Development and Validation of a Prediction Formula for Survival Time in Patients with Lung Squamous Cell Carcinoma

Development and Validation of a Prediction Formula for Survival Time in Patients with Lung Squamous Cell Carcinoma | 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 Development and Validation of a Prediction Formula for Survival Time in Patients with Lung Squamous Cell Carcinoma Yoshio Ichihashi, Teruyoshi Amagai, Shinichi Nakatsuka, Kiyoaki Uryuu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6547462/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lung cancer has become the leading cause of cancer-related mortality. The 5-year overall survival rate of lung squamous cell carcinoma (LUSC) remains less than 15%. Identifying patients who are likely to experience short or long survival has clinical utility by helping to minimize overtreatment or undertreatment. However, there is currently no method to predict survival time based on available information. Aim To develop and validate a prediction formula for survival after surgery for LUSC using readily available biomarkers. Methods The inclusion criteria of patients were male, who was diagnosed LUSC underwent radical surgery in two hospitals between April 2015 and December 2018. The methods consist of three parts as pooled analysis, phase 1 and 2. Before analyzing the two phases, a pooled analysis was performed to determine whether data from two hospitals could be combined and analyzed together. In phase 1, the prediction formula was developed using biomarkers that were proved reliable. In phase 2, the validation test was conducted to verify the accuracy of the developed formula.Patient data were as follows: 1. Demographics, 2. Laboratory data of Complete blood count (CBC) and biochemistry values were measured prior to the LUSC radical operation, 3. Biomarkers included Advanced Lung Cancer Inflammation Index (ALI), Hemoglobin, Alb, Lymphocyte, Platelets, Neutrophil/Lymphocyte Ratio and Systemic immune-inflammation index, with hemoglobin concentration (Hb). 4. Outcome measure was 5-year survival, defined as survival status during 5 years after radical surgery. All data were compared between two groups divided by the survival status at 5 years after the operation. Results 69 patients with LUSC were enrolled. 1. In phase 1, The prediction formula was developed as follows: Survival time = [6.9 × ALI] + [93.4 × Hb] -198.6 (days). 2. In phase 2, Validation test using 39 enrolled LUSC patients showed that the error rate was − 4% (SD 28%). Conclusion The prediction formula of survival time in patients with squamous cell lung cancer was developed using ALI and Hb as follows: Survival time (days) = [6.9 × ALI] + [93.4 × Hb] -198.6. The ROC curve analysis showed that the cut-off value of ALI was 48.04 (AUC = 0.690, p = 0.007). The novel prediction formula was developed and seemed feasible with an error rate of -4% (standard deviation of 28%), indicating that the newly developed prediction formula seems to be clinically feasible. Lung Squamous Cell Carcinoma Prediction formula Survival time Figures Figure 1 Figure 2 Background Lung cancer has emerging as the leading cause of cancer-related mortality. According to the Global Cancer Observatory (GLOBOCAN), in 2020, there were approximately 2.2 million new cases of and 1.8 million deaths caused by lung cancer worldwide [1]. The most common type of lung cancer is non-small-cell lung cancer (NSCLC), which consists of adenocarcinoma, lung squamous cell carcinoma (LUSC), and large cell carcinoma, accounting for 85% of lung cancer [2]. The 5-year overall remains less than survival rate of LUSC is less than 15% [3]. Identification of patients who are likely to experience short or long survival has clinical utility by helping to minimize over- or under-treatment, potentially leading to improved clinical decision making [4]. Indicators for predicting survival as 30-day mortality [5, 6] or survival rate within 5 years using various biomarkers [7–10], raising protocol of biomarker-driven therapies [11], or genetic information mainly based on single cell mRNA or DNA basis [12–15] have been reported. They have gained attention as new prognostic biomarkers in a variety of cancers. These methods are included in a cross-sectional approach, such as overall survival or disease-free survival at certain time of 30 days, 90 days, 5 years, 10 years at the level of patient group. The hidden problem of cross-sectional methods may be that it only indicates whether a patient is alive or not at a certain point in time, and it is not possible to predict individual survival length of days. To date, there is no longitudinal method available to predict how long each patient will live from a longitudinal perspective. Therefore, it is not possible to predict the longitudinal outcome on a case-by-case basis. The clinical meaning of the survival prediction formula is to avoid over- or under-treatment and to reduce the incidence of treatment-related adverse events. Moreover, from the point of view of medical progress, the prediction signature of survival time can be used to evaluate the progress of treatment. Therefore, it seems reasonable that this method can be developed and used as an indicator for medical quality evaluation from a new horizon. In this study, we developed a prediction formula for survival time after radical surgery for LUSC using available biomarkers and examined its validity. Methods Aim To develop a prediction formula for survival after LUSC surgery as an individual longitudinal survival day prediction using readily available biomarkers and to prevent the occurrence of adverse events due to over- or under-treatment after surgery. Participants Eligibility Criteria The inclusion criteria of patients were male, who was diagnosed LUSC and underwent radical surgery at Osaka Medical and Pharmaceutical University (OMPU) Hospital between April 2007 and December 2015, or at Yao Tokushukai General (YTG) Hospital between April 2015. In addition, for validation test analysis of developed formula, patients with LUSC and underwent the operation during December 2018 at OMPU were included as validation test participants. This time period was chosen to complete the follow-up period more than 5 years after radical surgery. Exclusion criteria Exclusion criteria are as follows: 1, female, 2, male who has a loss of follow-up of 5 years after LUSC radical surgery, 3, pathology diagnosis was not squamous cell carcinoma. Variables Available for Prediction Formula Development The variables collected from the subject's electronic medical records were as follows: 1, Demographics , including sex, age, body mass index (BMI, body weight (kg) divided by height (meters) square, kg. m 2 , 2, Laboratory data of complete blood counts (CBC) and biochemistry, including white blood cell, total lymphocyte (TLC), neutrophile (Neu), hemoglobin (Hb), platelet (PLT), and albumin (Alb). Blood measurements were taken within 7 days of hospitalization prior to radical LUSC surgery, 3, Biomarker indices , that were readily available, were chosed, including inflammation index and surgical stress index - advanced lung cancer index (ALI) [15], Hb, Alb, TLC, PLT (HALP) score [16, 18], NTP-lymphocyte ratio (NLR) [19], and systemic immune-inflammation index (SII) [20]. The equations for calculating them are shown (Table 1 ), 4, Pathological measures included pathological TNM stage (pTNM stage) and PDPN immune-histological staining results were evaluated. Tumor stage was defined according to the Union for International Cancer Control (UICC) TNM classification, and histologic type was defined according to the World Health Organization classification. Table 1 Equations of biomarkers used in the current study 1, Advanced Lung Cancer Inflammation Index (ALI) = BMI x Alb/ NLR [15] 2, Hemoglobin Alb Lymphocyte Platelets (HALP) = Hb x Alb x TLC/ PLT [17, 18] 3, Neutrophil/Lymphocyte Ratio (NLR) = Neu/ TLC [19] 4, Systemic immune-inflammation index (SII) = PLT X NLR [20] Abbreviations, BMI = body weight (kg) / [height (m) ] 2 [kg/m2], Alb: serum albumin level (g/dL), Hb: hemoglobin concentration (g/dL), TLC: peripheral total lymphocyte count (count/μL), PLT: platelet count (count/μL), Neu: peripheral blood neutrophil count (count/μL). Outcome measure The outcome measure was 5-year survival, defined as alive or dead at the time of 5 years after radical surgery. Pooled Analysis of Inter-Institutional Comparison Prior to Phase 1 and 2 Analyses To determine whether data from two sites could be combined and analyzed together, all variables collected from two hospitals were compared as a pooled analysis. If there were no statistical differences, it could be interpreted that there was no subject bias from two sites; it could be determined that all data from two sites could be analyzed together in further analyses of Methods 1, 2, and 3. Method 1 Development of the Prediction formula - Phase 1 - Method 1.1 Comparison of data of survival and death within 5 years of LUSC surgery All cases were divided into two groups, a 5-year survival group and a death group at the time of 5 years passed after the radical operation. Then, all collected data were compared to discriminate them to draw the biomarker to predict survival cases. Method 1.2 Development of the Prediction Formula of survival time Using the variable for predicting survival days after the LUSC radical operation, we developed the prediction formula using multiple logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was also performed to determine the cutoff value of the biomarkers available in the prediction formula. Method 2 Validation Test of the Developed Prediction Formula - Phase 2 - To test the validity of the prediction formula of survival days for squamous cell carcinoma obtained in result 2–2, the validity of the prediction formula was tested for those who were male and underwent surgery in 2019 at OMPU Hospital, of whom the 5-year survival rate could be analyzed. To analyze the validation of the prediction formula, we defined the error rate in which the difference between the predicted days (A) obtained using the prediction formula and the actual days of survival (B) was calculated as [(A-B)/ B (%)]. Method 3 Kaplan-Meier analysis for the possibility of Prediction Using PDPN Staining Results The correlation between PDPN staining and survival rate was investigated. Here, patients' surgical specimens were analyzed by histological staging and PDPN staining strength, divided into three degrees of strong, weak positive, or negative. The details are described below. Kaplan-Meier analysis was performed to compare two groups divided by PDPN staining result, strong positive vs. the remaining including weak positive and negative staining (Fig. 1 ). PDPN staining as Pathological analysis LUSC surgical specimen was collected from each case at the time of surgery, and fixed in 10% formalin for 24–48 hours, embedded in paraffin, sectioned at 4 µm, and stained with hematoxylin and eosin (H&E). For histopathological evaluation by PDPN staining, thin surgical sections of formalin-fixed paraffin-embedded tissue blocks were used. The immunohistochemical techniques were performed using