Novel risk prediction models for prognosis and immunotherapies of NSCLC based on coagulation and fibrinolysis-related genes

Novel risk prediction models for prognosis and immunotherapies of NSCLC based on coagulation and fibrinolysis-related genes | 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 Novel risk prediction models for prognosis and immunotherapies of NSCLC based on coagulation and fibrinolysis-related genes Ying Zeng, Hongting Jiang, Zhonglian Wang, Cha Luo, Fei Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7175734/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 This study aimed to identify clotting and fibrinolysis related genes (CFRGs) influencing prognosis of non-small cell lung cancer (NSCLC) patients and explore their role in tumor immune microenvironment, with a focus on the F9 gene in lung adenocarcinoma (LUAD). Using TCGA and GEO databases, we screened differentially expressed CFRGs and constructed a risk model. Kaplan-Meier survival analysis and ROC curves evaluated the model's predictive efficiency. Cox regression models were applied to establish nomograms for 1, 2, and 3 years. We performed gene expression, somatic mutation, GSEA, immune microenvironment studies, and gene-drug interaction network analyses. The CFRGs-related risk model, including CSN1S1, F2RL1, F5, F9, FGA, HABP2, MMP9, and TFPI2, revealed that high-risk patients had worse survival outcomes. The expression levels of these genes and immune cell infiltration differed significantly across NSCLC, LUAD, and LUSC datasets. Tissue microarray analysis revealed higher F9 expression in LUAD tumor tissues, associated with poor prognosis. Differences in immune cell expression and immune checkpoints were observed between F9 high- and low-expression groups in LUAD. Our findings highlight CFRGs' role in NSCLC prognosis and immunity, identifying F9 as a LUAD prognostic indicator. coagulation and fibrinolysis non-small cell lung cancer immune biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Lung cancer has become the leading cause of cancer death worldwide and is expected to overtake ischaemic heart disease (IHD) as the leading cause of death by 2060 (1, 2). Non-small cell lung cancer (NSCLC) accounts for 80-85% of all lung cancer diagnoses, and 70% of patients have metastases at the time of diagnosis, leading to a poor prognosis. The median survival is usually less than 12 months, 5-year survival is only 10-15% (3-5). The histological types of NSCLC are diverse, including lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), large cell carcinoma, sarcomatoid carcinoma and carcinoid carcinoma. Among these, LUAD and LUSC are the most prevalent subtypes, with LUAD now surpassing LUSC as the predominant form of NSCLC (6, 7). The treatment and prognosis of NSCLC are highly dependent on the stage of the disease at diagnosis (8). Unfortunately, due to the lack of clearly specific symptoms early on, many patients have advanced disease with metastasis by the time they are diagnosed (9, 10). Despite this challenge, we have been able to significantly improve the detection rate of early lung cancer and thus the survival rate of patients with NSCLC through low-dose CT scans, liquid biopsies, and serum tumor marker detection (2, 11-13). At present, the therapeutic drugs for NSCLC are mainly divided into three categories: cytotoxic drugs, molecular targeted drugs and immunotherapy drugs (14). In recent years, advances in targeted therapies and immunotherapy have significantly extended the survival of patients with NSCLC (15, 16). Still, these treatments face a number of challenges, including the emergence of drug resistance, the lack of precise biomarkers to predict response to immunotherapy, and the limitations of the patient population that will benefit (less than 20%) (13, 17). Tumor biomarkers that can predict treatment response and prognosis hold substantial potential in the management of NSCLC (5). It is an important component of precision medicine, which is essential for early diagnosis and effective treatment. This helps to optimize clinical decision making, improve diagnostic accuracy, determine prognosis and predict efficacy, and ultimately improve patient survival and quality of life (18-20). Activation of coagulation and fibrinolysis systems is a common phenomenon in the occurrence and development of malignant tumors (21). Since Trousseau first described the link between malignant cell growth and the coagulation and fibrinolytic systems in 1865, numerous clinical and in vitro studies have further revealed the close relationship between them (22-24). Coagulation and fibrinolysis systems are associated with several key processes of malignant tumors (such as metastasis, invasion, poor prognosis, angiogenesis) and their response to immunotherapy (22, 25). Coagulation and fibrinolysis related genes (CFRGs) play an important role in a variety of cancers, including promoting tumor growth, invasion, migration, and angiogenesis (25-29). These genes have been identified as potential targets in the diagnosis and treatment of a variety of cancers (25). However, in NSCLC, the role of CFRGs has not been fully studied. Tumor microenvironment (TME) and immune infiltration play an important role in tumor initiation, development and drug efficacy (30). TME is a key agent of cancer occurrence, progression, and treatment outcome, and is closely related to immunotherapy outcomes (29, 31). Meng Fan et al. found a significant association between TME and coagulation and fibrinolysis-associated genes (CFRGs) in hepatocellular carcinoma (HCC), a finding that highlights the important role of TME in tumor development (29). In light of this, in-depth studies of the interactions between CFRGs and TME and immune cell infiltration are particularly critical when exploring potential therapeutic targets for NSCLC. This relationship may reveal new therapeutic opportunities, particularly in terms of improving immunotherapy response rates and efficacy. Therefore, this study aimed to explore the potential value of CFRGs in NSCLC and analyze how they interact with TME and immune cell infiltration, thereby providing new molecular markers and therapeutic strategies for the precision treatment of NSCLC. By analyzing transcriptome data from NSCLC, we identified eight CFRGs associated with survival in NSCLC patients and explored the potential clinical value of these genes in NSCLC through a range of bioinformatics approaches. In addition, the prognosis and immune infiltration of F9 gene in patients with lung adenocarcinoma were further investigated by means of lung adenocarcinoma tissue microarray (containing 92 lung adenocarcinoma tissue samples and 92 corresponding paracancer tissue samples) combined with a public database. In summary, this study provides a new molecular target for the prognosis of NSCLC based on coagulation and fibrinolysis related genes, and fills a gap in the study of F9 gene in LUAD. 2. Materials and methods 2.1 Data source Data on transcriptome sequencing for lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database, including a total of 110 control samples (59 LUAD and 51 LUSC) and 1043 tumor samples (541 LUAD and 502 LUSC). We combined the TCGA-LUAD and TCGA-LUSC data sets to construct a non-small cell lung cancer (NSCLC) dataset and randomly divided it into a training set (702 sample) and a test set (300 samples) at a ratio of 7:3. In addition, we downloaded the external validation set GSE50081 (181 NSCLC tumor tissue samples using the Affymetrix platform) from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo). In the GeneCards database, 136 genes related to coagulation and fibrinolysis (CFRGs) were identified by the keyword "coagulation and fibrinolysis" and the related score bbb50 threshold ( Table S1 ). Furthermore, the effect of F9 gene on prognosis of lung adenocarcinoma patients was also searched in Kaplan-Meier Plotter (K-M Plotter, https://kmplot.com/analysis/index.php?p=service&cancer=lung) online database. Gene correlation was analyzed through the GEPIA2 database (http://gepia2.cancer-pku.cn/#correlation) (32). 