Comparison of prognostic value of neutrophil-to-lymphocyte ratio (NLR) in patients with metastatic and non-metastatic cancers 

Comparison of prognostic value of neutrophil-to-lymphocyte ratio (NLR) in patients with metastatic and non-metastatic cancers | 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 Comparison of prognostic value of neutrophil-to-lymphocyte ratio (NLR) in patients with metastatic and non-metastatic cancers Ayman Azhary, Nooh Mohamed Hajhamed, Salahaldeen Ismail Mohammed, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6503959/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 Background Several studies have reported NLR as an independent prognostic factor in solid tumors. However, comparing the prognostic value among metastatic and non-metastatic cancers remains underexplored, which is the objective of this study. Methods A total of 182 patients with different types of metastatic and non-metastatic cancers were enrolled in this retrospective study. Of these patients, chemotherapy was the main treatment received (56.6%). We compared the prognostic value among metastatic and non-metastatic cancers. Overall survival (OS) was used as an outcome metric. Univariate and multivariate Cox proportional hazards analyses were used to investigate the association between NLR and OS. Results In this study, metastatic cancer patients with high NLR demonstrated the worst survival outcomes in Kaplan-Meier survival analysis, whereas non-metastatic cancer patients showed minimal survival decline. Conversely, metastatic and non-metastatic cancer patients with low NLR maintained the highest survival probability. Multivariate analysis further identified high NLR as an independent predictor of poor overall survival. NLR showed a maximum sensitivity (S1) of 1 and specificity (S2) of 0.75, with an optimal cut-off value of NLR = 2.75 using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) was slightly increased in patients with metastatic cancers (0.717), compared to those with non-metastatic cancers (0.715), indicating that NLR serves as a more effective predictor of survival in metastatic cancers. Conclusions In conclusion, the study findings indicate a significant association between high NLR and worse overall survival in metastatic cancers compared with minimal survival decline in non-metastatic cancers. NLR is still rolling in research articles, seeking gaps to reach routine clinical practice. Trial registration: Not applicable. Neutrophil-to-lymphocyte ratio (NLR) prognostic value metastatic cancers non-metastatic cancers overall survival (OS) Kaplan-Meier survival analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Cancer remains a leading global health challenge, with an estimated over 19 million new cases and 10 million deaths reported in 2022, according to GLOBOCAN. Lung cancer remains the most common cause of cancer-related mortality, followed by colorectal, liver, gastric, and breast cancer [ 1 ]. The rising incidence of cancer, particularly in developing countries, underscores the need for reliable prognostic indicators to assess disease progression and treatment response [ 2 ]. One such biomarker is the neutrophil-to-lymphocyte ratio (NLR), a simple and cost-effective marker from routine blood tests [ 3 ]. NLR reflects the balance between systemic inflammation and immune response, critical in cancer progression [ 4 ]. While neutrophils are key immune cells involved in infection control and inflammation, they also contribute to tumor development by promoting angiogenesis, immune suppression, and metastasis [ 5 ]. Conversely, lymphocytes play a crucial role in anti-tumor immunity. A high NLR is often associated with an unfavorable prognosis, as it may indicate increased tumor-promoting inflammation and a weakened immune response [ 6 ]. Numerous studies have established NLR as an independent prognostic factor in solid tumors, including breast, lung, hepatocellular, colorectal, and nasopharyngeal cancers [ 3 ]. A baseline NLR greater than 4 has been linked to poorer overall survival across various malignancies [ 7 ]. However, while NLR has been extensively studied in solid tumors, its role in hematological cancers remains unclear [ 8 ]. Additionally, most research has focused on pre-treatment NLR, while data on post-treatment NLR and its prognostic significance remain limited [ 9 ]. Given NLR's prognostic value, this study aims to compare NLR in Sudanese patients with metastatic and non-metastatic cancers, considering differences across tumor types and treatment statuses. Understanding these associations may provide insights into the potential utility of NLR in cancer prognosis and treatment monitoring in diverse patient populations. Methods Study design and data collection This is a retrospective study analyzing clinical and laboratory data from 182 cancer patients diagnosed between April 1, 2021, and October 31, 2023, in the Wad Madani Specialized Cancer Center in Aljazeera State, Sudan. Data were extracted from hospital registries, ensuring the inclusion of relevant clinical and demographic variables with prognostic significance. Demographic variables included age at diagnosis and gender. Clinical variables included cancer metastasis status (metastatic vs. non-metastatic) and treatment modality (chemotherapy, radiotherapy, surgery, targeted drug therapy, or combination therapy). Hematological parameters encompassed total white blood cell count, platelet count, neutrophil percentage, and lymphocyte percentage. Patient selection criteria Patients were included based on the availability of complete clinical and hematological data at diagnosis. The study cohort comprised individuals with metastatic and non-metastatic cancers, representing various tumor types. Patients with chronic diseases like diabetes mellitus, and hypertension were excluded. Statistical Analysis Descriptive statistics summarized demographic and clinical characteristics, with age categorized into three groups (18–40, 41–60, and > 60 years). The Gaussian distribution of the data was tested using the Shapiro-Wilks test. Where appropriate, mean [± SD)] or median [interquartile range (IQR)] and related parametric (t-test and ANOVA) were used. For categorical variables, the Chi-square test (χ2) was used. Overall survival (OS) was estimated using the Kaplan-Meier method and analyzed using univariate analysis. Then, multivariate analysis was performed to assess the associations between NLR and prognostic factors, adjusting for confounders. OS comparisons were made between low and high NLR groups. A cut-off value of NLR was calculated using the maximum point (sensitivity + specificity) of the Receiver Operating Characteristic (ROC) curve for the prediction of metastasis. Using this method, the cut-off value for NLR was 2.75. Statistical significance was set at P-value < 0.05, and all analyses were performed using R program 4.4.2. Results Patients’ demographics and clinical characteristics The study included one hundred eighty-two patients; 67 (36.8%) were males and 115 (63.2%) were females. These patients had nine main types of solid and hematological cancers, which were categorized as metastatic; 47 (25.8%) and non-metastatic; 135 (74.2%). The most prevalent cancer type represented in the study was breast cancer, encompassing 61 patients (33.5%) (Table 1 ). Patients’ groupings according to treatments were primarily chemotherapy (56.6%), while smaller groups underwent surgery (5%), targeted drug therapy (1.6%), radiotherapy (1.6%), or those who received no treatment (24.7%) (Table 1 ). Table 1 Demographic and clinical characteristics of the study patients. Characteristic Age (Mean ± SD, Median [IQR], Min: Max) 52.5 ± 15.1, 53 [40 − 33], 18:85 Total white blood cells (Mean ± SD, Median [IQR], Min: Max) 11.8 ± 24.2, 7 [ 5 – 10 ], 1.1:239 Platelet (Mean ± SD, Median [IQR], Min: Max) 243.1 ± 132.45, 210.5[159–321], 26:883 Relative neutrophil (Mean ± SD, Median [IQR], Min: Max) 57 ± 17.2,58 [ 48–68], 6:98 Relative lymphocyte (Mean ± SD, Median [IQR], Min: Max) 38 ± 17.4, 36[ 27- 47.75], 5:92 NLR (Mean ± SD, Median [IQR], Min: Max) 2.34 ± 2.3, 1.7 [ 1–3], 0.06:18 Age group 18–40 n = 47 (25.8%) 41–60 n = 83 (45.6%) > 60 n = 52 (28.6%) Gender Female n = 115 (63.2%) Male n = 67 (36.8%) Type of cancer Breast cancer n = 61 (33.5%) Gastrointestinal cancers n = 46 (25.3%) Genito-urinary cancers n = 30 (16.5%) Hematological cancers n = 23 (12.6%) Nasopharyngeal carcinoma n = 11 (6%) Hepatocellular carcinoma n = 6 (3.3%) Lung cancer n = 2 (1.1%) Skin cancer n = 2 (1.1%) Mesothelioma n = 1 (0.5%) Metastatic status Yes n = 47 (25.8%) No n = 135 (74.2%) Treatment type Chemotherapy n = 103 (56.6%) Chemotherapy/Surgery n = 13(7.1%) Surgery n = 9 (5%) Chemotherapy/Radiotherapy n = 4 (2.2%) Radiotherapy n = 3 (1.6%) Chemotherapy/Surgery/Radiotherapy n = 2 (1.2%) Targeted drug therapy (Sorafenib) n = 3 (1.6%) Naïve n = 45 (24.7%) Patient survival Yes n = 11 (6%) No n = 171 (94%) NLR categories High NLR n = 56 (30.8%) Low NLR n = 126 (69.2%) Variation in NLR values across patient subgroups Patients aged 41–60 years exhibited a higher frequency of elevated NLR values, 48 (57.8%), compared to both the 18–40 years and > 60 years age groups, 18 (38.3%) and 29 (55.8%). No statistically significant difference was observed for the NLR values across the different age groups; p-value = 0.620. Although female and male patients had a similar frequency of high NLR values (52.2%), there was no statistically significant difference in NLR values based on patients’ sex; p-value = 0.340. On the other hand, there was no statistically significant difference between different types of cancer and their relation to NLR value (p-value = 0.210); however, breast cancers exhibited a higher frequency of high NLR value (35 (57.4%) compared to other cancers. Based on cancer metastatic status, there was a statistically significant difference for cancer metastasis status and its relation to NLR value; p-value = < 0.001, the frequency of patients with metastatic cancers with high NLR value was higher than non-metastatic cancer patients; 76.6% and 48.2%, respectively. Based on treatment categories, chemotherapy exhibited a higher frequency of elevated NLR values, 54 (52.4%) compared to other treatments. There was no statistically significant difference in NLR values among the different treatments; p-value = 0.665. However, a statistically significant difference in NLR values was found concerning patients' survival outcomes and cancer types, with p-values of < 0.001 (Table 2 ). Table 3 presents the distribution of cancer types among the study patients according to metastasis status. Table 4 presents the treatment distribution of cancer patients according to sex and age group. Table 2 The distribution of NLR values across the patients’ demographic and clinical characteristics. NLR value Total P value Low [ 60 years 23 (44.2%) 29 (55.8%) 52 (28.6%) Sex 0.340 Male 32 (47.8%) 35 (52.2%) 67 (36.8%) Female 55 (47.8%) 60 (52.2%) 115 (63.2%) Metastatic status < 0.001 Non-Metastatic 76 (56.3%) 59 (48.2%) 135 (43.7%) Metastatic 11 (23.4%) 36 (76.6%) 47 (25.8%) Treatment type 0.665 Chemotherapy 49 (47.6%) 54 (52.4%) 103 (56.6%) Surgery 3 (33.3%) 6 (66.7%) 9 (4.9%) Radiotherapy 3 (100.0%) 0 (0.0%) 3 (1.6%) Targeted drug therapy (Sorafenib) 2 (66.7%) 1 (33.3%) 3 (1.6%) Chemotherapy/Surgery 8 (61.5%) 5 (38.5%) 13 (7.0%) Chemotherapy/Radiotherapy 1 (33.3%) 3 (66.7%) 4 (2.2%) Chemotherapy/Surgery/Radiotherapy 1 (50.0%) 1 (50.0%) 2 (1.1%) Naïve 20 (44.4%) 25 (55.6%) 45 (24.7%) Patient survival < 0.001 Yes 87 (50.9%) 84 (49.1%) 171 (94.0%) No 0 (0.0%) 11 (100.0%) 11 (6.0%) Cancer type 0.210 Breast cancer 26 (42.6%) 35 (57.4%) 61(33.5%) Gastrointestinal cancer 22 (47.8%) 24 (52.2%) 46 (25.3%) Genito-urinary cancers 14 (46.7%) 16 (53.3%) 30 (16.5%) Hematological cancers 16 (69.6%) 7 (30.4%) 23 (12.6%) Nasopharyngeal carcinoma 7 (63.6%) 4 (36.4%) 11 (6%) Hepatocellular carcinoma 2 (33.3%) 4 (66.7%) 6 (3.3%) Lung cancer 0 (0.0%) 2 (100.0%) 2 (1.1%) Skin cancer 0 (0.0%) 2 (100.0%) 2 (1.1%) Mesothelioma 0 (0.0%) 1 (100.0%) 1 (0.5%) Total 87 (47.8%) 95 (52.2%) 182 (100%) Table 3 The distribution of cancer types among the study patients. Cancer types Metastatic status Total Non-Metastatic Metastatic Breast cancer 45 (73.8%) 16 (26.2%) 61 (33.5%) Gastrointestinal cancers 34 (73.9%) 12 (26.1%) 46 (25.3%) Genito-urinary cancers 22 (73.0%) 8 (27.0%) 30 (16.5%) Hematological cancers 19 (82.6%) 4 (17.4%) 23 (12.6%) Nasopharyngeal carcinoma 9 (81.8%) 2 (18.2%) 11 (6%) Hepatocellular carcinoma 3 (50.0%) 3 (50.0%) 6 (3.3%) Lung cancer 1 (50.0%) 1 (50.0%) 2 (1.1%) Skin cancer 1 (50.0%) 1 (50.0%) 2 (1.1%) Mesothelioma 1 (100.0%) - 1 (0.5%) Total 135 (74.2%) 47 (25.8%) 182 (100%) Table 4 The distribution of cancer patients’ treatment according to sex and age group. Treatment type Sex Age groups Metastatic status Total Male Female 18–40 41–60 > 60 Non-Metastatic Metastatic Chemotherapy 33 (32.0%) 70 (68.0%) 34 (33.0%) 44 (43.0%) 25 (24.3%) 74 (72.0%) 29 (28.1%) 103 (56.6%) Surgery 2 (22.2%) 7(77.8%) 