importance as a potential biomarker in the treatment and prognosis assessment of glioma.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox Regression Analysis of Glioblastoma Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.369 (0.535,3.501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.845 (0.355, 2.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.837 (0.678,4.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtent of Surgical Resection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.535 (0.626,3.766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.287 (0.475,3.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.302 (0.118,0.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.756 (0.327,1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Correlation Between CD133\u0026thinsp;+\u0026thinsp;EVs Positivity and Quality of Life in Glioma Patients\u003c/h2\u003e \u003cp\u003eThree months after being included in the study, a comparative analysis of CD133\u0026thinsp;+\u0026thinsp;rate levels and the patients' anxiety, depression, and quality of life scores revealed a significant correlation between CD133\u0026thinsp;+\u0026thinsp;rate and both depression (Figuren\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and quality of life (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher CD133\u0026thinsp;+\u0026thinsp;rate was associated with higher depression scores and lower quality of life scores. CD133\u0026thinsp;+\u0026thinsp;rate did not appear to play a significant role in anxiety scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, p\u0026thinsp;=\u0026thinsp;0.7526).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe grave prognosis of high-grade gliomas is attributed to their invasiveness, high recurrence rates, and resistance to treatment. Gliomas is an extremely malignant diseases, often resulting in treatment failure and a short survival period (approximately 15 months) [23]. The significant molecular heterogeneity of this tumor means that even patients with the same clinical grade may experience vastly different disease progressions and treatment responses [24]. This heterogeneity intensifies the urgent need to identify effective prognostic markers and therapeutic targets.\u003c/p\u003e \u003cp\u003eCD133, a pentaspan transmembrane glycoprotein, has long been considered a marker of tumor stem cells in various solid tumors [25]. In malignant tumors, CD133\u0026thinsp;+\u0026thinsp;tumor cells exhibit strong recurrence and chemoresistance [26]. This is likely due to the critical role of CD133\u0026thinsp;+\u0026thinsp;cells in maintaining the tumor stem cell pool, thereby promoting the continuous growth and spread of the tumor. Early study found that high rate of CD133 is associated with increased recurrence rates and significantly reduced overall survival rates in colorectal cancer patients [27]. A similar pattern has been observed in non-small cell lung cancer [7], where patients with high CD133\u0026thinsp;+\u0026thinsp;rate had significantly shorter survival times than those with low. rate. These studies suggest that CD133 may promote tumor aggressiveness by supporting the self-renewal of tumor cells and their chemoresistance.\u003c/p\u003e \u003cp\u003eFurther research also indicates that CD133\u0026thinsp;+\u0026thinsp;rate is not only related to tumor aggressiveness but may also influence the tumor microenvironment, thereby indirectly promoting tumor growth and metastasis [28,29]. For instance, a study on pancreatic cancer [9] discovered that CD133 is associated with increased vascular density around tumors, possibly through the regulation of angiogenic factors. This potential link with angiogenesis provides a new perspective on the role of CD133 in tumor biology.\u003c/p\u003e \u003cp\u003eGiven these functions, it is plausible that high rate of CD133 is associated with disease progression and poor treatment response, which may directly impact patients' quality of life [30]. In this study, by analyzing CD133\u0026thinsp;+\u0026thinsp;rate through plasma and EVs, we have identified results consistent with previous literature, highlighting the role of CD133 as a negative prognostic factor in GBM. Moreover, this study uniquely reveals the correlation between CD133 positivity rates and patients' quality of life. Patients in the low CD133\u0026thinsp;+\u0026thinsp;rate group showed significantly higher quality of life scores compared to those in the high group. This statistically significant difference suggests a potential correlation between CD133\u0026thinsp;+\u0026thinsp;rate and quality of life, where differences in quality-of-life scores may reflect variations in disease control, symptom management, and overall well-being among patients with different CD133\u0026thinsp;+\u0026thinsp;EVs rate.