Systemic Inflammation with Sarcopenia Predict Survival in Patients with Gastric

Systemic Inflammation with Sarcopenia Predict Survival in Patients with Gastric | 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 Systemic Inflammation with Sarcopenia Predict Survival in Patients with Gastric Yuying Liu, Guotian Ruan, Yizhong Ge, Qinqin Li, Qi Zhang, Xi Zhang, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1079469/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective: The levels of platelet-related inflammation indicators and sarcopenia have been reported to affect the survival of patients with cancer. To evaluate the prognostic influence of platelet count (PLT), platelet–lymphocyte ratio (PLR), and systemic immune inflammation index (SII), and SII combined with sarcopenia on the survival of patients with gastric cancer (GC). Methods: A total of 1131 patients with GC (811 men and 320 women, average age: 59.45 years) were evaluated. Receiver operating characteristic curves were used to determine the best cut-off values of PLT, PLR, and SII, and univariate and multivariate Cox risk regression models were used to evaluate whether SII is an independent predictor of overall survival (OS). The prognostic SS (SII-sarcopenia) was established based on SII and sarcopenia. Finally, a comprehensive analysis of the prognostic SS was performed. Results: SII had the strongest prognostic effect. The SII and OS of patients with GC were in an inverted U-shape (adjusted HR = 1.06; 95% CI: 0.95-1.18; adjusted P = 0.271). In patients with SII >1800, SII was negatively correlated with OS (adjusted HR = 0.57; 95% CI: 0.29-1.12; adjusted P = 0.102), however, there is no statistical difference. Interestingly, a high SS was associated with a poorer prognosis. The higher the SS score, the worse the OS ( P <0.001). Conclusion: SII is an independent prognostic indicator of GC, and high SII is related to poor prognosis. A Higher SS score had worse survival. Thus, the prognostic SS is a reliable predictor of OS in patients with GC. Cancer Biology Oncology gastric cancer systemic immune inflammation index sarcopenia survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Gastric cancer (GC) is the fifth most common malignancy and the fourth leading cause of cancer-related mortality worldwide, with more than 1 million incident cases and 769,000 deaths recorded in 2020. The incidence of GC varies by region and is the highest in East Asia and Eastern Europe and lower in North America and Northern Europe(Sung et al., 2021 ). Most GC patients are diagnosed with advanced disease owing to lack of suitable biomarkers. Chronic inflammation plays an important role in the occurrence of GC. The European Prospective Survey of Cancer and Nutrition study investigated the association between the inflammatory potential of diet and the risk of GC in 476,160 subjects from 10 European countries. After 14 years of follow-up, the results showed that the inflammatory potential of the diet is associated with an increased risk of GC(Agudo et al., 2018 ). Systemic inflammation plays a key role in the pathogenesis and progression of cancer. As such, the role of the tumor microenvironment in tumorigenesis has also attracted increasing attention. However, the interaction between tumors and inflammation is complicated. Preoperative hematological inflammation biomarkers including blood neutrophil, lymphocyte, monocyte, and platelet count (PLT); albumin levels; and their combinations have received increasing attention in recent years. Particularly, studies have reported that PLT plays an important role in inflammatory diseases. In addition, PLTs are involved in the occurrence and metastasis of cancer(Bambace & Holmes, 2011 ). Another study showed that the cancer type-specific combination of PLT characteristics can be used to diagnose early-stage cancer(Sabrkhany et al., 2017 ). PLT-related inflammation biomarkers have also been identified to have a predictive value in various cancers(De Giorgi et al., 2019 ; Zhang, Zheng, Quan, & Du, 2021 ), including for the prognosis of GC. Among inflammatory biomarkers of inflammation, two PLT-related indicators are widely studied, namely, the platelet–lymphocyte ratio (PLR) and systemic immune inflammation index (SII)(Feliciano et al., 2017 ; Jomrich et al., 2021 ; Templeton et al., 2014 ). PLR independently predicts survival of patients with mucinous gastric cancer(Zhu, Gao, Liu, Li, & Xue, 2021 ), and SII may serve as a convenient marker of survival after radical surgery in GC patients(Q. Wang & Zhu, 2019 ). Sarcopenia is a syndrome that represents degeneration and systemic loss of skeletal muscle mass. According to recent surveys, the prevalence of sarcopenia is relatively high, ranging from 15% at the age of 65 to 50% at the age of 80. Patients with sarcopenia often have a higher incidence of infectious diseases, metabolic syndrome, insulin resistance, and cardiovascular disease. In patients with cancer cachexia, anorexia, malnutrition, and systemic inflammation, the catalytic effect of the metabolic state is enhanced, leading to sarcopenia. Therefore, sarcopenia is considered to be a manifestation of cancer cachexia(Fukushima, Takemura, Suzuki, & Koga, 2018 ). Recent studies have shown that sarcopenia has an impact on the prognosis of various cancers. The survival rate of patients with sarcopenia is significantly lower than that of patients with esophageal cancer(Jin et al., 2021 ), colorectal cancer(Xie et al., 2021 ), pancreatic cancer(Cho et al., 2021 ), lung cancer(Kawaguchi et al., 2021 ) without sarcopenia. In general, sarcopenia plays an important role in the prognosis of cancer patients. Although platelet-related inflammation indicators or sarcopenia have a certain role in predicting the survival of cancer patients, the prediction effect needs to be improved. Hence, we explored the relationship between the combination of the two and the survival of patients with GC. 2. Methods 2.1 Study Design and Population This observational cohort study analyzed the data of patients with GC who visited one of more than 40 clinical centers in China between 2012 and 2018. The exclusion criteria were missing or abnormal preoperative data (data=0) on neutrophil count, PLT, sarcopenia, tumor stage, and lymphocyte counts (Figure 1 ). In total, 1131 GC patients were evaluated. This study was approved by the Institutional Review Board of each hospital (Registration number: ChiCTR1800020329) and was conducted in accordance with the Declaration of Helsinki. All participants signed a written informed consent. 2.2 Assessments Sarcopenia was diagnosed using a combination of low appendicular skeletal muscle index (ASMI) and low handgrip strength (HGS), based on the Asian Sarcopenia Working Group updated consensus in 2020. The calculation method of ASM has been reported previously in detail(Wen, Wang, Jiang, & Zhang, 2011 ). Briefly, we used the following equation: ASM = 0.193 × body weight (kg) + 0.107 × height (cm) -4.157 × sex (male: 1; female: 2) -0.037 × age (years)–2.631. ASMI was defined as ASM (kg)/height 2 (m 2 )(Choi et al., 2021 ). Low muscle mass was defined as ASMI <7 kg/m 2 for males and <5.4 kg/m 2 for females. HGS was measured in the dominant hand using a Jamar dynamometer. HGS <28 kg (male) or <18 kg (female) was defined as insufficient muscle strength(L. K. Chen et al., 2020 ). Laboratory measurements included albumin levels, neutrophil counts, lymphocyte counts, and PLT counts. All blood tests were performed after fasting for at least 9 h before anti-tumor treatment within 48 h of the first hospitalization. Body mass index (BMI) was calculated as BMI (kg/m 2 ) = weight (kg) / height^2 (m 2 ). PLR was calculated as PLR = PLT/lymphocyte counts, while SII was calculated as SII= neutrophil counts × PLT/lymphocyte counts. The SII-Sarcopenia Score (SS) is established based on SII and sarcopenia. Information on smoking status and alcohol and tea consumption was obtained using lifestyle questionnaires. All research results were reviewed and determined by an independent endpoint determination committee, whose members were blinded to the specific tasks of the research team. Pathological staging was according to the TNM staging system of the American Joint Committee on Cancer (8th edition)(Amin et al., 2017 ). 2.3 Statistical Analysis The main endpoint was overall survival (OS), including death from any cause. Evidence of death was obtained from follow-up records. Predictive models were compared with the ROC curves and C-index. The chi-square test was used to compare the differences between the categorical variables in the baseline characteristics of the patients, and the t-test was used to compare continuous variables. The risk factors were modeled as continuous variables, and the optimal cut-off point was calculated using the Wilcoxon rank test. The chi-square test was used to model dichotomous and quartile SII and calculate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) for the OS of patients with GC. Adjusted variables included age, sex, tumor stage, drinking status, albumin level, neutrophil count, BMI, surgery, chemotherapy, and radiotherapy. The heterogeneity between subgroups was evaluated using Cox regression, and the interaction between SII and the subgroups was checked using the probability ratio. Kaplan-Meier curves of survival according to PLT, PLR, and SII levels were generated. All statistical analyses were performed using R software version 4.0.5 (Lucent Technologies). 