1 (11.1%) 4 (44.4%) 4 (44.4%) 7 (77.8%) 2 (22.2%) 9 (4.9%) Radiotherapy 1 (33.3%) 2 (66.7%) - 2 (66.7%) 1 (33.3%) 2 (66.7%) 1 (33.3%) 3 (1.6%) Targeted drug therapy (Sorafenib) - 3 (100.0%) - 1 (33.3%) 2 (66.7%) 2 (66.7%) 1 (33.3%) 3 (1.6%) Chemotherapy/Surgery 6(46.2%) 7(53.8%) 2 (15.4%) 5 (38.5%) 6 (46.1%) 10 (76.9%) 3 (23.1%) 13 (7.1%) Chemotherapy/Radiotherapy 1 (25.0%) 3 (75.0%) 1 (25.0%) 3 (75.0%) - 3 (75.0%) 1 (25.0%) 4 (2.2%) Chemotherapy/Surgery/Radiotherapy 1 (50.0%) 1 (50.0%) - 2 (100.0%) - 2 (100.0%) - 2 (1.1%) Naïve 23 (51.1%) 22 (48.9%) 9 (20.0%) 22 (48.9%) 14 (31.1%) 35 (78.0%) 10 (22.0%) 45 (24.7%) Total 67 (36.8) 115 (63.2) 47 (25.8%) 83 (45.6%) 52 (28.6%) 135 (74.2%) 47 (25.8%) 182 (100%) Relative lymphocyte, neutrophil, and platelet absolute counts Patients over 60 years demonstrated a higher tendency for increased peripheral blood relative neutrophil count (RNC) and absolute platelet count (APC) compared to younger age groups (58.9 ± 15.39 and 260.2 ± 161.33, respectively); however, these differences were not statistically significant (p-values = 0.171 and 0.203, respectively). In contrast, patients aged 20–40 years had the highest relative lymphocyte count (RLC); 43.1 ± 20.93, but again, no statistically significant difference was observed, p-value = 0.063. Regarding sex, females exhibited a significantly higher APC; 260.8 ± 143.87 than males; 212.8 ± 104.34, p-value = 0.018, whereas RNC and RLC means were similar between sexes, with no statistically significant differences; p-values > 0.05. Patients with metastatic cancer had lower RLC; 35.3 ± 16.79, and APC; 213.3 ± 152.70, but higher RNC; 60.8 ± 15.52, compared to non-metastatic cases; RLC: 38.9 ± 17.53, APC: 253.5 ± 123.55, RNC: 55.8 ± 17.59, though none of these differences were statistically significant; p-value > 0.05. Also, no significant differences were found in RNC, RLC, and APC based on treatment types; p-values were 0.407, 0.326, and 0.402, respectively, though chemotherapy/radiotherapy-treated patients had the highest RNC; 65.0 ± 11.46, radiotherapy patients had the highest RLC; 60.3 ± 20.26, and targeted drug therapy patients had the highest APC; 385.0 ± 198.37. Both RNC and RLC showed statistically significant differences concerning patient survival and cancer type; p-values < 0.001, with deceased patients having a higher mean RNC; 77.5 ± 11.44, and surviving patients showing higher RLC; 39.3 ± 16.96, and APC; 244.9 ± 133.90. Malignant melanoma, AML, and rectal adenocarcinoma had the highest means for RNC: 86.0 ± 0.00, RLC 91.0 ± 0.00, and APC 433.3 ± 261.29, respectively. However, APC differences were not statistically significant for survival or cancer type; p-values were 0.452 and 0.070, respectively (Table 5 ). Table 5 Analysis of relative lymphocyte, neutrophil, and absolute platelet counts based on patients’ demographic and clinical characteristics. RNC Mean ± SD P-value RLC Mean ± SD P-value APC Mean ± SD P-value Age group 0.171 0.063 0.203 20–40 years 53.0 ± 20.33 43.1 ± 20.93 258.0 ± 38.62 41–60 years 58.2 ± 16.12 36.4 ± 16.08 223.9 ± 105.24 > 60 years 58.9 ± 15.39 35.9 ± 15.09 260.2 ± 161.33 Gender 0.632 0.896 0.018 Male 56.6 ± 18.56 38.2 ± 17.94 212.8 ± 104.34 Female 57.5 ± 16.39 37.9 ± 17.11 260.8 ± 143.87 Metastatic status 0.082 0.210 0.073 Non-Metastatic 55.8 ± 17.59 38.9 ± 17.53 253.5 ± 123.55 Metastatic 60.8 ± 15.52 35.3 ± 16.79 213.3 ± 152.70 Treatment type 0.407 0.326 0.402 Chemotherapy 56.8 ± 16.75 37.9 ± 16.15 242.5 ± 131.06 Surgery 61.3 ± 9.59 35.3 ± 11.02 264.8 ± 106.62 Radiotherapy 36.3 ± 20.11 60.3 ± 20.26 220.0 ± 136.18 Targeted drug therapy (Sorafenib) 58.7 ± 25.89 36.7 ± 23.12 385.0 ± 198.37 Chemotherapy/Surgery 59.5 ± 17.01 35.6 ± 17.03 175.9 ± 100.11 Chemotherapy/Radiotherapy 65.0 ± 11.46 29.0 ± 10.99 250.3 ± 120.22 Chemotherapy/Surgery/Radiotherapy 44.0 ± 49.49 53.5 ± 44.55 242.0 ± 82.02 Naïve 57.3 ± 17.49 38.0 ± 19.61 251.1 ± 144.47 Patient survival < 0.001 < 0.001 0.452 Yes 55.7 ± 16.67 39.3 ± 16.96 244.9 ± 133.90 No 77.5 ± 11.44 18.2 ± 10.69 213.9 ± 108.44 Cancer type < 0.001 0.012 0.070 Breast cancer 60.8 ± 12.8 35.2 ± 13.6 262 ± 130 Gastrointestinal cancer 58.6 ± 16.6 36.6 ± 16.7 255 ± 155 Genito-urinary cancers 52.9 ± 15.3 42.2 ± 16.1 214 ± 107 Hematological cancers 45.0 ± 23.6 48.3 ± 24.3 195 ± 119 Nasopharyngeal carcinoma 53.9 ± 17.9 40.2 ± 16.7 180 ± 93.2 Hepatocellular carcinoma 65.3 ± 18.1 28.2 ± 17.5 331 ± 141 Lung cancer 73.0 ± 9.9 22.5 ± 7.78 386 ± 31.1 Skin cancer 80.0 ± 0.0 16.0 ± 0.0 201 ± 0.0 Mesothelioma 73.5 ± 17.7 21.0 ± 17.0 251 ± 72.1 Total 57.1 ± 17.18 38.0 ± 17.38 243.1 ± 132.46 Kaplan-Meier survival analysis The Kaplan-Meier survival analysis demonstrated a significant association between overall survival, those with baseline NLR below the median of the whole study (NLR median = 1.7) and those with baseline NLR equal to or above the median of the whole study (p-value 0.0037). Overall, patients with low NLR values exhibited the highest survival probability with minimal decline over time, whereas those with high NLR experienced the most rapid decrease, indicating a poorer prognosis (Fig. 1 A). Patients with metastatic cancer showed worse survival outcomes compared to non-metastatic cancer patients (Fig. 1 B). Among non-metastatic cancer patients, individuals with low NLR maintained the highest survival probability, while those with high NLR showed a minimal decline over time; p-value = 0.18 (Fig. 2 A). Similarly, for metastatic cancer patients, high NLR was associated with the worst survival outcomes, with a sharp decrease occurring within the first 600 days; p-value = 0.048 (Fig. 2 B). Figure 3 presents Kaplan-Meier survival curves for cancer types based on NLR values. Patients were then divided into groups according to demographic characteristics (sex, age), and overall survival in patients below and above baseline NLR was observed (Supplementary Figures S1 -5). Lastly, survival analysis for cancer and treatment types was significantly associated with overall survival; p-values were < 0.001 and 0.03, respectively (Supplementary Figures S6, S7). Cox proportional hazards model Univariate and corresponding multivariate Cox proportional hazards analyses detailed in Table 6 assessed the association between NLR and OS. High NLR was defined using the overall cohort median. In univariate analysis, all variables except metastasis status, chemotherapy, hepatocellular carcinoma, lung cancer and mesothelioma showed no significant association with survival. In multivariate analysis, the adjusted hazard ratio (HR) for OS in patients with high NLR was 5.68 [0.81–39.66]. However, age, sex, cancer type, metastasis status, and treatment type violated the proportional hazards assumption, necessitating stratified multivariable regression. This stratification resulted in significant changes to the HR, which ranged between (0.000000001 and 5.68), indicating that proportional hazards violations significantly impacted the association between NLR and survival. Classification Performance of NLR in Predicting Survival The prognostic performance of NLR was evaluated using receiver operating characteristic (ROC) curve analysis. NLR demonstrated a maximum sensitivity (S1) of 1 and specificity (S2) of 0.75 (S1 + S2 = 1.75), with an optimal cut-off value of NLR = 2.75. Figure 4 presents ROC curves showing differences in prognostic accuracy across metastatic status. The area under the curve (AUC) was relatively increased in patients with metastatic cancer (0.717) compared to those with non-metastatic cancer (0.715). These findings suggest that NLR is a more effective predictor of survival in metastatic cancers. Table 6 Univariate and multivariate analyses for overall survival of cancer patients. Overall Survival Univariate Multivariate HR [95% CI] P value HR [95% CI] P value Age > 60 (Ref) 18–40 1.2 [0.17–8.9] 0.830 1.9 [0.36–10.11] 0.440 41–60 1.5 [0.31–7.4] 0.600 4 [1.1-14.69] 0.036 Gender Female (Ref) Male 0.79 [0.22–2.78] 0.710 2.17 [0.87–11.5] 0.078 Cancer type Breast cancer (Ref) Gastrointestinal cancers 0.55 [0.09–3.42] 0.520 0.65 [0.14–2.9] 0.580 Genitourinary cancers 0.000000017 [N/A] 0.990 0.14 [0.0021-9.8] 0.370 Hematological cancers 3.11 [0.28–34.43] 0.350 2.5 [0.28–22.65] 0.40 Nasopharyngeal carcinoma 10 [0.79-145.14] 0.070 2.77 [0.2–37.2] 0.44 Hepatocellular carcinoma 12.1 [1.79–82.65] 0.010 11.2 [1.7–74.9] 0.012 Lung cancer 32.2 [2.5-416.10] 0.0077 43 [4.65–409.4] < 0.001 Skin cancer 0.000000042 [N/A] 0.990 0.000000014 [N/A] 0.990 Mesothelioma 21 [1.60-275.63] 0.020 100.4 [7.5–1469] < 0.001 Metastatic status Metastatic (Ref) Non- Metastatic 0.14 [0.030–0.71] 0.017 0.32 [0.076-1.4] 0.130 Treatment type Chemotherapy/Radiotherapy (Ref) Chemotherapy 0.039 [0.0032-0.47] 0.010 0.004 [0.0053-0.11] < 0. 001 Surgery 0.00000036 [N/A] 0.990 0.00000000048[N/A] 0.990 Radiotherapy 0.00000036 [N/A] 0.990 0.000000001 [N/A] 0.990 Targeted drug therapy (Sorafenib) 0.24 [0.010–5.42] 0.400 0.039 [0.0031-0.49] 0. 012 Chemotherapy/Surgery 0.051 [0.0024-1.13] 0.060 0.86 [0.12-6] 0.88 Chemotherapy/Surgery/ Radiotherapy 0.00000009 [N/A] 0.990 0.037 [0.0000057-245] 0.46 Naïve 2 [0.049–83.6] 0.700 3.5[0.024–504.1] 0.610 NLR categories Low NLR (Ref) High NLR 1.6e + 0 9 [N/A] 0.990 5.68 [0.81–39.66] 0.07 Discussion Cancer is a leading cause of mortality and morbidity worldwide. Chronic inflammation is currently recognized as an essential feature of cancer, and neutrophils are believed to be a central component of this process [ 10 , 11 ]. Neutrophilia is a common feature of cancer-associated chronic inflammation, often accompanied by relative lymphocytopenia and represents a significant decrease in the cell-mediated adaptive immune response [ 12 ]. The most direct evidence for the association between carcinogenesis and chronic systemic inflammation comes from patients treated with inhibitors of chronic inflammation who were susceptible to cancer progression before treatment and were able to achieve chemo-preventive potential thereafter [ 13 ]. It is increasingly recognized that neutrophils play an essential role in the initiation, progression, and metastasis of cancer [ 14 ]. Neutrophils accumulate in many types of human and murine tumors and regulate almost all steps of tumor progression [ 15 ]. Therefore, objective prognostic tools are needed to help physicians assess the prognosis of patients with advanced cancer [ 13 ]. The routine collection of the complete blood count in clinical practice with little expense to the patient makes the NLR a promising biomarker of the cancer patient's systemic inflammatory status [ 12 ]. Neutrophil-lymphocyte ratio (NLR) has been used as an inflammation-based prognostic marker for various malignancies, including solid and hematologic malignancies such as lymphomas and multiple myelomas [ 16 , 17 ]. Our study is a retrospective single-center study. We analyzed data from 182 cancer patients representing nine types of cancer. Our key finding is that NLR is more predictive for metastatic cancers than non-metastatic ones using receiver operating characteristic (ROC) curve analysis. High NLR showed higher frequency in patients aged 41–60 years with hepatocellular carcinoma, breast cancer and metastatic cancers. Regarding survival analysis, there was a significant association between high NLR and overall survival in metastatic cancers (p-value = 0.0075), in which high NLR was associated with the worst survival outcomes among metastatic cancers compared with minimal decline over time in non-metastatic cancers. This was evident when we performed univariate and multivariate analyses, in which non-metastatic cancers showed significant association in the univariate analysis. This means that non-metastatic cancers were associated with an 86% reduction in HR compared to metastatic cancers. After adjusting for the confounders in multivariate analysis, the effect turned insignificant but reduced the HR by 68% compared to metastatic cancers. Furthermore, we analyzed the overall survival according to age groups, and there was a significant association between NLR and overall survival among patients aged 41–60 years; p-value = 0.029, with those with high NLR showing a substantial decline approximately by 600 days, whereas patients with low NLR values exhibited the highest survival probability and this was clear when we adjusted confounders in multivariate analysis were aged 41–60 years associated with significant increase HR (4 times) compared to a reference group, Similarly, for sex, high NLR was significantly associated with poor survival outcomes in both males and females, with a moderate decrease occurring within the first 400 days; p-value = 0.092, whereas males demonstrated a substantial decline within the first 400 days; p-value = 0.047. On the other hand, in univariate analysis, the HR was reduced in male patients by 21%, while in multivariate analysis, after adjusting for other confounders, the HR increased by 2.17 times compared to female patients. This may be due to high NLR, affecting overall survival in male patients. Moreover, regarding overall survival analysis