Pan-Immune-Inflammation Value is a novel prognostic biomarker in advanced Gastric Cancer Patients

Pan-Immune-Inflammation Value is a novel prognostic biomarker in advanced Gastric Cancer Patients | 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 Pan-Immune-Inflammation Value is a novel prognostic biomarker in advanced Gastric Cancer Patients Mingyu Wang, Xinyi Wang, Yingjia Hu, Jian Li, Xingjian Niu, Fujing Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5235727/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Advanced gastric cancer (GC) is a common malignancy with a poor prognosis, which remains the leading cause of cancer death worldwide. Identifying novel biomarkers is needed to predict survival for this highly progressive cancer. Many studies have confirmed that pan-immune-inflammation value (PIV) is related to the prognosis of various tumors in recent years. However, the prognostic value of PIV remains unclear in gastric cancer. The purpose of this study was to discuss the prognostic role of PIV in stage III and IV gastric cancer. Methods: The clinical data of 646 patients with gastric cancer after gastrectomy were retrospectively analyzed. The calculation method of PIV is PIV=neutrophil count (10 9 /L) × platelet count (10 9 /L) × monocyte count (10 9 /L)/lymphocyte count (10 9 /L). Patients were divided into high and low PIV group by cut-off value based on receiver operating characteristic (ROC) curve. Effects of PIV and other IIB on survival were analyzed based on the ROC curves. Kaplan-Meier method was plotted to indicate the value of immune-inflammatory biomarkers (IIBs) in predicting the overall survival of gastric cancer. The overall survival (OS) in advanced gastric cancer patients were analyzed and univariate and multivariate statistics were used to evaluate the prognostic value. Results: PIV had the most significantly predictive value in advanced GC patients compared with other peripheral blood parameters and IIBs. Cases in the high PIV group were more likely to have low serum albumin (Alb) level, larger tumor size compared with those in the low PIV group. PIV was identified an independent prognostic indicator for survival outcome in advanced GC patients in univariate and multivariate models. Conclusions: This study confirmed that PIV can reflect the prognosis of advanced GC patients who have undergone gastrectomy, suggesting a potential application of PIV in GC treatment outcomes. Compared to other IIb indicators, PIV is more sensitive as a prognostic indicator. PIV will also provide some insight into the underlying mechanisms of immune and inflammatory effects in GC development and progression. Trial registration: This study was a retrospective analysis, which has no intervention on human participants. gastric cancer pan-immune-inflammation value prognosis overall survival prognosis Figures Figure 1 Figure 2 Figure 3 Background Gastric cancer (GC) is one of the most leading cause of cancer-related mortality worldwide and ranking as the third most common cancer in China[1]. Despite advances in diagnosis and treatment, GC remains an aggressive malignancy with poor prognosis[2]. In accordance with the guidelines of international oncology, perioperative chemoradiotherapy regimen is very important in patients with GC, including adjuvant and neoadjuvant treatment[3]. However, due to the clinical heterogeneous of GC, the response of patients who are received the standard therapy is variable. Approximately 50% of patients may not benefit from the treatment and eventually develop a risk of recurrence[4,5]. Therefore, it’s urgently needed to determine appropriate biomarkers for a better prognostic evaluation and prediction for treatment outcomes. Inflammation and immunity are considered as cancer hallmarks and proved to play a crucial role in tumor development[6]. Recently, several immune-inflammatory markers have been evaluated to investigate their prognostic role in different cancers[7–9]. These markers are generally shown as chronic systemic inflammatory markers, which are calculated by some parameters obtained from circulating blood elements from peripheral blood specimens, such as lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), and etc[10]. However, since more evidence have elucidated the complex interplay among immunity, inflammation and cancer, using a composite biomarker that includes all the immune-inflammatory elements has been found to show a more obvious correlation with clinical outcomes in several cancers[11]. Of note, the pan-immune-inflammation value (PIV) combines all the inflammatory populations, including the platelets, monocytes, neutrophils, and lymphocytes, which is considered as a newer biomarker to obtain a more robust prognostic power[12]. However, the prognostic role of PIV in patients Who Receive Curative Gastrectomy during Perioperative Period of GC is rarely reported. Here we aimed to retrospectively review the characteristics of GC patients and investigate the potential role of PIV as a predictive biomarker in these patients who underwent curative gastrectomy during Perioperative Period. Methods Patient selection In current retrospective study, 646 patients who underwent curative gastrectomy were included at the First and Second Affiliated Hospital of Harbin Medical University between December 2018 and October 2023. The inclusion criteria were as follows: (a) pathologically confirmed primary gastric adenocarcinoma; (b) stage III-IV according to the American Joint Committee on Cancer (AJCC) 8th edition [13]; (c) patients underwent D2 gastrectomy and. The exclusion criteria were as follows: (a) patients who suffered with hematological disorders; (b) patients who received immunomodulatory treatment; (c) patients treated with neoadjuvant chemotherapy or radiotherapy before operation. A total of 646 eligible patients were included in this study. All preoperative laboratory data were retrieved. This study was approved by the Ethics Committee of Second Affiliated Hospital of Harbin Medical University, and all patients had written informed consent. Data collection All medical and surgical records were retrospectively reviewed and clinicopathological data of participants were collected from the patients’ records including gender age, tumor