\u003c/p\u003e \u003cp\u003eMonitoring CD133 in plasma and EVs can serve not only as a critical tool for GBM diagnosis and prognosis but also helps assess patient quality of life, thereby guiding more personalized treatment strategies. This non-invasive biomarker monitoring could improve disease management and ultimately enhance overall patient outcomes. Monitoring CD133 may help identify high-risk patient groups who might need more intensive monitoring and treatment strategies. For patients with high CD133\u0026thinsp;+\u0026thinsp;EVs rate, there may be a need to intensify management of depression and declines in quality of life during treatment to enhance their quality of life and overall effectiveness of treatment. Due to its retrospective cohort design, causal relationships cannot be established. The relatively small sample size may limit the statistical power of the results. Future research should validate these findings in a larger patient cohort and consider including more biomarkers to enhance the predictive capability of the model.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe study of CD133 not only reveals its multifaceted roles in tumor biology but also highlights its potential value in improving patient quality of life and enhancing treatment outcomes. As our understanding of this marker deepens, future therapeutic strategies could become more targeted, aiming to reduce the aggressiveness of GBM and improve patient survival quality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglioblastomas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular vesicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD133\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCluster of differentiation 133\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD44\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCluster of differentiation 44\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHOQOL-BREF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization Quality of Life-BREF\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHAM-A\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHamilton Anxiety Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHamilton Depression Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eELISA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnzyme-Linked Immunosorbent Assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by Natural Science Foundation of Hunan Province, grant number No. S2024JJMSXM2846.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.J. submitted ethics approval, collected the data, and started the original draft of the manuscript. F.L. was the supervising investigator; he prepared the original elements of the protocol and supervised the data collection. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy and ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMaterljan E, Materljan B, Sep\u0026egrave;i J, Tu\u0026scaron;kan-Mohar L, Zamolo G. Epidemiology of Central Nervous System Tumors in Labin Area, Croatia, 1974-200. \u003cem\u003eCroat Med J\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eOhka F, Natsume A, Wakabayashi T. Current Trends in Targeted Therapies for Glioblastoma Multiforme. \u003cem\u003eNeurology Research International\u003c/em\u003e. 2012;2012:1-13. doi:10.1155/2012/878425\u003c/li\u003e\n\u003cli\u003eLathia JD, Mack SC, Mulkearns-Hubert EE, Valentim CLL, Rich JN. Cancer stem cells in glioblastoma.\u003c/li\u003e\n\u003cli\u003eChen XY, Pan DL, Xu JH, et al. Serum Inflammatory Biomarkers Contribute to the Prognosis Prediction in High-Grade Glioma. \u003cem\u003eFront Oncol\u003c/em\u003e. 2022;11:754920. doi:10.3389/fonc.2021.754920\u003c/li\u003e\n\u003cli\u003eDas S, Dey MK, Devireddy R, Gartia MR. Biomarkers in Cancer Detection, Diagnosis, and Prognosis. \u003cem\u003eSensors\u003c/em\u003e. 2023;24(1):37. doi:10.3390/s24010037\u003c/li\u003e\n\u003cli\u003ePleskač P, Fargeas CA, Veselska R, Corbeil D, Skoda J. Emerging roles of prominin-1 (CD133) in the dynamics of plasma membrane architecture and cell signaling pathways in health and disease. \u003cem\u003eCell Mol Biol Lett\u003c/em\u003e. 2024;29(1):41. doi:10.1186/s11658-024-00554-0\u003c/li\u003e\n\u003cli\u003eWang S, Xu ZY, Wang LF, Su W. CD133+ cancer stem cells in lung cancer. Front Biosci (Landmark Ed). 2013 Jan 1;18(2):447-53. doi: 10.2741/4113.