according to cancer types, hepatocellular carcinoma, lung cancer, and mesothelioma demonstrated the worst survival probability, with a sharp decrease approximately by 300 days compared to other cancer types, and this was clear when we performed univariate and multivariate analyses, where hepatocellular carcinoma, lung cancer, and mesothelioma showed a significant association with poor overall survival in both univariate and multivariate analyses. Several studies and two meta-analyses consistently reported that NLR is an unfavorable prognostic indicator for patients with gastrointestinal, pulmonary, renal, breast and gynecological cancers [ 18 ]. New studies have shown that inflammatory markers such as NLR play an essential role in predicting survival in various malignancies, including colorectal, breast, ovarian, stomach, and bladder cancers. In a study by Yuan et al., neutrophil count was found to have a significant association with overall survival (OS), whereas lymphocyte count did not. Although not as substantial as the neutrophil count, NLR was linked to survival. High NLR promotes tumor growth and inhibits the anti-tumor response, resulting in disease progression and shorter time to develop metastases, particularly among breast cancer patients diagnosed in the advanced stages. Furthermore, research revealed that neutrophils release of growth factors and proteolytic enzymes throughout the body, including VEGF and matrix metalloproteinase-9, which enhance tumor invasion, metastasis, and angiogenesis [ 19 – 22 ]. Our results are in agreement with previous studies, showing a significant association between NLR and cancer types, with high NLR demonstrating the worst survival outcome over time. Similarly, there was a statistically significant difference in cancer metastasis status and its relation to NLR, with a higher frequency of high NLR among metastatic than non-metastatic cancer patients. In addition, RNC showed statistically significant differences with patient survival and cancer type; p-values < 0.001. Researchers mainly focused on inflammatory markers before treatment, while dynamic changes in inflammatory markers after chemotherapy were not considered. Changes in inflammatory markers during chemotherapy could be a valuable tool to assess prognosis, as chemotherapy can alter the inflammatory response [ 19 ]. Yamada et al. showed that NLR after neoadjuvant chemotherapy (NAC) was a better prognostic factor than that before NAC for patients with esophageal squamous cell carcinoma (ESCC) (23). Hashimoto et al. showed that a higher NLR after receiving the first cycle of first-line chemotherapy could be a prognostic marker in locally advanced or metastatic Upper Tract Urothelial Carcinoma (UTUC) patients [ 24 ]. In our study, regarding Kaplan-Meier survival analysis according to treatment types, chemotherapy/radiotherapy combination demonstrated the worst survival probability with a sharp decrease within the first 150 days compared to chemotherapy alone, which is significantly associated with good overall survival, especially those have low NLR value; p-value = 0.037. This was evident when we performed univariate and multivariate analyses, where chemotherapy showed a significant association in both univariate and multivariate analysis suggesting that chemotherapy has a more substantial effect in reducing HR by 96.1% and 99.6%, respectively, even after adjusting for other confounders, it remained a highly significant factor in reducing risk compared to reference treatment, confirming that chemotherapy has an independent protective effect on overall survival. Regarding NLR across metastatic and non-metastatic cancers, research indicates that elevated NLR is consistently associated with poorer overall survival (OS) in metastatic cancer patients, highlighting its role in tumor-induced inflammation and immune response. A study conducted by Zhou et al. indicated that high NLR correlates with shorter OS, with a pooled hazard ratio (HR) of 1.82, indicating a significant prognostic value in metastatic cases [ 25 ]. A previous study by Zhang et al. investigated the predictive rule of NLR for distant metastasis in gastric cancer (GC) patients and concluded that NLR is useful in predicting the presence of distant metastases with a recommendation to clinicians to pay attention to high NLR and conducted further investigations to detect distant metastases earlier [ 26 ]. In non-metastatic cancers, NLR is also a prognostic marker, affecting outcomes such as survival rates and response to treatment. High NLR is associated with poor prognosis across various non-metastatic cancers, such as colorectal and lung cancers [ 27 ]. Several studies demonstrated that elevated NLR was linked to advanced or aggressive breast cancer [ 22 ]. In our study, high NLR was most predominant among breast cancer (N = 35) patients, mainly with metastasis. The role of NLR remains unclear in many types of leukemias and lymphomas [ 8 ]. Our study included 23 patients with various types of hematological cancers, including acute myeloid leukemia (AML), chronic myeloid leukemia (CML), acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), non-Hodgkin lymphoma (NHL), multiple myeloma, and lymphoplasmocytic lymphoma. Notably, as opposed to solid organ cancers, low NLR was predominant, with few cases of multiple myeloma and non-Hodgkin lymphoma showing high NLR values. Notably, the highest NLR value of 18.0 was seen in one CML treated with hydroxyurea. Further prospective studies are encouraged to evaluate the predictive role of NLR in the context of hematological cancers, as most previous NLR studies focused on solid tumors. To the best of our knowledge, this the first study comparing prognostic value of NLR across metastatic and non-metastatic cancers from Sudan. This study has limitations, such as the small overall sample and per cancer type, as well as possible selection and time-dependent assessment biases as a retrospective study. Therefore, further prospective studies with larger sample sizes must confirm our findings. Conclusions In conclusion, our results revealed that high NLR has a higher predictive value, particularly among metastatic cancer patients receiving chemotherapy. Despite this prognostic value in this study and previous ones, the introduction of this biomarker into clinical practice is still challenging, mainly due to the lack of optimal cut-off value and significant variation of the association between NLR and overall survival across studies and this is due to further research to standardize its use. Abbreviations ALL Acute lymphocytic leukemia AML Acute myeloid leukemia APC Absolute platelet count AUC Area under the curve CLL Chronic lymphocytic leukemia CML Chronic myeloid leukemia HR Hazard ratio IQR Interquartile range NHL Non-Hodgkin lymphoma NLR Neutrophil-to-lymphocyte ratio OS Overall survival ROC Receiver Operating Characteristic Declarations Ethics approval and consent to participate This study was conducted in accordance with ethical guidelines for retrospective research. Due to the retrospective nature of the study, the Institutional Review Board (IRB) of Wad Madani Specialized Cancer Center waived the requirement for informed consent. Patient confidentiality was maintained by anonymizing all data. The study was approved by the IRB of Wad Madani Specialized Cancer Center. Consent for publication Not applicable. Availability of data and materials The dataset supporting the conclusions of this article is included within the article (and its additional file). Competing interests The authors declare no competing interests. Funding No funding was received Authors' contributions A.A, S.I.M, A.B.M, W.A.S, and E.A.E conceived the study and formulated the study design. A.A, M.A.M.B, B.H, and S.Y performed data collection. N.M.H and N.S.M did the statistical analysis and data presentation. A.A, N.S.M, N.M.H, M.E.H, A.E.A, and R.M.A.E wrote/edited the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Cupp MA, Cariolou M, Tzoulaki I, Aune D, Evangelou E, Berlanga-Taylor AJ. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 2020;18(1):360. Uribe-Querol E, Rosales C. Neutrophils in Cancer: Two Sides of the Same Coin. J Immunol Res. 2015;2015:1–21. Quail DF, Amulic B, Aziz M, Barnes BJ, Eruslanov E, Fridlender ZG, et al. Neutrophil phenotypes and functions in cancer: A consensus statement. J Exp Med. 2022;219(6):e20220011. Shang B, Cui H, Xie R, Wu J, Shi H, Bi X, et al. Neutrophil extracellular traps primed intercellular communication in cancer progression as a promising therapeutic target. Biomark Res. 2023;11(1):24. Socorro Faria S, Fernandes PC Jr, Barbosa Silva MJ, Lima VC, Fontes W, Freitas-Junior R et al. The neutrophil-to-lymphocyte ratio: a narrative review. ecancermedicalscience [Internet]. 2016 Dec 12 [cited 2025 Mar 16];10. Available from: http://www.ecancer.org/journal/10/full/702-the-neutrophil-to-lymphocyte-ratio-a-narrative-review.php Zucker A, Winter A, Lumley D, Karwowski P, Jung M, Kao J. Prognostic role of baseline neutrophil–to–lymphocyte ratio in metastatic solid tumors. Mol Clin Oncol [Internet]. 2020 Jul 16 [cited 2025 Mar 16]; Available from: http://www.spandidos-publications.com/ 10.3892/mco.2020.2095 Stefaniuk P, Szymczyk A, Podhorecka M. The Neutrophil to Lymphocyte and Lymphocyte to Monocyte Ratios as New Prognostic Factors in Hematological Malignancies – A Narrative Review. Cancer Manag Res. 2020;12:2961–77. Kim JY, Jung EJ, Kim JM, Lee HS, Kwag SJ, Park JH, et al. Dynamic changes of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio predicts breast cancer prognosis. BMC Cancer. 2020;20(1):1206. Rosell A, Aguilera K, Hisada Y, Schmedes C, Mackman N, Wallén H, et al. Prognostic value of circulating markers of neutrophil activation, neutrophil extracellular traps, coagulation and fibrinolysis in patients with terminal cancer. Sci Rep. 2021;11(1):5074. Rawat K, Syeda S, Shrivastava A. Neutrophil-derived granule cargoes: paving the way for tumor growth and progression. Cancer Metastasis Rev. 2021;40(1):221–44. Howard R, Kanetsky PA, Egan KM. Exploring the prognostic value of the neutrophil-to-lymphocyte ratio in cancer. Sci Rep. 2019;9(1):19673. Wang X, Teng F, Kong L, Yu J. Pretreatment neutrophil-to-lymphocyte ratio as a survival predictor for small-cell lung cancer. OncoTargets Ther. 2016;9:5761–70. Ng MSF, Tan L, Wang Q, Mackay CR, Ng LG. Neutrophils in cancer—unresolved questions. Sci China Life Sci. 2021;64(11):1829–41. Huang H, Zhang H, Onuma AE, Tsung A. Neutrophil Elastase and Neutrophil Extracellular Traps in the Tumor Microenvironment. In: Birbrair A, editor. Tumor Microenvironment [Internet]. Cham: Springer International Publishing; 2020 [cited 2025 Mar 29]. pp. 13–23. (Advances in Experimental Medicine and Biology; vol. 1263). Available from: https://link.springer.com/ 10.1007/978-3-030-44518-8_2 Mady M, Prasai K, Tella SH, Yadav S, Hallemeier CL, Rakshit S, et al. Neutrophil to lymphocyte ratio as a prognostic marker in metastatic gallbladder cancer. HPB. 2020;22(10):1490–5. Yilmaz S, Çeneli Ö. Prognostic Value of Neutrophil to Lymphocyte Ratio in Acute Myeloid Leukemia. Open J Intern Med. 2023;13(03):131–8. Orditura M, Galizia G, Diana A, Saccone C, Cobellis L, Ventriglia J, et al. Neutrophil to lymphocyte ratio (NLR) for prediction of distant metastasis-free survival (DMFS) in early breast cancer: a propensity score-matched analysis. ESMO Open. 2016;1(2):e000038. Nemoto T, Endo S, Isohata N, Takayanagi D, Nemoto D, Aizawa M, et al. Change in the neutrophil–to–lymphocyte ratio during chemotherapy may predict prognosis in patients with advanced or metastatic colorectal cancer. Mol Clin Oncol. 2021;14(5):107. Yuan J, Liang H, Li J, Li M, Tang B, Ma H, et al. Peripheral blood neutrophil count as a prognostic factor for patients with hepatocellular carcinoma treated with sorafenib. Mol Clin Oncol. 2017;7(5):837–42. Liu Z, Liang Y, Tang X, Qu H. Decrease in Blood Neutrophil-to-Lymphocyte Ratio Indicates Better Survival After Neoadjuvant Chemotherapy in Patients With Advanced Gastric Cancer. Front Surg. 2021;8:745748. Gago-Dominguez M, Matabuena M, Redondo CM, Patel SP, Carracedo A, Ponte SM, et al. Neutrophil to lymphocyte ratio and breast cancer risk: analysis by subtype and potential interactions. Sci Rep. 2020;10(1):13203. Yamada M, Tanaka K, Yamasaki M, Yamashita K, Makino T, Saito T, et al. Neutrophil–to–lymphocyte ratio after neoadjuvant chemotherapy as an independent prognostic factor in patients with esophageal squamous cell carcinoma. Oncol Lett. 2022;25(2):58. Hashimoto M, Fujita K, Nakayama T, Fujimoto S, Hamaguchi M, Nishimoto M, et al. Higher neutrophil-to-lymphocyte ratio after the first cycle of the first-line chemotherapy is associated with poor cancer specific survival of upper urinary tract carcinoma patients. Transl Androl Urol. 2021;10(7):2838–47. Zhou K, Wan J, Li Y, Yuan Y, Liu Q, Li H, et al. Prognostic value of pre-treatment neutrophil-to-lymphocyte ratio in patients with brain metastasis from cancer: a meta-analysis. Sci Rep. 2024;14(1):24789. Zhang X, Wang X, Li W, Sun T, Diao D, Dang C. Predictive value of neutrophil-to-lymphocyte ratio for distant metastasis in gastric cancer patients. Sci Rep. 2022;12(1):10269. Kapoor S. Neutrophil to lymphocyte ratio and its association with tumor prognosis in systemic malignancies. J Surg Oncol. 