anti- PDPN antibody (D2-40, DAKO) to evaluate its expression. Lymphatic endothelium was used as a strong positive control, and the presence of cancer cells stained with anti- PDPN antibody resulted in PDPN positive results. PDPN staining results were classified into three as follows: strong positive (Supplementary Fig. 1), weak positive (Supplementary Fig. 2), and negative (Supplementary Fig. 3). All immunohistochemical data were evaluated by a single pathologist (S.N.) who was blinded to the clinical status of the patients. Sample size calculation First, to calculate sample size in the developing prediction formula in phase 1, with a 95% confidence level and 5% margin error, and population proportion and population size, we set at 20% and 100, respectively. From these settings, the sample size was calculated as 72. Second, to calculate the sample size for the external validation test in phase 2, the number of subjects was set at 70, the confidence level was set at 90%, and the margin of error was set at 10%. As a result, the number of subjects required was 35, where 35 was set as the lowest limit of subject number of validation test. Statistical analysis Prognostic factors independent of sex, age, Alb, CRP, Hb, PLT, total lymphocyte count (TLC), neutrophil count, monocyte count, ALI, HALP, NLR, and SII as proteomics indicators were identified. Overall survival rate (OS) was calculated from the date of death from any cause after surgery. P < 0.05 was considered a statistically significant difference. All statistics were performed using SPSS version 29 (IBM, Armonk, NY, USA). Results 77 cases were included in the study. Among them, the result of sample size calculation is 72 as described in the statistics session. Among 77 cases, 58 cases were from OMPU and 19 cases were from YTG Hospital. To see female subjects, 6 from OMPU and 2 from YTG hospital, a total of 8 females were excluded. As the remaining 52 and 17 from two hospitals, a total of 69 cases were proceeded to further analysis (Fig. 1 ). The postoperative follow-up period was set at 60 months. The median age was 73.5 years (IQR: 55, 86 years). In terms of pathological stage classification, IA, IB, IIA, IIB, IIIA, IIB, and IV were 20, 23, 3, 13, 9, 0, and 1 case, respectively. There were 14 cases with lymph node metastasis. Result of the pooled analysis of the inter-institutional comparison Data from the two institutions were compared to examine the demographics, laboratory data, and outcomes of patients who underwent LUSC curative surgery at the two institutions. No statistically significant differences were found (Supplementary table 1 ). Therefore, it was determined that all data from the two institutions could be combined for further analysis. Therefore, data from two institutions were combined for analysis in the following methods. Result 1.1 Comparison of Survival and Death Groups in Developing Set Comparing the survival and death groups, statistically significant differences were observed in subject demographics and blood test, including BMI, Alb, and Hb (Table 2 ). Table 2 Comparison of data of survivors and death at the 5-year after the radical operation. Measures Total Survivors Deaths p-v alue Subject number 69 38 31 Demographics Age, Years old 73 (70, 78) 72 (68, 78) 73 (71, 78) 0.246 BMI, kg/m 2 22.0 (20.0, 24.3) 23.3 (21.0, 24.9) 20.8 (18.8, 23.7) 0.004 Laboratory index Alb, g/dL 4.1 (3.6, 4.3) 4.1 (3.9, 4.4) 3.8 (3.5, 4.2) 0.014 CRP, mg/dL 0.20 (0.10, 1.18) 0.15 (0.07, 0.73) 0.40 (0.10, 1.87) 0.075 Hb, g/dL 13.5 (12.1, 14.5) 14.0 (12.9, 15.0) 12.4 (11.1, 14.2) 0.002 PLT, 10 4 /µL 22.5 (18.2, 27.1) 22.7 (17.7, 26.8) 22.0 (18.7, 28.2) 0.942 TLC, counts/µL 1667 (1268, 2049) 1724 (1387, 2225) 1532 (1156, 1985) 0.132 Neutrophile, counts/µL 4108 (2888, 4966) 4035 (2905, 5054) 4192 (2871, 4943) 0.856 Mono, counts/µL 388 (302, 494) 351 (288, 492) 417 (347, 504) 0.221 PLR 143.89 (111.69, 170.93) 125.42 (99.35, 165.72) 152.37 (134.55, 210.53) 0.070 NLR 2.30 (1.78, 3.38) 2.25 (1.53, 3.27) 2.43 (2.08, 3.76) 0.144 ALI 36.02 (26.01, 46.34) 40.77 (31.07, 57.95) 32.61 (20.58, 39.90) 0.007 SII, 10 4 /µL 55.4 (37.4, 76.1) 52.9 (33.4, 72.2) 60.9 (37.8, 92.4) 0.167 HALP 37.87 (27.01, 50.71) 43.94 (31.48, 55.92) 35.57 (23.50, 40.86) 0.009 Pathological measures p Stage Ⅰ AB, n (%) 43 (62) 27 (71) 16 (52) 0.097 PDPN strong positive, n (%) 41 (59) 24 (63) 17 (55) 0.484 Outcome measure Survival days After operation 1826 (684, 1826) 1826 (1826, 1826) 621 (331, 1133) < 0.001 All data are expressed in medium (25%ile, 75%ile), Mann-Whitney's U test and the chi-square or Fisher's exact test for categorical variables. Result 1.2 Multiple logistic regression analysis for detecting Variables as confound available in the prediction formula To identify the available biomarkers to discriminate survival and death within 5 years after surgery, multiple logistic regression analysis for odds ratio (OR), 95% confidence interval (95% CI) and p-value for predicting 5-year survival was performed (Table 3 ).As the results, ALI and Hb were independent variables for 5-year survival prediction. The results of comparisons of HALP, NLR, and SII had no significant OR. Table 3 Results of multiple logistic regression analysis for detecting Variables as confound available in the prediction formula Variable OR (95%CI) p-v alue Age 1.003 (0.920–1.093) 0.945 ALI 0.961 (0.928–0.996) 0.030 Hb 0.670 (0.474–0.946) 0.023 Podoplanin 0.987 (0.319–3.057) 0.982 Result 1.3 Development of the prediction formula In Result 1.2.1, the two important features that were significant for predicting survival were ALI and Hb. Using the two features, the prediction formula of 5-year survival days after radical surgery for patients with LUSC was drawn as follows: Survival days = [6.9 × ALI] + [93.4 × Hb] -198.6. Then, ROC curve analysis was performed to determine the cutoff value of ALI for predicting 5-year survival. As a result, the cutoff value of ALI was 48.04 (AUC = 0.690, p = 0.007) (Supplementary Fig. 4). These statistical results of the ROC curve analysis with the meaningful high AUC and low p-value are interpreted that this prediction formula is relatively acceptable for clinical availability. Result 2. Results of Validation Test The number of subjects for the validation set of 39 cases, which satisfied the sample size of 33 cases calculated in method 2 and resulted in sufficient sample size for an external validation test. The relationship between actual survival time (days) and error rate are shown in Fig. 2 . The average error rate was − 4% with a standard deviation of 28%. Result 3. Results of Investigation of Correlation Between PDPN Staining Results and Survival Staining of surgical LUSC specimens with PDPN antibody revealed that 41 (13 strong positive, 28 weak positive) and 28 cases were PDPN positive and negative, respectively. In the next step, all cases were divided into two subgroups, one with strong PDPN and the other with weak and negative PDPN. Comparing all data including survival between two groups, statistically significant differences were found in age (p-value 0.003), Alb (p-value 0.010), NLR and SII (Table 4 ). Table 4 Comparison of outcome measures between result of PDPN staining, strong positive vs. weak positive + negative. PDPN staining Total Strong positive Others p-v alue Subject number 69 13 56 Demographics Age, Years old 73 (70, 78) 75 (73, 83) 72 (69, 78) 0.026 BMI, kg/m 2 22.0 (20.0, 24.3) 22.4 (18.7, 25.3) 22.0 (20.0, 24.1) 0.921 Laboratory index ALI 36.02 (26.01, 46.34) 46.27 (33.53, 69.73) 35.39 (24.07, 45.14) 0.053 HALP 37.87 (27.01, 50.71) 37.87 (30.89, 79.32) 37.52 (24.70, 50.23) 0.349 SII 55.4 (37.4, 76.1) 39.3 (29.1, 59.2) 59.1 (38.4, 80.4) 0.044 Pathological measures p Stage ⅠAB, n (%) 43 (62) 10 (77) 33 (59) 0.189 PDPN-strong positive, n (%) 41 (59) 13 (100) 28 (50) < 0.001 Abbreviations, ALI: advanced lung cancer inflammation index, BMI: body mass index, HALP: hemoglobin albumin lymphocyte platelet, NLR: neutrophile-lymphocyte ratio, SII: systemic immune-inflammation index, PDPN: podoplanin, Then, Kaplan-Meier analysis was performed to compare two groups. As the results of Kaplan-Meier analysis, a trend toward longer survival in the strong positive group was observed. However, there was no significant difference in survival between the two groups (Supplementary Fig. 5). This result is interpreted that there was a trend of survival of the PDPN strong positive patients with better than them of the others. Discussion An Error rate of the developed Prediction Formula seems acceptable In Fig. 1 , which shows the difference between [predicted - actual] survival days of the prediction formula and the error rate, the error rate was within +/- 100% in 38 of the 39 cases, and the average error was − 4% with a standard deviation of 28%. In general, an error rate of 5% or less is considered allowable limit of error, guaranteed by principles [21]. From this aspect, the validation test of this formula showed that the error in 38 of 39 cases, i.e., 97% of cases, was clinically usable. In addition, the correlation was shown to increase as the actual survival time increased. In other words, the prediction formula for survival days obtained this time showed a tendency for the error to reverse from negative to positive at 1000 days, and as the survival time increased beyond 1000 days, the error obtained by the prediction formula tended to increase. However, the error was within + 60% at 2000 days of actual survival day or 5.5 years, as shown on the horizontal axis of Fig. 2 . In other words, when the predicted survival time exceeds 2000 days, the survival time tends to be underestimated by the prediction formula, which means that it is necessary to use this formula while keeping in mind that the actual probability of survival is higher than the predicted number of days when the predicted survival day exceeds 2000 days or 5.5 years. What diseases use ALI as a prognostic indicator and Why? ALI is an inflammation index that was originally developed in 2013 as a prognostic indicator for stage IV non-small cell lung cancer (NSCLC). Since then, it has become more widely used as a prognostic indicator not only for other cancers, but also for chronic diseases whose pathology is primarily inflammatory. As of April 1, 2025, a literature search using ALI as a keyword on PubMed yielded 261 articles. Of the 261 articles, 165 were not related to the ALI and the remaining 96 were articles that used ALI as a prognostic indicator for various diseases. Clarifying 96 articles furthermore, lung cancer-related articles, non-lung cancer-related, lifestyle-related diseases, and other diseases of article number of 29, 34, 9, and 34, respectively (Table 5 ). Focusing on lung cancer, NSCLC had the largest number of articles with 16, while no article dealing with a prediction formula using ALI or the others has been reported to date. In addition, no ALI-related articles were found for LUSC. In contrast to lung cancer, 34 ALI-related articles were found for cancers other than lung cancer. The largest number of ALI-article was on gastrointestinal cancer (15 articles, Table 5 ). Table 5 The 261 ALI-related articles were grouped into four disease categories, with the diseases and