2.2 Screening of DE-CFRGs and functional analysis Prior to identifying differentially expressed genes (DEGs) within NSCLC dataset, principal component analysis (PCA) was applied to examine potential batch effects. The DEGs were subsequently recognized via edgeR package (|log2FC| > 1, false discovery rate (FDR)-corrected p < 0.05) (33). Differentially expressed cancer-related genes (DE-CFRGs) were identified by intersecting the DEGs with cancer-related gene sets. Thereafter, the potential roles of DE-CFRGs in NSCLC were explored, with clusterProfiler package applied for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses (p < 0.05, q < 0.05, minimum gene set size (minGSSize) = 5). Additionally, a volcano plot generated by "ggplot2" package was used for visualization of DEGs, while the DE-CFRGs were visualized through a Venn diagram. 2.3 Establishment and evaluation of prognostic models Within the training set, assessment of the potential value of CFRGs for predicting overall survival (OS) of NSCLC patients was conducted. Specifically, the least absolute shrinkage and selection operator (LASSO) was conducted on DE-CFRGs by glmnet package, with family = “cox”, maxit = 10000 to obtain the feature genes. Then, biomarkers were obtained by multivariate cox regression analysis based on feature genes (direction = both). Based on the biomarkers related to coagulation and fibrinolysis, a prognostic risk model was constructed, and the risk scores related to coagulation and fibrinolysis were calculated according to the formula: We classified the 702 tumor samples in training set into high-risk group (HRG) and low-risk group (LRG) according to the median prognostic value. Afterwards, in order to further evaluate the effectiveness of the prognostic model, we plotted the OS survival curve to determine the survival difference between risk groups. At the same time, the AUC area of the model was calculated by ROC to evaluate the validity of the model. Finally, to further evaluate the accuracy and generalizability of the prognostic model, we validated the survival prognosis score model multiple times through test sets and external validation sets (GSE50081). 2.4 Clinicopathological characteristics and nomogram construction To further investigate the influence of clinicopathological characteristics and risk models on NSCLC prognosis, we conducted a comprehensive analysis that incorporated age, gender, and tumor characteristics within the training cohort. Key clinicopathological variables, including tumor type, stage, size or extent of invasion (T), distant metastasis (M), and regional lymph node invasion and metastasis (N), were examined in conjunction with the risk model. Our objective was to assess whether significant differences in risk scores existed among various clinical information groups. Utilizing both univariate and multivariate Cox regression analyses, independent clinical factors impacting patient prognosis were recognized, including only those clinicopathological parameters with p < 0.01. Furthermore, we employed the rms package to develop nomograms for predicting patient outcomes at 1-year, 2-year, and 3-year intervals (34). The predictive accuracy of the nomogram model for NSCLC was subsequently assessed using calibration curves. 2.5 Somatic mutation analysis and GSEA After obtaining somatic mutation information of NSCLC samples in the training set, Maftools package (35) was employed to evaluate the potential associations between genetic mutation profiles and risk scores related to coagulation and fibrinolysis, with the top 25 genes with the highest mutation rates in HRG and LRG were visualized, respectively. Moreover to elucidate differential biological pathways influenced by risk scores in NSCLC, Gene Set Enrichment Analysis (GSEA) was employed via clusterProfiler package (36) (p < 0.05, FDR < 0.25, minimum gene set size (minGSSize) = 5). In detail, we utilized the dataset h.all.v7.4.symbols.gmt from the MSigDB database (https://www.gsea-msigdb.org/gsea) as the background set for enrichment analysis. Using the "edgeR" package, we calculated the differential expression (low vs. high) for all genes between HRG and LRG, ranking the genes in descending order based on these differential values. 2.6 Immune landscape analysis The altered immune landscape was further investigated. Specifically, CIBERSORT (37) was employed to conduct immunoinfiltration analysis on control and tumor samples derived from the NSCLC, LUAD, and LUSC datasets. After filtering samples with p < 0.05, the proportions of 22 distinct immune cell types within samples were ascertained. The Wilcoxon rank-sum test was utilized to evaluated the infiltration differences in 22 immune cell types between control and tumor samples. Furthermore, Spearman correlation analysis was performed to investigate relationships among differential immune cells, biomarkers, risk scores, and differential immune cells, utilizing "ggcor" package. Immune checkpoints serve as critical regulators of immune evasion in cancer (38). Therefore, we selected routine immune checkpoints (PD_L1, CTLA_4, LAG_3, GAL9, HAVCR2, TIM_3, PD_1, PD_1LG2, TIGHT, ADORA2A, BTLA, CD160, CD274, CSF1R, IL10, KIR2DL1, KIR2DL3, LGALS9, TGFB1, CD96, PDCD1, PDCD1LG2, TGFBR1, TIGIT; some genes were not detected in the data matrix) to analyze its expression in HRG and LRG. The Tumor Immune Dysfunction and Exclusion (TIDE) platform (http://tide.dfci.harvard.edu/) represents a sophisticated computational framework engineered to model mechanisms of tumor immune evasion. It is extensively utilized to forecast the therapeutic response to immune checkpoint blockade therapies, including inhibitors directed against PD-1 (programmed cell death protein 1) and CTLA-4 (cytotoxic T lymphocyte-associated protein 4) (39, 40). An elevated TIDE predictive score correlates with a greater propensity for immune evasion, implying that such patients are more likely to demonstrate reduced responsiveness to immunotherapeutic interventions (41). Therefore, TIDE algorithm analysis was conducted on HRG and LRG. 2.7 Expression analysis of biomarkers and the drug-gene network We employed the Wilcoxon rank-sum test to assess the differential expression of biomarkers between tumor and normal control samples within both TCGA-LUAD and TCGA-LUSC datasets. Furthermore, for providing clues for treatment, we utilized the DGidb database (https://www.dgidb.org/) to identify potential targeted biomarkers and their associated drug formulations that exhibited consistent differential expression patterns across both LUAD and LUSC datasets. Subsequently, we constructed a gene-drug interaction network using Cytoscape software to visually represent and conduct an in-depth analysis of the potential connections between these biomarkers and drugs. 