2013;107(5):560–560. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigures1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6503959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457167299,"identity":"de110eb4-194b-45cf-bc46-fa83ec5102ae","order_by":0,"name":"Ayman Azhary","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFACHijNzHzwAYjLR7wWdrZkAxCXjXgt/DxmEiCaoBb+/rPHPvPUHI42Z2ZLq/yaYyfDxsD88NENPFokDpxLns1z7HDuzmbmY7dltyUDHcZmbJyDz5qDPcbMPGyHczccZku7LbkNyAZ6RxqfFvnDPEAt/0BaeMyKJbfVE9ZicAyohbcNooXx47bDhLUYnuExZpzblw5yWLI047bjPGzMBPwid/6MMcObb9a5G84fPvjx57Zqe3725oeP8XofCJh4GJrBDGZwHDETUA4CjD8Y6mCMUTAKRsEoGAWYAAADYUWDo484zwAAAABJRU5ErkJggg==","orcid":"","institution":"Al Mughtaribeen University","correspondingAuthor":true,"prefix":"","firstName":"Ayman","middleName":"","lastName":"Azhary","suffix":""},{"id":457167300,"identity":"f6bd8651-b54b-44fb-b5b2-095c6a5377ca","order_by":1,"name":"Nooh Mohamed Hajhamed","email":"","orcid":"","institution":"Sirius Training and Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Nooh","middleName":"Mohamed","lastName":"Hajhamed","suffix":""},{"id":457167301,"identity":"12cf6455-23c3-48ad-9523-e71d97ff2679","order_by":2,"name":"Salahaldeen Ismail Mohammed","email":"","orcid":"","institution":"Omdurman Islamic University","correspondingAuthor":false,"prefix":"","firstName":"Salahaldeen","middleName":"Ismail","lastName":"Mohammed","suffix":""},{"id":457167302,"identity":"7b904efc-e63b-469c-8e09-4415fb9496c6","order_by":3,"name":"Abdullah M. 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A.","lastName":"Ebrahim","suffix":""},{"id":457167306,"identity":"40def845-09c0-473e-b45c-f4a7cac71f6d","order_by":7,"name":"Mohamed Abdelaziz Mohamed Balla","email":"","orcid":"","institution":"Omdurman Islamic University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Abdelaziz Mohamed","lastName":"Balla","suffix":""},{"id":457167307,"identity":"ffea3928-afc7-4eef-9884-2cf91ad652bd","order_by":8,"name":"Batol Hassan","email":"","orcid":"","institution":"University of Gezira","correspondingAuthor":false,"prefix":"","firstName":"Batol","middleName":"","lastName":"Hassan","suffix":""},{"id":457167308,"identity":"eb9e8b0e-708c-406b-85ac-938e2f728467","order_by":9,"name":"Sitelbanat Yassin","email":"","orcid":"","institution":"University of Gezira","correspondingAuthor":false,"prefix":"","firstName":"Sitelbanat","middleName":"","lastName":"Yassin","suffix":""},{"id":457167309,"identity":"e9b9e479-c150-43e2-a16a-4edd0b6fc411","order_by":10,"name":"Elgaylani Abdalla Eltayeb","email":"","orcid":"","institution":"University of Gezira","correspondingAuthor":false,"prefix":"","firstName":"Elgaylani","middleName":"Abdalla","lastName":"Eltayeb","suffix":""},{"id":457167310,"identity":"2714f08f-7405-4d6b-bea1-64ea869ddee0","order_by":11,"name":"Waleed Azhary Sir Alkhatim","email":"","orcid":"","institution":"University of Sinnar","correspondingAuthor":false,"prefix":"","firstName":"Waleed","middleName":"Azhary Sir","lastName":"Alkhatim","suffix":""},{"id":457167311,"identity":"dba32eb1-0f6f-4541-b0d9-48214f7a0066","order_by":12,"name":"Nouh Saad Mohamed","email":"","orcid":"","institution":"Sirius Training and Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Nouh","middleName":"Saad","lastName":"Mohamed","suffix":""}],"badges":[],"createdAt":"2025-04-22 11:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6503959/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6503959/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82899514,"identity":"2e52ae2c-16ef-40b6-b837-f4cf60880651","added_by":"auto","created_at":"2025-05-16 13:19:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier\u003c/strong\u003e \u003cstrong\u003eSurvival Analysis.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e: patients with low NLR or high NLR, \u003cstrong\u003eB:\u003c/strong\u003epatients with metastatic or non-metastatic cancer. Censoring, indicated by tick marks, is representing patients who were either lost to follow-up or remained alive at the last recorded time point. P indicates statistical significance.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6503959/v1/9f58f8dfa6e2817a73b6f426.jpeg"},{"id":82898506,"identity":"67b431af-a0ae-4b92-8fc8-5843f290e408","added_by":"auto","created_at":"2025-05-16 13:11:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":146144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier\u003c/strong\u003e \u003cstrong\u003eSurvival Analysis according to cancer metastatic status based on NLR values.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e: non-metastatic cancer, \u003cstrong\u003eB\u003c/strong\u003e: metastatic cancer. Censoring, indicated by tick marks, is representing patients who were either lost to follow-up or remained alive at the last recorded time point. P indicates statistical significance.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6503959/v1/6637991c8c5b89496c35eed9.jpeg"},{"id":82898508,"identity":"b6675bd3-0035-425a-9584-ca83aea90ce3","added_by":"auto","created_at":"2025-05-16 13:11:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":447458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier\u003c/strong\u003e \u003cstrong\u003eSurvival Analysis according to cancer types\u003c/strong\u003e \u003cstrong\u003ebased on NLR values. \u003c/strong\u003eA: breast cancer, B: gastrointestinal cancers, C: hematological cancers, D: genitourinary cancers, E: hepatocellular carcinoma, F: nasopharyngeal carcinoma. Censoring, indicated by tick marks, represents patients who were either lost to follow-up or remained alive at the last recorded time. P indicates statistical significance.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6503959/v1/19bf91d2fa7e318cba346d53.jpeg"},{"id":82899513,"identity":"0551dd02-8abf-4b19-ac0d-3411607334eb","added_by":"auto","created_at":"2025-05-16 13:19:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109844,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) analysis of for predicting metastatic and non-metastatic cancer cases.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6503959/v1/de62645a2026058092f8df39.png"},{"id":87313103,"identity":"311bffdd-8b2e-42ac-8149-53d197691f90","added_by":"auto","created_at":"2025-07-22 15:16:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2552993,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6503959/v1/56ab66a3-9db7-4be0-ae94-5a9e85c493e5.pdf"},{"id":82898513,"identity":"963e8da9-2bf4-40b4-9976-544ac758de94","added_by":"auto","created_at":"2025-05-16 13:11:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":403377,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6503959/v1/08bf342ca8e4d822dcb8eb0c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of prognostic value of neutrophil-to-lymphocyte ratio (NLR) in patients with metastatic and non-metastatic cancers ","fulltext":[{"header":"Background","content":"\u003cp\u003eCancer remains a leading global health challenge, with an estimated over 19\u0026nbsp;million new cases and 10\u0026nbsp;million deaths reported in 2022, according to GLOBOCAN. Lung cancer remains the most common cause of cancer-related mortality, followed by colorectal, liver, gastric, and breast cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The rising incidence of cancer, particularly in developing countries, underscores the need for reliable prognostic indicators to assess disease progression and treatment response [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne such biomarker is the neutrophil-to-lymphocyte ratio (NLR), a simple and cost-effective marker from routine blood tests [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. NLR reflects the balance between systemic inflammation and immune response, critical in cancer progression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While neutrophils are key immune cells involved in infection control and inflammation, they also contribute to tumor development by promoting angiogenesis, immune suppression, and metastasis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Conversely, lymphocytes play a crucial role in anti-tumor immunity. A high NLR is often associated with an unfavorable prognosis, as it may indicate increased tumor-promoting inflammation and a weakened immune response [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous studies have established NLR as an independent prognostic factor in solid tumors, including breast, lung, hepatocellular, colorectal, and nasopharyngeal cancers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A baseline NLR greater than 4 has been linked to poorer overall survival across various malignancies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, while NLR has been extensively studied in solid tumors, its role in hematological cancers remains unclear [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, most research has focused on pre-treatment NLR, while data on post-treatment NLR and its prognostic significance remain limited [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven NLR's prognostic value, this study aims to compare NLR in Sudanese patients with metastatic and non-metastatic cancers, considering differences across tumor types and treatment statuses. Understanding these associations may provide insights into the potential utility of NLR in cancer prognosis and treatment monitoring in diverse patient populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data collection\u003c/h2\u003e \u003cp\u003eThis is a retrospective study analyzing clinical and laboratory data from 182 cancer patients diagnosed between April 1, 2021, and October 31, 2023, in the Wad Madani Specialized Cancer Center in Aljazeera State, Sudan. Data were extracted from hospital registries, ensuring the inclusion of relevant clinical and demographic variables with prognostic significance. Demographic variables included age at diagnosis and gender. Clinical variables included cancer metastasis status (metastatic vs. non-metastatic) and treatment modality (chemotherapy, radiotherapy, surgery, targeted drug therapy, or combination therapy). Hematological parameters encompassed total white blood cell count, platelet count, neutrophil percentage, and lymphocyte percentage.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient selection criteria\u003c/h3\u003e\n\u003cp\u003ePatients were included based on the availability of complete clinical and hematological data at diagnosis. The study cohort comprised individuals with metastatic and non-metastatic cancers, representing various tumor types. Patients with chronic diseases like diabetes mellitus, and hypertension were excluded.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics summarized demographic and clinical characteristics, with age categorized into three groups (18\u0026ndash;40, 41\u0026ndash;60, and \u0026gt;\u0026thinsp;60 years). The Gaussian distribution of the data was tested using the Shapiro-Wilks test. Where appropriate, mean [\u0026plusmn;\u0026thinsp;SD)] or median [interquartile range (IQR)] and related parametric (t-test and ANOVA) were used. For categorical variables, the Chi-square test (χ2) was used. Overall survival (OS) was estimated using the Kaplan-Meier method and analyzed using univariate analysis. Then, multivariate analysis was performed to assess the associations between NLR and prognostic factors, adjusting for confounders. OS comparisons were made between low and high NLR groups. A cut-off value of NLR was calculated using the maximum point (sensitivity\u0026thinsp;+\u0026thinsp;specificity) of the Receiver Operating Characteristic (ROC) curve for the prediction of metastasis. Using this method, the cut-off value for NLR was 2.75. Statistical significance was set at P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and all analyses were performed using R program 4.4.