number of articles included in any of four categories. Category Subtype Article Total Supplementary reference number article number Lung cancer NSCLC 16 29 6, 15, 21, 28, 37, 41, 46, 52, 55, 62, 73, 75, 78, 84, 87, 94. SCLC 4 32, 61, 68, 93. Lung cancer 7 2, 7, 34, 49, 82, 83, 90. Lung adenocarcinoma 2 29, 69. Non-lung cancer Gastrointestinal tract 14 34 3, 8, 14, 16, 22, 33, 42, 44, 56 , 63, 65, 74, 79, 95. HCC 6 9, 24, 30, 39, 50, 76. Head Neck 2 35, 59. MM 2 43, 57. Urinary tract 2 45, 92. Malignant lymphoma 2 86, 96. Pancreas 2 54, 71. Cholangiocarcinoma 1 27 Skin 1 26 Neuroblastoma 1 25 Unknown 1 47 Life-related diseases HT 2 9 5, 11. DM 2 1, 36. CKD 3 10, 40, 64. Fatty Iiver 2 72, 91. Others Heart failure 5 24 12, 13, 18, 19, 23. Asthma 4 31, 38, 51, 85. ACS 3 20, 67, 89. MI 3 60, 66, 88. Others 9 4, 58 (stroke), 17, 48 (RA) , 53 (Pulmonary fibrosis) , 70 (CAP), 77 (pneumonia) , 80 (chronic pain), 81 (gallstone) Total 96 96 The four categories are shown from #1 to #4 as follows: #1, lung cancer, divided into four subcategories such as non-small cell lung cancer (NSMLC), small cell lung cancer (SCLC), overall lung cancer, and lung cancer adenocarcinoma, #2, Non-lung cancers , including gastrointestinal tract, hepato-cellular carcinoma (HCC), Head & neck cancers, malignant melanoma (MM), urinary tract cancers, malignant lymphoma, pancreatic cancer, cholangiocarcinoma, skin cancer, neuroblastoma, and unknown cancers, #3, Non-communicable Life-related diseases , including hypertension (HT), diabetes mellites (DM), chronic kidney disease (CKD), and fatty liver, and #4, The others, including heart failure, asthma, acute coronary syndrome (ACS), myocardial infarction (MI), and others (stroke, rheumatic arthritis, pulmonary fibrosis, community-acquired pneumonia (CAP), pneumonia, chronic pain, and gallstone. The list of ALI-related 96 articles are shown in supplementary table 2. The other subject of ALI-related articles to use ALI to predict the prognosis was lifestyle-related diseases, including heart failure, asthma, acute coronary syndrome and myocardial infarction. This analysis of 96 ALI-related articles showed that ALI is also useful for predicting the prognosis of lifestyle-related diseases other than cancer. One of the reasons for this may be that ALI involves BMI and Alb, which is an inflammatory reflection, in its calculation. The similar relationship between prognosis of cancers and ALI might be related to malnutrition revealed by BMI and an inflammatory response. However, to clarify why ALI is useful for predicting lung cancer prognosis, further analysis is needed to explain the reason why ALI might be related to the survival time of cancer patients as shown in the current study. Lack of Clinical significance of the predictive value of PDPN in LUSC The mucous sialoglycoprotein podoplanin (PDPN) is widely used as a histopathological marker to differentiate lymphatic vessels from blood vessels due to its expression on lymphatic vessel endothelial cells [22, 23]. A study to determine whether PDPN expressed in LUSC correlates with 5-year survival; PDPN-positive cases are often considered to have an unfavorable prognosis due to immunosuppressive microenvironments [24, 25]. On the other hand, there are also reports showing the opposite and the results are not consistent. Therefore, we decided to investigate whether PDPN positivity is associated with survival, and if so, whether it is a good or bad prognostic factor, and to compare it with other proteomic markers. As a result, contrary to our expectations, the association between PDPN and survival prognosis was not proved. Further additional studies of LUSC are needed to clarify whether or not there is predictive value of the PDPN-staining findings. Strength and limitations The strength of this study is, to our knowledge, the first report to predict survival days in patients with LUSC using ALI and Hb. The predictive indicators currently used for overall or disease-free survival are survival at 30 days, 90 days, 5 years, 10 years, and so on in the patient group. This index is a cross-sectional indicator that deals with a group of patients. The problem with this indicator is that it only indicates whether a patient is alive or not at a certain point in time, and it is not possible to predict individual survival days. In this respect, the prediction formula for survival days obtained in the current study is a longitudinal indicator of individual survival days. Future clinical application and verification are required. The limitations of this study will now be highlighted. First, the effectiveness of PDPN as a prognostic predictor could not be clarified. The reasons for this may be related to lack of subjects and retrospective research style. In future studies, it will be necessary to increase the number of subjects and to conduct a prospective study with not only early staged but also advanced staged subjects. The mechanism behind why ALI and Hb are useful in the prediction formula is unclear. To clarify this, it will be necessary to scientifically clarify the significance of the four indices included in ALI, BMI, Alb, NLR (Neu, TLC), in terms of LUSC survival time. Second, simple size must be small. Although the number of subjects was extremely limited and met the sample size calculation, it is expected that reliability would be improved by studying a larger number of subjects. Conclusion The prediction formula of survival time in patients with squamous cell lung cancer was developed using ALI and Hb as follows: Survival time (days) = [6.9 × ALI] + [93.4 × Hb] -198.6. The ROC curve analysis showed that the cut-off value for ALI was 48.04 (AUC = 0.690, p = 0.007), indicating that the newly developed prediction formula seems to be clinically feasible. Abbreviations ALI: advanced lung cancer inflammation index, BMI = body weight (kg) / [height (m) ] 2 [kg/m 2 ], Alb: serum albumin level (g/dL), HALP: hemoglobin albumin lymphocyte platelet, Hb: hemoglobin concentration (g/dL), NLR: neutrophile-lymphocyte ratio, PLT: platelet count (count/μL), PDPN: podoplanin, SII: systemic immune-inflammation index, TLC: peripheral total lymphocyte count (count/μL). Declarations Author Contributions declaration Y.I., A.T.. and T.A.i formulated the original idea; Yoshio Ichihashi completed the database search, data extraction, and analysis; S.N., K.U., H.H., K.O., and T.A. drafted the manuscript; K.S., N.H. reviewed and revised the manuscript for important intellectual content; and all authors provided final approval of the version to be submitted. All authors have read and agreed to the published version of the manuscript. Statement of ethics This cooperative study was conducted and approved by the Internal Review Board (IRB) of the Osaka Medical & Pharmaceutical University. The approval number is 2021-185-3 dated on March 20, 2025. The ethics approval from the Internal Review Board (IRB) of Osaka Medical & Pharmaceutical University approved that instead of obtaining informed consent from all participants, the ethics committee would post an opt-out notice at the hospital and on its website to confirm that no participant refused to participate. In addition, this study has been conducted in accordance with the Declaration of Helsinki. Human Ethics and Consent to Participate declaration s: Not applicable. Ethics approval and consent to participate : This cooperative study was conducted and approved by the Internal Review Board (IRB) of the Osaka Medical & Pharmaceutical University. The approval number is 2021-185-3 dated on March 20, 2025. The ethics approval from the Internal Review Board (IRB) of Osaka Medical & Pharmaceutical University approved that instead of obtaining informed consent from all participants, the ethics committee would post an opt-out notice at the hospital and on its website to confirm that no participant refused to participate. In addition, this study has been conducted in accordance with the Declaration of Helsinki. Funding Declaration: Not applicable. Consent for publication: Not applicable. Availability of data and materials: All data included in this article is available in the supplementary files. References Sung H, Ferlay J Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A Cancer J. Clin. 2021, 71, 209–249. Yu H, Pang Z, Li G, Gu T. Bioinformatics analysis of differentially expressed miRNAs in non-small cell lung cancer. J Clin Lab Anal. 2021;35(2):e23588. doi: 10.1002/jcla.23588. Lu T, Yang X, Huang Y, Zhao M, Li M, Ma K, et al. Trends in the incidence, treatment, and survival of patients with lung cancer in the last four decades. Cancer Manag Res. 2019;11:943–53. doi: 10.2147/CMAR.S187317. Miller HA, van Berkel VH, Frieboes HB. Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data. Metabolomics. 2022; 18(8):57. doi: 10.1007/s11306-022-01918-3. Razdan S, Slijivich M, Pfail J, Wiklund P, Sfakianos J, Wainganlar N. Predictive morbidity and mortality after radical cystectomy using calculators: A comprehensive review of the literature. Urol Oncol. 2021; 39(2): 109-120. doi: 10.1016/j.urolonc.2020.09.032. Wang X, Zhao M, Zhang C, Chen H, Liu X, An Y, Zhang L, Guo X. Establishment and clinical application of the nomogram related to risk or prognosis of hepatocellular carcinoma: A review. J Hepatocell Carcinoma. 2023; 10: 1389-1398. doi: 10.2147/JHC.S417123. Chen JW, Dhahbi J. Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection doi: 10.1038/s41598-021-92725-8.methods. Sci Rep. 2021; 11(1): 13323. Pezzella F. Early squamous cell lung carcinoma: prognostic biomarker for the many. Thorax. 2019;74(6): 527-528. doi: 10.1136/thoraxjnl-2018-212829. Okabe N, Ezaki J, Yamaura T, Muto S, Osugi J, Tamura H, et al. FAM83B is a novel biomarker for diagnosis and prognosis of lung squamous cell carcinoma. Int J Oncol. 2015;46(3):999-1006. doi: 10.3892/ijo.2015.2817. Zamay TN, Zamay GS, Kolovskaya OS, Zukov RA, Petrova MM, Garguan A, et al. Current and prospective proytein biomarkers of lung cancer. Cancers (Basel). 2017; 9(11):155. doi: 10.3390/cancers9110155. Redman MW, Papadimitrakpopoulou VA, Minichiello K, Hirsch RF, Mack PC, Schwartz LH, et al. Biomarker-driven therapies for previously treated squamous non-small-cell lung cancer (Lung-MAP SWOG S1400): a biomarker-driven master protocol. Lancet Oncol. 2020;21(12)1589-1601. doi: 10.1016/S1470-2045(20)30475-7. Li Q, Wang R, Yang Z, Li W, Yang J, Wang Z, et al. Molecular profiling of human non-small cell lung cancer by single-cell RNA-seq. Genome Med. 2022; 14(1):87. doi: 10.1186/s13073-022-01089-9. He B, Wei C, Cai Q, Zhang P, Shi S, Peng X, et al. Switched alternative splicing events as attractive features in lung squamous cell carcinoma. Cancer Cell Int. 2022; 22(1): 5. doi: 10.1186/s12935-021-02429-2. Zhang S, Liu Y, Liu K, Hu X, Gu X. A review of current developments in RNA modifications in lung cancer. Cancer Cell Int. 2024; 24(1):347. doi: 10.1186/s12935-024-03528-6. Bu Y, Liu Y, Hu C, Yuan D, Luo L, Li M, et al. MSR1 in lung squamous cell carcinoma: Prognostic and immunological values in pan- and single-cell analyses and a cohort study. Int Immunopharmacol. 