Proportions of Beneficial Factors in MLR, NLR, PLR and D-dimer in Preoperative Peripheral Blood of Patients With Early Stage Lung Cancer as Predictors of Patient Survival After Surgery

Proportions of Beneficial Factors in MLR, NLR, PLR and D-dimer in Preoperative Peripheral Blood of Patients With Early Stage Lung Cancer as Predictors of Patient Survival After Surgery | 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 Proportions of Beneficial Factors in MLR, NLR, PLR and D-dimer in Preoperative Peripheral Blood of Patients With Early Stage Lung Cancer as Predictors of Patient Survival After Surgery jun wang, huawei li, ran xu, tong lu, jiaying zhao, Pengfei zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1194999/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective The purpose of this paper is to predict the following items. preoperative baseline monocyte-to-lymphocyte ratio (MLR)、neutrophil-to-lymphocyte ratio (NLR) Platura-to-lymphocyte ratio (PLR) and dimeric fibrin fragment D (D-dimer) associated with clinical outcome in patients with Early Lung Cancer (LC). Methods We performed a retrospective analysis of 376 patients with LC. Progression-free survival (PFS) and overall survival (OS) were assessed by Kaplan-Meier, and univariate and multivariate Cox regression analyses were performed to identify prognostic factors. Finally, multivariate Cox regression analysis was used to evaluate the influence of favorable factors on patients’ OS and PFS combined with the basic clinical characteristics of the patient Results Among the variables screened by univariate Cox regression, MLR < 0.22, NLR < 1.99, PLR < 130.55 and D-Dimer < 70.5 (ng/ml) were significantly associated with both better OS and PFS. In multivariate Cox regression analysis, it was determined that MLR and D-Dimer had a better independent correlation with OS (p = 0.009, p = 0.05, respectively), while MLR was only better independently associated with PFS (P = 0.005). Furthermore, according to the number of favorable factors, patients with none of these factors had a significantly worse prognosis than patients with at least one of these factors. Conclusion  Baseline characteristics of low MLR, low NLR, low PLR and low D-dimer were associated with better outcomes. Pulmonology Peripheral blood biomarkers Early lung cancer MLR NLR PLR D-dimer Figures Figure 1 Figure 2 Background With the increasing morbidity and mortality in China, malignant tumors have become the main cause of death, among which lung cancer is the most common cancer and the main cause of death 1,2 . With the development of science and technology, the treatment of lung cancer also presents a variety of methods including surgery, chemotherapy and immunotherapy.However, due to the new coronavirus epidemic, some operations cannot be performed in time. Nevertheless, studies have shown that the benefit of delayed surgical treatment for patients with early-stage non-small cell lung cancer is still better than immediate radiotherapy 3 . Nevertheless, there is still a wide variation in OS and PFS in post-operative patients. Currently, the prediction of survival in postoperative lung cancer patients relies mainly on tumor node metastases (TNM) staging 4 . Although genetic and some molecular tests have shown great promise in predicting patient prognosis 5 , their huge financial burden makes it difficult to be widely available in most otherwise affluent cancer families. Therefore, we are working to identify novel circulating biomarkers that can successfully predict patient outcomes during routine preoperative testing. In recent years, many studies have demonstrated the important role of peripheral blood markers in the prognosis of patients with various tumors. Patients with bladder cancer with a low NLR 6 had significantly better clinical survival outcomes than those with high NLR 6 .In patients with HER2+ breast cancer, lower NLR and lower MLR show longer survival status 6,7 . It was reported that Plasma D-dimer was regarded as a prognostic marker for various types of malignancies, which included non-small-cell lung carcinoma (NSCLC) 8 . In addition, peripheral blood biomarkers have shown good prognostic ability in a variety of solid tumors including melanoma 9 , colorectal cancer 10 , esophageal cancer 11 and pancreatic cancer 12 . The above studies strongly suggest that single peripheral blood biomarkers have good prognostic ability in patients with malignancies, but whether there is a superimposed effect or whether these peripheral blood parameter indicators interact with each other needs further elucidation. Methods Patients This study retrospectively reviewed the medical records of all LC patients with stage I and stage II that who were treated with standard lobectomy at the Department of Thoracic Surgery, Second Affiliated Hospital of Harbin Medical University, Heilongjiang Province, China, from January 2015 to July 2017. Inclusion criteria: (1) preoperative imaging suggestive of a mass confined to a single lung lobe; (2) no distant metastases; (3) no preoperative adjuvant medication; (4) no hematological malignancies; (5) complete clinical and follow-up information; and (6) survival for at least 30 days postoperatively. Patients were followed up every three months after surgery via outpatient clinics or over the phone, with the last follow-up visit for all patients on June 30, 2020. This paper has been approved by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University. Because the study was retrospective, informed consent from patients was not required. Patient data confidentiality rules are consistent with the Declaration of Helsinki. Data collection Peripheral blood biomarkers including neutrophil count (10^9/L), monocyte count (10^9/L), lymphocyte count (10^9/L), platelet count (10^9/L) and D-dimer (ng/ml) were collected from the electronic medical record within 3 days prior to the procedure, as well as the patient's age, gender, BMI, underlying disease history, pathology profile and ECOG at that time scores and other basic clinical information. NLR and MLR were calculated as follows: NLR equals neutrophil count/lymphocyte count; Second, MLR equals monocyte count/lymphocyte count and PLR equals platelet count/lymphocyte count. Survival rates were analyzed by PFS and OS. Statistical analysis According to receiver operating Characteristic (ROC) curve analysis, the critical values of MLR, NLR, PLR and D-dimer can be determined and the highest Yorden index (defined as sensitivity + specificity -1) can be used to predict OS. This study used SPSS version 25.0 (IBM Corp., Armonk, NY, USA) for baseline statistics of patient clinical data. Moreover, OS and PFS were analyzed according to the Kaplan-Meier estimate by the log-rank test. Univariate and multivariate Cox regression were used to determine 95% confidence interval (CI) and risk ratio (HR). Clinical characterizations, including smoking status (former or never), age, multivariate cox regression was used to analyze the performance of patients in the Eastern tumor cooperative group. A P value less than 0.05 was considered statistically significant. P <0.05 on both sides was considered statistically significant. Results Patient demographics and clinical characteristics As shown in Table 1, 376 patients with early LC who underwent surgery were finally screened out. The results showed that there were 218 cases (58%) in males and 158 cases (42%) in females. The median age was 60(53,65) years. In our population, the majority of patients had an ECOG physical status of 0-1 (320/376, 85.1%) and the greatest number of patients had lung adenocarcinoma (n=233/376, 62%). We identified four preoperative peripheral blood biomarkers by referring to relevant studies and combining them with clinical significance 13,14 Some of the peripheral blood biomarkers for all patients are shown in (Table 2). Basis on the best CUT-OFF, low MLR (239/376, 63,6%), high NLR (207/376, 55.1%), low PLR (209/376, 55.6%) and high D-Dimer (263/376, 69.7%) were more predominant. Univariate and Multivariate Cox Analysis for Survival Outcome The results of OS and PFS based on cells counts of peripheral blood were shown in Figure 1: pre-treatment NLR < 0.216 (log-rank test p < 0.0001), NLR < 1.987 (p = 0.00016), PLR < 130.55 (p = 0.0034) and D-dimer < 70.5 ml/ng (0.00016)). In univariate Cox regressions analysis, sex, smoking, pathology type, MLR, PLR, NLR, D-dimer, and ECOG PS were associated with OS (p < 0.0001, 0.0037, 0.00021, 0.00045 and 0.0001, respectively). The above parameters were correlated with PFS (P<0.0001, 0.01429, 0.00025, 0.01265, 0.0001, respectively) (Table 3). In multivariate COX regressions, the preoperative peripheral blood biomarkers MLR and D-Dimer (p=0.009145, 0.005376, respectively), which we observed, maintained their significance for OS. However, D-dimer was not significant in PFS (p = 0.07662) (Table 4). Multivariate Model for Survival of Patients Then, according to the quantitative analysis results of favorable factors, including MLR<0.22, NLR<1.99, PLR<130.55 