size, T stage, N stage, M stage, TNM stage, tumor location, surgical approach, CEA, CA19-9, CA125, AFP, Alb. Divide gastric cancer into gastric body cancer, gastric fundus cancer, and gastric antral cancer by location. The number of blood cells in the blood routine including lymphocytes, neutrophils, monocytes, and platelets. The patient’s laboratory data were collected one week before surgery (at first diagnosis, before any treatment). For each patient, the pretreatment PIV was calculated using Fucà’s original formula[14]: PIV=neutrophil count (10 9 /L) × platelet count (10 9 /L) × monocyte count (10 9 /L)/lymphocyte count (10 9 /L). NLR=neutrophil count (10 9 /L)/lymphocyte count (10 9 /L); SII= [neutrophil count (10 9 /L) ×platelet count (10 9 /L)]/lymphocyte count (10 9 /L); PLR= platelet count (10 9 /L)/ lymphocyte count (10 9 /L); MLR= monocyte count (10 9 /L)/lymphocyte count (10 9 /L). Tumor staging is based on the American Joint Committee on Cancer (AJCC) 8th edition gastric cancer classification. Statistical Analysis ROC curve analysis was performed to determine the cut-off values for PIV, NLR, MLR, and PLR. Correlations between high or low PIV groups and clinicopathological features were analyzed using the c 2 or the Fisher's exact test when appropriate. Kaplan-Meier curves were used to visualize survival probabilities over time, the log-rank test was employed to compare survival curves between groups based on the indicator. The Cox regression analyses were conducted to evaluate the influence of clinic-pathological parameters on clinical survival outcomes. All statistical analyses were performed using SPSS 20.0 statistics software (IBM, USA). Statistical significance was defined as p < 0.05. Results Patient Characteristics A total of 646 patients with advanced stage III-IV gastric cancer who received Curative Gastrectomy during Perioperative Period were enrolled in this study, and the clinicopathological characteristics of the patients are shown in Table 1. Laparotomy was performed in 295 (45.7%) patients while the others received laparoscopic operation. Briefly, the patients age ranged from 23-89 years. Among all patients, there were 170 (26.3 %) females and 476 (73.7 %) males. 366(56.7%) patients with tumor size larger than 5cm. Of all 646 patients stage III 461(71.4%) and stage IV 185(28.6%), 112(17.3) patients without lymph node metastasis, 48(7.4%) patients with 1-2 lymph node metastases, 133(20.6%) patients with 3-6 lymph node metastases and 353(54.6%) patients with more than 6. 183(28.3%) patients had distant metastases.（Table 1） Table 1. Clinicopathological characteristics of advanced GC patients Characteristics % n=646 Gender Male 476 73.7 Female 170 26.3 Age, y 23-60 248 38.4 61-89 398 61.6 Size ≤5 280 43.3 >5 366 56.7 TNM 3 461 71.4 4 N 0 1 2 3 185 112 48 133 353 28.6 17.3 7.4 20.6 54.6 M 0 463 71.7 1 183 28.3 Location body of stomach 184 28.5 antrum of stomach fundus of stomach 402 60 62.2 9.3 Surgical Approach laparotomy 295 45.7 laparoscopic 351 54.3 Comparison of prognosis between PIV and its components and other IIBs Of all components including LYM(AUC=0.423) MON(AUC=0.632) PLT(AUC=0.636) and NEU(AUC=0.669), PIV(AUC=0.725) shows a better prognostic value (Fig 1a). When compared with other IIBs (SII NLR PLR and MLR), PIV has the largest AUC (0.725), which shows advantage in predictive value over SII(AUC=0.714) NLR(AUC=0.672) PLR(AUC=0.664) and MLR(AUC=0.670) (Fig 1b). Correlation between PIV and clinicopathological characteristics of advanced gastric cancer patients There were statistically significant differences in tumor size. While analysis of our data shows that gender, age, location and TNM stage correlate little with PIV ( p ＞0.05, Table 2). Patients in the high PIV group were more likely to have larger tumor sizes than those in the low PIV group. (Table 2) Table 2. Correlation between PIV and clinicopathological characteristics of advanced GC patients Characteristics Low PIV (n = 303) High PIV (n = 343) P -value Gender Male 229 247 0.304 Female 74 96 Age, y ≤ 60 117 131 0.912 > 60 186 212 Size ≤5 105 175 ＜0.005 ＞5 198 168 Location body of stomach 85 99 0.912 antrum of stomach fundus of stomach 191 27 211 33 TNM 3 212 249 0.461 4 91 94 M 0 213 250 0.466 1 90 93 N 0 53 59 0.158 1 23 25 2 66 67 3 X 123 38 166 26 Pearson’s correlation coefficient showed that PIV was positively correlated with CEA (r = 0.468, p < 0.05), and CA19-9 (r = 0.137, p < 0.05), negatively correlated with Alb (r = −0.126, p 0.05) (Table 3). In addition, cases in the high PIV group were more likely to have low Alb level, larger tumor size compared with those in the low PIV group. Table 3. Pearson’s correlation coefficient Variables median(range) r p-value CEA 2.39(0.2-1000) 0.468 ＜0.05 CA19-9 CA125 AFP Alb 12.46(0.6-1000) 15.31(1.87-1713) 2.32(0.5-1000) 36.5(14.4-51.6) 0.137 0.120 -0.140 -0.126 ＜0.05 0.188 0.775 ＜0.05 Kaplan-Meier curves of survival and subgroup analyses The survival curves of GC are shown in Fig.2. The overall survival curve (38.9% vs. 78.4%, p < 0.01) was significantly worse in patients with high PIV than those in the low group. To better understand the effect of PIV prognosis between TNM3 and TNM4 stage, we performed subgroup analyses and confirmed the statistically significant differences between stage III(Fig.3a) and IV(Fig.3b) (TNM III: 48.6% vs. 92.4%, p ＜0.01; TNM IV: 32.8% vs. 78.4%, p ＜0.01). As a result of univariate and multivariate analyses, PIV all appeared as an independent predictor in OS outcomes (Table 4). Besides, in multivariate analysis comprising the variables, tumor size and TNM stages caused a statistically significant difference in OS survival outcomes (Table 4). These results indicated that PIV was an independent value to predict survival in advanced GC patients. Table 4. Univariate and Multivariate analysis of prognostic factors for overall survival in advanced GC patients Covariate Univariable Multivariable HR (95%CI) P -value HR (95%CI) P -value Age, y Gender Tumor size TNM stage Location Surgical Approach CEA CA125 CA19-9 AFP Alb PIV MLR NLR PLR 1.015 (0.791-1.302) 0.950 (0.721-1.252) 2.629 (1.990-3.472) 4.454 (3.482-5.697) 0.962 (0.783-1.182) 0.767 (0.600-0.979) 1.002 (1.001-1.003) 1.000 （0.999-1.001） 1.001 （1.001-1.002） 1.002 （1.000-1.003） 0.958 （0.937-0.978） 1.000 (1.000-1.001) 2.522 (2.147-2.962) 1.079 (1.065-1.093) 1.002 (1.001-1.003) 0.907 0.714 ＜ 0.001 ＜ 0.001 0.712 0.330 ＜ 0.001 0.971 ＜ 0.001 0.035 ＜ 0.001 ＜ 0.001 0.001 0.001 0.001 0.985 (0.502-1.933) 0.851 (0.402-1.801) 3.740 (1.725-8.108) 5.196 (2.538-10.638) 1.152 (0.703-1.888) 1.602 (0.769-3.338) 0.994 (0.989-0.998) 0.996 (0.992-1.001) 1.001 (1.000-1.003) 1.003 (1.000-1.005) 0.956 (0.894-1.022) 1.001 (1.000-1.001) 0.943 (0.348-2.557) 1.019 (0.925-1.122) 1.000 (0.997-1.003) 0.985 0.673 0.001 0.001 0.573 0.208 0.010 0.094 0.097 0.017 0.188 0.005 0.908 0.705 0.861 Discussion In the current retrospective study, we demonstrated a inflammation-based biomarker PIV, as an independent predictor for these GC patients for the first time. PIV was associated with several clinicopathological characteristics of GC and had an extensive influence on OS outcomes, suggesting that PIV might be useful for more accurate stratification and individualized treatment for GC patients receiving Curative Gastrectomy during Perioperative Period. When compared with other traditional IIB indicators, PIV has a more sensitive prognostic ability. Since GC is a heterogeneous cancer with poor prognosis, it’s of great value to investigate a practical biomarker to find out the patients who can benefit from perioperative regimen for GC after curative gastrectomy. Inflammation plays an important role in various stages of tumor development[15]. In today's continuously advancing medical technology, accurate prediction of the condition of cancer patients has important clinical significance[16]. Compared with traditional IIB indicators, PIV, as a newly proposed inflammation indicator in 2020, has a guiding role in the prognosis of patients with metastatic colorectal cancer[17]. In subsequent studies, PIV has been confirmed to reflect the prognosis of various malignant tumors, including renal cell carcinoma, lung cancer, renal cell cancer, and prostate cancer[7–9,18,19]. The role of tumor microenvironment (TME) on tumor growth and progression have been well known in the last two decades, due to the complex interplay between large numbers of inflammatory or fibroblastic cells and tumor cells[20]. Previous studies have demonstrated that some immunological elements facilitate TME by producing pro-tumor cytokines and forming a dense tumor stroma[21]. In general, peripheral blood lymphocytes reflect the immune activation status and are responsible for antitumor-specific immune response[22]. However, circulating monocytes are sources of many immunosuppressive cells, including tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs), which were associated with immunosuppression and tumor progression[23]. Additionally, neutrophils also make great contribution to tumor cell proliferation and migration by secreting cytokines, chemokines, and growth factors and inhibiting CD8 + T lymphocyte-mediated antitumor activity[24]. Finally, platelets are other cells promoting cancer-favored microenvironment and correlating with tumor metastasis and adverse outcomes[25]. Therefore, systemic and local inflammation of solid cancers has shown a landscape and several systemic inflammatory response indicators have been researched for their prognostic value in cancer patients, especially in GC patients, who underwent a poor clinical outcomes even if receiving gastrectomy and perioperative chemoradiotherapy regimen[26,27]. In this regard, several blood-derived immune-inflammatory indexes which are easy-to obtain have been explored and shown significant prognostic usefulness, including LMR, NLR, MLR, and PLR indexes[28]. When compared with these common IIBs, our results suggested that PIV has the largest AUC, which was the best index for Inflammatory prognosis analysis. Conclusions In this research, we found that high PIV was connected with low Alb and large tumor size. We suspect that high PIV index indicate that patients are in a state of pro-tumor inflammation, in which the tumor volume grows and the body's consumption increases resulting in a decrease in serum protein. In the process of tumorigenesis, the balance of pro-tumor and anti-tumor effects always affects the occurrence and development of tumor, PIV can also be used as a tumor evaluation factor. By analyzing the data of stage III and IV gastric cancer patients, we found that the OS in the low PIV group was significantly higher than that in the high PIV group, which indicate an outstanding ability to judge patient outcomes. Limitations of this study are as follows. As a single-center retrospective study, results may be biased, lack external validation, and may not be representative of the broader population. Although we take the patient's blood indicators before surgery, PIV alone is an indicator of inflammation, which is influenced by the patient's overall body state. This study demonstrated the effect of PIV on the prognosis of gastric cancer, but external experimental confirmation was lacking. Considering these problems, PIV as a prognostic indicator of gastric cancer still needs to be validated by more experiments. Abbreviations AUC Area Under Curves GC Gastric Cancer MLR Monocyte to Lymphocyte Ratio IIB Immune-inflammatory biomarker NLR Neutrophil to Lymphocyte Ratio OS Overall Survival PIV Pan Immune-inflammation Value PLR Platelet to Lymphocyte Ratio TME Tumor Microenvironment MDSC LYM MON PLT NEU TAM Myeloid-derived suppressor cells Lymphocyte Monocyte Platelet Neutrophil Tumor-associated macrophage Declarations Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki. The studies involving human participants were reviewed and approved by YJSKY2023-157. This study was approved by the Ethics Committee of Second Affiliated Hospital of Harbin Medical University, and all patients had written informed consent. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no conflict of interest. Funding This work was supported by the Haiyan Foundation of Harbin Medical University Cancer Hospital under Grant No.JJZD2024-18, Harbin Medical University Cancer Hospital Top Young Talent Project under Grant No.BJQN2021-03 Authors’ contributions WM: designed and analyzed data, and contributed writing; NX: designed and analyzed data, and contributed writing; WX, HY, LJ: analyzed data; WF: designed and supervised research, analyzed data, and wrote the paper. Acknowledgements Not applicable References Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396:635–48. Matsuoka T, Yashiro M. Biomarkers of gastric cancer: Current topics and future perspective. 