2025; 145: 113811. doi: 10.1016/j.intimp.2024.113811. Jafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer 2013; 13: 158. Chen XL, Xue L, Wang W, Chen HN, Zhang WH, et al. Prognostic significance of the combination of preoperative hemoglobin, albumin, lymphocyte and platelet in patients with gastric carcinoma: a retrospective cohort study. Oncotarget. 2015;6(38):41370-82. doi: 10.18632/oncotarget.5629. Gursoy V, Sadri S, Kucukelyas HD, Hunutlu FC, Pinar IE, Yegen ZS, et al. HALP scores as a novel prognostic factor for patients with myelodysplastic syndromes. Sci Rep. 2024;14(1):13843. doi: 10.1038/s41598-024-64166-6. Buonacera A, Stancanelli B, Colaci M, Malatino L. Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases. Int J Mol Sci. 2022;23(7):3636. doi: 10.3390/ijms23073636. Selahattin Vural S, Ali Muhtaroğlu A, Mert Güngör M. Systemic immune-inflammation index: A new marker in differentiation of different thyroid diseases. Medicine (Baltimore). 2023;102(31):e34596. doi: 10.1097/MD.0000000000034596. Fraser CG. Biological variation: From principles to practice. Clinica Chimica Acta 2003; 331(1):173-174. DOI: 10.1016/S0009-8981(03)0007S2-X. Schoppmann SF, Birner P, Studer P, Breiteneder-Gelef S. Lymphatic microvessel density and lymphovascular invasion assessed by anti-podoplanin immunostaining in human breast cancer. Anticancer Res. 2001;21(4A):2351–5. Breiteneder-Gelef S, Soleiman A, Horvat R, Amann G, Kowalski H, Kerjaschki D. Podoplanin–a specifc marker for lymphatic endothelium expressed in angiosarcoma. Verh Dtsch Ges Pathol. 1999;83:270–5. Sakai T, Aokage K, Neri S, Nakamura H, Nomura S, Tane K, Miyoshi T, Sugano M, Kojima M, Fujii S, Kuwata T, Ochiai A, Iyoda A, Tsuboi M, Ishii G. Link between tumor-promoting fibrous microenvironment and an immunosuppressive microenvironment in stage I lung adenocarcinoma. Lung Cancer. 2018;126:64–7 Suzuki J, Aokage K, Neri S, Sakai T, Hashimoto H, Su YH, Yamazaki S, Nakamura H, Tane K, Miyoshi T, Sugano M, Kojima M, Fujii S, Kuwata T, Ochiai A, Tsuboi M, Ishii G. Relationship between podoplanin-expressing cancer-associated fibroblasts and the immune microenvironment of early lung squamous cell carcinoma. Lung Cancer. 2021;153:1–10. Additional Declarations No competing interests reported. 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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-6547462","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464279857,"identity":"f3a5df96-f6f2-4fc1-be35-9014b22230b5","order_by":0,"name":"Yoshio Ichihashi","email":"","orcid":"","institution":"Osaka Medical and Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Yoshio","middleName":"","lastName":"Ichihashi","suffix":""},{"id":464279858,"identity":"091b5d26-8ac3-4b08-b672-624bf424ef44","order_by":1,"name":"Teruyoshi Amagai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYNACAwY5BgYeGO8AUVoMjEnVwmCQ2IDQQgDwTzv8+MWPgj/pG46fPfjgA4OdHAPjWfzWSNxOM7PsMTDI3XAmL9lwBkMy0IXnEvBbczvBzIAHpOVAjpk0D8MBoAvPGODVIX87/ZvhHwODdIPzb4jUYnA7x/gx0JYEgxvE2mJ4O6eMWcbA2HDmjTfGhjMMko3ZCPlF7nb65o9v/sjJ853PMXzwocJOjl+CQIgBAZsEiFQAqzMAcc8Q0sHA/AFEyjfA+Pw9BLWMglEwCkbByAIAwEBHyk/gHfoAAAAASUVORK5CYII=","orcid":"","institution":"Jikei University of Health Care Sciences","correspondingAuthor":true,"prefix":"","firstName":"Teruyoshi","middleName":"","lastName":"Amagai","suffix":""},{"id":464279859,"identity":"ccf55f73-e843-46ce-a2c4-57d173d87ece","order_by":2,"name":"Shinichi Nakatsuka","email":"","orcid":"","institution":"Yao Tokushukai General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shinichi","middleName":"","lastName":"Nakatsuka","suffix":""},{"id":464279860,"identity":"3ac6f58e-da9e-4c27-b598-43990bf9ae18","order_by":3,"name":"Kiyoaki Uryuu","email":"","orcid":"","institution":"Yao Tokushukai General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kiyoaki","middleName":"","lastName":"Uryuu","suffix":""},{"id":464279861,"identity":"0c2a2d12-c119-4d28-aada-c95402e84fe0","order_by":4,"name":"Hiromasa Harada","email":"","orcid":"","institution":"Yao Tokushukai General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hiromasa","middleName":"","lastName":"Harada","suffix":""},{"id":464279862,"identity":"fae45b15-e4f2-4224-9211-c221664eb2f8","order_by":5,"name":"Kaoru Ochi","email":"","orcid":"","institution":"Hokusetsu General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kaoru","middleName":"","lastName":"Ochi","suffix":""},{"id":464279863,"identity":"084e5a7f-afd1-4396-a8ba-8575d323dcd8","order_by":6,"name":"Ayako Tsunou","email":"","orcid":"","institution":"Kitauwa Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ayako","middleName":"","lastName":"Tsunou","suffix":""},{"id":464279864,"identity":"aa24d63e-7ec6-44b8-a2e2-aedbaa783222","order_by":7,"name":"Kiyoshi Sato","email":"","orcid":"","institution":"Osaka Medical and Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Kiyoshi","middleName":"","lastName":"Sato","suffix":""},{"id":464279865,"identity":"3175960d-f838-4dcf-8796-37d6e1e1dc81","order_by":8,"name":"Nobuharu Hanaoka","email":"","orcid":"","institution":"Osaka Medical and Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Nobuharu","middleName":"","lastName":"Hanaoka","suffix":""}],"badges":[],"createdAt":"2025-04-28 11:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6547462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6547462/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83897330,"identity":"76996838-0ddf-40aa-90de-2784f3f34d81","added_by":"auto","created_at":"2025-06-04 08:57:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105028,"visible":true,"origin":"","legend":"\u003cp\u003eThe timeline of the current study. The upper and lower raw represents the two hospitals consisting of the study and two phases, respectively.\u003c/p\u003e\n\u003cp\u003eThe data was collected from Osaka Pharmaceutical Medical (OMPU) Hospital period between 2007 and 2015, in Phase 1 and 2019 in Phase 2. The data was collected also from Yao Tokushukai General (YTG) Hospital in period between 2015 and 2018 in Phase 1.\u003c/p\u003e\n\u003cp\u003eAbbreviation, OMPH: Osaka Medical and Pharmaceutical University Hospital, PDPN: podoplanin, YTG: Yao Tokushukai General Hospital.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6547462/v1/037af4a7e7e7511d72e2c596.jpg"},{"id":83897331,"identity":"ce4cc648-17ef-41bc-a8e6-24cf418bbde4","added_by":"auto","created_at":"2025-06-04 08:57:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24761,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between Actual Survival Day and Error Rate, defined as the difference of [Predicted Survival Day – Actual survival day / Actual survival day] (%).\u003c/p\u003e\n\u003cp\u003eThe average error rate was -4% (standard deviation of 28%) when comparing actual survival days to predicted survival days using the developed prediction formula.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6547462/v1/6632d8ba8146cdda3c41c902.jpg"},{"id":101294119,"identity":"327ed02b-00bf-41fa-823e-b646740cb0d3","added_by":"auto","created_at":"2026-01-28 08:44:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2607054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6547462/v1/82705639-28af-4086-b527-243fe561e372.pdf"},{"id":83897334,"identity":"e244f090-d090-42e8-8614-89e6a9600567","added_by":"auto","created_at":"2025-06-04 08:57:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7269766,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfilesichihashi.docx","url":"https://assets-eu.researchsquare.com/files/rs-6547462/v1/efaaf6290dea7789b9d177b7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Prediction Formula for Survival Time in Patients with Lung Squamous Cell Carcinoma","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer has emerging as the leading cause of cancer-related mortality. According to the Global Cancer Observatory (GLOBOCAN), in 2020, there were approximately 2.2\u0026nbsp;million new cases of and 1.8\u0026nbsp;million deaths caused by lung cancer worldwide [1]. The most common type of lung cancer is non-small-cell lung cancer (NSCLC), which consists of adenocarcinoma, lung squamous cell carcinoma (LUSC), and large cell carcinoma, accounting for 85% of lung cancer [2]. The 5-year overall remains less than survival rate of LUSC is less than 15% [3]. Identification of patients who are likely to experience short or long survival has clinical utility by helping to minimize over- or under-treatment, potentially leading to improved clinical decision making [4]. Indicators for predicting survival as 30-day mortality [5, 6] or survival rate within 5 years using various biomarkers [7\u0026ndash;10], raising protocol of biomarker-driven therapies [11], or genetic information mainly based on single cell mRNA or DNA basis [12\u0026ndash;15] have been reported. They have gained attention as new prognostic biomarkers in a variety of cancers. These methods are included in a cross-sectional approach, such as overall survival or disease-free survival at certain time of 30 days, 90 days, 5 years, 10 years at the level of patient group. The hidden problem of cross-sectional methods may be that it only indicates whether a patient is alive or not at a certain point in time, and it is not possible to predict individual survival length of days. To date, there is no longitudinal method available to predict how long each patient will live from a longitudinal perspective. Therefore, it is not possible to predict the longitudinal outcome on a case-by-case basis. The clinical meaning of the survival prediction formula is to avoid over- or under-treatment and to reduce the incidence of treatment-related adverse events. Moreover, from the point of view of medical progress, the prediction signature of survival time can be used to evaluate the progress of treatment. Therefore, it seems reasonable that this method can be developed and used as an indicator for medical quality evaluation from a new horizon. In this study, we developed a prediction formula for survival time after radical surgery for LUSC using available biomarkers and examined its validity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eTo develop a prediction formula for survival after LUSC surgery as an individual longitudinal survival day prediction using readily available biomarkers and to prevent the occurrence of adverse events due to over- or under-treatment after surgery.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants Eligibility Criteria\u003c/h3\u003e\n\u003cp\u003eThe inclusion criteria of patients were male, who was diagnosed LUSC and underwent radical surgery at Osaka Medical and Pharmaceutical University (OMPU) Hospital between April 2007 and December 2015, or at Yao Tokushukai General (YTG) Hospital between April 2015. In addition, for validation test analysis of developed formula, patients with LUSC and underwent the operation during December 2018 at OMPU were included as validation test participants. This time period was chosen to complete the follow-up period more than 5 years after radical surgery.\u003c/p\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cp\u003eExclusion criteria are as follows: 1, female, 2, male who has a loss of follow-up of 5 years after LUSC radical surgery, 3, pathology diagnosis was not squamous cell carcinoma.