3. Results 3.1 Patient Characteristics In total, 811 and 320 of the patients were male and female, respectively. The average age was 59.45 years. The optimal cut-off values predictive of survival were 270.50 for PLT, 149.57 for PLR, and 712.58 for SII, respectively (Supplemental Figure 1). Accordingly, low and high PLT, PLR, and SII were defined as ≤270.50 and >270.50, ≤149.57 and >149.57, and ≤712.58 and >712.58, respectively. In total, 798 and 333 patients had low and high PLT, 640 and 491 patients had low PLR and high PLR, and 498 and 633 patients had low SII and high SII, respectively. The clinicopathological characteristics of the patients are shown in Table 1 . Table 1 Baseline patient characteristics Total (n=1131) PLT ≤270.50 (n=798) PLT >270.50 (n=333) P value PLR ≤149.57 (n=640) PLR>149.57 (n=491) P value SII ≤712.58 (n=498) SII >712.58 (n=633) P value Sex (%) <0.001 0.649 0.099 Male 811 (71.71) 598 (74.94) 213 (63.96) 455 (71.09) 356 (72.51) 370 (74.30) 441 (69.67) Female 320 (28.29) 200 (25.06) 120 (36.04) 185 (28.91) 135 (27.49) 128 (25.70) 192 (30.33) Smoking (%) 0.034 0.135 0.542 No 585 (51.72) 396 (49.62) 189 (56.76) 344 (53.75) 241 (49.08) 252 (50.60) 333 (52.61) Yes 546 (48.28) 402 (50.38) 144 (43.24) 296 (46.25) 250 (50.92) 246 (49.40) 300 (47.39) Drinking (%) 0.058 <0.001 0.342 No 881 (77.90) 616 (77.19) 265 (79.58) 524 (81.88) 357 (72.71) 395 (79.32) 486 (76.78) Yes 250 (22.10) 182 (22.81) 68 (20.42) 116 (18.12) 134 (27.29) 103 (20.68) 147 (23.22) Tea consumption (%) 0.015 0.282 0.589 No 837 (74.01) 589 (73.81) 248 (74.47) 482 (75.31) 355 (72.30) 373 (74.90) 464 (73.30) Yes 294 (25.99) 209 (26.19) 85 (25.53) 158 (24.69) 136 (27.70) 125 (25.10) 169 (26.70) Tumor stage (%) 0.216 0.031 0.136 Ⅰ 143 (12.64) 112 (14.04) 31 (9.31) 85 (13.28) 58 (11.81) 71 (14.26) 72 (11.37) Ⅱ 260 (22.99) 192 (24.06) 68 (20.42) 149 (23.28) 111 (22.61) 120 (24.10) 140 (22.12) Ⅲ 389 (34.39) 274 (34.34) 115 (34.53) 236 (36.88) 153 (31.16) 174 (34.94) 215 (33.97) Ⅳ 339 (29.97) 220 (27.57) 119 (35.74) 170 (26.56) 169 (34.42) 133 (26.71) 206 (32.54) Sarcopenia (%) 0.146 0.448 0.164 No 902 (79.75) 623 (78.07) 279 (83.78) 516 (80.62) 386 (78.62) 407 (81.73) 495 (78.20) Yes 229 (20.25) 175 (21.93) 54 (16.22) 124 (19.38) 105 (21.38) 91 (18.27) 138 (21.80) Surgery (%) 0.004 <0.001 0.006 No 542 (47.92) 382 (47.87) 160 (48.05) 348 (54.37) 194 (39.51) 262 (52.61) 280 (44.23) Yes 589 (52.08) 416 (52.13) 173 (51.95) 292 (45.62) 297 (60.49) 236 (47.39) 353 (55.77) Chemotherapy (%) 0.029 <0.001 0.003 No 694 (61.36) 493 (61.78) 201 (60.36) 356 (55.62) 338 (68.84) 281 (56.43) 413 (65.24) Yes 437 (38.64) 305 (38.22) 132 (39.64) 284 (44.38) 153 (31.16) 217 (43.57) 220 (34.76) Radiotherapy (%) 0.107 1.00 0.153 No 1120 (99.03) 793 (99.37) 327 (98.20) 634 (99.06) 486 (98.98) 496 (99.60) 624 (98.58) Yes 11 (0.97) 5 (0.63) 6 (1.80) 6 (0.94) 5 (1.02) 2 (0.40) 9 (1.42) Age, years (%) 59.45 (11.39) 60.02 (11.24) 58.08 (11.65) 0.170 59.10 (11.41) 59.91 (11.36) 0.235 58.98 (10.85) 59.82 (11.79) 0.218 BMI, kg/m 2 21.58 (3.42) 21.30 (19.00, 23.80) 21.50 (19.40, 24.10) 0.136 21.35 (19.00, 23.92) 21.40 (19.20, 23.90) 0.889 21.60 (19.10, 24.10) 21.30 (19.10, 23.80) 0.227 Albumin, g/L 37.31 (5.63) 37.55 (34.00, 41.77) 36.40 (33.00, 40.00) 0.199 38.70 (35.00, 42.12) 35.40 (32.05, 39.15) <0.001 38.90 (35.20, 42.40) 35.90 (32.60, 39.80) <0.001 Neutrophil count, 10 9 /L 4.91 (5.43) 3.50 (2.32, 5.27) 4.90 (3.30, 6.77) 0.227 2.90 (2.07, 3.82) 5.79 (4.50, 7.88) <0.001 3.42 (2.40, 5.17) 4.37 (2.80, 6.47) <0.001 Lymphocyte count, 10 9 /L 1.55 (1.48) 1.40 (1.00, 1.82) 1.50 (1.10, 2.00) 0.081 1.65 (1.24, 2.05) 1.16 (0.85, 1.52) <0.001 1.85 (1.50, 2.20) 1.12 (0.80, 1.47) <0.001 PLT, 10 9 /L 236.55 (97.86) 196.00 (155.00, 232.00) 330.00 (295.00, 372.00) 2.325 202.50 (153.00, 249.00) 265.00 (211.00, 334.00) <0.001 187.00 (143.00, 238.00) 259.00 (208.00, 330.00) <0.001 PLR 234.53 (371.74) 138.60 (97.09, 191.53) 226.52 (164.13, 318.00) 0.327 120.18 (89.71, 162.36) 229.11 (172.03, 314.12) <0.001 103.47 (80.41, 125.53) 221.33 (175.40, 312.86) <0.001 SII 1019.24 (1757.98) 491.32 (267.68, 830.85) 1019.96 (657.64, 2000.56) 0.488 365.30 (230.19, 512.88) 1260.29 (889.26, 2065.10) <0.001 346.38 (213.10, 545.78) 953.00 (603.75, 1807.67) <0.001 Notes: Continuous variables are presented as the mean ± standard deviation (SD). Meanwhile, BMI, albumin, neutrophil count, lymphocyte count, PLT, PLR, and SII are presented as the median (quartile range). Categorical variables are presented as numbers and percentages. Differences in normally and non-normally distributed baseline characteristics are compared using the chi-square test or t-test and using Wilcoxon rank sum test, respectively. Abbreviations: BMI, body mass index; PLT, platelet count; PLR, platelet lymphocyte ratio; SII, systemic immune inflammation index; P , probability 3.2 Predictive capabilities of PLT, PLR, and SII The C-index of each prognostic model is shown in Table 2 . Among the three, SII had the highest C-index at 0.561, followed by PLR at 0.544. PLT had the lowest C-index at 0.533. Similarly, the ROC curve showed that SII had the best predictive capability, and PLT had the least. SII and the OS of patients with GC tended to have a negative correlation, but it was not significant (per SD increment-HR=1.06; 95% CI: 0.95-1.18) (Table 3 ). The high SII group had poorer OS than did the low SII group (adjusted HR=1.45; 95% CI: 1.19-1.75; adjusted P =0.001). When SII was divided into quartiles (Q1, ≤330.2; Q2, >330.2, ≤611.9; Q3, >611.9, ≤1123.2; and Q4, >1123.2), the Q2 group showed a higher risk of death (adjusted HR = 1.43, 95% CI: 1.08-1.89, adjusted P = 0.012; Q3 group: adjusted HR = 1.67, 95% CI: 1.27-2.19, adjusted P <0.001; Q4 group: adjusted HR = 1.84; 95% CI: 1.39-2.44; adjusted P <0.001). The curves before and after adjustment showed an inverted U-shaped trend (Figure 2 ). Table 2 C-indexes of PLT, PLR and SII for overall survival Variables PLT PLR SII C-index 0.533 0.544 0.561 P value 0.013 0.749 0.229 Notes: PLT, platelet count; PLR, platelet lymphocyte ratio; SII, systemic immune inflammation index; P , probability Table 3 Association between SII and OS in patients with gastric cancer according to Cox regression models adjusted for potential confounders SII patients Unadjusted Adjusted P value HR (95% CI) P value HR (95% CI) Per SD 1131 0.023 1.13 (1.02-1.25) 0.271 1.06 (0.95-1.18) By cutoff ≤712.58 640 ref. ref. >712.58 491 <0.001 1.50 (1.25-1.79) <0.001 1.45 (1.19-1.75) By quartile Q1 (≤330.2) 283 ref. ref. Q2 (330.2-611.9) 283 0.109 1.25 (0.95-1.64) 0.012 1.43 (1.08-1.89) Q3 (611.9-1123.2) 282 0.003 1.50 (1.15-1.95) 1123.2) 283 <0.001 1.65 (1.27-2.14) <0.001 1.84 (1.39-2.44) P for trend 1131 <0.001 1.18 (1.09-1.28) <0.001 1.21 (1.11-1.32) Notes: Data are presented as hazard ratios (95% confidence intervals). The analyses are adjusted for age, sex, tumor stage, drinking status, albumin level, BMI, surgery, chemotherapy, and radiotherapy. SII, systemic immune inflammation index; HR, hazard ratio; CI, confidence interval; P , probability; BMI, body mass index; Q, quarter Table 3 SS prognostic score construction SS Score Patients (n) SII≤712.58 and No-sarcopenia 0 516 SII>712.58 and No-sarcopenia 1 386 SII≤712.58 and Sarcopenia 1 124 SII>712.58 and Sarcopenia 2 105 Notes: SII, systemic immune inflammation index; SS, SII-Sarcopenia. To exclude the influence of radiotherapy and chemotherapy on SII, we conducted a sensitivity analysis, and consistent results were obtained (Supplemental Table 1). In addition, we also performed COX regression analysis on patients undergoing surgery, radiotherapy and chemotherapy, and the results showed the same trend (Supplemental Table 2, Supplemental Table 3). High SII is related to poor prognosis. Using SII as a continuous variable for Cox regression analysis, low SII (≤1800) was significantly associated with poor prognosis (adjusted HR=1.16; 95% CI: 1.06-1.28; adjusted P =0.002). High SII (>1800) was also negatively correlated with poor prognosis, but there is no significant difference in this negative correlation (adjusted HR = 0.57; 95% CI: 0.29-1.12; adjusted P =0.102) (Supplemental Table 4). 3.3 Subgroup Analyses The relationship between SII and OS among the different subgroups was evaluated using stratified analysis (Figure 4 ), including age, gender, drinking status, BMI, tumor stage, and sarcopenia. We found significant interactions between SII and tumor stage ( P <0.001). GC patients with stage III-IV tumors (adjusted HR=1.77, 95% CI: 1.45-2.18, adjusted P <0.001) had significantly worse survival than did patients with stage I-II tumors (adjusted HR = 1.29, 95% CI: 0.75-2.22, adjusted P =0.353). Combined analysis of SII and tumor stage showed that patients with high SII and high tumor stage had the worst survival (Supplemental Figure 2). 3.4 Overall Survival Kaplan-Meier curves of OS according to SII, sarcopenia, and SS are shown in Figure 5 . High SII and sarcopenia were significantly related to the difference in OS in the univariate Cox proportional hazard regression ( P <0.001). In addition, in the model comprising SII and sarcopenia showed that patients with high SII and sarcopenia had worse OS. Similarly, patients with a higher SS score also had worse OS ( P <0.001). Discussion This multicenter cohort study found that among the PLT-related inflammation indicators PLT, PLR, and SII, SII has the best prognostic indication. Further analysis showed that patients with high SII is associated with poor OS in GC patients (Figure 3), and the HR gradually declined as SII increased to >1800. However, Cox regression analysis of SII showed that as the SII increased, survival worsened. A statistically significant positive correlation was found in the subgroup analysis with SII 1800, HR=0.57, the statistically significant results were not shown. This might be due to the small number of patients with SII >1800. In addition, high SII combined with sarcopenia was associated with poor OS in patients with GC. The higher the SS score, the worse was the patient’s OS (Figure 5). These results have also been supported by a number of studies. Cytokines are key components of the inflammatory process. PLTs play an indispensable role in the development and metastasis of cancer. Once activated, PLTs can bind to tumor cells through P-selectin, which can be found on the surface of PLTs and bind to the CD24 ligand. In addition, PLTs promote tumor growth and metastasis by releasing pro-angiogenesis and growth factors. Therefore, a large number of studies have focused on the prognostic implant of PLT-related inflammation indicators in cancer patients, but the results have been conflicting. Therefore, other indicators that play an important role in predicting cancer survival have been evaluated (Kurtoglu, Kokcu, Celik, Sari, & Tosun, 2015; Peng et al., 2017; Sun, Ju, Han, Sun, & Wang, 2018; J. J. Wang et al., 2019; L. Wang et al., 2017). Notably, PLR and SII both include PLT PLR is considered to be a marker of endogenous residual anti-precancerous inflammation and procoagulant response in malignant tumors (Proctor et al., 2011). It is also considered to be a sensitive marker that could predict