and D-DIMER <70.5(ng/ mL).As shown in Figure 2, Sixty-eight patients (18%) showed no significant reduction in PFS and OS compared to those who had 1 and 2(group II, at 71 and 93, respectively) or three and four (group III, at 102 and 42, respectively) (Kaplan Meier analysis and survival rates compared to P < 0.0001, respectively). Multivariate COX analysis including clinically important Covariates (age, gender, BMI, ECOG ps, etc.) confirmed that the number of favorable factors was closely associated to PFS and OS (Table 5). Discussion To date, biomarkers remain a major focus of research in the field of Oncology, whether in the diagnosis of disease, the assessment of efficacy of treatment or the prognosis of patients. The connection between inflammation and cancer was first explored in 1863 by Rudolf Virchow et al. 15 . Since then, more and more studies have further confirmed the value of inflammatory markers in the diagnosis and prognosis evaluation of various malignant tumors. The inflammatory response is an organism's antagonistic response to noxious stimuli, both exogenous and endogenous 16,17 .Inflammation associated with cancer has a dramatic impact on the tumor microenvironment, which consists of tumor cells and inflammatory cells that release various cytokines and chemotactic factors 18 . Among them, NLR, PLR, MLR and D-dimer are closely related to inflammation and immune status of cancer patients, and have been applied to predict the prognosis of patients with various solid tumors 9–12 . NLR, PLR and MLR, as indicators of inflammation, are obtained from peripheral blood neutrophilic granulocyte, platelets and monocytes compared to lymphocytes, respectively. Therefore, the ratios between them can similarly indicate the role of inflammatory mediators in tumors. The function of inflammatory mediators in the tumor microenvironment is not fully understood, but there are several potential mechanisms that could provide a simple explanation for their role. (1) Lymphocytes are an important component of an organism's immune system, playing a major role in the body's immune surveillance and serving as a protective prognostic factor for patients with malignancies 19 . CD8 cytotoxic T lymphocytes (CTL) are the primary immune cells that target tumors. During cancer progression, CTL become dysfunctional and suppressed due to immune-related tolerance and immunosuppression within the tumor microenvironment (TME) 20 . (2) The role of neutrophilic granulocyte in the body is to regulate immunity by producing tumor necrosis factor (TNF)-α, a cytokine that impairs CD8T cell activity and increases vascular permeability, thus suppressing the immune system by inhibiting the immune activity of lymphocytes and ultimately leading to progression and metastasis 21,22 . (3) Monocytes play an important role as a protective immune factor in suppressing tumor growth by inducing the recruitment and function of lymphocytes in TME and interacting with adaptive immunity, especially in peripheral blood where monocytes are involved in paracrine signaling and produce many inflammatory cytokines and chemokines, including tumor necrosis factor α 23 . (4) Platelets in plasma play a vital part in tumor hematogenous metastasis and are a prerequisite for it. The mechanism of action may be that platelets protect tumor cells from shear and NK cell attack 24 . (5) Although the mechanism of plasma D-Dimer in tumor development is still unclear, some studies have reported that elevated plasma D-Dimer levels in breast cancer patients are associated with progesterone receptor expression, TNM staging and metastasis in breast cancer 25 . Although a number of studies have demonstrated that NLR, PLR, MLR and D-Dimer can be used as potential prognostic biomarkers in patients with a variety of solid tumors, these studies have more or less analyzed only one or two of these inflammatory indicators as markers. MLR and D-dimer were also proposed in our study as independent predictive markers of prognosis in patients with surgically treated lung cancer. However, we are not aware of any studies that have examined whether there is superposition between these markers or whether there is an interaction between them. For this reason, we divided these markers into three groups according to the number of beneficial factors, and the results of a multifactorial COX regression show that patients in the group without a single beneficial factor had the worst prognosis compared to the other two groups, while patients in the group with the most beneficial factors had the best prognosis. It is reasonable to believe that the number of beneficial peripheral blood biomarkers described above could be a valid predictor of the prognostic status of patients. There are some shortcomings in our study, as a retrospective study, selection bias may occur in the investigation and therefore the results need to be further clarified in a multicenter prospective study. In addition, only patients with operable early-stage lung cancer were selected, while it is unknown whether patients with inoperable advanced lung cancer have a superimposed effect in these tumor markers, and further studies are needed. Conclusion MLR and D-Dimer can be inexpensive and convenient independent predictors of prognosis in patients with surgically treatable early-stage lung cancer, and their predictive superposition can play a greater role in individualizing patient treatment. Abbreviations MLR: monocyte-to-lymphocyte ratio NLR: neutrophil-to-lymphocyte ratio PLR: Platura-to-lymphocyte ratio D-dimer: dimeric fibrin fragment D LC: Lung Cancer PFS: Progression-free survival OS: overall survival TNM: tumor node metastases NSCLC: non-small-cell lung cancer ROC: receiver operating Characteristic CI: confidence interval HR: risk ratio CTL: cytotoxic T lymphocytes Declarations Ethics approval and consent to participate For this study, the IRB approved our application for waiver of informed consent.Because of our IRB regulations, informed consent can be exempted as long as all the following conditions are met： The risk to the subjects in this study is not greater than the minimum risk. (Minimum risk refers to the possibility and degree of the expected risk in the research is not greater than the risk of daily life, or routine physical examination or psychological test.) Exemption of informed consent will not adversely affect the rights and health of subjects.Note: The patient only needs to accept the normal diagnosis and treatment process of the disease, and any medical treatment and rights will not be affected. The subjects' privacy and personally identifiable information are protected.Note: The personal information of patients in the study is confidential. Information that can identify patients will not be disclosed to members other than the research team. All study members and study sponsors are required to keep patient identities confidential. The patient file will only be available to researchers. In order to ensure that the research is carried out in accordance with regulations, when necessary, the government management department or the members of the ethics committee can review the patient data in the research unit according to the regulations. When the results of this research are published, no personal information about the patients will be disclosed. If informed consent is required, the research will not be possible (patients have the right to know that their medical records/specimen may be used for research, and their refusal or disagreement to participate in the research is not the reason why the research cannot be implemented or the informed consent is exempted).Note: Patients have the right to know that their tissue samples may be used for research and have the right to refuse to participate in the research. This study does not use medical records and specimens that patients/subjects have specifically refused to use in the past. Research using human body materials or data with identifiable information has failed to find the subject, and the research project does not involve personal privacy or commercial interests. We believe that our research meets the above conditions. This study has been reviewed by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University, with approval number:KY2021-254. Consent for publication This study is a retrospective study, and the ethics committee's exemption consent application has been passed, and the relevant data is only used in this study Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due [Our ethics committee stipulates that the information that can identify patients will not be disclosed to members other than the research team] but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding No funding Authors' contributions WJ and LHW wrote the main manuscript text . XR prepared figures 1. LT prepared figures 2. ZJY、ZPF、QLD、ZSQ and GJD prepared table 1-5 All authors reviewed the manuscript. 