\u003c/p\u003e\n\u003ch3\u003eVariables Available for Prediction Formula Development\u003c/h3\u003e\n\u003cp\u003eThe variables collected from the subject's electronic medical records were as follows: 1, \u003cb\u003eDemographics\u003c/b\u003e, including sex, age, body mass index (BMI, body weight (kg) divided by height (meters) square, kg. m\u003csup\u003e2\u003c/sup\u003e, 2, \u003cb\u003eLaboratory data\u003c/b\u003e of complete blood counts (CBC) and biochemistry, including white blood cell, total lymphocyte (TLC), neutrophile (Neu), hemoglobin (Hb), platelet (PLT), and albumin (Alb). Blood measurements were taken within 7 days of hospitalization prior to radical LUSC surgery, 3, \u003cb\u003eBiomarker indices\u003c/b\u003e, that were readily available, were chosed, including inflammation index and surgical stress index - advanced lung cancer index (ALI) [15], Hb, Alb, TLC, PLT (HALP) score [16, 18], NTP-lymphocyte ratio (NLR) [19], and systemic immune-inflammation index (SII) [20]. The equations for calculating them are shown (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 4, \u003cb\u003ePathological measures\u003c/b\u003e included pathological TNM stage (pTNM stage) and PDPN immune-histological staining results were evaluated. Tumor stage was defined according to the Union for International Cancer Control (UICC) TNM classification, and histologic type was defined according to the World Health Organization classification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEquations of biomarkers used in the current study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1, Advanced Lung Cancer Inflammation Index (ALI) = BMI x Alb/ NLR [15]\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2, Hemoglobin Alb Lymphocyte Platelets (HALP) = Hb x Alb x TLC/ PLT [17, 18]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3, Neutrophil/Lymphocyte Ratio (NLR) = Neu/ TLC [19]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4, Systemic immune-inflammation index (SII) = PLT X NLR [20]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003eAbbreviations, BMI = body weight (kg) / [height (m) ] 2 [kg/m2], Alb: serum albumin level (g/dL), Hb: hemoglobin concentration (g/dL), TLC: peripheral total lymphocyte count (count/\u0026mu;L), PLT: platelet count (count/\u0026mu;L), Neu: peripheral blood neutrophil count (count/\u0026mu;L).\u003c/p\u003e\n\u003ch3\u003eOutcome measure\u003c/h3\u003e\n\u003cp\u003eThe outcome measure was 5-year survival, defined as alive or dead at the time of 5 years after radical surgery.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePooled Analysis of Inter-Institutional Comparison Prior to Phase 1 and 2 Analyses\u003c/h2\u003e \u003cp\u003eTo determine whether data from two sites could be combined and analyzed together, all variables collected from two hospitals were compared as a pooled analysis. If there were no statistical differences, it could be interpreted that there was no subject bias from two sites; it could be determined that all data from two sites could be analyzed together in further analyses of Methods 1, 2, and 3.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMethod 1 Development of the Prediction formula - Phase 1 -\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMethod 1.1 Comparison of data of survival and death within 5 years of LUSC surgery\u003c/h2\u003e \u003cp\u003eAll cases were divided into two groups, a 5-year survival group and a death group at the time of 5 years passed after the radical operation. Then, all collected data were compared to discriminate them to draw the biomarker to predict survival cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMethod 1.2 Development of the Prediction Formula of survival time\u003c/h2\u003e \u003cp\u003eUsing the variable for predicting survival days after the LUSC radical operation, we developed the prediction formula using multiple logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was also performed to determine the cutoff value of the biomarkers available in the prediction formula.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMethod 2 Validation Test of the Developed Prediction Formula - Phase 2 -\u003c/h2\u003e \u003cp\u003eTo test the validity of the prediction formula of survival days for squamous cell carcinoma obtained in result 2–2, the validity of the prediction formula was tested for those who were male and underwent surgery in 2019 at OMPU Hospital, of whom the 5-year survival rate could be analyzed. To analyze the validation of the prediction formula, we defined the error rate in which the difference between the predicted days (A) obtained using the prediction formula and the actual days of survival (B) was calculated as [(A-B)/ B (%)].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMethod 3 Kaplan-Meier analysis for the possibility of Prediction Using PDPN Staining Results\u003c/h2\u003e \u003cp\u003eThe correlation between PDPN staining and survival rate was investigated.\u003c/p\u003e \u003cp\u003eHere, patients' surgical specimens were analyzed by histological staging and PDPN staining strength, divided into three degrees of strong, weak positive, or negative. The details are described below. Kaplan-Meier analysis was performed to compare two groups divided by PDPN staining result, strong positive vs. the remaining including weak positive and negative staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePDPN staining as Pathological analysis\u003c/h2\u003e \u003cp\u003eLUSC surgical specimen was collected from each case at the time of surgery, and fixed in 10% formalin for 24–48 hours, embedded in paraffin, sectioned at 4 µm, and stained with hematoxylin and eosin (H\u0026amp;E). For histopathological evaluation by PDPN staining, thin surgical sections of formalin-fixed paraffin-embedded tissue blocks were used. The immunohistochemical techniques were performed using anti- PDPN antibody (D2-40, DAKO) to evaluate its expression. Lymphatic endothelium was used as a strong positive control, and the presence of cancer cells stained with anti- PDPN antibody resulted in PDPN positive results. PDPN staining results were classified into three as follows: strong positive (Supplementary Fig.\u0026nbsp;1), weak positive (Supplementary Fig.\u0026nbsp;2), and negative (Supplementary Fig.\u0026nbsp;3). All immunohistochemical data were evaluated by a single pathologist (S.N.) who was blinded to the clinical status of the patients.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSample size calculation\u003c/h2\u003e \u003cp\u003eFirst, to calculate sample size in the developing prediction formula in phase 1, with a 95% confidence level and 5% margin error, and population proportion and population size, we set at 20% and 100, respectively. From these settings, the sample size was calculated as 72. Second, to calculate the sample size for the external validation test in phase 2, the number of subjects was set at 70, the confidence level was set at 90%, and the margin of error was set at 10%. As a result, the number of subjects required was 35, where 35 was set as the lowest limit of subject number of validation test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePrognostic factors independent of sex, age, Alb, CRP, Hb, PLT, total lymphocyte count (TLC), neutrophil count, monocyte count, ALI, HALP, NLR, and SII as proteomics indicators were identified. Overall survival rate (OS) was calculated from the date of death from any cause after surgery. P \u0026lt; 0.05 was considered a statistically significant difference. All statistics were performed using SPSS version 29 (IBM, Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e77 cases were included in the study. Among them, the result of sample size calculation is 72 as described in the statistics session. Among 77 cases, 58 cases were from OMPU and 19 cases were from YTG Hospital. To see female subjects, 6 from OMPU and 2 from YTG hospital, a total of 8 females were excluded. As the remaining 52 and 17 from two hospitals, a total of 69 cases were proceeded to further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The postoperative follow-up period was set at 60 months. The median age was 73.5 years (IQR: 55, 86 years). In terms of pathological stage classification, IA, IB, IIA, IIB, IIIA, IIB, and IV were 20, 23, 3, 13, 9, 0, and 1 case, respectively. There were 14 cases with lymph node metastasis.\u003c/p\u003e\u003ch2\u003eResult of the pooled analysis of the inter-institutional comparison\u003c/h2\u003e\u003cp\u003eData from the two institutions were compared to examine the demographics, laboratory data, and outcomes of patients who underwent LUSC curative surgery at the two institutions. No statistically significant differences were found (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, it was determined that all data from the two institutions could be combined for further analysis. Therefore, data from two institutions were combined for analysis in the following methods.\u003c/p\u003e\u003ch2\u003eResult 1.1 Comparison of Survival and Death Groups in Developing Set\u003c/h2\u003e\u003cp\u003eComparing the survival and death groups, statistically significant differences were observed in subject demographics and blood test, including BMI, Alb, and Hb (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of data of survivors and death at the 5-year after the radical operation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-v\u003c/em\u003ealue\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject number\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Years old\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (70, 78)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (68, 78)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (71, 78)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e22.0 (20.0, 24.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e23.3 (21.0, 24.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e20.8 (18.8, 23.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAlb, g/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.1 (3.6, 4.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.1 (3.9, 4.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.8 (3.5, 4.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCRP, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.20 (0.10, 1.18)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.15 (0.07, 0.73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.40 (0.10, 1.87)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.075\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHb, g/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e13.5 (12.1, 14.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e14.0 (12.9, 15.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12.4 (11.1, 14.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePLT, 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/µL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e22.5 (18.2, 27.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e22.7 (17.7, 26.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e22.0 (18.7, 28.