certain types of advanced cancer, treatment response, and prognosis (Zhou et al., 2014). In addition, an elevated SII indicates that a highly inflammatory tumor microenvironment. SII can be measured easily at an affordable cost using a reproducible method, making it a promising prognostic indicator in clinical applications(Dong et al., 2020). Sarcopenia has also been confirmed to be related to poor prognosis in patients with cancer, thus making it a valuable prognostic indicator. A meta-analysis in 2016 showed that most studies on SMI and prognosis of patients with cancer were published after 2012, and more than half of the studies were published after 2015. This indicates an increasing attention to the prognostic value of SMI in this population (Shachar, Williams, Muss, & Nishijima, 2016). Skeletal muscle loss after surgery has been confirmed to be significantly negatively correlated with adverse postoperative outcomes in patients with non-small cell lung cancer(Takamori et al., 2020). However, sarcopenia was measured using skeletal muscle area, which is different from our research. A retrospective analysis showed that SMI is an independent predictor of OS in patients with breast cancer(Hua et al., 2020). This association between SMI and cancer prognosis could be because certain tumors could induce systemic inflammation, and sarcopenia might be a reflection of more radical tumor metabolism. Cancer patients generally have varying degenerative diseases, causing loss of muscle mass, strength, and dysfunction. The occurrence of these degenerative diseases is influenced by several factors including malnutrition, insufficient physical activity, comorbidities, and other factors directly related to pathophysiology and treatment-related toxicities. There have been several studies on sarcopenia combined with inflammatory indicators. Chen et al. retrospective analyzed the prognostic significance of preoperative SII in patients with colorectal cancer and concluded that SII has better predictive capability than PLR(J. H. Chen et al., 2017). Consistent findings were found in the current study. A 2021 study on the synergistic effect of sarcopenia and systemic inflammation on the survival of patients with oral cancer found that sarcopenia and systemic inflammation may have a negative synergistic prognostic effect on patients with advanced oral squamous cell carcinoma. (Lee et al., 2021). Despite differences in the assessment method for sarcopenia between this and the current study, it was confirmed that the ASM equation model was in good agreement with the dual X-ray absorbance (DXA) values(Wen et al., 2011). In addition, our study also combined the patients’ grip strength. A recent study reported that the combination of skeletal sarcopenia and PLR can help identify the survival risk of patients (Yamahara, Mizukoshi, Lee, & Ikegami, 2021). Based on these findings and of another research(Hirahara et al., 2019), we established the SS score according to the combination of SII and sarcopenia. In the stratified analysis, there was a significant interaction between tumor stage and SII. High SII patients with stage III-IV tumors showed significantly worse survival. This result was consistent with other studies that patients with stage III and IV tumors had worse 3-year OS rates than those with stage I and II tumors (16% and 9% vs 75% and 52%). The higher the tumor stage, the worse the survival rate (L. J. Chen, Chang, & Chang, 2021). Consistent findings were observed in the current study. To our best knowledge, this is the first cohort study to explore the relationship between SII combined with sarcopenia and OS in patients with GC. The advantage of this study is that we first screened out the index SII that has the best predictive indication among platelet-related inflammation indexes, and combined with sarcopenia on this basis, we obtained a better predictive model SS. However, the following limitations should be also considered. First, the adopted anthropometric equations that had been validated in Chinese individuals to estimate muscle mass, rather than bioimpedance analysis (BIA) or DXA recommended by the European Working Group on Sarcopenia in Older People(Cruz-Jentoft et al., 2010) and Asian Working Group for Sarcopenia(L. K. Chen et al., 2014). However, DXA is expensive and the patients might have to expose to X-rays. There are very few BIAs in hospitals in mainland China, and the cut-off point for defining low muscle mass on BIA among elderly Chinese individuals has not been established(Zeng et al., 2015). Second, only the PLT was recorded, and thus, PLT-related inflammation indexes did not included the mean PLT volume and PLT distribution width. Third, there was no record of diet and socioeconomic status, which might have affected muscle loss and cancer death. Fourth, this study only included the Chinese population and did not represent other ethnic groups. Future research needs to investigate whether reducing systemic inflammation and increasing muscle mass can prolong patient survival and the underlying mechanisms on their influence in patients with GC. Conclusion Systemic inflammation and sarcopenia are relaed to the prognostic significance of GC patients. Among the PLT-related inflammation indicators PLT, PLR, and SII, SII has the best prognostic accuracy. In this study, SII independently predicted survival, thus making it a possible reliable indicator of GC prognosis. In addition, patients with both inflammation and sarcopenia have significantly worse survival than do patients without these impacts. Abbreviations GC: gastric cancer; PLT: platelet count; PLR: platelet lymphocyte ratio; SII: systemic immune inflammation index; OS: overall survival; HR: hazard ratio; 95% CIs: 95% confidence intervals; P : probability; AUC: area under the curve; NLR: neutrophil-to-lymphocyte ratio; ASMI: appendicular skeletal muscle index; HGS: low handgrip strength; ASM: appendix skeletal muscle mass; BMI: body mass index; ROC: receiver operating characteristic; Q: quarter; SD: standard deviation; SMI: skeletal mass index; BIA: Bioimpedance analysis; DXA: dual X-ray absorptance Declarations Acknowledgments We thank all the patients who participated in this study for their active cooperation and valuable contributions. We would also like to express our gratitude to the 40 clinical centers for providing the data used in this study. Funding: This work was supported by the National Key Research and Development Program [grant number 2017YFC1309200]. Competing interests: The authors have no relevant financial or non-financial interests to disclose. Author Contributions: All authors contributed to the study conception and design. R.G.T., L.Y.Y., S.H.P., and W.Z.P. designed research; L.Y. Y and G.Y. Z and L.Q.Q. conducted research; Z.Q., Z.X. and S.M.M. analyzed data; W.M., Y.Q.H. and S.H.P. provided essential materials; L.Y.Y., T.M., Z.X.W., L.X.R., Z.K.P. and Y.M.wrote the paper; H.C.L., L.T., X.H.L., L.X.Y., L.S.Q. verified the results; L.Y. Y. had primary responsibility for final content. All authors read and approved the final manuscript. Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate: This study followed the Helsinki declaration. All participants signed an informed consent form and this study was approved by the Institutional Review Board of each hospital (Registration number: ChiCTR1800020329). Consent to participate: Because of the retrospective nature of this study, consent to participate for inclusion was waived. Consent to publish: Because of the retrospective nature of this study, consent to publish was waived. References Agudo A, Cayssials V, Bonet C, Tjønneland A, Overvad K, Boutron-Ruault MC, Jakszyn P (2018) Inflammatory potential of the diet and risk of gastric cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Am J Clin Nutr 107(4):607–616 Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Winchester DP (2017) The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. 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Clin Cancer Res 25(13):3839–3846 Dong M, Shi Y, Yang J, Zhou Q, Lian Y, Wang D, Fan R (2020) Prognostic and clinicopathological significance of systemic immune-inflammation index in colorectal cancer: a meta-analysis. Ther Adv Med Oncol 12:1758835920937425 Feliciano EMC, Kroenke CH, Meyerhardt JA, Prado CM, Bradshaw PT, Kwan ML, Caan BJ (2017) Association of Systemic Inflammation and Sarcopenia With Survival in Nonmetastatic Colorectal Cancer: Results From the C SCANS Study. JAMA Oncol 3(12):e172319 Fukushima H, Takemura K, Suzuki H, Koga F (2018) Impact of Sarcopenia as a Prognostic Biomarker of Bladder Cancer.Int J Mol Sci, 19(10). doi: 10.3390/ijms19102999 Hirahara T, Arigami T, Yanagita S, Matsushita D, Uchikado Y, Kita Y, Natsugoe S (2019) Combined neutrophil-lymphocyte ratio and platelet-lymphocyte ratio predicts chemotherapy response and prognosis in patients with advanced gastric cancer. BMC Cancer 19(1):672 Hua X, Deng JP, Long ZQ, Zhang WW, Huang X, Wen W, Lin HX (2020) Prognostic significance of the skeletal muscle index and an inflammation biomarker in patients with breast cancer who underwent postoperative adjuvant radiotherapy. Curr Probl Cancer 44(2):100513 Jin SB, Tian ZB, Ding XL, Guo YJ, Mao T, Yu YN, Jing X (2021) The Impact of Preoperative Sarcopenia on Survival Prognosis in Patients Receiving Neoadjuvant Therapy for Esophageal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 11:619592 Jomrich G, Paireder M, Kristo I, Baierl A, Ilhan-Mutlu A, Preusser M, Schoppmann SF (2021) High Systemic Immune-Inflammation Index is an Adverse Prognostic Factor for Patients With Gastroesophageal Adenocarcinoma. Ann Surg 273(3):532–541 Kawaguchi Y, Hanaoka J, Ohshio Y, Okamoto K, Kaku R, Hayashi K, Akazawa A (2021) Does sarcopenia affect postoperative short- and long-term outcomes in patients with lung cancer?