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Patel TH, Cecchini M. Targeted Therapies in Advanced Gastric Cancer. Curr Treat Options Oncol [Internet]. 2020 [cited 2024 Sep 30];21. Available from: https://pubmed.ncbi.nlm.nih.gov/32725377/ Hsueh C, Tao L, Zhang M, Cao W, Gong H, Zhou J, et al. The prognostic value of preoperative neutrophils, platelets, lymphocytes, monocytes and calculated ratios in patients with laryngeal squamous cell cancer. Oncotarget [Internet]. 2017 [cited 2024 Sep 30];8:60514–27. Available from: https://pubmed.ncbi.nlm.nih.gov/28947990/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5235727","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":365405168,"identity":"3c3807be-a7dc-4425-a84f-e953d38c6e23","order_by":0,"name":"Mingyu Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingyu","middleName":"","lastName":"Wang","suffix":""},{"id":365405169,"identity":"38bcea5a-b395-4ebb-b437-fefa14faaef1","order_by":1,"name":"Xinyi Wang","email":"","orcid":"","institution":"Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Wang","suffix":""},{"id":365405170,"identity":"33558891-3055-4a76-ad4b-bc57cf23f5bc","order_by":2,"name":"Yingjia Hu","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital, Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yingjia","middleName":"","lastName":"Hu","suffix":""},{"id":365405171,"identity":"c9b94454-f202-434f-a773-21cbd59def3c","order_by":3,"name":"Jian Li","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital, Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Li","suffix":""},{"id":365405172,"identity":"920eb379-b528-4fd5-b62d-2e199bf30a18","order_by":4,"name":"Xingjian Niu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACZiBOAFJsDAyMjxkYDoAFJYjVwmxMnBYkwCZNlBZzdh4ziYc7atn52M8eqy7ccyfa4ADzwds8DHZ5uLRYNvMYGySeOc7MxpOXdnvGs2e5Gw6wJVvzMCQX49JicJjH8EFi2zGgX3LMbvMcOAzUwmMmzcNwILEBtxaDA2At/G/MiiFa+L8R0gKypYaZTSLHjBlqCxsBLWzFBoltB4Ba3hhL8xx4ljvzMJux5RyDZNxazh/eJvmzrS5Zvj/H8DPPgTu5fcebH954U2GHUwsUHE5GsJnBRuFXDwR1dgSVjIJRMApGwcgFAKKXVYuUkl94AAAAAElFTkSuQmCC","orcid":"","institution":"Harbin Medical University Cancer Hospital, Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xingjian","middleName":"","lastName":"Niu","suffix":""},{"id":365405173,"identity":"a0d7e8f1-8c31-4545-b6d0-d24a691aea91","order_by":5,"name":"Fujing Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fujing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-10-10 02:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5235727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5235727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67203221,"identity":"bd1d9d14-6c0b-47bf-a1ce-d2bde763c7ec","added_by":"auto","created_at":"2024-10-22 10:24:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":307445,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of PIV, LYM, MLR, PLR and NLR values in GC patients(a). ROC curve analysis of PIV, MLR, PLR, SII and NLR values in GC patients(b).\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5235727/v1/172cd46ba9b80e6258aff6a9.png"},{"id":67203220,"identity":"f3c6567e-8993-4918-9f79-322f13193cfb","added_by":"auto","created_at":"2024-10-22 10:24:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107163,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for OS in GC patients in high and low PIV groups.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5235727/v1/9159f2d6da11ab583622243e.png"},{"id":67202462,"identity":"f5f224eb-de60-4af8-9b4d-0dcf531d3d4d","added_by":"auto","created_at":"2024-10-22 10:16:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155204,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for OS in GC patients with stage III and stage IV(a and b) according to PIV.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5235727/v1/b1bbb494aa31e1cbec63a5ae.png"},{"id":89371147,"identity":"9ff77994-3d33-4467-9f3f-d365ab1055c0","added_by":"auto","created_at":"2025-08-19 10:08:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1248764,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5235727/v1/642f0f7a-6da1-4693-9dd8-2a0e51157098.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pan-Immune-Inflammation Value is a novel prognostic biomarker in advanced Gastric Cancer Patients","fulltext":[{"header":"Background","content":"\u003cp\u003eGastric cancer (GC) is one of the most leading cause of cancer-related mortality worldwide and ranking as the third most common cancer in China[1]. Despite advances in diagnosis and treatment, GC remains an aggressive malignancy with poor prognosis[2]. In accordance with the guidelines of international oncology, perioperative chemoradiotherapy regimen is very important in patients with GC, including adjuvant and neoadjuvant treatment[3]. However, due to the clinical heterogeneous of GC, the response of patients who are received the standard therapy is variable. Approximately 50% of patients may not benefit from the treatment and eventually develop a risk of recurrence[4,5]. Therefore, it\u0026rsquo;s urgently needed to determine appropriate biomarkers for a better prognostic evaluation and prediction for treatment outcomes.\u003c/p\u003e\n\u003cp\u003eInflammation and immunity are considered as cancer hallmarks and proved to play a crucial role in tumor development[6]. Recently, several immune-inflammatory markers have been evaluated to investigate their prognostic role in different cancers[7\u0026ndash;9]. These markers are generally shown as chronic systemic inflammatory markers, which are calculated by some parameters obtained from circulating blood elements from peripheral blood specimens, such as lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), and etc[10]. However, since more evidence have elucidated the complex interplay among immunity, inflammation and cancer, using a composite biomarker that includes all the immune-inflammatory elements has been found to show a more obvious correlation with clinical outcomes in several cancers[11]. Of note, the pan-immune-inflammation value (PIV) combines all the inflammatory populations, including the platelets, monocytes, neutrophils, and lymphocytes, which is considered as a newer biomarker to obtain a more robust prognostic power[12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, the prognostic role of PIV in patients Who Receive Curative Gastrectomy during Perioperative Period of GC is rarely reported. Here we aimed to retrospectively review the characteristics of GC patients and investigate the potential role of PIV as a predictive biomarker in these patients who underwent curative gastrectomy during Perioperative Period.