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Patients’ characteristics at baseline Characteristic Total（N=376） Median (25%,75%) or mea±SD Age 60（53，65） Sex Female Male 158 （42%） 218 （58%） BMI 23.86 ± 3.2 Address Country Town 155 （41.2%） 221 （58.8%） Smoking status Former Never 164 （43.6%） 43.6 （56.4%） Tumor site RUL LUL RLL LLL RML 81 （21.5%） 103 （27.4%） 82 （21.8%） 83 （22.1%） 27 （7.2%） Histologic subtype Adeno. Squamous. SCLC Another 233 （62%） 90 （23.9%） 25 （6.6%） 28 （7.4%） Differentiation Well Moderately Poorly 66 （17.6%） 90 （23.9%） 220 （58.5%） PIS Yes No 94 （25%） 282 （75%） TNM staging I II 205 （54.5%） 171 （45.5%） ECOG PS 0-1 2-4 320 （85.1%） 56 （14.9%） Basic illness Hypertension Diabetes 59 （15.7%） 20 （5.3%） Tumor Size 32.68±16.62 Lung function FEV1 FEV1% DLCO 2.37±0.63 78.65±9.84 6.74±1.91 BMI, body mass index; ECOG PS, Eastern Cooperative Oncology Group performance status; TNM staging, tumor, nodes, metastasis-classification staging；PIS：Pleural invasion status；HOM：History of malignancy；RUL：Right upper lobe；RML：Right middle lobe；RLL：Right lower lobe；LUL：Left upper lobe；LLL：Left lower lobe； Table 2. Patient's peripheral blood data Characteristic Median (25%,75%) Total(n=376) High（%） low（%） Neutrophil count 4.07（3.13，5.04）(10^9/L) Lymphocyte count 1.87（1.5，2.34）(10^9/L) Monocyte count 0.36（0.25，0.46）(10^9/L) Platelet count 237（196，285）(10^9/L) D-Dimer 110.5（62.25，168）(ng/ml) 263（69.7） 113（30.3） MLR 0.22（0.14，0.25） 137（36.4） 239（63.6） NLR 2.61（1.58，2.94） 207（55.1） 169（44.9） PLR 137.6（96.73，162.73） 167（44.4） 209（55.6） MLR, monocyte to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio Table3. Univariate analyses of biomarkers for OS and PFS. Characteristic Reference OS PFS HR 95% CI p-value HR 95% CI p-value sex female 2.44 1.59-3.75 0.000048 2.26 1.51-3.40 0.000081 age 1.01 0.99-1.03 0.241034 1.01 0.99-1.03 0.257189 smoking no 1.69 1.16-2.46 0.006738 1.53 1.07-2.20 0.021356 pathology another SCLC squama. adeno. 1.73 2.86 1.96 0.85-3.51 1.49-5.47 1.29-3.00 0.128456 0.001567 0.001793 1.70 2.53 1.82 0.87-3.32 1.33-4.81 1.21-2.75 0.121719 0.004787 0.004036 size 1.01 0.99-1.02 0.326866 1.01 1.00-1.02 0.262015 P IS no 1.78 1.20-2.64 0.003969 1.96 1.35-2.85 0.000426 TNM Ⅰ 1.25 0.86-1.83 0.238301 1.32 0.92-1.89 0.133186 BMI 0.97 0.92-1.03 0.369197 0.98 0.93-1.04 0.527315 MLR high 0.39 0.27-0.57 0.000001 0.43 0.30-0.61 <0.0001 NLR high 0.46 0.31-0.70 0.00021 0.49 0.33-0.72 0.000252 PLR high 0.57 0.39-0.83 0.003706 0.64 0.44-0.91 0.014286 D-Dimer high 0.40 0.24-0.67 0.000453 0.57 0.37-0.89 0.012654 ECGO PS 2-4 0.26 0.18-0.39 <0.0001 0.21 0.14-0.31 <0.0001 Table4. multivariate analyses of biomarkers for OS and PFS. Characteristic Reference OS PFS HR 95% CI p-value HR 95% CI p-value sex female 1.72 1.06-2.80 0.02843 1.51 0.96-2.39 0.077308 Pathology another SCLC squama. adeno. 1.41 2.25 1.56 0.69-2.90 1.14-4.44 0.98-2.46 0.344902 0.018932 0.05821 1.47 2.44 1.61 0.75-2.91 1.26-4.72 1.03-2.51 0.265219 0.008322 0.037925 PIS no 1.48 0.97-2.28 0.069652 1.61 1.07-2.40 0.021767 MLR high 0.56 0.37-0.87 0.009145 0.57 0.39-0.85 0.005487 PLR high 0.74 0.49-1.12 0.157511 D-Dimer high 0.48 0.29-0.80 0.005376 0.67 0.43-1.04 0.076619 ECGO PS 2-4 0.28 0.18-0.42 <0.0001 0.23 0.15-0.34 <0.0001 Table5. Multivariate COX regression analysis of OS and PFS grouped by beneficial factors group Total（n=376） OS PFS HR 95% CI p-value HR 95% CI p-value Ⅰ(0) 68 Reference Reference Ⅱ (1-2) 164 0.40 0.26-0.62 <0.0001 0.48 0.31-0.74 0.00083 Ⅲ(3-4) 144 0.29 0.17-0.48 <0.0001 0.37 0.23-0.59 <0.0001 Covariables included age, Eastern Cooperative Oncology Group performance status (0-1 or 2-4), smoking status (former or never).I grope: with no beneficial factors，II grope：There are 1-2 beneficial factors，III grope：There are 3-4 beneficial factors Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 18 Feb, 2022 Reviews received at journal 18 Feb, 2022 Reviews received at journal 30 Jan, 2022 Reviewers agreed at journal 18 Jan, 2022 Reviewers agreed at journal 17 Jan, 2022 Reviewers invited by journal 05 Jan, 2022 Editor assigned by journal 04 Jan, 2022 Editor invited by journal 27 Dec, 2021 Submission checks completed at journal 27 Dec, 2021 First submitted to journal 22 Dec, 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. 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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-1194999","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":72808493,"identity":"1e73b0e4-4b2f-4e19-8b37-8762d267f15e","order_by":0,"name":"jun wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"jun","middleName":"","lastName":"wang","suffix":""},{"id":72808494,"identity":"9bfa5d65-62ac-41b9-bff2-a7e443e300ec","order_by":1,"name":"huawei li","email":"","orcid":"","institution":"The Second Affiliated 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zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACPgYGNhAtZ8DAAxZgbCCkhQ2qxZh0LYkbiNcikf7swc8dtenbpc8e3czDYCO74QDzswf4tSSkG/aeOZ67sy8v7TYPQ5rxhgNs5gYEtByT4G07lrvhDI8ZUMvhxA0HeNgk8GtJbJP823Ys3QCi5T8xWpLZpHnbahKgWg4QoYXnGZu0bNsBww1n+NJuzjFINp55mM0MrxZ+9vRnkm/b6uQNzvAeu/Gmwk6273jzM7xaGAQSQORhKA8UVMx41YOsOQAi6wgpGwWjYBSMgpEMAPzcRqwmZBvFAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Linyou","middleName":"","lastName":"zhang","suffix":""}],"badges":[],"createdAt":"2021-12-22 08:59:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1194999/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1194999/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16869340,"identity":"e712d4f1-1f41-42a1-baad-7cdae071375b","added_by":"auto","created_at":"2021-12-30 14:40:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":720783,"visible":true,"origin":"","legend":"\u003cp\u003ePFS (a, c, e, g) and OS (b, d, f, h) curves of patients stratified according to peripheral blood markers (MLR, NLR,PLR and D-dimer). p Values were calculated with the log-rank test\u003c/p\u003e","description":"","filename":"figure1reviewcopy.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1194999/v1/1fed9650110e14e8477a3e8c.jpg"},{"id":16869305,"identity":"0e7c4195-d024-4a99-93f5-98662f327d47","added_by":"auto","created_at":"2021-12-30 14:37:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":552391,"visible":true,"origin":"","legend":"\u003cp\u003ePFS rate (A),and OS rate (B) were determined for patients in groups I, II, and III (non one,one and two ,three and four factors, respectively). p Values were calculated with the log-rank test\u003c/p\u003e","description":"","filename":"figure2reviewcopy.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1194999/v1/6b8b29066b77427a6d18e48b.jpg"},{"id":16869341,"identity":"ccdebaf3-698e-4a69-9166-299bf06f7e32","added_by":"auto","created_at":"2021-12-30 14:40:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":669366,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1194999/v1/40fa3823-5a18-4ce9-ad25-c9110d1cef36.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eProportions of Beneficial Factors in MLR, NLR, PLR and D-dimer in Preoperative Peripheral Blood of Patients With Early Stage Lung Cancer as Predictors of Patient Survival After Surgery\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the increasing morbidity and mortality in China, malignant tumors have become the main cause of death, among which lung cancer is the most common cancer and the main cause of death\u003csup\u003e1,2\u003c/sup\u003e. With the development of science and technology, the treatment of lung cancer also presents a variety of methods including surgery, chemotherapy and immunotherapy.However, due to the new coronavirus epidemic, some operations cannot be performed in time. Nevertheless, studies have shown that the benefit of delayed surgical treatment for patients with early-stage non-small cell lung cancer is still better than immediate radiotherapy\u003csup\u003e3\u003c/sup\u003e. Nevertheless, there is still a wide variation in OS and PFS in post-operative patients. Currently, the prediction of survival in postoperative lung cancer patients relies mainly on tumor node metastases (TNM) staging\u003csup\u003e4\u003c/sup\u003e. Although genetic and some molecular tests have shown great promise in predicting patient prognosis\u003csup\u003e5\u003c/sup\u003e, their huge financial burden makes it difficult to be widely available in most otherwise affluent cancer families. Therefore, we are working to identify novel circulating biomarkers that can successfully predict patient outcomes during routine preoperative testing.