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; demographics and clinical characteristics\u003c/h2\u003e \u003cp\u003eThe study included one hundred eighty-two patients; 67 (36.8%) were males and 115 (63.2%) were females. These patients had nine main types of solid and hematological cancers, which were categorized as metastatic; 47 (25.8%) and non-metastatic; 135 (74.2%). The most prevalent cancer type represented in the study was breast cancer, encompassing 61 patients (33.5%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients\u0026rsquo; groupings according to treatments were primarily chemotherapy (56.6%), while smaller groups underwent surgery (5%), targeted drug therapy (1.6%), radiotherapy (1.6%), or those who received no treatment (24.7%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eDemographic and clinical characteristics of the study patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, Median [IQR], Min: Max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1, 53 [40\u0026thinsp;\u0026minus;\u0026thinsp;33], 18:85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal white blood cells\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, Median [IQR], Min: Max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.2, 7 [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], 1.1:239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, Median [IQR], Min: Max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243.1\u0026thinsp;\u0026plusmn;\u0026thinsp;132.45, 210.5[159\u0026ndash;321], 26:883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelative neutrophil\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, Median [IQR], Min: Max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2,58 [ 48\u0026ndash;68], 6:98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelative lymphocyte\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, Median [IQR], Min: Max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4, 36[ 27- 47.75], 5:92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, Median [IQR], Min: Max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3, 1.7 [ 1\u0026ndash;3], 0.06:18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;47 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;83 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;52 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;115 (63.2%)\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=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;67 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;61 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;46 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenito-urinary cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;30 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematological cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;23 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasopharyngeal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;11 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;6 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesothelioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetastatic status\u003c/b\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;47 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;135 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;103 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;13(7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;9 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;3 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Surgery/Radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;2 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted drug therapy (Sorafenib)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;3 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u0026iuml;ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;45 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatient survival\u003c/b\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;11 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;171 (94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;56 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;126 (69.2%)\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=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariation in NLR values across patient subgroups\u003c/h2\u003e \u003cp\u003ePatients aged 41\u0026ndash;60 years exhibited a higher frequency of elevated NLR values, 48 (57.8%), compared to both the 18\u0026ndash;40 years and \u0026gt;\u0026thinsp;60 years age groups, 18 (38.3%) and 29 (55.8%). No statistically significant difference was observed for the NLR values across the different age groups; p-value\u0026thinsp;=\u0026thinsp;0.620. Although female and male patients had a similar frequency of high NLR values (52.2%), there was no statistically significant difference in NLR values based on patients\u0026rsquo; sex; p-value\u0026thinsp;=\u0026thinsp;0.340.\u003c/p\u003e \u003cp\u003eOn the other hand, there was no statistically significant difference between different types of cancer and their relation to NLR value (p-value\u0026thinsp;=\u0026thinsp;0.210); however, breast cancers exhibited a higher frequency of high NLR value (35 (57.4%) compared to other cancers.\u003c/p\u003e \u003cp\u003eBased on cancer metastatic status, there was a statistically significant difference for cancer metastasis status and its relation to NLR value; p-value\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001, the frequency of patients with metastatic cancers with high NLR value was higher than non-metastatic cancer patients; 76.6% and 48.2%, respectively.\u003c/p\u003e \u003cp\u003eBased on treatment categories, chemotherapy exhibited a higher frequency of elevated NLR values, 54 (52.4%) compared to other treatments. There was no statistically significant difference in NLR values among the different treatments; p-value\u0026thinsp;=\u0026thinsp;0.665. However, a statistically significant difference in NLR values was found concerning patients' survival outcomes and cancer types, with p-values of \u0026lt;\u0026thinsp;0.001 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the distribution of cancer types among the study patients according to metastasis status. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the treatment distribution of cancer patients according to sex and age group.\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\u003eThe distribution of NLR values across the patients\u0026rsquo; demographic and clinical characteristics.\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNLR value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow [\u0026lt;\u0026thinsp;median(1.7)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e[\u0026ge;\u0026thinsp;median (1.7)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (57.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.340\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\u003e32 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\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\u003e55 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetastatic status\u003c/b\u003e\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Metastatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (76.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment type\u003c/b\u003e\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\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted drug therapy (Sorafenib)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Surgery/Radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u0026iuml;ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatient survival\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \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\u003e87 (50.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84 (49.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (94.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\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\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(33.5%)\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\u003eGastrointestinal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (25.3%)\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\u003eGenito-urinary cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (16.5%)\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\u003eHematological cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (69.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (12.6%)\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\u003eNasopharyngeal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (6%)\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\u003eHepatocellular carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.3%)\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\u003eLung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.1%)\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\u003eSkin cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.1%)\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\u003eMesothelioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5%)\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\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e87 (47.8%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95 (52.2%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e182 (100%)\u003c/b\u003e\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=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe distribution of cancer types among the study patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMetastatic status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Metastatic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetastatic\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\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenito-urinary cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (73.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematological cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (82.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasopharyngeal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesothelioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e135 (74.2%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e47 (25.8%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e182 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe distribution of cancer patients\u0026rsquo; treatment according to sex and age group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMetastatic status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-Metastatic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetastatic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (32.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (68.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e103 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted\u0026nbsp;drug\u0026nbsp;therapy (Sorafenib)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (46.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10 (76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Surgery/Radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u0026iuml;ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35 (78.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e67 (36.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e115 (63.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e47 (25.8%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e83 (45.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e52 (28.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e135 (74.2%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e47 (25.8%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e182 (100%)\u003c/b\u003e\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\n\u003ch3\u003eRelative lymphocyte, neutrophil, and platelet absolute counts\u003c/h3\u003e\n\u003cp\u003ePatients over 60 years demonstrated a higher tendency for increased peripheral blood relative neutrophil count (RNC) and absolute platelet count (APC) compared to younger age groups (58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.39 and 260.2\u0026thinsp;\u0026plusmn;\u0026thinsp;161.33, respectively); however, these differences were not statistically significant (p-values\u0026thinsp;=\u0026thinsp;0.171 and 0.203, respectively). In contrast, patients aged 20\u0026ndash;40 years had the highest relative lymphocyte count (RLC); 43.1\u0026thinsp;\u0026plusmn;\u0026thinsp;20.93, but again, no statistically significant difference was observed, p-value\u0026thinsp;=\u0026thinsp;0.063. Regarding sex, females exhibited a significantly higher APC; 260.8\u0026thinsp;\u0026plusmn;\u0026thinsp;143.87 than males; 212.8\u0026thinsp;\u0026plusmn;\u0026thinsp;104.34, p-value\u0026thinsp;=\u0026thinsp;0.018, whereas RNC and RLC means were similar between sexes, with no statistically significant differences; p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003ePatients with metastatic cancer had lower RLC; 35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.79, and APC; 213.3\u0026thinsp;\u0026plusmn;\u0026thinsp;152.70, but higher RNC; 60.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.52, compared to non-metastatic cases; RLC: 38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53, APC: 253.5\u0026thinsp;\u0026plusmn;\u0026thinsp;123.55, RNC: 55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.59, though none of these differences were statistically significant; p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05. Also, no significant differences were found in RNC, RLC, and APC based on treatment types; p-values were 0.407, 0.326, and 0.402, respectively, though chemotherapy/radiotherapy-treated patients had the highest RNC; 65.