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn current retrospective study, 646 patients who underwent curative gastrectomy were included at the First and Second Affiliated Hospital of Harbin Medical University between December 2018 and October 2023. The inclusion criteria were as follows: (a) \u0026nbsp;pathologically confirmed primary gastric adenocarcinoma; (b) stage III-IV according to the American Joint Committee on Cancer (AJCC) 8th edition\u0026nbsp;[13]; (c) patients underwent D2 gastrectomy and. The exclusion criteria were as follows: (a) patients who suffered with hematological disorders; (b) patients who received immunomodulatory treatment; (c) patients treated with neoadjuvant chemotherapy or radiotherapy before operation. A total of 646 eligible patients were included in this study. All preoperative laboratory data were retrieved. This study was approved by the Ethics Committee of Second Affiliated Hospital of Harbin Medical University, and all patients had written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll medical and surgical records were retrospectively reviewed and clinicopathological data of participants were collected from the patients\u0026rsquo; records including gender age, tumor size, T stage, N stage, M stage, TNM stage, tumor location, surgical approach, CEA, CA19-9, CA125, AFP, Alb. Divide gastric cancer into gastric body cancer, gastric fundus cancer, and gastric antral cancer by location. The number of blood cells in the blood routine including lymphocytes, neutrophils, monocytes, and platelets. The patient\u0026rsquo;s laboratory data were collected one week before surgery (at first diagnosis, before any treatment). For each patient, the pretreatment PIV was calculated using Fuc\u0026agrave;\u0026rsquo;s original formula[14]: PIV=neutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; platelet count (10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; monocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)/lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L). NLR=neutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)/lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L); SII= [neutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times;platelet count (10\u003csup\u003e9\u003c/sup\u003e/L)]/lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L); PLR= platelet count (10\u003csup\u003e9\u003c/sup\u003e/L)/ lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L); MLR= monocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)/lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L). \u0026nbsp;Tumor staging is based on the American Joint Committee on Cancer (AJCC) 8th edition gastric cancer classification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis was performed to determine the cut-off values for PIV, NLR, MLR, and PLR. Correlations between high or low PIV groups and clinicopathological features were analyzed using the\u0026nbsp;c\u003csup\u003e2\u003c/sup\u003e or the Fisher\u0026apos;s exact test when appropriate. Kaplan-Meier curves were used to visualize survival probabilities over time, the log-rank test was employed to compare survival curves between groups based on the indicator. The Cox regression analyses were conducted to evaluate the influence of clinic-pathological parameters on clinical survival outcomes. All statistical analyses were performed using SPSS 20.0 statistics software (IBM, USA). Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 646 patients with advanced stage III-IV gastric cancer who received Curative Gastrectomy during Perioperative Period were enrolled in this study, and the clinicopathological characteristics of the patients are shown in Table 1. Laparotomy was performed in 295 (45.7%) patients while the others received laparoscopic operation. Briefly, the patients age ranged from 23-89 years. Among all patients, there were 170 (26.3 %) females and 476 (73.7 %) males. 366(56.7%) patients with tumor size larger than 5cm. Of all 646 patients stage III 461(71.4%) and stage IV 185(28.6%), 112(17.3) patients without lymph node metastasis, 48(7.4%) patients with 1-2 lymph node metastases, 133(20.6%) patients with 3-6 lymph node metastases and 353(54.6%) patients with more than 6. 183(28.3%) patients had distant metastases.（Table 1）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eClinicopathological characteristics of advanced GC patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003en=646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61-89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026le;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026gt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebody of stomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eantrum of stomach\u003c/p\u003e\n \u003cp\u003efundus of stomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.2\u003c/p\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurgical Approach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elaparotomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elaparoscopic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of prognosis between PIV and its components and other IIBs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf all components including LYM(AUC=0.423) MON(AUC=0.632) PLT(AUC=0.636) and NEU(AUC=0.669), PIV(AUC=0.725) shows a better prognostic value (Fig 1a).