\u003c/p\u003e\n\u003cp\u003eIn recent years, many studies have demonstrated the important role of peripheral blood markers in the prognosis of patients with various tumors. Patients with bladder cancer with a low NLR\u003csup\u003e6\u003c/sup\u003e had significantly better clinical survival outcomes than those with high NLR\u003csup\u003e6\u003c/sup\u003e.In patients with HER2+ breast cancer, lower NLR and lower MLR show longer survival status\u003csup\u003e6,7\u003c/sup\u003e. It was reported that Plasma D-dimer was regarded as a prognostic marker for various types of malignancies, which included non-small-cell lung carcinoma (NSCLC)\u003csup\u003e8\u003c/sup\u003e. In addition, peripheral blood biomarkers have shown good prognostic ability in a variety of solid tumors including melanoma\u003csup\u003e9\u003c/sup\u003e, colorectal cancer\u003csup\u003e10\u003c/sup\u003e, esophageal cancer\u003csup\u003e11\u003c/sup\u003e and pancreatic cancer\u003csup\u003e12\u003c/sup\u003e. The above studies strongly suggest that single peripheral blood biomarkers have good prognostic ability in patients with malignancies, but whether there is a superimposed effect or whether these peripheral blood parameter indicators interact with each other needs further elucidation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003ePatients\u003c/p\u003e\n\u003cp\u003eThis study retrospectively reviewed the medical records of all LC patients with stage I and stage II that who were treated with standard lobectomy at the Department of Thoracic Surgery, Second Affiliated Hospital of Harbin Medical University, Heilongjiang Province, China, from January 2015 to July 2017. Inclusion criteria: (1) preoperative imaging suggestive of a mass confined to a single lung lobe; (2) no distant metastases; (3) no preoperative adjuvant medication; (4) no hematological malignancies; (5) complete clinical and follow-up information; and (6) survival for at least 30 days postoperatively. Patients were followed up every three months after surgery via outpatient clinics or over the phone, with the last follow-up visit for all patients on June 30, 2020. This paper has been approved by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University. Because the study was retrospective, informed consent from patients was not required. Patient data confidentiality rules are consistent with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral blood biomarkers including neutrophil count (10^9/L), monocyte count (10^9/L), lymphocyte count (10^9/L), platelet count (10^9/L) and D-dimer (ng/ml) were collected from the electronic medical record within 3 days prior to the procedure, as well as the patient\u0026apos;s age, gender, BMI, underlying disease history, pathology profile and ECOG at that time scores and other basic clinical information. NLR and MLR were calculated as follows: NLR equals neutrophil count/lymphocyte count; Second, MLR equals monocyte count/lymphocyte count and PLR equals platelet count/lymphocyte count. Survival rates were analyzed by PFS and OS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to receiver operating Characteristic (ROC) curve analysis, the critical values of MLR, NLR, PLR and D-dimer can be determined and the highest Yorden index (defined as sensitivity + specificity -1) can be used to predict OS. This study used SPSS version 25.0 (IBM Corp., Armonk, NY, USA) for baseline statistics of patient clinical data. Moreover, OS and PFS were analyzed according to the Kaplan-Meier estimate by the log-rank test. Univariate and multivariate Cox regression were used to determine 95% confidence interval (CI) and risk ratio (HR). Clinical characterizations, including smoking status (former or never), age, multivariate cox regression was used to analyze the performance of patients in the Eastern tumor cooperative group. A P value less than 0.05 was considered statistically significant. P \u0026lt;0.05 on both sides was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient demographics and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 1, 376 patients with early LC who underwent surgery were finally screened out. The results showed that there were 218 cases (58%) in males and 158 cases (42%) in females. The median age was 60(53,65) years. In our population, the majority of patients had an ECOG physical status of 0-1 (320/376, 85.1%) and the greatest number of patients had lung adenocarcinoma (n=233/376, 62%).\u003c/p\u003e\n\n\u003cp\u003eWe identified four preoperative peripheral blood biomarkers by referring to relevant studies and combining them with clinical significance\u003csup\u003e13,14\u003c/sup\u003e Some of the peripheral blood biomarkers for all patients are shown in (Table 2). Basis on the best CUT-OFF, low MLR (239/376, 63,6%), high NLR (207/376, 55.1%), low PLR (209/376, 55.6%) and high D-Dimer (263/376, 69.7%) were more predominant.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eUnivariate and Multivariate Cox Analysis for Survival Outcome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of OS and PFS based on cells counts of peripheral blood were shown in Figure 1: pre-treatment NLR \u0026lt; 0.216 (log-rank test p \u0026lt; 0.0001), NLR \u0026lt; 1.987 (p = 0.00016), PLR \u0026lt; 130.55 (p = 0.0034) and D-dimer \u0026lt; 70.5 ml/ng (0.00016)). In univariate Cox regressions analysis, sex, smoking, pathology type, MLR, PLR, NLR, D-dimer, and ECOG PS were associated with OS (p \u0026lt; 0.0001, 0.0037, 0.00021, 0.00045 and 0.0001, respectively). The above parameters were correlated with PFS (P\u0026lt;0.0001, 0.01429, 0.00025, 0.01265, 0.0001, respectively) (Table 3). In multivariate COX regressions, the preoperative peripheral blood biomarkers MLR and D-Dimer (p=0.009145, 0.005376, respectively), which we observed, maintained their significance for OS. However, D-dimer was not significant in PFS (p = 0.07662) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Model for Survival of Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThen, according to the quantitative analysis results of favorable factors, including MLR\u0026lt;0.22, NLR\u0026lt;1.99, PLR\u0026lt;130.55 and D-DIMER \u0026lt;70.5(ng/ mL).As shown in Figure 2, Sixty-eight patients (18%) showed no significant reduction in PFS and OS compared to those who had 1 and 2(group II, at 71 and 93, respectively) or three and four (group III, at 102 and 42, respectively) (Kaplan Meier analysis and survival rates compared to P \u0026lt; 0.0001, respectively). Multivariate COX analysis including clinically important Covariates (age, gender, BMI, ECOG ps, etc.) confirmed that the number of favorable factors was closely associated to PFS and OS (Table 5).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo date, biomarkers remain a major focus of research in the field of Oncology, whether in the diagnosis of disease, the assessment of efficacy of treatment or the prognosis of patients. The connection between inflammation and cancer was first explored in 1863 by Rudolf Virchow et al.\u003csup\u003e15\u003c/sup\u003e. Since then, more and more studies have further confirmed the value of inflammatory markers in the diagnosis and prognosis evaluation of various malignant tumors. The inflammatory response is an organism\u0026apos;s antagonistic response to noxious stimuli, both exogenous and endogenous\u003csup\u003e16,17\u003c/sup\u003e.Inflammation associated with cancer has a dramatic impact on the tumor microenvironment, which consists of tumor cells and inflammatory cells that release various cytokines and chemotactic factors\u003csup\u003e18\u003c/sup\u003e. \u0026nbsp;Among them, NLR, PLR, MLR and D-dimer are closely related to inflammation and immune status of cancer patients, and have been applied to predict the prognosis of patients with various solid tumors\u003csup\u003e9\u0026ndash;12\u003c/sup\u003e. NLR, PLR and MLR, as indicators of inflammation, are obtained from peripheral blood neutrophilic granulocyte, platelets and monocytes compared to lymphocytes, respectively. Therefore, the ratios between them can similarly indicate the role of inflammatory mediators in tumors.\u003c/p\u003e\n\u003cp\u003eThe function of inflammatory mediators in the tumor microenvironment is not fully understood, but there are several potential mechanisms that could provide a simple explanation for their role. (1) Lymphocytes are an important component of an organism\u0026apos;s immune system, playing a major role in the body\u0026apos;s immune surveillance and serving as a protective prognostic factor for patients with malignancies\u003csup\u003e19\u003c/sup\u003e. CD8 cytotoxic T lymphocytes (CTL) are the primary immune cells that target tumors. During cancer progression, CTL become dysfunctional and suppressed due to immune-related tolerance and immunosuppression within the tumor microenvironment (TME)\u003csup\u003e20\u003c/sup\u003e. (2) The role of neutrophilic granulocyte in the body is to regulate immunity by producing tumor necrosis factor (TNF)-\u0026alpha;, a cytokine that impairs CD8T cell activity and increases vascular permeability, thus suppressing the immune system by inhibiting the immune activity of lymphocytes and ultimately leading to progression and metastasis\u003csup\u003e21,22\u003c/sup\u003e. (3) Monocytes play an important role as a protective immune factor in suppressing tumor growth by inducing the recruitment and function of lymphocytes in TME and interacting with adaptive immunity, especially in peripheral blood where monocytes are involved in paracrine signaling and produce many inflammatory cytokines and chemokines, including tumor necrosis factor \u0026alpha; \u003csup\u003e23\u003c/sup\u003e. (4) Platelets