2.8 Tissue Microarray and Immunohistochemistry Based on the bioinformatics analysis results, F9 gene was selected as a target for clinical sample validation. Tissue microarray technology was used in this study. Lung adenocarcinoma tissue microarray included 92 cancer tissues and 92 paracancer tissues (Shanghai Xinchao Biotechnology Co., LTD.). The antigen was extracted by heating with citric acid after dewaxing and hydration. After antigen recovery, wash with PBS 3 times. The endogenous peroxidase activity was blocked with 3% H 2 O 2 , incubated at room temperature for 10 minutes, and washed 3 times with PBS. Sections were then blocked with 5% BSA for 1 hour at room temperature. The F9 antibody (proteintech, 21481-1-AP) was diluted (1:1000) and incubated at 4℃ overnight. After reheating for 1 hour and washing with PBS 3 times, sections were incubated with biotin-labeled secondary antibodies at room temperature for 10 minutes, followed by 4 PBS washes. Horseradish peroxidase-conjugated streptavidin was applied, incubated for 10 minutes, and washed 4 times with PBS. Subsequently, DAB was used for chromogenic development, and hematoxylin was applied as a counterstain. Images were captured under a microscop, with PBS serving as a negative control. Nuclear brown-yellow granules indicated F9 positivity. The IRS was calculated as the product of staining intensity (SI) and percentage of positive cells (PP), i.e. IRS=SI×PP. SI was graded as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong), while PP was categorized as 0 (negative), 1 (≤10%), 2 (11–50%), 3 (51–80%), or 4 (>80%). An IRS > 3 indicated positive immunoreactivity, with 3 5 strong. Immunohistochemical results were evaluated microscopically using the IRS scoring system. 2.9 Statistical analysis R language (version 4.1.3) was employed to conduct bioinformatics analyses. Besides, Wilcoxon rank-sum test and Chi-square test were employed in this study to assess differences between specific groups, setting significance threshold at p < 0.05. GraphPad Prism 9.5 software was applied for data visualization.Clinical trial registration number: Not applicable. 3. Results 3.1 Recognition and multiple functions of DE-CFRGs The general workflow of this current study is illustrated in Figure 1 . We combined both TCGA-LUAD and TCGA-LUSC data sets to construct the NSCLC dataset. PCA showed that there was no significant batch effect in the dataset ( Figure 2A ). We then screened 19,123 DEGs based on tumor and control samples from the NSCLC dataset, which exhibited markedly different expression levels in tumor and normal tissues ( Figure 2B ). Cross-testing of DEG between NSCLC and CFRGs produced a total of 73 DE-CFRGs, including 43 up-regulated and 30 down-regulated genes ( Figure 1C ). GO and KEGG analyses revealed the crucial function of DE-CFRGs in NSCLC and the important pathways involved. The results show that DE-CFRGs may be involved in biological processes like blood coagulation, hemostasis, wound healing, etc. And in HIF-1 signaling pathway, Malaria, complement and cogulation cascades and other important pathways play a role ( Figure 2D-E ). These results aid in understanding the multiple roles of coagulation and fibrinolysis in NSCLC progression. 3.2 Strong predictive power of CFRGs for NSCLC prognosis demonstrated by a risk model After LASSO and Cox regression analyses, eight key biomarkers were identified, including CSN1S1, F2RL1, F5, F9, FGA, HABP2, MMP9 and TFPI2, from 73 candidate CFRGs ( Figure 3A-C, Table 1 ). In the training set, in-depth analysis was conducted on the risk model constructed by these 8 genes, including survival state heat map, risk score distribution, and gene expression heat map ( Figure 3D ). Kaplan-Meier survival curve analysis showed that OS was significantly lower in HRG than in LRG (p<0.0001) ( Figure 3G ). For 1-year, 2-year, and 3-year OS, corresponding ROC curves were plotted and the resulting AUC values were 0.65, 0.66, and 0.63, respectively ( Figure 3J ). These results suggest that our risk model has good prognostic ability for patients with NSCLC. In addition, we also obtained consistent results on the test and validation sets ( Figure 3E-F,H-I,K-L ). The remarkable reliability and generalizability reveals the potential of this model as an invaluable instrument for tailored prognostic evaluation in clinical management of NSCLC. 3.3 Expression trends of biomarkers and a nomogram integrating risk scores and clinicopathological features It was found that CSN1S1, F2RL1, F5, F9, FGA, MMP9 and TFPI2 genes were significantly overexpressed in the HRG and HABP2 gene was significantly overexpressed in the LRG within training and validation sets ( Figure 4A ). In the TCGA-LUAD dataset, CSN1S1, F2RL1, F5, HABP2, MMP9 and TFPI2 genes were significantly overexpressed in tumor samples. In TCGA-LUSC, CSN1S1, F2RL1, F5, F2RL1 and F5 genes were highly expressed in tumor samples, while FGA, HABP2 and TFPI2 genes were significantly low expressed in tumor samples. We further explored the correlation between clinicopathological features and risk model prognosis ( Figure 4B ). The analysis showed that male patients showed a higher risk score. In terms of tumor staging, stage III patients exhibited markedly higher risk scores than stage I and II patients. In addition, in terms of tumor size (T), patients with stage T2 and T4 had higher risk scores than those with stage T1. However, there were no marked differences in risk scores between the age, lymph node metastasis and distant metastasis groups, as well as between the TCGA-LUAD and TCGA-LUSC datasets. Nomograms play an important role in individualized risk assessment, risk stratification, identification of high-risk patients, risk adjustment, and direct prediction of cancer patient survival (42, 43). This tool helps clinicians make personalized decisions (44, 45). Through Cox regression analyses, lymph node metastasis (pathologic_n), tumor stage (pathologic_stage), tumor size (pathologic_T), and risk score were finally identified as important prognostic indicators (p<0.01, Figure 4C ). The 1-year, 2-year and 3-year clinical multi-indicator charts were constructed ( Figure 4D ). The slope of corresponding calibration curve was observed to be close to 1, revealing the model calibration was excellent ( Figure 4E ). 3.4 Differential somatic mutation profiles and functional pathways altered by risk scores Somatic mutation profiles of HRG and LRG were illustrated in the training set ( Figure 5A ). Markedly, TP53 and TTN emerged as the genes with HIGHER mutation rates in HRG and LRG, with mutation rates of 68.6% and 60.9%, respectively. The predominant mutation types identified were missense mutation in both TP53 and TTN. These findings helped to uncover the potential impact of risk scores related to coagulation and fibrinolysis on mutation patterns of specific genes in NSCLC patients. The mutation gene correlation analysis showed ( Figure 5B ) that mutations in TP53 gene and FAT3,MUC17,NAV3 and SYNE1 genes were mutually exclusive. TTN gene mutation and TP53, ADAMTS12 COL11A1 gene mutation of mutually exclusive, such as with CSMD3 FAT3, PCDH15, RYR2, RYR3, SPTA1, SYNE1 such as gene mutations occur together. Through GSEA, pathways biologically linked to genes differing between HRG and LRG were revealed. Specifically, the differential genes were associated with the activities of pathways such as heme metabolism, protein_secretion, androgen response, apoptosis, and KRAS signal ( Figure 5C ). These findings suggest that CFRGs associated with NSCLC may play a potential role in biological processes like cell signaling, cell death, immune response, and metabolism. Coagulation and fibrinolysis may play critical roles in progression of NSCLC by influencing activities of these pathways. 