\u003c/p\u003e\n\u003cp\u003eWhen compared with other IIBs (SII NLR PLR and MLR), PIV has the largest AUC (0.725), which shows advantage in predictive value over SII(AUC=0.714) NLR(AUC=0.672) PLR(AUC=0.664) and MLR(AUC=0.670) (Fig 1b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between PIV and clinicopathological characteristics of advanced gastric cancer patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were statistically significant differences in tumor size. While analysis of our data shows that \u0026nbsp;gender, age, location and TNM stage correlate little with PIV (\u003cem\u003ep\u003c/em\u003e＞0.05, Table 2). Patients in the high PIV group were more likely to have larger tumor sizes than those in the low PIV group. (Table 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eCorrelation between PIV and clinicopathological characteristics of advanced GC patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"478\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow PIV\u003c/p\u003e\n \u003cp\u003e(n = 303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh PIV\u003c/p\u003e\n \u003cp\u003e(n = 343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026le; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026le;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＞5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebody of stomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eantrum of stomach\u003c/p\u003e\n \u003cp\u003efundus of stomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePearson\u0026rsquo;s correlation coefficient showed that PIV was positively correlated with CEA (r = 0.468, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and CA19-9 (r = 0.137, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), negatively correlated with Alb (r = \u0026minus;0.126, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). However, PIV was not correlated with CA125 or AFP (r = 0.120, and \u0026minus;0.140; \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) (Table 3). In addition, cases in the high PIV group were more likely to have low Alb level, larger tumor size compared with those in the low PIV group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003ePearson\u0026rsquo;s correlation coefficient\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emedian(range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.39(0.2-1000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCA19-9\u003c/p\u003e\n \u003cp\u003eCA125\u003c/p\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003cp\u003eAlb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.46(0.6-1000)\u003c/p\u003e\n \u003cp\u003e15.31(1.87-1713)\u003c/p\u003e\n \u003cp\u003e2.32(0.5-1000)\u003c/p\u003e\n \u003cp\u003e36.5(14.4-51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves of survival and subgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survival curves of GC are shown in Fig.2. The overall survival curve (38.9% vs. 78.4%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) was significantly worse in patients with high PIV than those in the low group. To better understand the effect of PIV prognosis between TNM3 and TNM4 stage, we performed subgroup analyses and confirmed the statistically significant differences between stage III(Fig.3a) and IV(Fig.3b) (TNM III: 48.6% vs. 92.4%, \u003cem\u003ep\u003c/em\u003e＜0.01; TNM IV: 32.8% vs. 78.4%, \u003cem\u003ep\u003c/em\u003e＜0.01).\u003c/p\u003e\n\u003cp\u003eAs a result of univariate and multivariate analyses, PIV all appeared as an independent predictor in OS outcomes (Table 4). Besides, in multivariate analysis comprising the variables, tumor size and TNM stages caused a statistically significant difference in OS survival outcomes (Table 4). These results indicated that PIV was an independent value to predict survival in advanced GC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Univariate and Multivariate analysis of prognostic factors for overall survival in advanced GC patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"470\" style=\"margin-right: calc(47%); width: 53%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8009%;\"\u003e\n \u003cp\u003eCovariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9882%;\"\u003e\n \u003cp\u003eUnivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.4745%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7676%;\"\u003e\n \u003cp\u003eMultivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 5.2761%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8009%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.3205%;\"\u003e\n \u003cp\u003eHR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.2902%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.8038%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8009%;\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003cp\u003eTNM stage\u003c/p\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003cp\u003eSurgical Approach\u003c/p\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003cp\u003eCA125\u003c/p\u003e\n \u003cp\u003eCA19-9\u003c/p\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003cp\u003eAlb\u003c/p\u003e\n \u003cp\u003ePIV\u003c/p\u003e\n \u003cp\u003eMLR\u003c/p\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.3205%;\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003cp\u003e(0.791-1.302)\u003c/p\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003cp\u003e(0.721-1.252)\u003c/p\u003e\n \u003cp\u003e2.629\u003c/p\u003e\n \u003cp\u003e(1.990-3.472)\u003c/p\u003e\n \u003cp\u003e4.454\u003c/p\u003e\n \u003cp\u003e(3.482-5.697)\u003c/p\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003cp\u003e(0.783-1.182)\u003c/p\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003cp\u003e(0.600-0.979)\u003c/p\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003cp\u003e(1.001-1.003)\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003cp\u003e（0.999-1.001）\u003c/p\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003cp\u003e（1.001-1.002）\u003c/p\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003cp\u003e（1.000-1.003）\u003c/p\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003cp\u003e（0.937-0.978）\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003cp\u003e(1.000-1.001)\u003c/p\u003e\n \u003cp\u003e2.522\u003c/p\u003e\n \u003cp\u003e(2.147-2.962)\u003c/p\u003e\n \u003cp\u003e1.079\u003c/p\u003e\n \u003cp\u003e(1.065-1.093)\u003c/p\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003cp\u003e(1.001-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.2902%;\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 