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.46, radiotherapy patients had the highest RLC; 60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.26, and targeted drug therapy patients had the highest APC; 385.0\u0026thinsp;\u0026plusmn;\u0026thinsp;198.37. Both RNC and RLC showed statistically significant differences concerning patient survival and cancer type; p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001, with deceased patients having a higher mean RNC; 77.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44, and surviving patients showing higher RLC; 39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.96, and APC; 244.9\u0026thinsp;\u0026plusmn;\u0026thinsp;133.90. Malignant melanoma, AML, and rectal adenocarcinoma had the highest means for RNC: 86.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00, RLC 91.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00, and APC 433.3\u0026thinsp;\u0026plusmn;\u0026thinsp;261.29, respectively. However, APC differences were not statistically significant for survival or cancer type; p-values were 0.452 and 0.070, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\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\u003eAnalysis of relative lymphocyte, neutrophil, and absolute platelet counts based on patients\u0026rsquo; demographic and clinical characteristics.\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=\"\u0026plusmn;\" 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=\"\u0026plusmn;\" 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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNC\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRLC\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e53.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e43.1\u0026thinsp;\u0026plusmn;\u0026thinsp;20.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e258.0\u0026thinsp;\u0026plusmn;\u0026thinsp;38.62\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\u003e41\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e223.9\u0026thinsp;\u0026plusmn;\u0026thinsp;105.24\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\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e260.2\u0026thinsp;\u0026plusmn;\u0026thinsp;161.33\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\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\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=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e38.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e212.8\u0026thinsp;\u0026plusmn;\u0026thinsp;104.34\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e57.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e260.8\u0026thinsp;\u0026plusmn;\u0026thinsp;143.87\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\u003e\u003cb\u003eMetastatic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Metastatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e253.5\u0026thinsp;\u0026plusmn;\u0026thinsp;123.55\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\u003eMetastatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e60.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e213.3\u0026thinsp;\u0026plusmn;\u0026thinsp;152.70\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\u003e\u003cb\u003eTreatment type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e56.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e242.5\u0026thinsp;\u0026plusmn;\u0026thinsp;131.06\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\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e61.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e264.8\u0026thinsp;\u0026plusmn;\u0026thinsp;106.62\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\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e220.0\u0026thinsp;\u0026plusmn;\u0026thinsp;136.18\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\u003eTargeted drug therapy (Sorafenib)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;25.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;23.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e385.0\u0026thinsp;\u0026plusmn;\u0026thinsp;198.37\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\u003eChemotherapy/Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e59.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e175.9\u0026thinsp;\u0026plusmn;\u0026thinsp;100.11\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\u003eChemotherapy/Radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e250.3\u0026thinsp;\u0026plusmn;\u0026thinsp;120.22\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\u003eChemotherapy/Surgery/Radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e44.0\u0026thinsp;\u0026plusmn;\u0026thinsp;49.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;44.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e242.0\u0026thinsp;\u0026plusmn;\u0026thinsp;82.02\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\u003eNa\u0026iuml;ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e57.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e38.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e251.1\u0026thinsp;\u0026plusmn;\u0026thinsp;144.47\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\u003e\u003cb\u003ePatient survival\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.452\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=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e55.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e244.9\u0026thinsp;\u0026plusmn;\u0026thinsp;133.90\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e77.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e18.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e213.9\u0026thinsp;\u0026plusmn;\u0026thinsp;108.44\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\u003e\u003cb\u003eCancer type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e60.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e262\u0026thinsp;\u0026plusmn;\u0026thinsp;130\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\u003eGastrointestinal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e255\u0026thinsp;\u0026plusmn;\u0026thinsp;155\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\u003eGenito-urinary cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e52.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e214\u0026thinsp;\u0026plusmn;\u0026thinsp;107\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\u003eHematological cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e48.3\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e195\u0026thinsp;\u0026plusmn;\u0026thinsp;119\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\u003eNasopharyngeal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e180\u0026thinsp;\u0026plusmn;\u0026thinsp;93.2\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\u003eHepatocellular carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e331\u0026thinsp;\u0026plusmn;\u0026thinsp;141\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\u003eLung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e386\u0026thinsp;\u0026plusmn;\u0026thinsp;31.1\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\u003eSkin cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e80.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e201\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\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\u003eMesothelioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e73.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e251\u0026thinsp;\u0026plusmn;\u0026thinsp;72.1\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\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e57.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e38.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e243.1\u0026thinsp;\u0026plusmn;\u0026thinsp;132.46\u003c/b\u003e\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\n\u003ch3\u003eKaplan-Meier survival analysis\u003c/h3\u003e\n\u003cp\u003eThe Kaplan-Meier survival analysis demonstrated a significant association between overall survival, those with baseline NLR below the median of the whole study (NLR median\u0026thinsp;=\u0026thinsp;1.7) and those with baseline NLR equal to or above the median of the whole study (p-value 0.0037). Overall, patients with low NLR values exhibited the highest survival probability with minimal decline over time, whereas those with high NLR experienced the most rapid decrease, indicating a poorer prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Patients with metastatic cancer showed worse survival outcomes compared to non-metastatic cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Among non-metastatic cancer patients, individuals with low NLR maintained the highest survival probability, while those with high NLR showed a minimal decline over time; p-value\u0026thinsp;=\u0026thinsp;0.18 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Similarly, for metastatic cancer patients, high NLR was associated with the worst survival outcomes, with a sharp decrease occurring within the first 600 days; p-value\u0026thinsp;=\u0026thinsp;0.048 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents Kaplan-Meier survival curves for cancer types based on NLR values. Patients were then divided into groups according to demographic characteristics (sex, age), and overall survival in patients below and above baseline NLR was observed (Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-5). Lastly, survival analysis for cancer and treatment types was significantly associated with overall survival; p-values were \u0026lt;\u0026thinsp;0.001 and 0.03, respectively (Supplementary Figures S6, S7).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCox proportional hazards model\u003c/h2\u003e \u003cp\u003eUnivariate and corresponding multivariate Cox proportional hazards analyses detailed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e assessed the association between NLR and OS. High NLR was defined using the overall cohort median. In univariate analysis, all variables except metastasis status, chemotherapy, hepatocellular carcinoma, lung cancer and mesothelioma showed no significant association with survival. In multivariate analysis, the adjusted hazard ratio (HR) for OS in patients with high NLR was 5.68 [0.81\u0026ndash;39.66]. However, age, sex, cancer type, metastasis status, and treatment type violated the proportional hazards assumption, necessitating stratified multivariable regression. This stratification resulted in significant changes to the HR, which ranged between (0.000000001 and 5.68), indicating that proportional hazards violations significantly impacted the association between NLR and survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClassification Performance of NLR in Predicting Survival\u003c/h2\u003e \u003cp\u003eThe prognostic performance of NLR was evaluated using receiver operating characteristic (ROC) curve analysis. NLR demonstrated a maximum sensitivity (S1) of 1 and specificity (S2) of 0.75 (S1\u0026thinsp;+\u0026thinsp;S2\u0026thinsp;=\u0026thinsp;1.75), with an optimal cut-off value of NLR\u0026thinsp;=\u0026thinsp;2.75. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents ROC curves showing differences in prognostic accuracy across metastatic status. The area under the curve (AUC) was relatively increased in patients with metastatic cancer (0.717) compared to those with non-metastatic cancer (0.715). These findings suggest that NLR is a more effective predictor of survival in metastatic cancers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analyses for overall survival of cancer 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=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 [0.17\u0026ndash;8.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 [0.36\u0026ndash;10.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e41\u0026ndash;60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 [0.31\u0026ndash;7.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 [1.1-14.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 [0.22\u0026ndash;2.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17 [0.87\u0026ndash;11.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55 [0.09\u0026ndash;3.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 [0.14\u0026ndash;2.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenitourinary cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000000017 [N/A]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14 [0.0021-9.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematological cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.11 [0.28\u0026ndash;34.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5 [0.28\u0026ndash;22.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasopharyngeal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 [0.79-145.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.77 [0.2\u0026ndash;37.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHepatocellular carcinoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.1 [1.79\u0026ndash;82.