in plasma play a vital part in tumor hematogenous metastasis and are a prerequisite for it. The mechanism of action may be that platelets protect tumor cells from shear and NK cell attack\u003csup\u003e24\u003c/sup\u003e. (5) Although the mechanism of plasma D-Dimer in tumor development is still unclear, some studies have reported that elevated plasma D-Dimer levels in breast cancer patients are associated with progesterone receptor expression, TNM staging and metastasis in breast cancer\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAlthough a number of studies have demonstrated that NLR, PLR, MLR and D-Dimer can be used as potential prognostic biomarkers in patients with a variety of solid tumors, these studies have more or less analyzed only one or two of these inflammatory indicators as markers. MLR and D-dimer were also proposed in our study as independent predictive markers of prognosis in patients with surgically treated lung cancer. However, we are not aware of any studies that have examined whether there is superposition between these markers or whether there is an interaction between them. For this reason, we divided these markers into three groups according to the number of beneficial factors, and the results of a multifactorial COX regression show that patients in the group without a single beneficial factor had the worst prognosis compared to the other two groups, while patients in the group with the most beneficial factors had the best prognosis. It is reasonable to believe that the number of beneficial peripheral blood biomarkers described above could be a valid predictor of the prognostic status of patients. There are some shortcomings in our study, as a retrospective study, selection bias may occur in the investigation and therefore the results need to be further clarified in a multicenter prospective study. In addition, only patients with operable early-stage lung cancer were selected, while it is unknown whether patients with inoperable advanced lung cancer have a superimposed effect in these tumor markers, and further studies are needed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMLR and D-Dimer can be inexpensive and convenient independent predictors of prognosis in patients with surgically treatable early-stage lung cancer, and their predictive superposition can play a greater role in individualizing patient treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMLR: monocyte-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eNLR: neutrophil-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003ePLR: Platura-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eD-dimer: dimeric fibrin fragment D\u003c/p\u003e\n\u003cp\u003eLC: Lung Cancer\u003c/p\u003e\n\u003cp\u003ePFS: Progression-free survival\u003c/p\u003e\n\u003cp\u003eOS: overall survival\u003c/p\u003e\n\u003cp\u003eTNM: tumor node metastases\u003c/p\u003e\n\u003cp\u003eNSCLC: non-small-cell lung cancer\u003c/p\u003e\n\u003cp\u003eROC: receiver operating Characteristic\u003c/p\u003e\n\u003cp\u003eCI: confidence interval\u003c/p\u003e\n\u003cp\u003eHR: risk ratio\u003c/p\u003e\n\u003cp\u003eCTL: cytotoxic T lymphocytes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this study, the IRB approved our application for waiver of informed consent.Because of our IRB regulations, informed consent can be exempted as long as all the following conditions are met：\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eThe risk to the subjects in this study is not greater than the minimum risk. (Minimum risk refers to the possibility and degree of the expected risk in the research is not greater than the risk of daily life, or routine physical examination or psychological test.)\u003c/li\u003e\n \u003cli\u003eExemption of informed consent will not adversely affect the rights and health of subjects.Note: The patient only needs to accept the normal diagnosis and treatment process of the disease, and any medical treatment and rights will not be affected.\u003c/li\u003e\n \u003cli\u003eThe subjects\u0026apos; privacy and personally identifiable information are protected.Note: The personal information of patients in the study is confidential. Information that can identify patients will not be disclosed to members other than the research team. All study members and study sponsors are required to keep patient identities confidential. The patient file will only be available to researchers. In order to ensure that the research is carried out in accordance with regulations, when necessary, the government management department or the members of the ethics committee can review the patient data in the research unit according to the regulations. When the results of this research are published, no personal information about the patients will be disclosed.\u003c/li\u003e\n \u003cli\u003eIf informed consent is required, the research will not be possible (patients have the right to know that their medical records/specimen may be used for research, and their refusal or disagreement to participate in the research is not the reason why the research cannot be implemented or the informed consent is exempted).Note: Patients have the right to know that their tissue samples may be used for research and have the right to refuse to participate in the research.\u003c/li\u003e\n \u003cli\u003eThis study does not use medical records and specimens that patients/subjects have specifically refused to use in the past.\u003c/li\u003e\n \u003cli\u003eResearch using human body materials or data with identifiable information has failed to find the subject, and the research project does not involve personal privacy or commercial interests.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe believe that our research meets the above conditions.\u003c/p\u003e\n\u003cp\u003eThis study has been reviewed by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University, with approval number:KY2021-254.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a retrospective study, and the ethics committee\u0026apos;s exemption consent application has been passed, and the relevant data is only used in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due [Our ethics committee stipulates that the information that can identify patients will not be disclosed to members other than the research team] but are available from the corresponding author on reasonable request.\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWJ and LHW wrote the main manuscript text .\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;XR prepared figures 1.\u003c/p\u003e\n\u003cp\u003eLT prepared figures 2.\u003c/p\u003e\n\u003cp\u003eZJY、ZPF、QLD、ZSQ and GJD prepared table 1-5\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the process of writing the thesis, I also received valuable opinions from many students, and I would like to express my sincere thanks to them. Especially, I wish to thank My best friend Ms. Li Mei for revising my English grammar and suggesting some wording.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e(1) \u0026nbsp; \u0026nbsp;Chen, W.; Zheng, R.; Baade, P. D.; Zhang, S.; Zeng, H.; Bray, F.; Jemal, A.; Yu, X. Q.; He, J. Cancer Statistics in China, 2015. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e66\u003c/em\u003e (2), 115\u0026ndash;132. https://doi.org/10.3322/caac.21338.\u003c/p\u003e\n\u003cp\u003e(2) \u0026nbsp; \u0026nbsp;Gao, S.; Li, N.; Wang, S.; Zhang, F.; Wei, W.; Li, N.; Bi, N.; Wang, Z.; He, J. Lung Cancer in People\u0026rsquo;s Republic of China. \u003cem\u003eJournal of Thoracic Oncology\u003c/em\u003e \u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e15\u003c/em\u003e (10), 1567\u0026ndash;1576. https://doi.org/10.1016/j.jtho.2020.04.028.\u003c/p\u003e\n\u003cp\u003e(3) \u0026nbsp; \u0026nbsp;Mayne, N. R.; Lin, B. 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A.; Castiglia, M.; Grassadonia, A.; De Tursi, M.; Irtelli, L.; Rizzo, S.; Bertani, A.; Gristina, V.; Barraco, N.; Russo, A.; Natoli, C.; Bazan, V. Analysis of Systemic Inflammatory Biomarkers in Neuroendocrine Carcinomas of the Lung: Prognostic and Predictive Significance of NLR, LDH, ALI, and LIPI Score. \u003cem\u003eTher Adv Med Oncol\u003c/em\u003e \u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e12\u003c/em\u003e, 1758835920942378. https://doi.org/10.1177/1758835920942378.\u003c/p\u003e\n\u003cp\u003e(15) \u0026nbsp;\u0026nbsp;Balkwill, F.; Mantovani, A. Inflammation and Cancer: Back to Virchow? \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e2001\u003c/strong\u003e, \u003cem\u003e357\u003c/em\u003e (9255), 539\u0026ndash;545. https://doi.org/10.1016/S0140-6736(00)04046-0.\u003c/p\u003e\n\u003cp\u003e(16) \u0026nbsp;\u0026nbsp;Diakos, C. I.; Charles, K. A.; McMillan, D. C.; Clarke, S. J. 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The Association of D-Dimer with Clinicopathological Features of Breast Cancer and Its Usefulness in Differential Diagnosis: A Systematic Review and Meta-Analysis. \u003cem\u003ePLoS One\u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e14\u003c/em\u003e (9), e0221374. https://doi.org/10.1371/journal.pone.0221374.