3.5 Close associations between CFRGs and immune microenvironment Immune cell infiltration plays an crucial role in the occurrence and progression of NSCLC. We hypothesized that CFRGs, as a prognostic indicator of NSCLC. We hypothesized that CFRGs, as a prognostic indicator of NSCLC, is associated with immune invasion. Therefore, infiltration levels of 22 immune cell types in the NSCLC, LUAD and LUSC datasets were further investigated. It was found that their infiltration levels exhibited differences between normal and tumor samples ( Figure 6A ). In NSCLC, cell types like naive B cell and M1 macrophages were significantly overexpressed, whereas cell types like M0 macrophages and resting mast cells exhibited markedly lower expression. Notably, the correlation between biomarkers and differential immune cells were illustrated ( Figure 6B ), and it found that TFPI2, FGA, HABP2, F2RL1, F5 and MMP9 genes were strongly correlated with differential immune cells, while CSN1S1 and F9 genes were relatively weakly correlated with differential immune cells. In addition, we constructed a biomarker risk score-differential immune cell correlation network ( Figure 6C ), which divided the genes into two groups: groupGene1=TFPI2+FGA+HABP2+F5, groupGene2=F2RL1+MMP9+CSN1S1+F9. The results show that in the NSCLC dataset, groupGene1 is significantly correlated with T foliicular helper cells and M0 macrophages, while groupGene2 is significantly correlated with M0 macrophages. In the LUAD dataset, groupGene1 was significantly correlated with activated CD4 memory T cells. groupGene2 was significantly associated with naive B cells, and riskScore was significantly associated with naive B cells and T foliicular helper cells. However, in the LUSC dataset, groupGene1 and groupGene2 had no significant correlation with differential immune cells. Consequently, coagulation and fibrinolysis might influence the progression of NSCLC by altering the immune infiltration levels of specific immune cell types, which are closely associated with specific biomarkers. 3.6 Evaluation of ICI treatment response of CFRGs and drug-gene network Immunotherapies, known as checkpoint inhibitors (CPIs), are currently the second most common cancer treatment for patients with advanced NSCLC (46). Our analysis showed no marked difference in immune checkpoints between HRG and LRG ( Figure 7A ). Therefore, we compared and analyzed the differences in immune checkpoint expression between tumor samples in the LUAD and LUSC datasets and between tumor samples and their respective control samples. It was showed that compared with LUAD tumor samples, immune checkpoints such as BTLA, CD160, CD244, and CTLA4 were significantly lower expressed in LUSC tumor samples, while CD274, TGFB1, and VTCN1 were significantly higher expressed ( Figure 7B ). In the LUAD dataset, immune checkpoints such as ADORA2A, CD160, KDR and TGFB1 were significantly underexpressed in tumor samples, while immune checkpoints such as BTLA, CTLA4, LAG3 and TIGIT were significantly overexpressed ( Figure 7C ). In the LUSC dataset, 18 immune checkpoints such as ADORA2A, BTLA and CD160 showed directional consistency with their expression in the LUAD dataset ( Figure 7D ). It is worth mentioning that there is no significant difference in the expression of IDO1, IL10 and IL10RB between tumor samples and control normal samples in LUAD dataset, while the expression of IDO1, IL10 and IL10RB is significantly low in LUSC dataset. In addition, there was no significant difference in the expression of PDCD1 in the LUSC dataset, but significantly high expression in the tumor samples in the LUAD dataset. The different expression patterns of these genes in LUAD and LUSC suggest that they may serve as potential biomarkers to distinguish the two lung cancer subtypes. To validate these findings, we utilized the TIDE algorithm to predict immunotherapy responses in LRG and HRG patients. The results showed no significant difference in TIDE scores between the HRG and LRG ( Figure 7E ), further confirming that risk scores may not have a direct impact on immunotherapy efficacy. Furthermore, utilizing eight CFRGs within the risk model, the GDidb database was employed to identify drugs that target biomarkers exhibiting similar trends of variation between control and tumor samples in both the LUAD and LUSC datasets. A total of 19 gene-drug pairs were discovered, with five approved drugs targeting F2RL1, six approved drugs targeting F9, seven approved drugs targeting FGA, and one drug targeting MMP9 (comprising four target genes and 19 drugs). Subsequently, the complex drug-gene interaction networks were constructed ( Figure 7F ). 3.7 Validation and immune infiltration of F9 gene in LUAD For further verifying the prognostic value of F9 gene in LUAD patients, we analyzed 92 pairs of tumor and para-cancer samples and related clinical data. Tissue samples were stained with immunohistochemistry, and F9 clinical data were analyzed for survival and correlation. The expression of F9 gene in cancer tissue and adjacent normal lung tissue is shown in Figure 8A . The positive expression of F9 gene in cancer tissues (78.4%) was markedly higher than that in non-cancer tissues (19.8%) (p0.05) ( Table 2 ). Importantly, it was showed that F9 gene was an independent adverse prognostic factor in patients with lung adenocarcinoma (p =0.03) ( Table3 ). Moreover, F9 gene was significantly higher expressed in tumor tissues than in adjacent normal tissues ( Figure 8B ). Furthermore, patients with positive expression of F9 had worse survival ( Figure 8C ). The Kaplan-meier plotter database also showed consistent results ( Figure 8D ). The ROC curve showed that the area under the curve (AUC) was 0.7622, p<0.0001 ( Figure 8E ), further confirming the prognostic value of F9 in LUAD patients. In addition, we explored the relationship between F9 gene and immune infiltration in the TCGA-LUAD dataset. According to the median value of F9 gene in the LUAD dataset, they were divided into high- and low-expression groups. GSEA showed the differential pathways of F9 gene between high-low expression groups, such as apical surface, IL6 JAK STAT3 signaling, and TGF beta signaling ( Figure 8F ). As shown in Figure 8G, in the LUAD data set, resting dendritic cells and activated natural killer (NK) cells are higher expressed in the low-expression group, while M0 macrophaged is higher expressed in the high-expression group. The expression of LAG3, CTLA4, PDCD1, TIGIT and other immune checkpoints was different between high- and low-expression groups ( Figure 8H ). We also verified the expression of ALK, EGFE, PDL1 and other three genes in the tissue chip samples, and analyzed their correlation with F9 gene. The results showed that there was no significant correlation between them, and the results retrieved in the GEPIA2 database further confirmed our conclusion ( Figure 8I ). Adenosine receptor A2A (ADORA2A), a a member of the G-protein-coupled receptor (GPCR) family, mediates intracellular signaling cascades,including the AKT and ERK pathways, through its interaction with adenosine. This receptor exhibits selective upregulation in neuroendocrine lung cancer (47). Existing studies have shown that adenosine A2A receptors (ADORA2A) in myeloid cells can inhibit T cell and NK cell responses in the solid tumor microenvironment (48), and can also inhibit macrophage-apoptosis by inhibiting the expression of tumor necrosis factor-α (TNF-α) (49). The significantly differential expression of ADORA2A1 may indicate that it is a potential immunotherapeutic target. Therapeutic strategies targeting ADORA2A1 may help modulate the immune response in the tumor microenvironment, thereby improving the effectiveness of immunotherapy. 