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.2 [1.7\u0026ndash;74.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.2 [2.5-416.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 [4.65\u0026ndash;409.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eSkin cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000000042 [N/A]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000000014 [N/A]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMesothelioma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 [1.60-275.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.4 [7.5\u0026ndash;1469]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003e\u003cb\u003eMetastatic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-\u003cb\u003eMetastatic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14 [0.030\u0026ndash;0.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 [0.076-1.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Radiotherapy (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.039 [0.0032-0.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004 [0.0053-0.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.\u003cb\u003e001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00000036 [N/A]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00000000048[N/A]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00000036 [N/A]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000000001 [N/A]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted drug therapy (Sorafenib)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24 [0.010\u0026ndash;5.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039 [0.0031-0.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.\u003cb\u003e012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.051 [0.0024-1.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 [0.12-6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Surgery/ Radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00000009 [N/A]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037 [0.0000057-245]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u0026iuml;ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 [0.049\u0026ndash;83.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5[0.024\u0026ndash;504.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow NLR (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6e\u0026thinsp;+\u0026thinsp;0 9 [N/A]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.68 [0.81\u0026ndash;39.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer is a leading cause of mortality and morbidity worldwide. Chronic inflammation is currently recognized as an essential feature of cancer, and neutrophils are believed to be a central component of this process [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Neutrophilia is a common feature of cancer-associated chronic inflammation, often accompanied by relative lymphocytopenia and represents a significant decrease in the cell-mediated adaptive immune response [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The most direct evidence for the association between carcinogenesis and chronic systemic inflammation comes from patients treated with inhibitors of chronic inflammation who were susceptible to cancer progression before treatment and were able to achieve chemo-preventive potential thereafter [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is increasingly recognized that neutrophils play an essential role in the initiation, progression, and metastasis of cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Neutrophils accumulate in many types of human and murine tumors and regulate almost all steps of tumor progression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, objective prognostic tools are needed to help physicians assess the prognosis of patients with advanced cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe routine collection of the complete blood count in clinical practice with little expense to the patient makes the NLR a promising biomarker of the cancer patient's systemic inflammatory status [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Neutrophil-lymphocyte ratio (NLR) has been used as an inflammation-based prognostic marker for various malignancies, including solid and hematologic malignancies such as lymphomas and multiple myelomas [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study is a retrospective single-center study. We analyzed data from 182 cancer patients representing nine types of cancer. Our key finding is that NLR is more predictive for metastatic cancers than non-metastatic ones using receiver operating characteristic (ROC) curve analysis. High NLR showed higher frequency in patients aged 41\u0026ndash;60 years with hepatocellular carcinoma, breast cancer and metastatic cancers. Regarding survival analysis, there was a significant association between high NLR and overall survival in metastatic cancers (p-value\u0026thinsp;=\u0026thinsp;0.0075), in which high NLR was associated with the worst survival outcomes among metastatic cancers compared with minimal decline over time in non-metastatic cancers. This was evident when we performed univariate and multivariate analyses, in which non-metastatic cancers showed significant association in the univariate analysis. This means that non-metastatic cancers were associated with an 86% reduction in HR compared to metastatic cancers. After adjusting for the confounders in multivariate analysis, the effect turned insignificant but reduced the HR by 68% compared to metastatic cancers.\u003c/p\u003e \u003cp\u003eFurthermore, we analyzed the overall survival according to age groups, and there was a significant association between NLR and overall survival among patients aged 41\u0026ndash;60 years; p-value\u0026thinsp;=\u0026thinsp;0.029, with those with high NLR showing a substantial decline approximately by 600 days, whereas patients with low NLR values exhibited the highest survival probability and this was clear when we adjusted confounders in multivariate analysis were aged 41\u0026ndash;60 years associated with significant increase HR (4 times) compared to a reference group, Similarly, for sex, high NLR was significantly associated with poor survival outcomes in both males and females, with a moderate decrease occurring within the first 400 days; p-value\u0026thinsp;=\u0026thinsp;0.092, whereas males demonstrated a substantial decline within the first 400 days; p-value\u0026thinsp;=\u0026thinsp;0.047. On the other hand, in univariate analysis, the HR was reduced in male patients by 21%, while in multivariate analysis, after adjusting for other confounders, the HR increased by 2.17 times compared to female patients. This may be due to high NLR, affecting overall survival in male patients.\u003c/p\u003e \u003cp\u003eMoreover, regarding overall survival analysis according to cancer types, hepatocellular carcinoma, lung cancer, and mesothelioma demonstrated the worst survival probability, with a sharp decrease approximately by 300 days compared to other cancer types, and this was clear when we performed univariate and multivariate analyses, where hepatocellular carcinoma, lung cancer, and mesothelioma showed a significant association with poor overall survival in both univariate and multivariate analyses.\u003c/p\u003e \u003cp\u003eSeveral studies and two meta-analyses consistently reported that NLR is an unfavorable prognostic indicator for patients with gastrointestinal, pulmonary, renal, breast and gynecological cancers [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. New studies have shown that inflammatory markers such as NLR play an essential role in predicting survival in various malignancies, including colorectal, breast, ovarian, stomach, and bladder cancers. In a study by Yuan et al., neutrophil count was found to have a significant association with overall survival (OS), whereas lymphocyte count did not. Although not as substantial as the neutrophil count, NLR was linked to survival. High NLR promotes tumor growth and inhibits the anti-tumor response, resulting in disease progression and shorter time to develop metastases, particularly among breast cancer patients diagnosed in the advanced stages. Furthermore, research revealed that neutrophils release of growth factors and proteolytic enzymes throughout the body, including VEGF and matrix metalloproteinase-9, which enhance tumor invasion, metastasis, and angiogenesis [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our results are in agreement with previous studies, showing a significant association between NLR and cancer types, with high NLR demonstrating the worst survival outcome over time. Similarly, there was a statistically significant difference in cancer metastasis status and its relation to NLR, with a higher frequency of high NLR among metastatic than non-metastatic cancer patients. In addition, RNC showed statistically significant differences with patient survival and cancer type; p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eResearchers mainly focused on inflammatory markers before treatment, while dynamic changes in inflammatory markers after chemotherapy were not considered. Changes in inflammatory markers during chemotherapy could be a valuable tool to assess prognosis, as chemotherapy can alter the inflammatory response [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Yamada et al. showed that NLR after neoadjuvant chemotherapy (NAC) was a better prognostic factor than that before NAC for patients with esophageal squamous cell carcinoma (ESCC) (23). Hashimoto et al. showed that a higher NLR after receiving the first cycle of first-line chemotherapy could be a prognostic marker in locally advanced or metastatic Upper Tract Urothelial Carcinoma (UTUC) patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our study, regarding Kaplan-Meier survival analysis according to treatment types, chemotherapy/radiotherapy combination demonstrated the worst survival probability with a sharp decrease within the first 150 days compared to chemotherapy alone, which is significantly associated with good overall survival, especially those have low NLR value; p-value\u0026thinsp;=\u0026thinsp;0.037. This was evident when we performed univariate and multivariate analyses, where chemotherapy showed a significant association in both univariate and multivariate analysis suggesting that chemotherapy has a more substantial effect in reducing HR by 96.1% and 99.6%, respectively, even after adjusting for other confounders, it remained a highly significant factor in reducing risk compared to reference treatment, confirming that chemotherapy has an independent protective effect on overall survival.\u003c/p\u003e \u003cp\u003eRegarding NLR across metastatic and non-metastatic cancers, research indicates that elevated NLR is consistently associated with poorer overall survival (OS) in metastatic cancer patients, highlighting its role in tumor-induced inflammation and immune response. A study conducted by Zhou et al. indicated that high NLR correlates with shorter OS, with a pooled hazard ratio (HR) of 1.82, indicating a significant prognostic value in metastatic cases [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A previous study by Zhang et al. investigated the predictive rule of NLR for distant metastasis in gastric cancer (GC) patients and concluded that NLR is useful in predicting the presence of distant metastases with a recommendation to clinicians to pay attention to high NLR and conducted further investigations to detect distant metastases earlier [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In non-metastatic cancers, NLR is also a prognostic marker, affecting outcomes such as survival rates and response to treatment. High NLR is associated with poor prognosis across various non-metastatic cancers, such as colorectal and lung cancers [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies demonstrated that elevated NLR was linked to advanced or aggressive breast cancer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In our study, high NLR was most predominant among breast cancer (N\u0026thinsp;=\u0026thinsp;35) patients, mainly with metastasis. The role of NLR remains unclear in many types of leukemias and lymphomas [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Our study included 23 patients with various types of hematological cancers, including acute myeloid leukemia (AML), chronic myeloid leukemia (CML), acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), non-Hodgkin lymphoma (NHL), multiple myeloma, and lymphoplasmocytic lymphoma. Notably, as opposed to solid organ cancers, low NLR was predominant, with few cases of multiple myeloma and non-Hodgkin lymphoma showing high NLR values. Notably, the highest NLR value of 18.0 was seen in one CML treated with hydroxyurea. Further prospective studies are encouraged to evaluate the predictive role of NLR in the context of hematological cancers, as most previous NLR studies focused on solid tumors.