-a systematic review and meta-analysis. J Thorac Dis 13(3):1358–1369 Kurtoglu E, Kokcu A, Celik H, Sari S, Tosun M (2015) Platelet Indices May be Useful in Discrimination of Benign and Malign Endometrial Lesions, and Early and Advanced Stage Endometrial Cancer. Asian Pac J Cancer Prev 16(13):5397–5400 Lee J, Liu SH, Dai KY, Huang YM, Li CJ, Chen JC, Chen YJ (2021) Sarcopenia and Systemic Inflammation Synergistically Impact Survival in Oral Cavity Cancer. Laryngoscope 131(5):E1530–e1538 Peng HX, Yang L, He BS, Pan YQ, Ying HQ, Sun HL, Wang SK (2017) Combination of preoperative NLR, PLR and CEA could increase the diagnostic efficacy for I-III stage CRC.J Clin Lab Anal, 31(5). doi: 10.1002/jcla.22075 Proctor MJ, Morrison DS, Talwar D, Balmer SM, Fletcher CD, O'Reilly DS, McMillan DC (2011) A comparison of inflammation-based prognostic scores in patients with cancer. A Glasgow Inflammation Outcome Study. Eur J Cancer 47(17):2633–2641 Sabrkhany S, Kuijpers MJE, van Kuijk SMJ, Sanders L, Pineda S, Damink O, Oude SWM, Egbrink MGA (2017) A combination of platelet features allows detection of early-stage cancer. Eur J Cancer 80:5–13 Shachar SS, Williams GR, Muss HB, Nishijima TF (2016) Prognostic value of sarcopenia in adults with solid tumours: A meta-analysis and systematic review. Eur J Cancer 57:58–67 Sun Z, Ju Y, Han F, Sun X, Wang F (2018) Clinical implications of pretreatment inflammatory biomarkers as independent prognostic indicators in prostate cancer.J Clin Lab Anal, 32(3). doi: 10.1002/jcla.22277 Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209–249 Takamori S, Tagawa T, Toyokawa G, Shimokawa M, Kinoshita F, Kozuma Y, Maehara Y (2020) Prognostic Impact of Postoperative Skeletal Muscle Decrease in Non-Small Cell Lung Cancer. Ann Thorac Surg 109(3):914–920 Templeton AJ, Ace O, McNamara MG, Al-Mubarak M, Vera-Badillo FE, Hermanns T, Amir E (2014) Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev 23(7):1204–1212 Wang JJ, Wang YL, Ge XX, Xu MD, Chen K, Wu MY, Li W (2019) Prognostic Values of Platelet-Associated Indicators in Resectable Lung Cancers. Technol Cancer Res Treat 18:1533033819837261 Wang L, Jia J, Lin L, Guo J, Ye X, Zheng X, Chen Y (2017) Predictive value of hematological markers of systemic inflammation for managing cervical cancer. Oncotarget 8(27):44824–44832 Wang Q, Zhu D (2019) The prognostic value of systemic immune-inflammation index (SII) in patients after radical operation for carcinoma of stomach in gastric cancer. J Gastrointest Oncol 10(5):965–978 Wen X, Wang M, Jiang CM, Zhang YM (2011) Anthropometric equation for estimation of appendicular skeletal muscle mass in Chinese adults. Asia Pac J Clin Nutr 20(4):551–556 Xie H, Wei L, Liu M, Yuan G, Tang S, Gan J (2021) Preoperative computed tomography-assessed sarcopenia as a predictor of complications and long-term prognosis in patients with colorectal cancer: a systematic review and meta-analysis. Langenbecks Arch Surg 406(6):1775–1788 Yamahara K, Mizukoshi A, Lee K, Ikegami S (2021) Sarcopenia with inflammation as a predictor of survival in patients with head and neck cancer. Auris Nasus Larynx 48(5):1013–1022 Zeng P, Wu S, Han Y, Liu J, Zhang Y, Zhang E, Zhang T (2015) Differences in body composition and physical functions associated with sarcopenia in Chinese elderly: reference values and prevalence. Arch Gerontol Geriatr 60(1):118–123 Zhang Y, Zheng L, Quan L, Du L (2021) Prognostic role of platelet-to-lymphocyte ratio in oral cancer: A meta-analysis. J Oral Pathol Med 50(3):274–279 Zhou X, Du Y, Huang Z, Xu J, Qiu T, Wang J, Liu P (2014) Prognostic value of PLR in various cancers: a meta-analysis. PLoS ONE 9(6):e101119 Zhu Z, Gao J, Liu Z, Li C, Xue Y (2021) Preoperative Platelet-to-Lymphocyte Ratio (PLR) for Predicting the Survival of Stage I-III Gastric Cancer Patients with a MGC Component. Biomed Res Int, 2021, 9678363. doi: 10.1155/2021/9678363 Supplementary Files OnlineResource.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 21 Nov, 2021 Reviewers invited by journal 21 Nov, 2021 Editor invited by journal 15 Nov, 2021 Editor assigned by journal 15 Nov, 2021 First submitted to journal 14 Nov, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1079469","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":64869220,"identity":"41333f30-4088-4a0c-b84e-db6957e9bda2","order_by":0,"name":"Yuying Liu","email":"","orcid":"","institution":"Capital Medical University Affiliated Beijing Shijitan Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuying","middleName":"","lastName":"Liu","suffix":""},{"id":64869221,"identity":"84e6b987-f6d7-4f6a-97e4-5641d8da8dfc","order_by":1,"name":"Guotian Ruan","email":"","orcid":"","institution":"Capital 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16:07:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1079469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1079469/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":15836479,"identity":"8c4dfa2c-f20c-4edd-a575-93a02ebfe48f","added_by":"auto","created_at":"2021-11-23 17:53:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64000,"visible":true,"origin":"","legend":"Flow chart","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1079469/v1/09cdd4f6b065cf5dffe53d94.png"},{"id":15836481,"identity":"e839dbdc-9fd4-49c3-bb03-bf67c2174cbe","added_by":"auto","created_at":"2021-11-23 17:53:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76705,"visible":true,"origin":"","legend":"Receiver operating characteristics (ROC) curve for PLT, PLR, SII based on overall survival\n \nNotes: PLT,platelet count; PLR, platelet lymphocyte ratio; SII, systemic immune inflammation index; AUC, area under the curve.\n","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1079469/v1/f23a4327ab640791a2519881.png"},{"id":15836480,"identity":"864143e8-74b5-4e7e-926e-d1434c3e05ee","added_by":"auto","created_at":"2021-11-23 17:53:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61985,"visible":true,"origin":"","legend":"Relationship between SII and OS in patients with gastric cancer\n\n \nNotes: Use cox regression to analyze SII (a: as continuous variable P=0.023; OR=1.13; 95% CI: 1.02,1.25) and adjusted (b: as continuous variable P=0.271; adjusted OR=1.06; 95% CI :0.95,1.18). The analyses are adjusted for age, sex, tumor stage, drinking status, albumin level, BMI, surgery, chemotherapy, and radiotherapy. SII, systemic immune inflammation index; HR, hazard ratio; CI, confidence interval; BMI, body mass index.\n \n","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1079469/v1/a2d343724342c1c6fcd86664.png"},{"id":15836483,"identity":"5da1e4b1-4bf0-46b7-b2dd-14cc4bf09b37","added_by":"auto","created_at":"2021-11-23 17:53:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":86120,"visible":true,"origin":"","legend":"The relationship between SII and the OS of patients with gastric cancer in different subgroups\n\n \nNotes: The cox regression model was used to calculate hazard ratios (HRs) and 95% confidence interval (CI). Each subgroup is adjusted for age, sex, tumor stage, drinking status, albumin level, BMI, surgery, chemotherapy, and radiotherapy. SII, systemic immune inflammation index; HR, hazard ratio; CI, confidence interval; P, probability; BMI, body mass index.\n\n","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1079469/v1/514807f2fbc68dbe84d27645.png"},{"id":15836482,"identity":"ffddc0d3-3988-49b4-afba-22abc379ba4d","added_by":"auto","created_at":"2021-11-23 17:53:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181265,"visible":true,"origin":"","legend":"Kaplan-Meier survival curves for overall survival for patients with gastric cancer with high SII(\u003e712.58) and sarcopenia versus low SII(≤712.58), no sarcopenia and SS, respectively.\n\n \n \nNotes: SII, systemic immune inflammation index; SS, SII-Sarcopenia; P, probability.\n\n","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1079469/v1/5dee8e97d5ba6af61c5bc489.png"},{"id":15836509,"identity":"3f492c92-e454-4daa-b882-2e97b532e092","added_by":"auto","created_at":"2021-11-23 17:54:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":936499,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1079469/v1/a916aa8a-448a-449a-9f90-8446b258ff25.pdf"},{"id":15836484,"identity":"48172b72-b227-4836-958b-f8b775e03065","added_by":"auto","created_at":"2021-11-23 17:53:59","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":263978,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1079469/v1/f93964494526507ba61c6e62.pdf"}],"financialInterests":"","formattedTitle":"Systemic Inflammation with Sarcopenia Predict Survival in Patients with Gastric","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer (GC) is the fifth most common malignancy and the fourth leading cause of cancer-related mortality worldwide, with more than 1 million incident cases and 769,000 deaths recorded in 2020. The incidence of GC varies by region and is the highest in East Asia and Eastern Europe and lower in North America and Northern Europe(Sung et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Most GC patients are diagnosed with advanced disease owing to lack of suitable biomarkers. Chronic inflammation plays an important role in the occurrence of GC. The European Prospective Survey of Cancer and Nutrition study investigated the association between the inflammatory potential of diet and the risk of GC in 476,160 subjects from 10 European countries. After 14 years of follow-up, the results showed that the inflammatory potential of the diet is associated with an increased risk of GC(Agudo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSystemic inflammation plays a key role in the pathogenesis and progression of cancer. As such, the role of the tumor microenvironment in tumorigenesis has also attracted increasing attention. However, the interaction between tumors and inflammation is complicated. Preoperative hematological inflammation biomarkers including blood neutrophil, lymphocyte, monocyte, and platelet count (PLT); albumin levels; and their combinations have received increasing attention in recent years. Particularly, studies have reported that PLT plays an important role in inflammatory diseases. In addition, PLTs are involved in the occurrence and metastasis of cancer(Bambace \u0026amp; Holmes, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Another study showed that the cancer type-specific combination of PLT characteristics can be used to diagnose early-stage cancer(Sabrkhany et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). PLT-related inflammation biomarkers have also been identified to have a predictive value in various cancers(De Giorgi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang, Zheng, Quan, \u0026amp; Du, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), including for the prognosis