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTab 1. Patients\u0026rsquo; characteristics at baseline\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.23580034423408%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.65404475043029%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"56.11015490533563%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal（N=376）\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Median (25%,75%) or mea\u0026plusmn;SD\u0026nbsp;\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.23580034423408%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"22.375215146299485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.440619621342513%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.948364888123923%\"\u003e\n \u003cp\u003e60（53，65）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e158 （42%）\u003c/p\u003e\n \u003cp\u003e218 （58%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e23.86 \u0026plusmn;\u0026nbsp;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAddress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003cp\u003eTown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e155 （41.2%）\u003c/p\u003e\n \u003cp\u003e221 （58.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e164 （43.6%）\u003c/p\u003e\n \u003cp\u003e43.6 （56.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.23580034423408%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.65404475043029%\"\u003e\n \u003cp\u003eRUL\u003c/p\u003e\n \u003cp\u003eLUL\u003c/p\u003e\n \u003cp\u003eRLL\u003c/p\u003e\n \u003cp\u003eLLL\u003c/p\u003e\n \u003cp\u003eRML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"56.11015490533563%\"\u003e\n \u003cp\u003e81 \u0026nbsp;（21.5%）\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e103 （27.4%）\u003c/p\u003e\n \u003cp\u003e82 \u0026nbsp;（21.8%）\u003c/p\u003e\n \u003cp\u003e83 \u0026nbsp;（22.1%）\u003c/p\u003e\n \u003cp\u003e27 \u0026nbsp;（7.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.23580034423408%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistologic subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.65404475043029%\"\u003e\n \u003cp\u003eAdeno.\u003c/p\u003e\n \u003cp\u003eSquamous.\u003c/p\u003e\n \u003cp\u003eSCLC\u003c/p\u003e\n \u003cp\u003eAnother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"56.11015490533563%\"\u003e\n \u003cp\u003e233 （62%）\u003c/p\u003e\n \u003cp\u003e90 \u0026nbsp;（23.9%）\u003c/p\u003e\n \u003cp\u003e25 \u0026nbsp;（6.6%）\u003c/p\u003e\n \u003cp\u003e28 \u0026nbsp;（7.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferentiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003cp\u003eModerately\u003c/p\u003e\n \u003cp\u003ePoorly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e66 \u0026nbsp;（17.6%）\u003c/p\u003e\n \u003cp\u003e90 \u0026nbsp;（23.9%）\u003c/p\u003e\n \u003cp\u003e220 （58.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e94 \u0026nbsp;（25%）\u003c/p\u003e\n \u003cp\u003e282 （75%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNM staging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e205 （54.5%）\u003c/p\u003e\n \u003cp\u003e171 （45.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG PS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003e0-1\u003c/p\u003e\n \u003cp\u003e2-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e320 （85.1%）\u003c/p\u003e\n \u003cp\u003e56 \u0026nbsp;（14.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic illness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e59 \u0026nbsp;（15.7%）\u003c/p\u003e\n \u003cp\u003e20 \u0026nbsp;（5.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e32.68\u0026plusmn;16.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.275862068965516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLung function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.689655172413794%\"\u003e\n \u003cp\u003eFEV1\u003c/p\u003e\n \u003cp\u003eFEV1%\u003c/p\u003e\n \u003cp\u003eDLCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.03448275862069%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30%\"\u003e\n \u003cp\u003e2.37\u0026plusmn;0.63\u003c/p\u003e\n \u003cp\u003e78.65\u0026plusmn;9.84\u003c/p\u003e\n \u003cp\u003e6.74\u0026plusmn;1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.275862068965516%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.5517241379310345%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.482758620689655%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI, body mass index; ECOG PS, Eastern Cooperative Oncology Group performance status; TNM staging, tumor, nodes, metastasis-classification staging；PIS：Pleural invasion status；HOM：History of malignancy；RUL：Right upper lobe；RML：Right middle lobe；RLL：Right lower lobe；LUL：Left upper lobe；LLL：Left lower lobe；\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Patient\u0026apos;s peripheral blood data\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"18.785310734463277%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"39.548022598870055%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (25%,75%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"41.666666666666664%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal(n=376)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eHigh（%）\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; low（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.785310734463277%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophil count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.548022598870055%\"\u003e\n \u003cp\u003e4.07（3.13，5.04）(10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.372881355932204%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.293785310734464%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.785310734463277%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocyte count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.548022598870055%\"\u003e\n \u003cp\u003e1.87（1.5，2.34）(10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.372881355932204%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.293785310734464%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.785310734463277%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonocyte count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.548022598870055%\"\u003e\n \u003cp\u003e0.36（0.25，0.46）(10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.372881355932204%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.293785310734464%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.785310734463277%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.548022598870055%\"\u003e\n \u003cp\u003e237（196，285）(10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.372881355932204%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.293785310734464%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.785310734463277%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-Dimer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.548022598870055%\"\u003e\n \u003cp\u003e110.5（62.25，168）(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.372881355932204%\"\u003e\n \u003cp\u003e263（69.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.293785310734464%\"\u003e\n \u003cp\u003e113（30.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.785310734463277%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.548022598870055%\"\u003e\n \u003cp\u003e0.22（0.14，0.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.372881355932204%\"\u003e\n \u003cp\u003e137（36.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.293785310734464%\"\u003e\n \u003cp\u003e239（63.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.785310734463277%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.548022598870055%\"\u003e\n \u003cp\u003e2.61（1.58，2.94）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.372881355932204%\"\u003e\n \u003cp\u003e207（55.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.293785310734464%\"\u003e\n \u003cp\u003e169（44.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.785310734463277%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.548022598870055%\"\u003e\n \u003cp\u003e137.6（96.73，162.73）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.372881355932204%\"\u003e\n \u003cp\u003e167（44.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.293785310734464%\"\u003e\n \u003cp\u003e209（55.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMLR, monocyte to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable3. Univariate analyses of biomarkers for OS and PFS.