18.6528%;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003cp\u003e(0.502-1.933)\u003c/p\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003cp\u003e(0.402-1.801)\u003c/p\u003e\n \u003cp\u003e3.740\u003c/p\u003e\n \u003cp\u003e(1.725-8.108)\u003c/p\u003e\n \u003cp\u003e5.196\u003c/p\u003e\n \u003cp\u003e(2.538-10.638)\u003c/p\u003e\n \u003cp\u003e1.152\u003c/p\u003e\n \u003cp\u003e(0.703-1.888)\u003c/p\u003e\n \u003cp\u003e1.602\u003c/p\u003e\n \u003cp\u003e(0.769-3.338)\u003c/p\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003cp\u003e(0.989-0.998)\u003c/p\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003cp\u003e(0.992-1.001)\u003c/p\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003cp\u003e(1.000-1.003)\u003c/p\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003cp\u003e(1.000-1.005)\u003c/p\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003cp\u003e(0.894-1.022)\u003c/p\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003cp\u003e(1.000-1.001)\u003c/p\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003cp\u003e(0.348-2.557)\u003c/p\u003e\n \u003cp\u003e1.019\u003c/p\u003e\n \u003cp\u003e(0.925-1.122)\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003cp\u003e(0.997-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9215%;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current retrospective study, we demonstrated a inflammation-based biomarker PIV, as an independent predictor for these GC patients for the first time. PIV was associated with several clinicopathological characteristics of GC and had an extensive influence on OS outcomes, suggesting that PIV might be useful for more accurate stratification and individualized treatment\u0026nbsp;for GC patients receiving Curative Gastrectomy during Perioperative Period. When compared with other traditional IIB indicators, PIV has a more sensitive prognostic ability.\u003c/p\u003e\n\u003cp\u003eSince GC is a heterogeneous cancer with poor prognosis, it\u0026rsquo;s of great value to investigate a practical biomarker to find out the patients who can benefit from perioperative regimen for GC after curative gastrectomy. Inflammation plays an important role in various stages of tumor development[15]. In today\u0026apos;s continuously advancing medical technology, accurate prediction of the condition of cancer patients has important clinical significance[16]. Compared with traditional IIB indicators, PIV, as a newly proposed inflammation indicator in 2020, has a guiding role in the prognosis of patients with metastatic colorectal cancer[17].\u0026nbsp;In subsequent studies, PIV has been confirmed to reflect the prognosis of various malignant tumors, including renal cell carcinoma, lung cancer, renal cell cancer, and prostate cancer[7\u0026ndash;9,18,19].\u003c/p\u003e\n\u003cp\u003eThe role of tumor microenvironment (TME) on tumor growth and progression have been well known in the last two decades, due to the complex interplay between large numbers of inflammatory or fibroblastic cells and tumor cells[20]. Previous studies have demonstrated that some immunological elements facilitate TME by producing pro-tumor cytokines and forming a dense tumor stroma[21]. In general, peripheral blood lymphocytes reflect the immune activation status and are responsible for antitumor-specific immune response[22]. However, circulating monocytes are sources of many immunosuppressive cells, including tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs), which were associated with immunosuppression and tumor progression[23]. Additionally, neutrophils also make great contribution to tumor cell proliferation and migration by secreting cytokines, chemokines, and growth factors and inhibiting CD8\u003csup\u003e+\u003c/sup\u003eT lymphocyte-mediated antitumor activity[24]. Finally, platelets are other cells promoting cancer-favored microenvironment and correlating with tumor metastasis and adverse outcomes[25]. Therefore, systemic and local inflammation of solid cancers has shown a landscape and several systemic inflammatory response indicators have been researched for their prognostic value in cancer patients, especially in GC patients, who underwent a poor clinical outcomes even if receiving gastrectomy and perioperative chemoradiotherapy regimen[26,27]. In this regard, several blood-derived immune-inflammatory indexes which are easy-to obtain have been explored and shown significant prognostic usefulness, including LMR, NLR, MLR, and PLR indexes[28]. When compared with these common IIBs, our results suggested that PIV has the largest AUC, which was the best index for Inflammatory prognosis analysis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this research, we found that high PIV was connected with low Alb and large tumor size. We suspect that high PIV index indicate that patients are in a state of pro-tumor inflammation, in which the tumor volume grows and the body\u0026apos;s consumption increases resulting in a decrease in serum protein. In the process of tumorigenesis, the balance of pro-tumor and anti-tumor effects always affects the occurrence and development of tumor, PIV can also be used as a tumor evaluation factor. By analyzing the data of stage III and IV gastric cancer patients, we found that the OS in the low PIV group was significantly higher than that in the high PIV group, which indicate an outstanding ability to judge patient outcomes.