of GC. Among inflammatory biomarkers of inflammation, two PLT-related indicators are widely studied, namely, the platelet\u0026ndash;lymphocyte ratio (PLR) and systemic immune inflammation index (SII)(Feliciano et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jomrich et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Templeton et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). PLR independently predicts survival of patients with mucinous gastric cancer(Zhu, Gao, Liu, Li, \u0026amp; Xue, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and SII may serve as a convenient marker of survival after radical surgery in GC patients(Q. Wang \u0026amp; Zhu, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSarcopenia is a syndrome that represents degeneration and systemic loss of skeletal muscle mass. According to recent surveys, the prevalence of sarcopenia is relatively high, ranging from 15% at the age of 65 to 50% at the age of 80. Patients with sarcopenia often have a higher incidence of infectious diseases, metabolic syndrome, insulin resistance, and cardiovascular disease. In patients with cancer cachexia, anorexia, malnutrition, and systemic inflammation, the catalytic effect of the metabolic state is enhanced, leading to sarcopenia. Therefore, sarcopenia is considered to be a manifestation of cancer cachexia(Fukushima, Takemura, Suzuki, \u0026amp; Koga, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent studies have shown that sarcopenia has an impact on the prognosis of various cancers. The survival rate of patients with sarcopenia is significantly lower than that of patients with esophageal cancer(Jin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), colorectal cancer(Xie et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), pancreatic cancer(Cho et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), lung cancer(Kawaguchi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) without sarcopenia. In general, sarcopenia plays an important role in the prognosis of cancer patients.\u003c/p\u003e \u003cp\u003eAlthough platelet-related inflammation indicators or sarcopenia have a certain role in predicting the survival of cancer patients, the prediction effect needs to be improved. Hence, we explored the relationship between the combination of the two and the survival of patients with GC.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eThis observational cohort study analyzed the data of patients with GC who visited one of more than 40 clinical centers in China between 2012 and 2018. The exclusion criteria were missing or abnormal preoperative data (data=0) on neutrophil count, PLT, sarcopenia, tumor stage, and lymphocyte counts (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In total, 1131 GC patients were evaluated.\u003c/p\u003e \u003cp\u003e This study was approved by the Institutional Review Board of each hospital (Registration number: ChiCTR1800020329) and was conducted in accordance with the Declaration of Helsinki. All participants signed a written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessments\u003c/h2\u003e \u003cp\u003eSarcopenia was diagnosed using a combination of low appendicular skeletal muscle index (ASMI) and low handgrip strength (HGS), based on the Asian Sarcopenia Working Group updated consensus in 2020. The calculation method of ASM has been reported previously in detail(Wen, Wang, Jiang, \u0026amp; Zhang, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Briefly, we used the following equation: ASM = 0.193 \u0026times; body weight (kg) + 0.107 \u0026times; height (cm) -4.157 \u0026times; sex (male: 1; female: 2) -0.037 \u0026times; age (years)\u0026ndash;2.631. ASMI was defined as ASM (kg)/height\u003csup\u003e2\u003c/sup\u003e (m\u003csup\u003e2\u003c/sup\u003e)(Choi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Low muscle mass was defined as ASMI \u0026lt;7 kg/m\u003csup\u003e2\u003c/sup\u003e for males and \u0026lt;5.4 kg/m\u003csup\u003e2\u003c/sup\u003e for females. HGS was measured in the dominant hand using a Jamar dynamometer. HGS \u0026lt;28 kg (male) or \u0026lt;18 kg (female) was defined as insufficient muscle strength(L. K. Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLaboratory measurements included albumin levels, neutrophil counts, lymphocyte counts, and PLT counts. All blood tests were performed after fasting for at least 9 h before anti-tumor treatment within 48 h of the first hospitalization. Body mass index (BMI) was calculated as BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) = weight (kg) / height^2 (m\u003csup\u003e2\u003c/sup\u003e). PLR was calculated as PLR = PLT/lymphocyte counts, while SII was calculated as SII= neutrophil counts \u0026times; PLT/lymphocyte counts. The SII-Sarcopenia Score (SS) is established based on SII and sarcopenia. Information on smoking status and alcohol and tea consumption was obtained using lifestyle questionnaires. All research results were reviewed and determined by an independent endpoint determination committee, whose members were blinded to the specific tasks of the research team. Pathological staging was according to the TNM staging system of the American Joint Committee on Cancer (8th edition)(Amin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe main endpoint was overall survival (OS), including death from any cause. Evidence of death was obtained from follow-up records. Predictive models were compared with the ROC curves and C-index. The chi-square test was used to compare the differences between the categorical variables in the baseline characteristics of the patients, and the t-test was used to compare continuous variables. The risk factors were modeled as continuous variables, and the optimal cut-off point was calculated using the Wilcoxon rank test. The chi-square test was used to model dichotomous and quartile SII and calculate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) for the OS of patients with GC.\u003c/p\u003e \u003cp\u003eAdjusted variables included age, sex, tumor stage, drinking status, albumin level, neutrophil count, BMI, surgery, chemotherapy, and radiotherapy. The heterogeneity between subgroups was evaluated using Cox regression, and the interaction between SII and the subgroups was checked using the probability ratio. Kaplan-Meier curves of survival according to PLT, PLR, and SII levels were generated. All statistical analyses were performed using R software version 4.0.5 (Lucent Technologies).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e3.1 Patient Characteristics\u003c/h2\u003e\n \u003cp\u003eIn total, 811 and 320 of the patients were male and female, respectively. The average age was 59.45 years. The optimal cut-off values predictive of survival were 270.50 for PLT, 149.57 for PLR, and 712.58 for SII, respectively (Supplemental Figure 1). Accordingly, low and high PLT, PLR, and SII were defined as \u0026le;270.50 and \u0026gt;270.50, \u0026le;149.57 and \u0026gt;149.57, and \u0026le;712.58 and \u0026gt;712.58, respectively. In total, 798 and 333 patients had low and high PLT, 640 and 491 patients had low PLR and high PLR, and 498 and 633 patients had low SII and high SII, respectively. The clinicopathological characteristics of the patients are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline patient characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n=1131)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLT \u0026le;270.50\u003c/p\u003e\n \u003cp\u003e(n=798)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLT \u0026gt;270.50\u003c/p\u003e\n \u003cp\u003e(n=333)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLR \u0026le;149.57\u003c/p\u003e\n \u003cp\u003e(n=640)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLR\u0026gt;149.57\u003c/p\u003e\n \u003cp\u003e(n=491)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSII \u0026le;712.58\u003c/p\u003e\n \u003cp\u003e(n=498)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSII \u0026gt;712.58\u003c/p\u003e\n \u003cp\u003e(n=633)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e811 (71.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e598 (74.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213 (63.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e455 (71.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356 (72.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e370 (74.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e441 (69.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e320 (28.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200 (25.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120 (36.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185 (28.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135 (27.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128 (25.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e192 (30.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e585 (51.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e396 (49.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189 (56.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344 (53.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e241 (49.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252 (50.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e333 (52.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e546 (48.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e402 (50.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144 (43.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e296 (46.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e250 (50.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246 (49.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300 (47.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e881 (77.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e616 (77.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265 (79.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e524 (81.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e357 (72.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e395 (79.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e486 (76.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e250 (22.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182 (22.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68 (20.