4. Discussion NSCLC is a heterogeneous disease with multiple histopathological and clinical features (50, 51). It is characterized by high morbidity, mortality, metastasis and recurrence rates, and low five-year survival rates (4, 5, 52). Therefore, the discovery of new molecular markers and therapeutic targets is critical to improving treatment outcomes in patients with NSCLC (53). Hypercoagulation and hyperfibrinolysis of blood are common in NSCLC patients, and these pathological states not only disturb the balance of the blood circulatory system, but also may aggravate the aggressiveness and migration ability of tumor cells (54). Clinical studies have shown that elevated coagulation and fibrinolysis levels are strongly associated with poor patient outcomes in a variety of cancers (55). In addition, cancer development and the immune system are closely related, especially in the tumor microenvironment, where tumor cells have developed multiple mechanisms to evade immune surveillance, resulting in impaired immune cell function and weakened anti-tumor immune response (25, 56). Based on this background, we explored prognostic CFRGs as potential biomarkers of NSCLC and analyzed their role in the NSCLC immune microenvironment, immune efficacy, and prognosis. This study is dedicated to exploring new research directions in the field of non-small cell lung cancer (NSCLC), aiming to provide innovative perspectives for clinical treatment strategies and promote the development of precision medicine, thereby providing patients with more personalized and effective treatment options. In this study, we aimed to explore the genes associated with coagulation and fibrinolysis that are associated with the prognosis of NSCLC. First, we selected 73 differentially expressed genes (DE-CFRGs) at the intersection of TCGA_LUAD dataset, TCGA_LUSC dataset and CFRGs dataset, among which 43 up-regulated genes and 30 down-regulated genes. Functional enrichment analysis revealed the relationship between these DE-CFRGs and blood coagulation, coagulation, coagulation, hemostasis,wound healing, and regulation of body fluid levels are closely related, suggesting that they may play a key role in tumor growth, angiogenesis, and metastasis in NSCLC. In addition, KEGG enrichment analysis showed that the signaling pathways involved in these genes include Complement and coagulation cascades, Malaria, African trypanosomiasis, HIF-1 signaling pathways, suggesting that they may play an important role in tumor immune escape, hypoxia response, and energy metabolism. Subsequently, by applying LASSO algorithm and multivariate COX proportional risk model, eight biomarker genes (CSN1S1, F2RL1, F5, F9, FGA, HABP2, MMP9, TFPI2) that are closely related to the prognosis of NSCLC were screened. Higher expression of the CSN1S1, F2RL1, F9, FGA, and TFPI2 genes is linked to a higher mortality risk in NSCLC patients, with CSN1S1, F2RL1, and F9 having the most impact. Conversely, increased HABP2 expression is linked to a lower mortality risk, while F5 and MMP9 expression levels show no significant impact on mortality risk. Casein α-s1 (CSN1S1) gene is a tumor suppressor with immunomodulatory function, which plays an important role in the growth and metastasis of cancer (57, 58). CSN1S1 expression can be up-regulated or down-regulated in different types of cancer (lung cancer) and can be evaluated by monitoring promoter methylation, gene mutations, and chromosome copy number variation (CNAs) (58, 59). CSN1S1 has been studied as a biomarker in various cancers (breast cancer, epithelial ovarian cancer, hepatocellular carcinoma, and lung squamous cell carcinoma, etc.) (58-61). It is important to note that there are different opinions about the expression of CSN1S1 in lung cancer. Mohsina Akter Mou et al. explored the expression level of CSN1S1 mRNA in normal and cancerous tissues based on public databases, and found that the expression level of CSN1S1 transcription was generally low in lung cancer tissues (58). However, Feng Xu et al . 's study indicated that CSN1S1, as a gene related to tumor suppressor gene p53 (TP53), was significantly overexpressed in lung squamous cell carcinoma tumor tissues and significantly correlated with PD-L1 expression(59). Coagulation factor II(thrombin) receptor-like 1 (F2RL1), also known as protease-activated receptor 2 (PAR-2), is a transmembrane G-protein-coupled receptor belonging to the PAR family, whose activation affects the cellular molecules and microenvironment of tumor cells during tumourgenesis (62-64). In patients with lung adenocarcinoma, high mRNA expression of F2RL1 is strongly associated with poor overall survival (OS) and relapse-free survival (RFS), suggesting that high expression of F2RL1 is associated with adverse prognosis (65, 66). Previous research has demonstrated that the inhibition of PAR2 can reverse non-small cell lung cancer (NSCLC) resistance to gefitinib through the β-arrestin-EGFR-ERK signaling pathway (67). Additionally, the suppression of PAR2 promoter methylation has been shown to upregulate PAR2 gene expression in lung adenocarcinoma (LUAD) cells, thereby influencing the regulation of cellular proliferation, migration, apoptosis, and invasion (62). Furthermore, inhibiting the promoter methylation of PAR2 can enhance the expression of PAR2 gene in lung adenocarcinoma (LUAD) cells, thereby participating in the regulation of the proliferation, migration, apoptosis and invasion of cancer cells (65). Fibrinogen α chain (FGA), also known as Fib2, is a plasma glycoprotein that affects the clotting cascade (68-70). FGA is not only involved in blood clotting, but it plays a role in a variety of cellular and physiological functions, including tumor cell apoptosis, tumor angiogenesis, and tumor development processes (69, 71). In the field of cancer research, the role and related mechanisms of FGA have been extensively studied, with low expression in lung cancer, stomach cancer, hepatocellular carcinoma, cholangiocarcinoma, cervical cancer, breast cancer and other cancers (69, 72-76),and high expression in ovarian cancer, gallbladder cancer, colorectal cancer and other cancers (77-79). Existing studies have shown that highly expressed FGA can inhibit the proliferation and metastasis of lung cancer cells by inhibiting the expression of integrin receptors and Akt /mTOR pathway, and FGA and ITGA5 can inhibit the mTOR pathway by reducing AKT phosphorylation (72). Existing studies have shown that highly expressed FGA can inhibit the proliferation and metastasis of lung cancer cells by inhibiting the expression of integrin receptors and Akt /mTOR pathway, and FGA and ITGA5 can inhibit the mTOR pathway by reducing AKT phosphorylation (71). Hyaluronan-binding protein 2 (HABP2), factor VIIactivating protease (FSAP), codes for a hyaluronic acid binding protein (80). Current studies on HABP2 have focused on thyroid cancer, especially familial non-medullary thyroid cancer (80-82). In addition, HABP2 is highly expressed in endometrial and colorectal cancer tumors and is associated with poor prognosis (80, 83). Existing studies have shown that HABP2 is elevated in different NSCLC tissues, and its expression is more specific in lung adenocarcinoma, and it can promote lung cancer progression through direct activation of uPA (84, 85). Tissue factor pathway inhibitor 2 (TFPI2), also known as stroma-associated serine protease inhibitor (MSPI), is a potent inhibitor of plasminase in ECM (86). TFPI2 belongs to the kunitz type serine protease inhibitor family, which plays an important role in cancer progression by regulating cell adhesion, proliferation, and migration, and has been shown to be a tumor suppressor gene in a variety of malignant tumors, including NSCLC (87, 88). The low expression of TFPI-2 gene in NSCLC is associated with increased tumor aggressiveness, and its gene methylation is an independent factor affecting the prognosis of patients with non-metastatic NSCLC (89, 90). In addition, TFPI-2 inhibits NSCLC angiogenesis by decreasing the expression of vascular endothelial growth factor and inhibits the activity of multiple matrix metalloproteinases (MMPs) to reduce tumor cell invasion and metastasis (91). The F9 gene, localized to chromosome Xq27 and approximately 34kb in length, encodes Human coagulation factor IX (FIX), a serine protease that plays an important role in the coagulation cascade (92, 