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.942\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTLC, counts/µL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1667 (1268, 2049)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1724 (1387, 2225)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1532 (1156, 1985)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.132\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNeutrophile, counts/µL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4108 (2888, 4966)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4035 (2905, 5054)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4192 (2871, 4943)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.856\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMono, counts/µL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e388 (302, 494)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e351 (288, 492)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e417 (347, 504)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.221\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e143.89 (111.69, 170.93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e125.42 (99.35, 165.72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e152.37 (134.55, 210.53)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.070\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.30 (1.78, 3.38)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.25 (1.53, 3.27)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.43 (2.08, 3.76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.144\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eALI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e36.02 (26.01, 46.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e40.77 (31.07, 57.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e32.61 (20.58, 39.90)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSII, 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/µL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e55.4 (37.4, 76.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e52.9 (33.4, 72.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e60.9 (37.8, 92.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.167\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHALP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e37.87 (27.01, 50.71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e43.94 (31.48, 55.92)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e35.57 (23.50, 40.86)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep Stage Ⅰ AB, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e43 (62)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e27 (71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e16 (52)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.097\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePDPN strong positive, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e41 (59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e24 (63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e17 (55)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.484\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome measure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSurvival days\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAfter operation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1826 (684, 1826)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1826 (1826, 1826)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e621 (331, 1133)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAll data are expressed in medium (25%ile, 75%ile), Mann-Whitney\u0026apos;s U test and the chi-square or Fisher\u0026apos;s exact test for categorical variables.\u003c/p\u003e\u003cp\u003e \u003cb\u003eResult 1.2 Multiple logistic regression analysis for detecting Variables as confound available in the prediction formula\u003c/b\u003e \u003c/p\u003e\u003cp\u003eTo identify the available biomarkers to discriminate survival and death within 5 years after surgery, multiple logistic regression analysis for odds ratio (OR), 95% confidence interval (95% CI) and p-value for predicting 5-year survival was performed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).As the results, ALI and Hb were independent variables for 5-year survival prediction. The results of comparisons of HALP, NLR, and SII had no significant OR.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of multiple logistic regression analysis for detecting Variables as confound available in the prediction formula\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-v\u003c/em\u003ealue\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.003 (0.920–1.093)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.961 (0.928–0.996)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.670 (0.474–0.946)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePodoplanin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.987 (0.319–3.057)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.982\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eResult 1.3 Development of the prediction formula\u003c/h2\u003e\u003cp\u003eIn Result 1.2.1, the two important features that were significant for predicting survival were ALI and Hb. Using the two features, the prediction formula of 5-year survival days after radical surgery for patients with LUSC was drawn as follows: Survival days = [6.9 × ALI] + [93.4 × Hb] -198.6. Then, ROC curve analysis was performed to determine the cutoff value of ALI for predicting 5-year survival. As a result, the cutoff value of ALI was 48.04 (AUC = 0.690, p = 0.007) (Supplementary Fig.\u0026nbsp;4). These statistical results of the ROC curve analysis with the meaningful high AUC and low p-value are interpreted that this prediction formula is relatively acceptable for clinical availability.\u003c/p\u003e\u003ch2\u003eResult 2. Results of Validation Test\u003c/h2\u003e\u003cp\u003eThe number of subjects for the validation set of 39 cases, which satisfied the sample size of 33 cases calculated in method 2 and resulted in sufficient sample size for an external validation test. The relationship between actual survival time (days) and error rate are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average error rate was − 4% with a standard deviation of 28%.\u003c/p\u003e\u003ch2\u003eResult 3. Results of Investigation of Correlation Between PDPN Staining Results and Survival\u003c/h2\u003e\u003cp\u003eStaining of surgical LUSC specimens with PDPN antibody revealed that 41 (13 strong positive, 28 weak positive) and 28 cases were PDPN positive and negative, respectively. In the next step, all cases were divided into two subgroups, one with strong PDPN and the other with weak and negative PDPN. Comparing all data including survival between two groups, statistically significant differences were found in age (p-value 0.003), Alb (p-value 0.010), NLR and SII (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of outcome measures between result of PDPN staining, strong positive vs. weak positive + negative.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDPN staining\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong positive\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-v\u003c/em\u003ealue\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject number\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Years old\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (70, 78)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (73, 83)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72 (69, 78)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e22.0 (20.0, 24.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e22.4 (18.7, 25.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e22.0 (20.0, 24.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.921\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eALI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e36.02 (26.01, 46.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e46.27 (33.53, 69.73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e35.39 (24.07, 45.14)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.053\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHALP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e37.87 (27.01, 50.71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e37.87 (30.89, 79.32)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e37.52 (24.70, 50.23)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.349\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e55.4 (37.4, 76.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e39.3 (29.1, 59.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e59.1 (38.4, 80.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep Stage ⅠAB, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e43 (62)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10 (77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e33 (59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.189\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePDPN-strong \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003epositive, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e41 (59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e28 (50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAbbreviations, ALI: advanced lung cancer inflammation index, BMI: body mass index, HALP: hemoglobin albumin lymphocyte platelet, NLR: neutrophile-lymphocyte ratio, SII: systemic immune-inflammation index, PDPN: podoplanin,\u0026nbsp;\u003c/p\u003e\u003cp\u003eThen, Kaplan-Meier analysis was performed to compare two groups. As the results of Kaplan-Meier analysis, a trend toward longer survival in the strong positive group was observed. However, there was no significant difference in survival between the two groups (Supplementary Fig.\u0026nbsp;5). This result is interpreted that there was a trend of survival of the PDPN strong positive patients with better than them of the others.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eAn Error rate of the developed Prediction Formula seems acceptable\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which shows the difference between [predicted - actual] survival days of the prediction formula and the error rate, the error rate was within +/- 100% in 38 of the 39 cases, and the average error was \u0026minus;\u0026thinsp;4% with a standard deviation of 28%. In general, an error rate of 5% or less is considered allowable limit of error, guaranteed by principles [21]. From this aspect, the validation test of this formula showed that the error in 38 of 39 cases, i.e., 97% of cases, was clinically usable. In addition, the correlation was shown to increase as the actual survival time increased. In other words, the prediction formula for survival days obtained this time showed a tendency for the error to reverse from negative to positive at 1000 days, and as the survival time increased beyond 1000 days, the error obtained by the prediction formula tended to increase. However, the error was within +\u0026thinsp;60% at 2000 days of actual survival day or 5.5 years, as shown on the horizontal axis of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In other words, when the predicted survival time exceeds 2000 days, the survival time tends to be underestimated by the prediction formula, which means that it is necessary to use this formula while keeping in mind that the actual probability of survival is higher than the predicted number of days when the predicted survival day exceeds 2000 days or 5.5 years.