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this the first study comparing prognostic value of NLR across metastatic and non-metastatic cancers from Sudan. This study has limitations, such as the small overall sample and per cancer type, as well as possible selection and time-dependent assessment biases as a retrospective study. Therefore, further prospective studies with larger sample sizes must confirm our findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our results revealed that high NLR has a higher predictive value, particularly among metastatic cancer patients receiving chemotherapy. Despite this prognostic value in this study and previous ones, the introduction of this biomarker into clinical practice is still challenging, mainly due to the lack of optimal cut-off value and significant variation of the association between NLR and overall survival across studies and this is due to further research to standardize its use.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute lymphocytic leukemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAML\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute myeloid leukemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAPC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbsolute platelet count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCLL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic lymphocytic leukemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCML\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic myeloid leukemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\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\"\u003e\u003cb\u003eIQR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNHL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-Hodgkin lymphoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with ethical guidelines for retrospective research. Due to the retrospective nature of the study, the Institutional Review Board (IRB) of Wad Madani Specialized Cancer Center waived the requirement for informed consent. Patient confidentiality was maintained by anonymizing all data. The study was approved by the IRB of Wad Madani Specialized Cancer Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is included within the article (and its additional file).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.A, S.I.M, A.B.M, W.A.S, and E.A.E conceived the study and formulated the study design. A.A, M.A.M.B, B.H, and S.Y performed data collection. N.M.H and N.S.M did the statistical analysis and data presentation. A.A, N.S.M, N.M.H, M.E.H, A.E.A, and R.M.A.E wrote/edited the manuscript. All authors read and approved the final manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCupp MA, Cariolou M, Tzoulaki I, Aune D, Evangelou E, Berlanga-Taylor AJ. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 2020;18(1):360.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUribe-Querol E, Rosales C. Neutrophils in Cancer: Two Sides of the Same Coin. J Immunol Res. 2015;2015:1\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuail DF, Amulic B, Aziz M, Barnes BJ, Eruslanov E, Fridlender ZG, et al. Neutrophil phenotypes and functions in cancer: A consensus statement. J Exp Med. 2022;219(6):e20220011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang B, Cui H, Xie R, Wu J, Shi H, Bi X, et al. Neutrophil extracellular traps primed intercellular communication in cancer progression as a promising therapeutic target. Biomark Res. 2023;11(1):24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSocorro Faria S, Fernandes PC Jr, Barbosa Silva MJ, Lima VC, Fontes W, Freitas-Junior R et al. The neutrophil-to-lymphocyte ratio: a narrative review. ecancermedicalscience [Internet]. 2016 Dec 12 [cited 2025 Mar 16];10. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ecancer.org/journal/10/full/702-the-neutrophil-to-lymphocyte-ratio-a-narrative-review.php\u003c/span\u003e\u003cspan address=\"http://www.ecancer.org/journal/10/full/702-the-neutrophil-to-lymphocyte-ratio-a-narrative-review.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZucker A, Winter A, Lumley D, Karwowski P, Jung M, Kao J. Prognostic role of baseline neutrophil\u0026ndash;to\u0026ndash;lymphocyte ratio in metastatic solid tumors. Mol Clin Oncol [Internet]. 2020 Jul 16 [cited 2025 Mar 16]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.spandidos-publications.com/\u003c/span\u003e\u003cspan address=\"http://www.spandidos-publications.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/mco.2020.2095\u003c/span\u003e\u003cspan address=\"10.3892/mco.2020.2095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefaniuk P, Szymczyk A, Podhorecka M. The Neutrophil to Lymphocyte and Lymphocyte to Monocyte Ratios as New Prognostic Factors in Hematological Malignancies \u0026ndash; A Narrative Review. Cancer Manag Res. 2020;12:2961\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JY, Jung EJ, Kim JM, Lee HS, Kwag SJ, Park JH, et al. Dynamic changes of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio predicts breast cancer prognosis. BMC Cancer. 2020;20(1):1206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosell A, Aguilera K, Hisada Y, Schmedes C, Mackman N, Wall\u0026eacute;n H, et al. Prognostic value of circulating markers of neutrophil activation, neutrophil extracellular traps, coagulation and fibrinolysis in patients with terminal cancer. Sci Rep. 2021;11(1):5074.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawat K, Syeda S, Shrivastava A. Neutrophil-derived granule cargoes: paving the way for tumor growth and progression. Cancer Metastasis Rev. 2021;40(1):221\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoward R, Kanetsky PA, Egan KM. Exploring the prognostic value of the neutrophil-to-lymphocyte ratio in cancer. Sci Rep. 2019;9(1):19673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Teng F, Kong L, Yu J. Pretreatment neutrophil-to-lymphocyte ratio as a survival predictor for small-cell lung cancer. OncoTargets Ther. 2016;9:5761\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg MSF, Tan L, Wang Q, Mackay CR, Ng LG. Neutrophils in cancer\u0026mdash;unresolved questions. Sci China Life Sci. 2021;64(11):1829\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang H, Zhang H, Onuma AE, Tsung A. Neutrophil Elastase and Neutrophil Extracellular Traps in the Tumor Microenvironment. In: Birbrair A, editor. Tumor Microenvironment [Internet]. Cham: Springer International Publishing; 2020 [cited 2025 Mar 29]. pp. 13\u0026ndash;23. (Advances in Experimental Medicine and Biology; vol. 1263). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/\u003c/span\u003e\u003cspan address=\"https://link.springer.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-030-44518-8_2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-44518-8_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMady M, Prasai K, Tella SH, Yadav S, Hallemeier CL, Rakshit S, et al. Neutrophil to lymphocyte ratio as a prognostic marker in metastatic gallbladder cancer. HPB. 2020;22(10):1490\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYilmaz S, \u0026Ccedil;eneli \u0026Ouml;. Prognostic Value of Neutrophil to Lymphocyte Ratio in Acute Myeloid Leukemia. Open J Intern Med. 2023;13(03):131\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrditura M, Galizia G, Diana A, Saccone C, Cobellis L, Ventriglia J, et al. Neutrophil to lymphocyte ratio (NLR) for prediction of distant metastasis-free survival (DMFS) in early breast cancer: a propensity score-matched analysis. ESMO Open. 2016;1(2):e000038.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNemoto T, Endo S, Isohata N, Takayanagi D, Nemoto D, Aizawa M, et al. Change in the neutrophil\u0026ndash;to\u0026ndash;lymphocyte ratio during chemotherapy may predict prognosis in patients with advanced or metastatic colorectal cancer. Mol Clin Oncol. 2021;14(5):107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan J, Liang H, Li J, Li M, Tang B, Ma H, et al. Peripheral blood neutrophil count as a prognostic factor for patients with hepatocellular carcinoma treated with sorafenib. Mol Clin Oncol. 2017;7(5):837\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Liang Y, Tang X, Qu H. Decrease in Blood Neutrophil-to-Lymphocyte Ratio Indicates Better Survival After Neoadjuvant Chemotherapy in Patients With Advanced Gastric Cancer. Front Surg. 2021;8:745748.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGago-Dominguez M, Matabuena M, Redondo CM, Patel SP, Carracedo A, Ponte SM, et al. Neutrophil to lymphocyte ratio and breast cancer risk: analysis by subtype and potential interactions. Sci Rep. 2020;10(1):13203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamada M, Tanaka K, Yamasaki M, Yamashita K, Makino T, Saito T, et al. Neutrophil\u0026ndash;to\u0026ndash;lymphocyte ratio after neoadjuvant chemotherapy as an independent prognostic factor in patients with esophageal squamous cell carcinoma. Oncol Lett. 2022;25(2):58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashimoto M, Fujita K, Nakayama T, Fujimoto S, Hamaguchi M, Nishimoto M, et al. Higher neutrophil-to-lymphocyte ratio after the first cycle of the first-line chemotherapy is associated with poor cancer specific survival of upper urinary tract carcinoma patients. Transl Androl Urol. 2021;10(7):2838\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou K, Wan J, Li Y, Yuan Y, Liu Q, Li H, et al. Prognostic value of pre-treatment neutrophil-to-lymphocyte ratio in patients with brain metastasis from cancer: a meta-analysis. Sci Rep. 2024;14(1):24789.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Wang X, Li W, Sun T, Diao D, Dang C. Predictive value of neutrophil-to-lymphocyte ratio for distant metastasis in gastric cancer patients. Sci Rep. 2022;12(1):10269.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapoor S. Neutrophil to lymphocyte ratio and its association with tumor prognosis in systemic malignancies. J Surg Oncol. 2013;107(5):560\u0026ndash;560.\u003c/span\u003e\u003c/li\u003e\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":"Neutrophil-to-lymphocyte ratio (NLR), prognostic value, metastatic cancers, non-metastatic cancers, overall survival (OS), Kaplan-Meier survival analysis","lastPublishedDoi":"10.21203/rs.3.rs-6503959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6503959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSeveral studies have reported NLR as an independent prognostic factor in solid tumors. However, comparing the prognostic value among metastatic and non-metastatic cancers remains underexplored, which is the objective of this study.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 182 patients with different types of metastatic and non-metastatic cancers were enrolled in this retrospective study. Of these patients, chemotherapy was the main treatment received (56.6%). We compared the prognostic value among metastatic and non-metastatic cancers. Overall survival (OS) was used as an outcome metric. Univariate and multivariate Cox proportional hazards analyses were used to investigate the association between NLR and OS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, metastatic cancer patients with high NLR demonstrated the worst survival outcomes in Kaplan-Meier survival analysis, whereas non-metastatic cancer patients showed minimal survival decline. Conversely, metastatic and non-metastatic cancer patients with low NLR maintained the highest survival probability. Multivariate analysis further identified high NLR as an independent predictor of poor overall survival. NLR showed a maximum sensitivity (S1) of 1 and specificity (S2) of 0.75, with an optimal cut-off value of NLR\u0026thinsp;=\u0026thinsp;2.75 using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) was slightly increased in patients with metastatic cancers (0.717), compared to those with non-metastatic cancers (0.715), indicating that NLR serves as a more effective predictor of survival in metastatic cancers.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn conclusion, the study findings indicate a significant association between high NLR and worse overall survival in metastatic cancers compared with minimal survival decline in non-metastatic cancers. NLR is still rolling in research articles, seeking gaps to reach routine clinical practice.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Comparison of prognostic value of neutrophil-to-lymphocyte ratio (NLR) in patients with metastatic and non-metastatic cancers ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 13:11:47","doi":"10.21203/rs.3.rs-6503959/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":"fe46fce8-b7df-4da7-9e47-0006074fd5ba","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-22T15:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 13:11:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6503959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6503959","identity":"rs-6503959","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}