93). Currently, the F9 gene is widely studied as a hemophilia defect factor, and its deficiency can cause hemophilia type B (also known as Christmas disease) (94, 95). In addition, Xiaoge Gao et al . found that the expression of F9 gene was related to the prognosis of hepatocellular carcinoma through bioinformatics studies (96), Paula Carpintero-Fernandez et al . identified clotting factor IX (F9) as a regulator of aging through genome-wide CRISPR/Cas9 screening (97), Proteomic studies by Zhao Xu et al have revealed that F9 may be a potential biomarker for pseudoexfoliative syndrome (PEX) and pseudoexfoliative glaucoma (PEXG) (98). However, no studies have investigated the link between the F9 gene and non-small cell lung cancer. Through public database screening and tissue chip studies, we found that F9 gene, as a key gene of coagulation and fibrinolysis, may be a potential prognostic biomarker of LUAD. Moreover, F9 gene was associated with immune infiltration of lung adenocarcinoma. In this study, we constructed a prognostic gene-associated risk model based on eight coagulation and fibrinolysis related genes (CFRGs) and rigorously evaluated and validated it with an internal validation set and an external GSE50081 dataset. The results showed that the model was able to significantly distinguish between high-risk group (HRG) and low-risk group (LRG) non-small cell lung cancer (NSCLC) patients, with the HRG having a significantly lower overall survival rate than the LRG, a finding that highlights the potential of the model in identifying patients with poorer prognosis. Further, the area under ROC curve (AUC) values of the model at 1 year, 2 years and 3 years were 0.65, 0.66 and 0.63, respectively, and these values were close to 0.7, indicating that our prognostic risk model had good predictive ability. In addition, by combining prognostic risk model with clinicopathological features, the predictive power and clinical usability of the model can be improved. It was found that there were statistically significant differences in risk scores for clinicopathological factors such as gender, Stage, N, and riskScore, and pathologic_t and riskScore were independent prognostic factors for NSCLC patients . In order to further verify the predictive performance of the model, we drew 1-year, 2-year, 3-year clinical multi-indicator columns and their correction curves. The calibration curve is close to a straight line with a slope of 1, which indicates that the prediction results of the model are highly reliable and provide a solid basis for clinical decision making. Somatic mutation analysis revealed that TP53 and TTN were the most commonly mutated genes in the non-small cell lung cancer (NSCLC) training dataset. The tumor suppressor gene TP53 is the most commonly mutated gene in human cancer cells, affecting about 50% of patients with non-small cell lung cancer, and TP53 mutation is associated with poor prognosis in advanced NSCLC (99, 100). The TTN gene encodes the largest human protein, titin. In non-small cell lung cancer (NSCLC), TTN gene mutation is strongly associated with patient responsiveness to immune checkpoint blockade (ICB) therapy, and TTN mutation status in Circulatory tumor DNA (ctDNA) predicts treatment effect. Indicating its potential as a biomarker for predicting therapeutic response (101). In lung squamous cell carcinoma, TTN gene mutation is positively associated with tumor mutation load (TMB) and is associated with better prognosis, suggesting that TTN mutation may be a potential prognostic indicator of LUSC (102). In lung adenocarcinoma, reduced expression of the TTN gene is associated with poor prognosis in patients and may serve as a prognostic biomarker and potential immunotherapeutic target (103). Through GSEA analysis, 16 biological pathways of differentially expressed genes (DEGs) were found to be significantly enriched between HRG and LRG, such as heme metabolism, androgen response and other pathways. The enrichment of these pathways indicates that these genes play a complex role in tumor development, involving several key biological processes such as blood metabolism, protein synthesis and release, androgen response, cell apoptosis, and cell signaling. These findings provide new insights into the complex biological behavior of tumors and may help in the discovery of new therapeutic targets. We also performed tumor immune microenvironment and immune checkpoint analyses. Immune cells and molecules in the tumor microenvironment are strongly associated with tumor prognosis in NSCLC (104). We evaluated 22 immune cell types in NSCLC, LUAD, and LUSC3 datasets, and only retained statistically significant trusted samples. The results showed that there were significant differences in the expression of these 22 immune cells in different datasets. This reflects the diversity and variability of immune cell composition in the tumor microenvironment. This difference may be related to the biology of different lung cancer subtypes, the stage of progression, or the response to treatment. The correlation between biomarkers and immune cells showed that seven biomarkers, including FGA, HABP2, F2RL1, F5, MMP9 and CSN1S1, were correlated with differential immune cells to varying degrees in the three datasets, while only F9 gene was not significantly correlated in the three datasets. This may imply that F9 has a different role in the tumor immune microenvironment than other screened biomarkers and that F9 may influence tumor development and prognosis through non-immune cell-dependent pathways. There was no statistical difference in immune checkpoints between HRG and LRG, and TIDE analysis showed no significant difference between HRG and LRG. It is suggested that this risk model can effectively distinguish prognosis, but it may not directly affect the immune escape mechanism of tumors. We selected F9 gene as a target for further study to explore its prognostic and immune value in LUAD patients. In lung adenocarcinoma microarray samples, we observed that the positive expression rate of F9 gene in tumor samples was significantly higher than that in neighboring normal samples, indicating that the expression of F9 gene in lung adenocarcinoma was significantly up-regulated. In addition, high expression of F9 gene was associated with poor prognosis in LUAD patients, suggesting that F9 gene may be a marker of poor prognosis. The AUC value of the ROC curve was 0.7622 , indicating that the model based on F9 gene has good stability and predictive ability, and can be used as an effective tool to predict the prognosis of LUAD patients. Multivariate Cox analysis further confirmed that F9 gene was an independent prognostic risk factor in LUAD patients, emphasizing the importance of F9 gene in prognostic evaluation. Our study also found that F9 gene had no significant correlation with three genes, including ALK, EGFR and PDL1, which was further confirmed in the GEPIA2 database, suggesting that F9 gene may influence the prognosis of LUAD through a pathway independent of these known genes. Based on the analysis of TCGA-LUAD dataset, the differential expression pathway between the high expression group and the low expression group of F9 gene is closely related to immune response and tumor microenvironment. These results suggest that F9 gene may be involved in tumor immune escape and immune regulation by affecting APICAL_SURFACE, IL6_JAK_STAT3_SIGNALING pathways. Immune cell analysis showed that resting dendritic cells and activated NK cells were higher expressed in the group with low F9 expression, while M0 macrophageswas higher in the group with high F9 expression. This may indicate that F9 gene expression levels are related to the infiltration and functional status of specific immune cell types. The