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eWhat diseases use ALI as a prognostic indicator and Why?\u003c/h2\u003e \u003cp\u003eALI is an inflammation index that was originally developed in 2013 as a prognostic indicator for stage IV non-small cell lung cancer (NSCLC). Since then, it has become more widely used as a prognostic indicator not only for other cancers, but also for chronic diseases whose pathology is primarily inflammatory. As of April 1, 2025, a literature search using ALI as a keyword on PubMed yielded 261 articles. Of the 261 articles, 165 were not related to the ALI and the remaining 96 were articles that used ALI as a prognostic indicator for various diseases. Clarifying 96 articles furthermore, lung cancer-related articles, non-lung cancer-related, lifestyle-related diseases, and other diseases of article number of 29, 34, 9, and 34, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Focusing on lung cancer, NSCLC had the largest number of articles with 16, while no article dealing with a prediction formula using ALI or the others has been reported to date. In addition, no ALI-related articles were found for LUSC. In contrast to lung cancer, 34 ALI-related articles were found for cancers other than lung cancer. The largest number of ALI-article was on gastrointestinal cancer (15 articles, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe 261 ALI-related articles were grouped into four disease categories, with the diseases and number of articles included in any of four categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSupplementary reference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003earticle number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung cancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e6, 15, 21, 28, 37, 41, 46, 52, 55,\u003c/p\u003e \u003cp\u003e62, 73, 75, 78, 84, 87, 94.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSCLC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e32, 61, 68, 93.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLung cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2, 7, 34, 49, 82, 83, 90.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLung adenocarcinoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e29, 69.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-lung cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGastrointestinal tract\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3, 8, 14, 16, 22, 33, 42, 44, 56\u003c/b\u003e,\u003c/p\u003e \u003cp\u003e\u003cb\u003e63, 65, 74, 79, 95.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9, 24, 30, 39, 50, 76.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHead Neck\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e35, 59.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e43, 57.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUrinary tract\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e45, 92.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMalignant lymphoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e86, 96.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePancreas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e54, 71.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCholangiocarcinoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSkin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNeuroblastoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLife-related diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5, 11.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1, 36.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e10, 40, 64.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFatty Iiver\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e72, 91.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHeart failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12, 13, 18, 19, 23.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAsthma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e31, 38, 51, 85.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eACS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e20, 67, 89.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e60, 66, 88.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4, 58 (stroke), 17, 48 (RA)\u003c/b\u003e,\u003c/p\u003e \u003cp\u003e\u003cb\u003e53 (Pulmonary fibrosis)\u003c/b\u003e,\u003c/p\u003e \u003cp\u003e\u003cb\u003e70 (CAP), 77 (pneumonia)\u003c/b\u003e,\u003c/p\u003e \u003cp\u003e\u003cb\u003e80 (chronic pain), 81 (gallstone)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe four categories are shown from #1 to #4 as follows: \u003cstrong\u003e#1, lung cancer,\u003c/strong\u003e divided into four subcategories such as non-small cell lung cancer (NSMLC), small cell lung cancer (SCLC), overall lung cancer, and lung cancer adenocarcinoma, \u003cstrong\u003e#2, Non-lung cancers\u003c/strong\u003e, including gastrointestinal tract, hepato-cellular carcinoma (HCC), Head \u0026amp; neck cancers, malignant melanoma (MM), urinary tract cancers, malignant lymphoma, pancreatic cancer, cholangiocarcinoma, skin cancer, neuroblastoma, and unknown cancers, \u003cstrong\u003e#3, Non-communicable Life-related diseases\u003c/strong\u003e, including hypertension (HT), diabetes mellites (DM), chronic kidney disease (CKD), and fatty liver, and \u003cstrong\u003e#4, The others,\u003c/strong\u003e including heart failure, asthma, acute coronary syndrome (ACS), myocardial infarction (MI), and others (stroke, rheumatic arthritis, pulmonary fibrosis, community-acquired pneumonia (CAP), pneumonia, chronic pain, and gallstone.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The list of ALI-related 96 articles are shown in supplementary table 2.\u003c/p\u003e\u003cp\u003eThe other subject of ALI-related articles to use ALI to predict the prognosis was lifestyle-related diseases, including heart failure, asthma, acute coronary syndrome and myocardial infarction. This analysis of 96 ALI-related articles showed that ALI is also useful for predicting the prognosis of lifestyle-related diseases other than cancer. One of the reasons for this may be that ALI involves BMI and Alb, which is an inflammatory reflection, in its calculation. The similar relationship between prognosis of cancers and ALI might be related to malnutrition revealed by BMI and an inflammatory response. However, to clarify why ALI is useful for predicting lung cancer prognosis, further analysis is needed to explain the reason why ALI might be related to the survival time of cancer patients as shown in the current study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLack of Clinical significance of the predictive value of PDPN in LUSC\u003c/h2\u003e \u003cp\u003eThe mucous sialoglycoprotein podoplanin (PDPN) is widely used as a histopathological marker to differentiate lymphatic vessels from blood vessels due to its expression on lymphatic vessel endothelial cells [22, 23]. A study to determine whether PDPN expressed in LUSC correlates with 5-year survival; PDPN-positive cases are often considered to have an unfavorable prognosis due to immunosuppressive microenvironments [24, 25]. On the other hand, there are also reports showing the opposite and the results are not consistent. Therefore, we decided to investigate whether PDPN positivity is associated with survival, and if so, whether it is a good or bad prognostic factor, and to compare it with other proteomic markers. As a result, contrary to our expectations, the association between PDPN and survival prognosis was not proved. Further additional studies of LUSC are needed to clarify whether or not there is predictive value of the PDPN-staining findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eStrength and limitations\u003c/h2\u003e \u003cp\u003eThe strength of this study is, to our knowledge, the first report to predict survival days in patients with LUSC using ALI and Hb. The predictive indicators currently used for overall or disease-free survival are survival at 30 days, 90 days, 5 years, 10 years, and so on in the patient group. This index is a cross-sectional indicator that deals with a group of patients. The problem with this indicator is that it only indicates whether a patient is alive or not at a certain point in time, and it is not possible to predict individual survival days. In this respect, the prediction formula for survival days obtained in the current study is a longitudinal indicator of individual survival days. Future clinical application and verification are required.\u003c/p\u003e \u003cp\u003eThe limitations of this study will now be highlighted. First, the effectiveness of PDPN as a prognostic predictor could not be clarified. The reasons for this may be related to lack of subjects and retrospective research style. In future studies, it will be necessary to increase the number of subjects and to conduct a prospective study with not only early staged but also advanced staged subjects. The mechanism behind why ALI and Hb are useful in the prediction formula is unclear. To clarify this, it will be necessary to scientifically clarify the significance of the four indices included in ALI, BMI, Alb, NLR (Neu, TLC), in terms of LUSC survival time. Second, simple size must be small. Although the number of subjects was extremely limited and met the sample size calculation, it is expected that reliability would be improved by studying a larger number of subjects.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prediction formula of survival time in patients with squamous cell lung cancer was developed using ALI and Hb as follows: Survival time (days) = [6.9 \u0026times; ALI] + [93.4 \u0026times; Hb] -198.6. The ROC curve analysis showed that the cut-off value for ALI was 48.04 (AUC\u0026thinsp;=\u0026thinsp;0.690, p\u0026thinsp;=\u0026thinsp;0.007), indicating that the newly developed prediction formula seems to be clinically feasible.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALI: advanced lung cancer inflammation index,\u0026nbsp;BMI = body weight (kg) / [height (m) ] \u003csup\u003e2\u003c/sup\u003e [kg/m\u003csup\u003e2\u003c/sup\u003e], Alb: serum albumin level (g/dL), HALP: hemoglobin albumin lymphocyte platelet, Hb: hemoglobin concentration (g/dL), NLR: neutrophile-lymphocyte ratio, PLT: platelet count (count/\u0026mu;L), PDPN: podoplanin, SII: systemic immune-inflammation index, TLC: peripheral total lymphocyte count (count/\u0026mu;L).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Y.I., A.T.. and T.A.i formulated the original idea; Yoshio Ichihashi completed the database search, data extraction, and analysis; S.N., K.U., H.H., K.O., and T.A. drafted the manuscript; K.S., N.H. reviewed and revised the manuscript for important intellectual content; and all authors provided final approval of the version to be submitted. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cooperative study was conducted and approved by the Internal Review Board (IRB) of the Osaka Medical \u0026amp; Pharmaceutical University. The approval number is 2021-185-3 dated on March 20, 2025. The ethics approval from the Internal Review Board (IRB) of Osaka Medical \u0026amp; Pharmaceutical University approved that instead of obtaining informed consent from all participants, the ethics committee would post an opt-out notice at the hospital and on its website to confirm that no participant refused to participate. In addition, this study has been conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declaration\u003c/strong\u003es: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e:\u0026nbsp;This cooperative study was conducted and approved by the Internal Review Board (IRB) of the Osaka Medical \u0026amp; Pharmaceutical University. The approval number is 2021-185-3 dated on March 20, 2025. The ethics approval from the Internal Review Board (IRB) of Osaka Medical \u0026amp; Pharmaceutical University approved that instead of obtaining informed consent from all participants, the ethics committee would post an opt-out notice at the hospital and on its website to confirm that no participant refused to participate. In addition, this study has been conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAll data included in this article is available in the supplementary files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A Cancer J. Clin. 2021, 71, 209\u0026ndash;249.\u003c/li\u003e\n\u003cli\u003eYu H, Pang Z, Li G, Gu T. Bioinformatics analysis of differentially expressed miRNAs in non-small cell lung cancer. J Clin Lab Anal. 2021;35(2):e23588. doi: 10.1002/jcla.23588.\u003c/li\u003e\n\u003cli\u003eLu T, Yang X, Huang Y, Zhao M, Li M, Ma K, et al. Trends in the incidence, treatment, and survival of patients with lung cancer in the last four decades. Cancer Manag Res. 2019;11:943\u0026ndash;53. doi: 10.2147/CMAR.S187317.\u003c/li\u003e\n\u003cli\u003eMiller HA, van Berkel VH, Frieboes HB. Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data. Metabolomics. 2022; 18(8):57. doi: 10.1007/s11306-022-01918-3.\u003c/li\u003e\n\u003cli\u003eRazdan S, Slijivich M, Pfail J, Wiklund P, Sfakianos J, Wainganlar N. Predictive morbidity and mortality after radical cystectomy using calculators: A comprehensive review of the literature. Urol Oncol. 2021; 39(2): 109-120. doi: 10.1016/j.urolonc.2020.09.032.\u003c/li\u003e\n\u003cli\u003eWang X, Zhao M, Zhang C, Chen H, Liu X, An Y, Zhang L, Guo X. Establishment and clinical application of the nomogram related to risk or prognosis of hepatocellular carcinoma: A review. J Hepatocell Carcinoma. 2023; 10: 1389-1398. doi: 10.2147/JHC.S417123.\u003c/li\u003e\n\u003cli\u003eChen JW, Dhahbi J. Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection doi: 10.1038/s41598-021-92725-8.methods. Sci Rep. 2021; 11(1): 13323. \u003c/li\u003e\n\u003cli\u003ePezzella F. Early squamous cell lung carcinoma: prognostic biomarker for the many. Thorax. 2019;74(6): 527-528. doi: 10.1136/thoraxjnl-2018-212829.\u003c/li\u003e\n\u003cli\u003eOkabe N, Ezaki J, Yamaura T, Muto S, Osugi J, Tamura H, et al. FAM83B is a novel biomarker for diagnosis and prognosis of lung squamous cell carcinoma. Int J Oncol. 2015;46(3):999-1006. doi: 10.3892/ijo.2015.2817.\u003c/li\u003e\n\u003cli\u003eZamay TN, Zamay GS, Kolovskaya OS, Zukov RA, Petrova MM, Garguan A, et al. Current and prospective proytein biomarkers of lung cancer. Cancers (Basel). 2017; 9(11):155. doi: 10.3390/cancers9110155.\u003c/li\u003e\n\u003cli\u003eRedman MW, Papadimitrakpopoulou VA, Minichiello K, Hirsch RF, Mack PC, Schwartz LH, et al. Biomarker-driven therapies for previously treated squamous non-small-cell lung cancer (Lung-MAP SWOG S1400): a biomarker-driven master protocol. Lancet Oncol. 2020;21(12)1589-1601. doi: 10.1016/S1470-2045(20)30475-7.\u003c/li\u003e\n\u003cli\u003eLi Q, Wang R, Yang Z, Li W, Yang J, Wang Z, et al. Molecular profiling of human non-small cell lung cancer by single-cell RNA-seq. Genome Med. 2022; 14(1):87. doi: 10.1186/s13073-022-01089-9.\u003c/li\u003e\n\u003cli\u003eHe B, Wei C, Cai Q, Zhang P, Shi S, Peng X, et al. Switched alternative splicing events as attractive features in lung squamous cell carcinoma. Cancer Cell Int. 2022; 22(1): 5. doi: 10.1186/s12935-021-02429-2.\u003c/li\u003e\n\u003cli\u003eZhang S, Liu Y, Liu K, Hu X, Gu X. A review of current developments in RNA modifications in lung cancer. Cancer Cell Int. 2024; 24(1):347. doi: 10.1186/s12935-024-03528-6.\u003c/li\u003e\n\u003cli\u003eBu Y, Liu Y, Hu C, Yuan D, Luo L, Li M, et al. MSR1 in lung squamous cell carcinoma: Prognostic and immunological values in pan- and single-cell analyses and a cohort study. Int Immunopharmacol. 2025; 145: 113811. doi: 10.1016/j.intimp.2024.113811.\u003c/li\u003e\n\u003cli\u003eJafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer 2013; 13: 158.\u003c/li\u003e\n\u003cli\u003eChen\u003csup\u003e \u003c/sup\u003eXL, Xue\u003csup\u003e \u003c/sup\u003eL, Wang\u003csup\u003e \u003c/sup\u003eW, Chen\u003csup\u003e \u003c/sup\u003eHN, Zhang\u003csup\u003e \u003c/sup\u003eWH, et al. Prognostic significance of the combination of preoperative hemoglobin, albumin, lymphocyte and platelet in patients with gastric carcinoma: a retrospective cohort study. Oncotarget. 2015;6(38):41370-82. doi: 10.18632/oncotarget.5629.\u003c/li\u003e\n\u003cli\u003eGursoy V, Sadri S, Kucukelyas HD, Hunutlu FC, Pinar IE, Yegen ZS, et al. HALP scores as a novel prognostic factor for patients with myelodysplastic syndromes. Sci Rep. 2024;14(1):13843. doi: 10.1038/s41598-024-64166-6.\u003c/li\u003e\n\u003cli\u003eBuonacera A, Stancanelli B, Colaci M, Malatino L. Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases. Int J Mol Sci. 2022;23(7):3636. doi: 10.3390/ijms23073636.\u003c/li\u003e\n\u003cli\u003eSelahattin Vural S, Ali Muhtaroğlu A, Mert G\u0026uuml;ng\u0026ouml;r M. Systemic immune-inflammation index: A new marker in differentiation of different thyroid diseases. Medicine (Baltimore). 2023;102(31):e34596. doi: 10.1097/MD.0000000000034596.\u003c/li\u003e\n\u003cli\u003eFraser CG. Biological variation: From principles to practice. Clinica Chimica Acta 2003; 331(1):173-174. DOI: 10.1016/S0009-8981(03)0007S2-X.\u003c/li\u003e\n\u003cli\u003eSchoppmann SF, Birner P, Studer P, Breiteneder-Gelef S. Lymphatic microvessel density and lymphovascular invasion assessed by anti-podoplanin immunostaining in human breast cancer. Anticancer Res. 2001;21(4A):2351\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eBreiteneder-Gelef S, Soleiman A, Horvat R, Amann G, Kowalski H, Kerjaschki D. Podoplanin\u0026ndash;a specifc marker for lymphatic endothelium expressed in angiosarcoma. Verh Dtsch Ges Pathol. 1999;83:270\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eSakai T, Aokage K, Neri S, Nakamura H, Nomura S, Tane K, Miyoshi T, Sugano M, Kojima M, Fujii S, Kuwata T, Ochiai A, Iyoda A, Tsuboi M, Ishii G. Link between tumor-promoting fibrous microenvironment and an immunosuppressive microenvironment in stage I lung adenocarcinoma. Lung Cancer. 2018;126:64\u0026ndash;7\u003c/li\u003e\n\u003cli\u003eSuzuki J, Aokage K, Neri S, Sakai T, Hashimoto H, Su YH, Yamazaki S, Nakamura H, Tane K, Miyoshi T, Sugano M, Kojima M, Fujii S, Kuwata T, Ochiai A, Tsuboi M, Ishii G. Relationship between podoplanin-expressing cancer-associated fibroblasts and the immune microenvironment of early lung squamous cell carcinoma. Lung Cancer. 2021;153:1\u0026ndash;10.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung Squamous Cell Carcinoma, Prediction formula, Survival time","lastPublishedDoi":"10.21203/rs.3.rs-6547462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6547462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLung cancer has become the leading cause of cancer-related mortality. The 5-year overall survival rate of lung squamous cell carcinoma (LUSC) remains less than 15%. Identifying patients who are likely to experience short or long survival has clinical utility by helping to minimize overtreatment or undertreatment. However, there is currently no method to predict survival time based on available information.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eTo develop and validate a prediction formula for survival after surgery for LUSC using readily available biomarkers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe inclusion criteria of patients were male, who was diagnosed LUSC underwent radical surgery in two hospitals between April 2015 and December 2018. The methods consist of three parts as pooled analysis, phase 1 and 2. Before analyzing the two phases, a pooled analysis was performed to determine whether data from two hospitals could be combined and analyzed together. In phase 1, the prediction formula was developed using biomarkers that were proved reliable. In phase 2, the validation test was conducted to verify the accuracy of the developed formula.Patient data were as follows: 1. Demographics, 2. Laboratory data of Complete blood count (CBC) and biochemistry values were measured prior to the LUSC radical operation, 3. Biomarkers included Advanced Lung Cancer Inflammation Index (ALI), Hemoglobin, Alb, Lymphocyte, Platelets, Neutrophil/Lymphocyte Ratio and Systemic immune-inflammation index, with hemoglobin concentration (Hb). 4. Outcome measure was 5-year survival, defined as survival status during 5 years after radical surgery. All data were compared between two groups divided by the survival status at 5 years after the operation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e69 patients with LUSC were enrolled. 1. In phase 1, The prediction formula was developed as follows: Survival time = [6.9 \u0026times; ALI] + [93.4 \u0026times; Hb] -198.6 (days). 2. In phase 2, Validation test using 39 enrolled LUSC patients showed that the error rate was \u0026minus;\u0026thinsp;4% (SD 28%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe prediction formula of survival time in patients with squamous cell lung cancer was developed using ALI and Hb as follows: Survival time (days) = [6.9 \u0026times; ALI] + [93.4 \u0026times; Hb] -198.6. The ROC curve analysis showed that the cut-off value of ALI was 48.04 (AUC\u0026thinsp;=\u0026thinsp;0.690, p\u0026thinsp;=\u0026thinsp;0.007). The novel prediction formula was developed and seemed feasible with an error rate of -4% (standard deviation of 28%), indicating that the newly developed prediction formula seems to be clinically feasible.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Prediction Formula for Survival Time in Patients with Lung Squamous Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 08:57:04","doi":"10.21203/rs.3.rs-6547462/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ceaee588-9f59-4b75-a95b-513ee2c2a0e0","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T08:42:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 08:57:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6547462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6547462","identity":"rs-6547462","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}