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"34.5679012345679%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"34.74426807760141%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePFS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.764705882352942%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.925831202046037%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.18158567774936%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.020460358056265%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.925831202046037%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.18158567774936%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003esex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e2.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e1.59-3.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.000048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e2.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e1.51-3.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.000081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003eage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e1.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.99-1.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.241034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e1.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.99-1.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.257189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003esmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e1.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e1.16-2.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.006738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e1.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e1.07-2.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.021356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003epathology\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eanother\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSCLC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003esquama.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003eadeno.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003cp\u003e2.86\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.85-3.51\u003c/p\u003e\n \u003cp\u003e1.49-5.47\u003c/p\u003e\n \u003cp\u003e1.29-3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.128456\u003c/p\u003e\n \u003cp\u003e0.001567\u003c/p\u003e\n \u003cp\u003e0.001793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.70\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.87-3.32\u003c/p\u003e\n \u003cp\u003e1.33-4.81\u003c/p\u003e\n \u003cp\u003e1.21-2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.121719\u003c/p\u003e\n \u003cp\u003e0.004787\u003c/p\u003e\n \u003cp\u003e0.004036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003esize\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e1.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.99-1.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.326866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e1.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e1.00-1.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.262015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003eIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e1.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e1.20-2.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.003969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e1.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e1.35-2.85\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.000426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e1.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.86-1.83\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.238301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e1.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.92-1.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.133186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.92-1.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.369197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e0.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.93-1.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.527315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e0.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.27-0.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.000001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e0.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.30-0.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e0.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.31-0.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.00021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e0.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.33-0.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.000252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e0.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.39-0.83\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.003706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.44-0.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.014286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-Dimer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e0.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.24-0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.000453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e0.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.37-0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e0.012654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.52212389380531%\"\u003e\n \u003cp\u003e\u003cstrong\u003eECGO PS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e2-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.141592920353983%\"\u003e\n \u003cp\u003e0.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.18-0.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.31858407079646%\"\u003e\n \u003cp\u003e0.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.097345132743364%\"\u003e\n \u003cp\u003e0.14-0.31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.274336283185841%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable4. multivariate analyses of biomarkers for OS and PFS.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"17.491166077738516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"13.250883392226148%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"34.45229681978799%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"34.80565371024735%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePFS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.959287531806616%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.338422391857506%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.575063613231553%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.213740458015268%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.829516539440203%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.083969465648856%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cstrong\u003esex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.289241622574956%\"\u003e\n \u003cp\u003e1.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.403880070546737%\"\u003e\n \u003cp\u003e1.06-2.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.874779541446209%\"\u003e\n \u003cp\u003e0.02843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.465608465608465%\"\u003e\n \u003cp\u003e1.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.051146384479718%\"\u003e\n \u003cp\u003e0.96-2.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003e0.077308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathology\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eanother\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSCLC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003esquama.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003eadeno.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.289241622574956%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.41\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.403880070546737%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.69-2.90\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.14-4.44\u003c/p\u003e\n \u003cp\u003e0.98-2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.874779541446209%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.344902\u003c/p\u003e\n \u003cp\u003e0.018932\u003c/p\u003e\n \u003cp\u003e0.05821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.465608465608465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.47\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.051146384479718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.75-2.91\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.26-4.72\u003c/p\u003e\n \u003cp\u003e1.03-2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.265219\u003c/p\u003e\n \u003cp\u003e0.008322\u003c/p\u003e\n \u003cp\u003e0.037925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.289241622574956%\"\u003e\n \u003cp\u003e1.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.403880070546737%\"\u003e\n \u003cp\u003e0.97-2.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.874779541446209%\"\u003e\n \u003cp\u003e0.069652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.465608465608465%\"\u003e\n \u003cp\u003e1.