expression of immune checkpoint LAG3, CTLA4, PDCD1, TIGIT and ADORA2A was different between high- and low- expression groups of F9, which may affect the response of LUAD patients to immune checkpoint inhibitor treatment, providing potential biomarkers for the individualization of immunotherapy. Conclusion At present, a large number of biomarkers related to the coagulation system in cancers have been reported in the literature, but the role of coagulation and fibrinolysis related genes in NSCLC has not been systematically studied. This study aims to fill this gap by analyzing the transcriptomic data of NSCLC, using bioinformatics methods to screen out coagulation and fibrinolytic genes closely related to the prognosis of NSCLC, and explore their potential clinical value in NSCLC. In addition, our study focused specifically on the F9 gene and found that it may be a potential prognostic biomarker for patients with LUAD. The expression level of F9 gene is closely related to the prognosis of LUAD patients, and there is a certain correlation with the immune microenvironment, especially the immune checkpoint ADORA2A, which provides new evidence for the role of F9 gene in NSCLC. Declarations Acknowledgements Not applicable. Funding This study was supported by the National Natural Science Foundation of China (No. 82160529), the Yunnan Revitalization Talent Support Program (No. RLQB20220014), Yunnan Health Training Project of High Level Talents (No. H-2024021) and the 535 Talent Project of the First Affiliated Hospital of Kunming Medical University (No. 2023535D17). Availability of data and materials This study used GSE50081 data sets can be found in the GEO database (https://www.ncbi.nlm.nih.gov/geo), TCGA can be found in the data set (https://portal.gdc.cancer.gov/). Survival curve from K-M Plotter (https://kmplot.com/analysis/index.php?p=service&cancer=lung) and GEPIA2 (http://gepia2.cancer-pku.cn/#correlation) Available in an online database. Further inquiries can be directed to the corresponding authors. Ethical approval and consent to participate Our research was carried out in accordance with the Declaration of Helsinki. 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Discovery of An Orally Effective Factor IX-Transferrin Fusion Protein for Hemophilia B. Int J Mol Sci. 2019;21(1). Chernyi N, Gavrilova D, Saruhanyan M, Oloruntimehin ES, Karabelsky A, Bezsonov E, et al. Recent Advances in Gene Therapy for Hemophilia: Projecting the Perspectives. Biomolecules. 2024;14(7). Batty P, Lillicrap D. Advances and challenges for hemophilia gene therapy. Hum Mol Genet. 2019;28(R1):R95-r101. Gao X, Ren X, Wang F, Ren X, Liu M, Cui G, et al. Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma. Sci Rep. 2024;14(1):9509. Carpintero-Fernández P, Borghesan M, Eleftheriadou O, Pan-Castillo B, Fafián-Labora JA, Mitchell TP, et al. Genome wide CRISPR/Cas9 screen identifies the coagulation factor IX (F9) as a regulator of senescence. Cell Death Dis. 2022;13(2):163. Xu Z, Ke Y, Feng Q, Tuerdimaimaiti A, Zhang D, Dong L, et al. Proteomic characteristics of the aqueous humor in Uyghur patients with pseudoexfoliation syndrome and pseudoexfoliative glaucoma. Exp Eye Res. 2024;243:109903. Cole AJ, Zhu Y, Dwight T, Yu B, Dickson KA, Gard GB, et al. Comprehensive analyses of somatic TP53 mutation in tumors with variable mutant allele frequency. Sci Data. 2017;4:170120. Jiang W, Cheng H, Yu L, Zhang J, Wang Y, Liang Y, et al. Mutation patterns and evolutionary action score of TP53 enable identification of a patient population with poor prognosis in advanced non-small cell lung cancer. Cancer Med. 2023;12(6):6649-58. Su C, Wang X, Zhou J, Zhao J, Zhou F, Zhao G, et al. Titin mutation in circulatory tumor DNA is associated with efficacy to immune checkpoint blockade in advanced non-small cell lung cancer. Transl Lung Cancer Res. 2021;10(3):1256-65. Zou S, Ye J, Hu S, Wei Y, Xu J. Mutations in the TTN Gene are a Prognostic Factor for Patients with Lung Squamous Cell Carcinomas. Int J Gen Med. 2022;15:19-31. Chen J, Wen Y, Su H, Yu X, Hong R, Chen C, et al. Deciphering Prognostic Value of TTN and Its Correlation With Immune Infiltration in Lung Adenocarcinoma. Front Oncol. 2022;12:877878. Yao Y, Yang G, Lu G, Ye J, Cui L, Zeng Z, et al. Th22 Cells/IL-22 Serves as a Protumor Regulator to Drive Poor Prognosis through the JAK-STAT3/MAPK/AKT Signaling Pathway in Non-Small-Cell Lung Cancer. J Immunol Res. 2022;2022:8071234. Tables Table 1 . The information of 8 prognosis-related genes. Gene symbol Gene ID Full name Location Function of the encoded protein CSN1S1 1446 casein alpha s1 Cytoplasm、membrane Predicted to be involved in response to dehydroepiandrosterone, response to estradiol, and response to steroid hormone F2RL1 2150 F2R like trypsin receptor 1 cytoplasm G protein-coupled receptors encoded by F2RL1 promote vasodilation and blood pressure reduction, and are involved in inflammatory responses and immune regulation F5 2153 coagulation factor V Golgi apparatus The FV gene encodes an important cofactor in the clotting cascade, which is involved in the activation of clotting factor X, thereby promoting the conversion of prothrombin to thrombin F9 2158 coagulation factor IX Secreted to blood Factor IX is a vitamin K-dependent plasma protein involved in the intrinsic pathway of blood clotting FGA 2243 fibrinogen alpha chain Endoplasmic reticulum FGA plays a key role in blood clotting and wound healing, and is involved in hemostasis and early wound repair, pregnancy success, immune response, etc HABP2 3026 hyaluronan binding protein 2 Secreted to blood HABP2 activates coagulation factor VII and may negatively regulate cell proliferation and cell migration as a tumor suppressor MMP9 4318 matrix metallopeptidase 9 Cytoplasm、membrane MMP9 is involved in local proteolysis and leukocyte migration of extracellular matrix, osteoclasts, degradation of fibronectin, but not laminatin or Pz peptide TFPI2 7980 tissue factor pathway inhibitor 2 Cytoplasm TFPI2 regulates plasminase-mediated matrix remodeling. Inhibition of trypsin, plasminase, VIIa factor/tissue factor and weak Xa facton. There was no effect on thrombin. *Information in a table from The NCBI database (https://www.ncbi.nlm.nih.gov/gene/), and THE HUMAN PROTEIN ATLAS database (https://www.proteinatlas.org/). Table 2. Association of F9 expression with clinicopathological features of lung adenocarcinoma. Factors Cases (n) F9 expression P value Negative Positive Cancer 88 19(21.6%) 69(78.4%) 0.000 Non-cancerous 86 69(80.2%) 17(19.8%) Age(years) 0.138 < 60 33 12(36.4%) 21(63.6%) ≥ 60 55 12(21.8%) 43(78.2%) Gender 0.430 Male 49 15(30.6%) 34(69.4%) Female 39 9(23.1%) 30(76.9%) T_stage① 0.623 T1 21 7(33.3%) 14(66.7%) T2 36 8(22.2%) 28(77.8%) T3 11 2(18.2%) 9(81.8%) T4 8 3(37.5%) 5(62.5%) T_stage② 1.000 T1+T2 57 15(26.3%) 42(73.3%) T3+T4 19 5(26.3%) 14(73.7%) TNM stage① 0.732 I 25 7(28.0%) 18(72.0%) II 14 3(21.4%) 11(78.6%) III 23 5(21.7%) 18(78.3%) IV 2 1(50.0%) 1(50.0%) unknown 24 8(33.3%) 16(66.7%) 0.766 TNM stage② 0.882 I-II 39 10(25.6%) 29(74.4%) III-IV 25 6(24.0%) 19(76.0%) unknown 24 8(33.3%) 16(66.7%) 0.729 Lymph node metastasis 0.759 Yes 33 8(24.8%) 25(75.8%) No 34 9(26.5%) 25(73.5%) Unknown 21 7(33.3%) 14(66.7%) Positive lymph node ratio (LNR) 0.194 ≤ 20% 44 20(45.5%) 24(54.5%) > 20% 21 6(28.6%) 15(71.4%) Pathological grade① 0.429 I 1 0(0.0%) 1(100.0%) II 55 18(32.7%) 37(67.3%) III 32 6(18.8%) 26(81.3%) Pathological grade② 0.175 I-II 56 18(32.1%) 38(67.9%) III 32 6(18.8%) 26(81.3%) Maximum tumor diameter① 0.655 ≤ 3cm 31 9(29.0%) 22(71.0%) > 3cm 45 11(24.4%) 34(75.6%) Maximum tumor diameter② ≤ 3cm 31 9(29.0%) 22(71.0%) 0.506 3cm 5cm 12 4(33.3%) 8(66.7%) Maximum tumor diameter③ 0.547 ≤ 5cm 64 16(25.0%) 48(75.0%) > 5cm 12 4(33.3%) 8(66.7%) Maximum tumor