\u003c/li\u003e\n\u003cli\u003eRichardson GD, Robson CN, Lang SH, Neal DE, Maitland NJ, Collins AT. CD133, a novel marker for human prostatic epithelial stem cells. \u003cem\u003eJournal of Cell Science\u003c/em\u003e. 2004;117(16):3539-3545. doi:10.1242/jcs.01222\u003c/li\u003e\n\u003cli\u003eImmervoll H, Hoem D, Sakariassen P\u0026Oslash;, Steffensen OJ, Molven A. Expression of the \u0026ldquo;stem cell marker\u0026rdquo; CD133 in pancreas and pancreatic ductal adenocarcinomas. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2008;8(1):48. doi:10.1186/1471-2407-8-48\u003c/li\u003e\n\u003cli\u003eSuetsugu A, Nagaki M, Aoki H, Motohashi T, Kunisada T, Moriwaki H. Characterization of CD133+ hepatocellular carcinoma cells as cancer stem/progenitor cells. \u003cem\u003eBiochemical and Biophysical Research Communications\u003c/em\u003e. 2006;351(4):820-824. doi:10.1016/j.bbrc.2006.10.128\u003c/li\u003e\n\u003cli\u003eKarbanov\u0026aacute; J, Missol-Kolka E, Fonseca AV, et al. The Stem Cell Marker CD133 (Prominin-1) Is Expressed in Various Human Glandular Epithelia. \u003cem\u003eJ Histochem Cytochem\u003c/em\u003e. 2008;56(11):977-993. doi:10.1369/jhc.2008.951897\u003c/li\u003e\n\u003cli\u003eKlein WM, Wu BP, Zhao S, Wu H, Klein-Szanto AJP, Tahan SR. Increased expression of stem cell markers in malignant melanoma. \u003cem\u003eModern Pathology\u003c/em\u003e. 2007;20(1):102-107. doi:10.1038/modpathol.3800720\u003c/li\u003e\n\u003cli\u003eFlorek M, Haase M, Marzesco AM, et al. Prominin-1/CD133, a neural and hematopoietic stem cell marker, is expressed in adult human differentiated cells and certain types of kidney cancer. \u003cem\u003eCell Tissue Res\u003c/em\u003e. 2005;319(1):15-26. doi:10.1007/s00441-004-1018-z\u003c/li\u003e\n\u003cli\u003eZhang M, Song T, Yang L, et al. Nestin and CD133: valuable stem cell-specific markers for determining clinical outcome of glioma patients. \u003cem\u003eJ Exp Clin Cancer Res\u003c/em\u003e. 2008;27(1):85. doi:10.1186/1756-9966-27-85\u003c/li\u003e\n\u003cli\u003eBrocco D, Simeone P, Buca D, et al. Blood Circulating CD133+ Extracellular Vesicles Predict Clinical Outcomes in Patients with Metastatic Colorectal Cancer. \u003cem\u003eCancers\u003c/em\u003e. 2022;14(5):1357. doi:10.3390/cancers14051357\u003c/li\u003e\n\u003cli\u003eSherman L, Sleeman J, Herrlich P, Ponta H. Hyaluronate receptors: key players in growth, differentiation, migration and tumor progression. \u003cem\u003eCurrent Opinion in Cell Biology\u003c/em\u003e. 1994;6(5):726-733. doi:10.1016/0955-0674(94)90100-7\u003c/li\u003e\n\u003cli\u003eJordan AR, Racine RR, Hennig MJP, Lokeshwar VB. The Role of CD44 in Disease Pathophysiology and Targeted Treatment. \u003cem\u003eFront Immunol\u003c/em\u003e. 2015;6. doi:10.3389/fimmu.2015.00182\u003c/li\u003e\n\u003cli\u003eChen C, Zhao S, Karnad A, Freeman JW. The biology and role of CD44 in cancer progression: therapeutic implications. \u003cem\u003eJ Hematol Oncol\u003c/em\u003e. 2018;11(1):64. doi:10.1186/s13045-018-0605-5\u003c/li\u003e\n\u003cli\u003eBrown DV, Filiz G, Daniel PM, et al. Expression of CD133 and CD44 in glioblastoma stem cells correlates with cell proliferation, phenotype stability and intra-tumor heterogeneity. Harrison JK, ed. \u003cem\u003ePLoS ONE\u003c/em\u003e. 2017;12(2):e0172791. doi:10.1371/journal.pone.0172791\u003c/li\u003e\n\u003cli\u003eTuck MK, Chan DW, Chia D, et al. Standard Operating Procedures for Serum and Plasma Collection: Early Detection Research Network Consensus Statement \u003cem\u003eStandard Operating Procedure Integration Working Group\u003c/em\u003e. \u003cem\u003eJ Proteome Res\u003c/em\u003e. 2009;8(1):113-117. doi:10.1021/pr800545q\u003c/li\u003e\n\u003cli\u003eKoshy B, Gopal Das C, Rajashekarachar Y, Bharathi D, Hosur S. A cross-sectional comparative study on the assessment of quality of life in psychiatric patients under remission treated with monotherapy and polypharmacy. \u003cem\u003eIndian J Psychiatry\u003c/em\u003e. 2017;59(3):333. doi:10.4103/psychiatry.IndianJPsychiatry_126_16\u003c/li\u003e\n\u003cli\u003eMoriya RM, Urbano MR, Vargas HO, et al. Digital mental health interventions for anxiety and mood disorders patients: A 24-week follow-up. \u003cem\u003eClinical eHealth\u003c/em\u003e. 2023;6:114-120. doi:10.1016/j.ceh.2023.09.002\u003c/li\u003e\n\u003cli\u003eThakkar JP, Dolecek TA, Horbinski C, et al. Epidemiologic and Molecular Prognostic Review of Glioblastoma. \u003cem\u003eCancer Epidemiology, Biomarkers \u0026amp; Prevention\u003c/em\u003e. 2014;23(10):1985-1996. doi:10.1158/1055-9965.EPI-14-0275\u003c/li\u003e\n\u003cli\u003eChen R, Smith-Cohn M, Cohen AL, Colman H. Glioma Subclassifications and Their Clinical Significance. \u003cem\u003eNeurotherapeutics\u003c/em\u003e. 2017;14(2):284-297. doi:10.1007/s13311-017-0519-x\u003c/li\u003e\n\u003cli\u003eBrugnoli F, Grassilli S, Al-Qassab Y, Capitani