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.051146384479718%\"\u003e\n \u003cp\u003e1.07-2.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003e0.021767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.289241622574956%\"\u003e\n \u003cp\u003e0.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.403880070546737%\"\u003e\n \u003cp\u003e0.37-0.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.874779541446209%\"\u003e\n \u003cp\u003e0.009145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.465608465608465%\"\u003e\n \u003cp\u003e0.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.051146384479718%\"\u003e\n \u003cp\u003e0.39-0.85\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003e0.005487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.289241622574956%\"\u003e\n \u003cp\u003e0.74\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.403880070546737%\"\u003e\n \u003cp\u003e0.49-1.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.874779541446209%\"\u003e\n \u003cp\u003e0.157511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"34.74426807760141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-Dimer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.289241622574956%\"\u003e\n \u003cp\u003e0.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.403880070546737%\"\u003e\n \u003cp\u003e0.29-0.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.874779541446209%\"\u003e\n \u003cp\u003e0.005376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.465608465608465%\"\u003e\n \u003cp\u003e0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.051146384479718%\"\u003e\n \u003cp\u003e0.43-1.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003e0.076619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cstrong\u003eECGO PS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003e2-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.289241622574956%\"\u003e\n \u003cp\u003e0.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.403880070546737%\"\u003e\n \u003cp\u003e0.18-0.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.874779541446209%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.465608465608465%\"\u003e\n \u003cp\u003e0.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.051146384479718%\"\u003e\n \u003cp\u003e0.15-0.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.227513227513228%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable5. Multivariate COX regression analysis of OS and PFS grouped by beneficial factors\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003egroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"17.937853107344633%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal（n=376）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"37.28813559322034%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"36.440677966101696%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePFS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.497120921305182%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.62763915547025%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.547024952015356%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.829174664107486%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.042226487523994%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.456813819577736%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eⅠ(0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.937853107344633%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"37.28813559322034%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"36.440677966101696%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.345120226308346%\"\u003e\n \u003cp\u003e\u003cstrong\u003eⅡ\u003c/strong\u003e\u003cstrong\u003e(1-2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.963224893917964%\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.841584158415841%\"\u003e\n \u003cp\u003e0.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.042432814710043%\"\u003e\n \u003cp\u003e0.26-0.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.456859971711458%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.506364922206506%\"\u003e\n \u003cp\u003e0.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.295615275813295%\"\u003e\n \u003cp\u003e0.31-0.74\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.548797736916548%\"\u003e\n \u003cp\u003e0.00083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.345120226308346%\"\u003e\n \u003cp\u003e\u003cstrong\u003eⅢ(3-4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.963224893917964%\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.841584158415841%\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.042432814710043%\"\u003e\n \u003cp\u003e0.17-0.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.456859971711458%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.506364922206506%\"\u003e\n \u003cp\u003e0.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.295615275813295%\"\u003e\n \u003cp\u003e0.23-0.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.548797736916548%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCovariables included age, Eastern Cooperative Oncology Group performance status (0-1 or 2-4), smoking status (former or never).I grope: with no beneficial factors，II grope：There are 1-2 beneficial factors，III grope：There are 3-4 beneficial factors\u003c/p\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Peripheral blood biomarkers, Early lung cancer, MLR, NLR, PLR, D-dimer","lastPublishedDoi":"10.21203/rs.3.rs-1194999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1194999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective\u003c/p\u003e\u003cp\u003eThe purpose of this paper is to predict the following items. preoperative baseline monocyte-to-lymphocyte ratio (MLR)、neutrophil-to-lymphocyte ratio (NLR) Platura-to-lymphocyte ratio (PLR) and dimeric fibrin fragment D (D-dimer) associated with clinical outcome in patients with Early Lung Cancer (LC).\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eWe performed a retrospective analysis of 376 patients with LC. Progression-free survival (PFS) and overall survival (OS) were assessed by Kaplan-Meier, and univariate and multivariate Cox regression analyses were performed to identify prognostic factors. Finally, multivariate Cox regression analysis was used to evaluate the influence of favorable factors on patients’ OS and PFS combined with the basic clinical characteristics of the patient \u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eAmong the variables screened by univariate Cox regression, MLR \u0026lt; 0.22, NLR \u0026lt; 1.99, PLR \u0026lt; 130.55 and D-Dimer \u0026lt; 70.5 (ng/ml) were significantly associated with both better OS and PFS. In multivariate Cox regression analysis, it was determined that MLR and D-Dimer had a better independent correlation with OS (p = 0.009, p = 0.05, respectively), while MLR was only better independently associated with PFS (P = 0.005). Furthermore, according to the number of favorable factors, patients with none of these factors had a significantly worse prognosis than patients with at least one of these factors.\u003c/p\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003cp\u003e\u003cspan class=\"ql-cursor\"\u003e\u003c/span\u003eBaseline characteristics of low MLR, low NLR, low PLR and low D-dimer were associated with better outcomes.\u003c/p\u003e","manuscriptTitle":"Proportions of Beneficial Factors in MLR, NLR, PLR and D-dimer in Preoperative Peripheral Blood of Patients With Early Stage Lung Cancer as Predictors of Patient Survival After Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-12-30 14:37:32","doi":"10.21203/rs.3.rs-1194999/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-02-18T21:58:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-02-18T17:17:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-01-30T22:25:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8324f784-ca84-45e2-bbd4-8944604af77f","date":"2022-01-18T17:46:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a6f54c47-407a-41ab-8e5d-c9525c50421b","date":"2022-01-17T23:11:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-05T15:38:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-05T03:15:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-12-28T03:37:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-12-28T03:23:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2021-12-22T08:45:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"369d4214-722e-42bc-b823-719f4be64f67","owner":[],"postedDate":"December 30th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":9433658,"name":"Pulmonology"}],"tags":[],"updatedAt":"2022-03-21T04:14:14+00:00","versionOfRecord":[],"versionCreatedAt":"2021-12-30 14:37:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1194999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1194999","identity":"rs-1194999","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}