\u003c/p\u003e\n\u003cp\u003eLimitations of this study are as follows. As a single-center retrospective study, results may be biased, lack external validation, and may not be representative of the broader population. Although we take the patient\u0026apos;s blood indicators before surgery, PIV alone is an indicator of inflammation, which is influenced by the patient\u0026apos;s overall body state. This study demonstrated the effect of PIV on the prognosis of gastric cancer, but external experimental confirmation was lacking. Considering these problems, PIV as a prognostic indicator of gastric cancer still needs to be validated by more experiments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArea Under Curves\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGastric Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMonocyte to Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImmune-inflammatory biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeutrophil to Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePan Immune-inflammation Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatelet to Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor Microenvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMDSC\u003c/p\u003e\n \u003cp\u003eLYM\u003c/p\u003e\n \u003cp\u003eMON\u003c/p\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003cp\u003eNEU\u003c/p\u003e\n \u003cp\u003eTAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMyeloid-derived suppressor cells\u003c/p\u003e\n \u003cp\u003eLymphocyte\u003c/p\u003e\n \u003cp\u003eMonocyte\u003c/p\u003e\n \u003cp\u003ePlatelet\u003c/p\u003e\n \u003cp\u003eNeutrophil\u003c/p\u003e\n \u003cp\u003eTumor-associated macrophage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki. The studies involving human participants were reviewed and approved by YJSKY2023-157.\u0026nbsp;This study was approved by the Ethics Committee of Second Affiliated Hospital of Harbin Medical University, and all patients had written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Haiyan Foundation of Harbin Medical University Cancer Hospital under Grant No.JJZD2024-18, Harbin Medical University Cancer Hospital Top Young Talent Project under Grant No.BJQN2021-03\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWM: designed and analyzed data, and contributed writing; NX: designed and analyzed data, and contributed writing; WX, HY, LJ: analyzed data; WF: designed and supervised research, analyzed data, and wrote the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. 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No NETs no TIME: Crosstalk between neutrophil extracellular traps and the tumor immune microenvironment. Front Immunol [Internet]. 2022 [cited 2024 Sep 30];13. Available from: https://pubmed.ncbi.nlm.nih.gov/36618417/\u003c/li\u003e\n\u003cli\u003eLin Y, Xu J, Lan H. Tumor-associated macrophages in tumor metastasis: biological roles and clinical therapeutic applications. J Hematol Oncol [Internet]. 2019 [cited 2024 Sep 30];12. Available from: https://pubmed.ncbi.nlm.nih.gov/31300030/\u003c/li\u003e\n\u003cli\u003eH\u0026ouml;gner A, Moehler M. Immunotherapy in Gastric Cancer. Curr Oncol. 2022;29:1559\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003ePatel TH, Cecchini M. Targeted Therapies in Advanced Gastric Cancer. Curr Treat Options Oncol [Internet]. 2020 [cited 2024 Sep 30];21. Available from: https://pubmed.ncbi.nlm.nih.gov/32725377/\u003c/li\u003e\n\u003cli\u003eHsueh C, Tao L, Zhang M, Cao W, Gong H, Zhou J, et al. 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Available from: https://pubmed.ncbi.nlm.nih.gov/28947990/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gastric cancer, pan-immune-inflammation value, prognosis, overall survival, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-5235727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5235727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Advanced gastric cancer (GC) is a common malignancy with a poor prognosis, which remains the leading cause of cancer death worldwide. Identifying novel biomarkers is needed to predict survival for this highly progressive cancer. Many studies have confirmed that pan-immune-inflammation value (PIV) is related to the prognosis of various tumors in recent years. However, the prognostic value of PIV remains unclear in gastric cancer. The purpose of this study was to discuss the prognostic role of PIV in stage III and IV gastric cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe clinical data of 646 patients with gastric cancer after gastrectomy were retrospectively analyzed. The calculation method of PIV is PIV=neutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L) × platelet count (10\u003csup\u003e9\u003c/sup\u003e/L) × monocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)/lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L). Patients were divided into high and low PIV group by cut-off value based on receiver operating characteristic (ROC) curve. Effects of PIV and other IIB on survival were analyzed based on the ROC curves. Kaplan-Meier method was plotted to indicate the value of immune-inflammatory biomarkers (IIBs) in predicting the overall survival of gastric cancer. The overall survival (OS) in advanced gastric cancer patients were analyzed and univariate and multivariate statistics were used to evaluate the prognostic value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e PIV had the most significantly predictive value in advanced GC patients compared with other peripheral blood parameters and IIBs. Cases in the high PIV group were more likely to have low serum albumin (Alb) level, larger tumor size compared with those in the low PIV group. PIV was identified an independent prognostic indicator for survival outcome in advanced GC patients in univariate and multivariate models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study confirmed that PIV can reflect the prognosis of advanced GC patients who have undergone gastrectomy, suggesting a potential application of PIV in GC treatment outcomes. Compared to other IIb indicators, PIV is more sensitive as a prognostic indicator. PIV will also provide some insight into the underlying mechanisms of immune and inflammatory effects in GC development and progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e This study was a retrospective analysis, which has no intervention on human participants.\u003c/p\u003e","manuscriptTitle":"Pan-Immune-Inflammation Value is a novel prognostic biomarker in advanced Gastric Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 10:16:31","doi":"10.21203/rs.3.rs-5235727/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"033186a6-8823-48d0-8e11-8ed0a5e15178","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-19T10:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-22 10:16:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5235727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5235727","identity":"rs-5235727","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}