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116 (18.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134 (27.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103 (20.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147 (23.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTea consumption (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e837 (74.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e589 (73.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e248 (74.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e482 (75.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355 (72.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e373 (74.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e464 (73.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e294 (25.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209 (26.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85 (25.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158 (24.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136 (27.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125 (25.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169 (26.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor stage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143 (12.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112 (14.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31 (9.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85 (13.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58 (11.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71 (14.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72 (11.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260 (22.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e192 (24.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68 (20.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149 (23.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111 (22.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120 (24.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140 (22.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e389 (34.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e274 (34.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115 (34.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236 (36.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153 (31.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174 (34.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215 (33.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e339 (29.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220 (27.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119 (35.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170 (26.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169 (34.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133 (26.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206 (32.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSarcopenia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e902 (79.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e623 (78.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e279 (83.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e516 (80.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e386 (78.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e407 (81.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e495 (78.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229 (20.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175 (21.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54 (16.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124 (19.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105 (21.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91 (18.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138 (21.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgery (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e542 (47.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e382 (47.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160 (48.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e348 (54.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194 (39.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e262 (52.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280 (44.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e589 (52.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e416 (52.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173 (51.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e292 (45.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e297 (60.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236 (47.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e353 (55.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e694 (61.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e493 (61.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201 (60.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356 (55.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e338 (68.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281 (56.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e413 (65.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437 (38.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e305 (38.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (39.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e284 (44.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153 (31.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217 (43.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220 (34.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadiotherapy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1120 (99.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e793 (99.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e327 (98.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e634 (99.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e486 (98.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e496 (99.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e624 (98.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.45 (11.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.02 (11.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.08 (11.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.10 (11.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.91 (11.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.98 (10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.82 (11.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.58 (3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.30 (19.00, 23.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.50 (19.40, 24.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.35 (19.00, 23.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.40 (19.20, 23.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.60 (19.10, 24.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.30 (19.10, 23.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.31 (5.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.55 (34.00, 41.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.40 (33.00, 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.70 (35.00, 42.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.40 (32.05, 39.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.90 (35.20, 42.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.90 (32.60, 39.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.91 (5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.50 (2.32, 5.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.90 (3.30, 6.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.90 (2.07, 3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.79 (4.50, 7.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.42 (2.40, 5.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.37 (2.80, 6.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.55 (1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40 (1.00, 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.50 (1.10, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.65 (1.24, 2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16 (0.85, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85 (1.50, 2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12 (0.80, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236.55 (97.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e196.00 (155.00, 232.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e330.00 (295.00, 372.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202.50 (153.00, 249.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265.00 (211.00, 334.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187.00 (143.00, 238.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259.00 (208.00, 330.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234.53 (371.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.60 (97.09, 191.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226.52 (164.13, 318.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.18 (89.71, 162.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229.11 (172.03, 314.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.47 (80.41, 125.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e221.33 (175.40, 312.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1019.24 (1757.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e491.32 (267.68, 830.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1019.96 (657.64, 2000.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e365.30 (230.19, 512.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1260.29 (889.26, 2065.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e346.38 (213.10, 545.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e953.00 (603.75, 1807.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eNotes: Continuous variables are presented as the mean \u0026plusmn; standard deviation (SD). Meanwhile, BMI, albumin, neutrophil count, lymphocyte count, PLT, PLR, and SII are presented as the median (quartile range). Categorical variables are presented as numbers and percentages. Differences in normally and non-normally distributed baseline characteristics are compared using the chi-square test or t-test and using Wilcoxon rank sum test, respectively.