S, Bertagnolo V. CD133 in Breast Cancer Cells: More than a Stem Cell Marker. \u003cem\u003eJournal of Oncology\u003c/em\u003e. 2019;2019:1-8. doi:10.1155/2019/7512632\u003c/li\u003e\n\u003cli\u003eLiu G, Yuan X, Zeng Z, et al. Analysis of gene expression and chemoresistance of CD133+ cancer stem cells in glioblastoma. \u003cem\u003eMol Cancer\u003c/em\u003e. 2006;5(1):67. doi:10.1186/1476-4598-5-67\u003c/li\u003e\n\u003cli\u003ePark YY, An CH, Oh ST, Chang ED, Lee J. Expression of CD133 is associated with poor prognosis in stage II colorectal carcinoma. \u003cem\u003eMedicine\u003c/em\u003e. 2019;98(32):e16709. doi:10.1097/MD.0000000000016709\u003c/li\u003e\n\u003cli\u003eLi Z. CD133: a stem cell biomarker and beyond. Published online 2013.\u003c/li\u003e\n\u003cli\u003eMoreno-Londo\u0026ntilde;o AP, Robles-Flores M. Functional Roles of CD133: More than Stemness Associated Factor Regulated by the Microenvironment. \u003cem\u003eStem Cell Rev and Rep\u003c/em\u003e. 2024;20(1):25-51. doi:10.1007/s12015-023-10647-6\u003c/li\u003e\n\u003cli\u003eLiu B lin, Liu S juan, Baskys A, et al. Platinum sensitivity and CD133 expression as risk and prognostic predictors of central nervous system metastases in patients with epithelial ovarian cancer. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2014;14(1):829. doi:10.1186/1471-2407-14-829\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":"gliomas, CD133, biomarker, extracellular vesicles","lastPublishedDoi":"10.21203/rs.3.rs-4347987/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4347987/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eGliomas are the most common malignant tumors in the central nervous system and have a poor prognosis. Circulating and plasma-derived extracellular vesicles (EVs) have been identified as effective biomarkers for the diagnosis and prognosis of gliomas, while Cluster of differentiation 133 (CD133) is closely associated with tumor aggressiveness, chemoresistance, and patient prognosis across various cancers. This study aims to evaluate the association between CD133 and malignancy, and prognosis of glioma patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study design was employed to compare plasma and plasma-derived CD133\u0026thinsp;+\u0026thinsp;EVs and CD44\u0026thinsp;+\u0026thinsp;EVs rates in 75 glioma patients and 38 healthy controls. Clinical and pathological parameters were compared using Mann-Whitney U tests or Kruskal-Wallis H tests about increased CD133\u0026thinsp;+\u0026thinsp;rate. Additionally, quality of life, anxiety, and depression were assessed using the WHOQOL-BREF, Hamilton Anxiety Rating Scale (HAM-A), and Hamilton Depression Rating Scale (HDRS) to observe differences between CD133 high group and CD133 low group. The disease-free survival rate and overall survival rate were calculated using the Kaplan-Meier method, and the resulting curves were compared using log-rank tests. The impact of various clinical pathological features on survival was further assessed using a stepwise Cox proportional hazards regression model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eQuantities of plasma CD44 and CD133\u0026thinsp;+\u0026thinsp;EVs contents were 1.25 and 1.21 times those of healthy controls, respectively, yet only the quantity of CD133\u0026thinsp;+\u0026thinsp;EVs was capable of differentiating glioma grades (P\u0026thinsp;=\u0026thinsp;0.001). Stratifying glioma patients based on CD133\u0026thinsp;+\u0026thinsp;EVs content revealed that the low rate group exhibited a significant survival advantage, with a mortality risk that was only 33.54% of the high rate group, which was statistically significant (P\u0026thinsp;=\u0026thinsp;0.0124).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;EVs rate is a significant prognostic indicator in glioma patients, where lower rate is associated with better survival rates. These findings support the potential value of CD133 as a biomarker in the diagnosis and therapeutic monitoring of gliomas.\u003c/p\u003e","manuscriptTitle":"Correlation Between Circulating CD133+ Extracellular Vesicles and the Malignancy and Prognosis of Gliomas: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-08 14:37:08","doi":"10.21203/rs.3.rs-4347987/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":"caa8d45f-a233-458d-87be-d38589ae5861","owner":[],"postedDate":"May 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-17T01:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-08 14:37:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4347987","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4347987","identity":"rs-4347987","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}