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eAbbreviations: BMI, body mass index; PLT, platelet count; PLR, platelet lymphocyte ratio; SII, systemic immune inflammation index; \u003cem\u003eP\u003c/em\u003e, probability\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e3.2 Predictive capabilities of PLT, PLR, and SII\u003c/h2\u003e\n \u003cp\u003eThe C-index of each prognostic model is shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Among the three, SII had the highest C-index at 0.561, followed by PLR at 0.544. PLT had the lowest C-index at 0.533. Similarly, the ROC curve showed that SII had the best predictive capability, and PLT had the least. SII and the OS of patients with GC tended to have a negative correlation, but it was not significant (per SD increment-HR=1.06; 95% CI: 0.95-1.18) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The high SII group had poorer OS than did the low SII group (adjusted HR=1.45; 95% CI: 1.19-1.75; adjusted \u003cem\u003eP\u003c/em\u003e=0.001). When SII was divided into quartiles (Q1, \u0026le;330.2; Q2, \u0026gt;330.2, \u0026le;611.9; Q3, \u0026gt;611.9, \u0026le;1123.2; and Q4, \u0026gt;1123.2), the Q2 group showed a higher risk of death (adjusted HR = 1.43, 95% CI: 1.08-1.89, adjusted \u003cem\u003eP\u003c/em\u003e = 0.012; Q3 group: adjusted HR = 1.67, 95% CI: 1.27-2.19, adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001; Q4 group: adjusted HR = 1.84; 95% CI: 1.39-2.44; adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001). The curves before and after adjustment showed an inverted U-shaped trend (Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eC-indexes of PLT, PLR and SII\u0026ensp;for overall\u0026ensp;survival\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNotes: PLT, platelet count; PLR, platelet lymphocyte ratio; SII, systemic immune inflammation index; \u003cem\u003eP\u003c/em\u003e, probability\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between SII and OS in patients with gastric cancer according to Cox regression models adjusted for potential confounders\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003epatients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (1.02-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (0.95-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBy cutoff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;712.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;712.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (1.25-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45 (1.19-1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBy quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1 (\u0026le;330.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2 (330.2-611.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (0.95-1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43 (1.08-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3 (611.9-1123.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (1.15-1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67 (1.27-2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4 (\u0026gt;1123.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65 (1.27-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84 (1.39-2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (1.09-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (1.11-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNotes: Data are presented as hazard ratios (95% confidence intervals). The analyses are adjusted for age, sex, tumor stage, drinking status, albumin level, BMI, surgery, chemotherapy, and radiotherapy.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eSII, systemic immune inflammation index; HR, hazard ratio; CI, confidence interval; \u003cem\u003eP\u003c/em\u003e, probability; BMI, body mass index; Q, quarter\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSS prognostic score construction\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSII\u0026le;712.58 and No-sarcopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSII\u0026gt;712.58 and No-sarcopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSII\u0026le;712.58 and Sarcopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSII\u0026gt;712.58 and Sarcopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNotes: SII, systemic immune inflammation index; SS, SII-Sarcopenia.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTo exclude the influence of radiotherapy and chemotherapy on SII, we conducted a sensitivity analysis, and consistent results were obtained (Supplemental Table 1). In addition, we also performed COX regression analysis on patients undergoing surgery, radiotherapy and chemotherapy, and the results showed the same trend (Supplemental Table 2, Supplemental Table 3). High SII is related to poor prognosis. Using SII as a continuous variable for Cox regression analysis, low SII (\u0026le;1800) was significantly associated with poor prognosis (adjusted HR=1.16; 95% CI: 1.06-1.28; adjusted \u003cem\u003eP\u003c/em\u003e=0.002). High SII (\u0026gt;1800) was also negatively correlated with poor prognosis, but there is no significant difference in this negative correlation (adjusted HR = 0.57; 95% CI: 0.29-1.12; adjusted \u003cem\u003eP\u003c/em\u003e =0.102) (Supplemental Table 4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.3 Subgroup Analyses\u003c/h2\u003e\n \u003cp\u003eThe relationship between SII and OS among the different subgroups was evaluated using stratified analysis (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), including age, gender, drinking status, BMI, tumor stage, and sarcopenia. We found significant interactions between SII and tumor stage (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). GC patients with stage III-IV tumors (adjusted HR=1.77, 95% CI: 1.45-2.18, adjusted \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) had significantly worse survival than did patients with stage I-II tumors (adjusted HR = 1.29, 95% CI: 0.75-2.22, adjusted \u003cem\u003eP\u003c/em\u003e=0.353). Combined analysis of SII and tumor stage showed that patients with high SII and high tumor stage had the worst survival (Supplemental Figure 2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.4 Overall Survival\u003c/h2\u003e\n \u003cp\u003eKaplan-Meier curves of OS according to SII, sarcopenia, and SS are shown in Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. High SII and sarcopenia were significantly related to the difference in OS in the univariate Cox proportional hazard regression (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). In addition, in the model comprising SII and sarcopenia showed that patients with high SII and sarcopenia had worse OS. Similarly, patients with a higher SS score also had worse OS (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis multicenter cohort study found that among\u0026nbsp;the PLT-related inflammation indicators PLT, PLR, and SII, SII has the best prognostic indication.\u0026nbsp;Further analysis showed that patients with high SII is associated with poor OS in GC patients (Figure 3), and the HR gradually declined as SII increased to \u0026gt;1800. However, Cox regression analysis of SII showed that as the SII increased, survival worsened. A statistically significant positive correlation was found in the subgroup analysis with SII \u0026lt;1800 as the cut-off. Whereas in subgroup of SII \u0026gt;1800, HR=0.57, the statistically significant results were not shown. This might be due to the small number of patients with SII \u0026gt;1800. In addition, high SII combined with sarcopenia was associated with poor OS in patients\u0026nbsp;with GC. The\u0026nbsp;higher the SS score, the worse was the patient\u0026rsquo;s OS (Figure 5). These results have also been supported by a number of studies.\u003c/p\u003e\n\u003cp\u003eCytokines are key components of the inflammatory process. PLTs play an indispensable role in the development and metastasis of cancer. Once activated, PLTs can bind to tumor cells through P-selectin, which can be found on the surface of PLTs and bind to\u0026nbsp;the CD24 ligand. In addition, PLTs promote tumor growth and metastasis by releasing pro-angiogenesis and growth factors. Therefore, a large number of studies have focused on the prognostic implant of PLT-related inflammation indicators in cancer patients, but the results have been conflicting. Therefore, other indicators that play an important role in predicting cancer survival have been evaluated\u0026nbsp;(Kurtoglu, Kokcu, Celik, Sari, \u0026amp; Tosun, 2015; Peng et al., 2017; Sun, Ju, Han, Sun, \u0026amp; Wang, 2018; J. J. Wang et al., 2019; L. Wang et al., 2017). Notably, PLR and SII both include PLT PLR is considered to be a marker of endogenous residual anti-precancerous inflammation and procoagulant response in malignant tumors\u0026nbsp;(Proctor et al., 2011). It is also considered to be a sensitive marker that could predict certain types of advanced cancer, treatment response,\u0026nbsp;and prognosis\u0026nbsp;(Zhou et al., 2014). In addition, an elevated SII indicates that a highly inflammatory tumor microenvironment. SII can be measured easily at an affordable cost using a reproducible method, making it a promising prognostic indicator in clinical applications(Dong et al., 2020).\u003c/p\u003e\n\u003cp\u003eSarcopenia has also been confirmed to be related to poor prognosis in patients with cancer, thus making it a valuable prognostic indicator. A meta-analysis in 2016 showed that most studies on SMI and prognosis of patients with cancer were published after 2012, and more than half of the studies were published after 2015. This indicates an increasing attention to the prognostic value of SMI in this population\u0026nbsp;(Shachar, Williams, Muss, \u0026amp; Nishijima, 2016). Skeletal muscle loss after surgery has been confirmed to be significantly negatively correlated with adverse postoperative outcomes in patients with non-small cell lung cancer(Takamori et al., 2020). However, sarcopenia was measured using skeletal muscle area, which is different from our research. A retrospective analysis showed that SMI is an independent predictor of OS in patients with breast cancer(Hua et al., 2020). This association between SMI and cancer prognosis could be because certain tumors could induce systemic inflammation, and sarcopenia might be a reflection of more radical tumor metabolism. Cancer\u0026nbsp;patients generally have varying degenerative diseases, causing loss of muscle mass, strength, and dysfunction. The occurrence of these degenerative diseases is influenced by several factors\u0026nbsp;including malnutrition, insufficient physical activity, comorbidities, and other factors directly related to pathophysiology and treatment-related toxicities.\u003c/p\u003e\n\u003cp\u003eThere have been several studies on sarcopenia combined with inflammatory indicators. Chen et al. retrospective analyzed the prognostic significance of preoperative SII in patients with colorectal cancer and concluded that SII has better predictive capability than PLR(J. H. Chen et al., 2017). Consistent findings were found in the current study. A 2021 study on the synergistic effect of sarcopenia and systemic inflammation on the survival of patients with oral cancer found that sarcopenia and systemic inflammation may have a negative synergistic prognostic effect on patients with advanced oral squamous cell carcinoma.\u0026nbsp;(Lee et al., 2021). Despite differences in the assessment method for sarcopenia between this and the current study, it was confirmed that the ASM equation model was in good agreement with the dual X-ray absorbance (DXA) values(Wen et al., 2011). In addition, our study also combined the patients\u0026rsquo; grip strength. A recent study reported that the combination of skeletal sarcopenia and PLR can help identify the survival risk of patients\u0026nbsp;(Yamahara, Mizukoshi, Lee, \u0026amp; Ikegami, 2021). Based on these findings and of another research(Hirahara et al., 2019), we established the SS score according to the combination of SII and sarcopenia.\u003c/p\u003e\n\u003cp\u003eIn the stratified analysis, there was a significant interaction between tumor stage and SII. High SII patients with stage III-IV tumors showed significantly worse survival. This result was consistent with other studies that patients with stage III and IV tumors had worse 3-year OS rates than those with stage I and II tumors (16% and 9% vs 75% and 52%). The higher the tumor stage, the worse the survival rate\u0026nbsp;(L. J. Chen, Chang, \u0026amp; Chang, 2021). Consistent findings were observed in the current study.\u003c/p\u003e\n\u003cp\u003eTo our best knowledge, this is the first cohort study to explore the relationship between SII combined with sarcopenia and OS in patients with GC. The advantage of this study is that we first screened out the index SII that has the best predictive indication among platelet-related inflammation indexes, and combined with sarcopenia on this basis, we obtained a better predictive model SS. However, the following limitations should be also considered. First, the adopted anthropometric equations that had been validated in Chinese individuals to estimate muscle mass, rather than bioimpedance analysis (BIA) or DXA recommended by the European Working Group on Sarcopenia in Older People(Cruz-Jentoft et al., 2010) and Asian Working Group for Sarcopenia(L. K. Chen et al., 2014). However, DXA is expensive and the patients might have to expose to X-rays. There are very few BIAs in hospitals in mainland China, and the cut-off point for defining low muscle mass on BIA among elderly Chinese individuals has not been established(Zeng et al., 2015). Second, only the PLT was recorded, and thus, PLT-related inflammation indexes did not included the mean PLT volume and PLT distribution width. Third, there was no record of diet and socioeconomic status, which might have affected muscle loss and cancer death. Fourth, this study only included the Chinese population and did not represent other ethnic groups. Future research needs to investigate whether reducing systemic inflammation and increasing muscle mass can prolong patient survival and the underlying mechanisms on their influence in patients with GC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSystemic inflammation and sarcopenia are relaed to the prognostic significance of GC patients. Among the PLT-related inflammation indicators PLT, PLR, and SII, SII has the best prognostic accuracy. In this study, SII independently predicted survival, thus making it a possible reliable indicator of GC prognosis. In addition, patients with both inflammation and sarcopenia have significantly worse survival than do patients without these impacts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGC: gastric cancer; PLT: platelet count; PLR: platelet lymphocyte ratio; SII: systemic immune inflammation index; OS: overall survival; HR: hazard ratio; 95% CIs: 95% confidence intervals; \u003cem\u003eP\u003c/em\u003e: probability; AUC: area under the curve; NLR: neutrophil-to-lymphocyte ratio; ASMI: appendicular skeletal muscle index; HGS: low handgrip strength; ASM: appendix skeletal muscle mass; BMI: body mass index; ROC: receiver operating characteristic; Q: quarter; SD: standard deviation; SMI: skeletal mass index; BIA: Bioimpedance analysis; DXA: dual X-ray absorptance\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe thank all the patients who participated in this study for their active cooperation and valuable contributions. We would also like to express our gratitude to the 40 clinical centers for providing the data used in this study.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program [grant number 2017YFC1309200].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. R.G.T., L.Y.Y., S.H.P., and W.Z.P. designed research; L.Y. Y and G.Y. Z and L.Q.Q. conducted research; Z.Q., Z.X. and S.M.M. analyzed data; W.M., Y.Q.H. and S.H.P. provided essential materials; L.Y.Y., T.M., Z.X.W., L.X.R., Z.K.P. and Y.M.wrote the paper; H.C.L., L.T., X.H.L., L.X.Y., L.S.Q. verified the results; L.Y. Y. had primary responsibility for final content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study followed the Helsinki declaration. All participants signed an informed consent form and this study was approved by the Institutional Review Board of each hospital (Registration number: ChiCTR1800020329).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent to participate:\u003c/h2\u003e\n\u003cp\u003eBecause of the retrospective nature of this study, consent to participate for inclusion was waived.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent to publish:\u003c/h2\u003e\n\u003cp\u003eBecause of the retrospective nature of this study, consent to publish was waived.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgudo A, Cayssials V, Bonet C, Tj\u0026oslash;nneland A, Overvad K, Boutron-Ruault MC, Jakszyn P (2018) Inflammatory potential of the diet and risk of gastric cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. 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Biomed Res Int, 2021, 9678363. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2021/9678363\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"gastric cancer, systemic immune inflammation index, sarcopenia, survival","lastPublishedDoi":"10.21203/rs.3.rs-1079469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1079469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThe levels of platelet-related inflammation indicators and sarcopenia have been reported to affect the survival of patients with cancer.\u003cstrong\u003e \u003c/strong\u003eTo evaluate the prognostic influence of platelet count (PLT), platelet–lymphocyte ratio (PLR), and systemic immune inflammation index (SII), and SII combined with sarcopenia on the survival of patients with gastric cancer (GC).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 1131 patients with GC (811 men and 320 women, average age: 59.45 years) were evaluated. Receiver operating characteristic curves were used to determine the best cut-off values of PLT, PLR, and SII, and univariate and multivariate Cox risk regression models were used to evaluate whether SII is an independent predictor of overall survival (OS). The prognostic SS (SII-sarcopenia) was established based on SII and sarcopenia.\u003cstrong\u003e \u003c/strong\u003eFinally,\u003cstrong\u003e \u003c/strong\u003ea comprehensive analysis of the prognostic SS was performed. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e SII had the strongest prognostic effect. The SII and OS of patients with GC were in an inverted U-shape (adjusted HR = 1.06; 95% CI: 0.95-1.18; adjusted \u003cem\u003eP\u003c/em\u003e = 0.271). In patients with SII \u0026gt;1800, SII was negatively correlated with OS (adjusted HR = 0.57; 95% CI: 0.29-1.12; adjusted \u003cem\u003eP\u003c/em\u003e = 0.102), however, there is no statistical difference. Interestingly, a high SS was associated with a poorer prognosis. The higher the SS score, the worse the OS (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e SII is an independent prognostic indicator of GC, and high SII is related to poor prognosis. A Higher SS score had worse survival. Thus, the prognostic SS is a reliable predictor of OS in patients with GC. \u003c/p\u003e","manuscriptTitle":"Systemic Inflammation with Sarcopenia Predict Survival in Patients with Gastric","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-23 17:53:57","doi":"10.21203/rs.3.rs-1079469/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-11-21T16:46:01+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-11-21T10:47:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Journal of Cancer Research and Clinical Oncology","date":"2021-11-15T16:04:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-11-15T07:45:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cancer Research and Clinical Oncology","date":"2021-11-14T11:07:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"58b89bcc-9369-4122-ab49-deb503b36a00","owner":[],"postedDate":"November 23rd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":8686310,"name":"Cancer Biology"},{"id":8686311,"name":"Oncology"}],"tags":[],"updatedAt":"2022-01-12T14:39:27+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-23 17:53:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1079469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1079469","identity":"rs-1079469","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}