diameter④ 0.939 ≤ 7cm 70 19(27.1%) 51(72.9%) > 7cm 6 1(16.7%) 5(83.3%) Tumor site 0.991 left lung 30 8(26.7%) 22(73.3%) right lung 56 15(26.8%) 41(73.2%) ALK expression 0.893 Negative 72 18(25.0%) 54(75.0%) Positive 9 3(33.3%) 6(66.7%) EGFR expression 0.877 Negative 53 14(26.4%) 39(73.6%) Positive 19 4(21.1%) 15(78.9%) unknown 16 6(37.5%) 10(62.5%) 0.540 PDL1 expression 0.243 Negative 18 7(38.9%) 11(61.1%) Positive 68 17(25.0%) 51(75.0%) Evaluation of PDL1 expression① 0.631 50% 11 2(18.2%) 9(81.8%) Evaluation of PDL1 expression② 0.682 ≤50% 75 22(29.3%) 53(70.7%) >50% 11 2(18.2%) 9(81.8%) Evaluation of PDL1 expression③ 0.556 ≤ 40% 74 22(29.7%) 52(70.3%) > 40% 12 2(16.7%) 10(83.3%) Evaluation of PDL1 expression④ 0.434 < 1% 18 7(38.9%) 11(61.1%) ≥1% and <50% 57 15(26.3%) 42(73.3%) ≥ 50% 11 2(18.2%) 9(81.8%) Evaluation of PDL1 expression⑤ 0.631 50% 11 2(18.2%) 9(72.1%) Table3. Univariate and multivariate analysis. Characteristic Univariate analysis Multivariate analysis HR(95%CI) P value HR(95%CI) P value F9 expression Negative vs Positive 2.316(1.1118-4.795) 0.024 2.596(1.094-6.160) 0.03 Age < 60 years vs ≥60 years 1.185(0.649-2.161) 0.581 —— —— Gender Female vs Male 1.146(0.641-2.047) 0.646 —— —— T_stage T1 vs T2 vs T3 vs T4 1.667(1.219-2.278) 0.001 T1-2 vs T3-4 3.076(1.569-6.031) 0.001 2.880(1.058-7.841) 0.038 TNM stage I vs II vs III vs IV 1.711(1.189-2.461) 0.004 I-II vs III-IV 2.599(1.377-4.905) 0.003 2.354(0.770-7.195) 0.133 Lymph node metastasis NO vs YES 1.565(0.832-2.944) 0.165 —— —— LNR ≤ 20% vs > 20% 2.102(1.104-4.001) 0.024 0.821(0.273-2.471) 0.726 Pathological grade I vs II vs III 1.743(0.983-3.090) 0.057 —— —— I-II vs III 1.723(0.962-3.086) 0.067 —— —— Maximum tumor diameter ≤ 3cm vs (3 5cm 1.225(0.809-1.855) 0.337 —— —— ≤ 5cm vs > 5cm 1.217(0.564-2.627) 0.617 —— —— ≤ 7cm vs > 7cm 3.344(1.288-8.685) 0.013 1.047(0.300-3.653) 0.942 Tumor site Left lung vs Right lung 1.069(0.591-1.933) 0.826 —— —— ALK expression Negative vs Positive 1.225(0.542-2.763) 0.625 —— —— EGFR expression Negative vs Positive 1.482(0.767-2.865) 0.242 —— —— PDL1 expression Negative vs Positive 1.214(0.599-2.457) 0.591 —— —— Evaluation of PDL1 expression 50% 1.388(0.966-1.996) 0.077 —— —— ≤ 50% vs > 50% 1.476(0.712-3.062) 0.295 —— —— ≤ 40% vs > 40% 1.649(0.818-3.324) 0.162 —— —— 50) vs ≥ 50% 1.261(0.790-2.014) 0.331 —— —— 50% 1.246(0.872-1.779) 0.226 —— —— 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-7175734","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505896178,"identity":"fd4cc4ee-3b82-49cd-860c-b27168b831bc","order_by":0,"name":"Ying Zeng","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zeng","suffix":""},{"id":505896179,"identity":"5607a8e8-4d73-46d8-8227-ef085ec0db1f","order_by":1,"name":"Hongting Jiang","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongting","middleName":"","lastName":"Jiang","suffix":""},{"id":505896180,"identity":"7604ef82-3900-4461-8305-dc7ec5774811","order_by":2,"name":"Zhonglian Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhonglian","middleName":"","lastName":"Wang","suffix":""},{"id":505896181,"identity":"26ce5740-d2c7-438b-94cb-f513a5308ce2","order_by":3,"name":"Cha Luo","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cha","middleName":"","lastName":"Luo","suffix":""},{"id":505896182,"identity":"18b09a57-87eb-4f80-83de-1be680b1a893","order_by":4,"name":"Fei Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Zhang","suffix":""},{"id":505896183,"identity":"57f7e657-7218-4a8c-b2e3-08d9133c1390","order_by":5,"name":"Qiaofen Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC9gYGhgMMDHJAVmPjww/EaOE5ANZiDGQdbjaWIFYLA1iLRHqbAA9RWiSSNx74UWEgZy75sI1BgsFOTreBoJa0goM9ZwyMLWcntj0oYEg2NjtAQIu9RI7BAd62P4kbbie2G0gwHEjcRkgLD1DLwb9tBvUbbh5sk+AhVsth3jaDBIMbjMRq4XlWcFjmjIHhhjOJwEA2IMIvPOzJmz++qTCQNzh+/OHDDxV2cgS1AIEBDjaRWkbBKBgFo2AUYAEAqmhD8rF1cmcAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qiaofen","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2025-07-21 09:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7175734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7175734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90315927,"identity":"28e79aef-3ede-4ebd-b2a0-62bacffe0bce","added_by":"auto","created_at":"2025-09-01 10:17:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4411910,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of this study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7175734/v1/681ce9118560a5a55cd61be8.png"},{"id":90314558,"identity":"bf0fa895-8a80-48c1-b7b0-8f8a02c80b8f","added_by":"auto","created_at":"2025-09-01 10:09:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2315738,"visible":true,"origin":"","legend":"\u003cp\u003eScreening and enrichment of DE-CFRGs in NSCLC. (A) PCA analysis was performed on the NSCLC dataset from the TCGA database. (B) Volcano map of DEGs between tumor and normal tissues in the NSCLC dataset, with red representing upregulation and blue representing downregulation. (C) Venn diagram of DE-CFRGs between DEGs and CFRGs. (D) GO enrichment analysis of 73 DE-CFRGs. (E)KEGG pathway enrichment of 73 DE-CFRGs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7175734/v1/221320393d2c7de73b48c75c.png"},{"id":90314587,"identity":"1a6345f8-6b1a-4abf-bbde-66a41af7d955","added_by":"auto","created_at":"2025-09-01 10:09:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3667333,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression and risk model evaluation. (A) LASSO coefficient path map of 18 characteristic genes. (B) LASSO regularization path. (C) Multivariate cox regression analysis. (D-F) Risk score distribution, survival status and gene expression heat map of training set, test set and validation set. (G-L) Kaplan–Meier survival analysis and ROC curves of training set, test set and validation set. Survival status: 1: dead, 0: lost to follow-up (including alive). Low: low-risk group, High: high-risk group. Time: days. AUC: area under the curve.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7175734/v1/bcda1d5732792f6e78e74021.png"},{"id":90314584,"identity":"8c301576-f3d3-4b69-9392-278bbc82d39f","added_by":"auto","created_at":"2025-09-01 10:09:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3084561,"visible":true,"origin":"","legend":"\u003cp\u003eBiomarker expression analysis and prognostic Analysis of NSCLC. (A) Biomarker expression analysis between high-low risk groups in the test set and validation set, as well as between TCGA-LUAD and TCGA-LUSC tumor samples and control normal samples. (B) Risk scores were assessed under different clinical information subgroups. (C) Univariate and multivariate Cox analyses of clinical-pathological factors. (D-E) Nomogram of the prognostic model and Calibration of the 1,2,3-year prognostic model. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7175734/v1/3310593594b5d551c4b6c96f.png"},{"id":90314561,"identity":"83e3d7f3-6a38-4928-8726-bcc8a371e7a3","added_by":"auto","created_at":"2025-09-01 10:09:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4634220,"visible":true,"origin":"","legend":"\u003cp\u003eSomatic mutation analysis, GSEA analysis and immune infiltration analysis. (A) The somatic mutation waterfall plot of high and low-risk groups based on mutation data from the training set of NSCLC patients. (B) Mutated gene correlation analysis heat map. (C) Enrichment analysis of GSEA between high and low-risk groups. *p \u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7175734/v1/07d2461df170774e59435ede.png"},{"id":90314563,"identity":"5fc0053f-a875-4adc-b944-fa90cedfbc8e","added_by":"auto","created_at":"2025-09-01 10:09:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3322637,"visible":true,"origin":"","legend":"\u003cp\u003eImmunoinfiltration analysis of three datasets (NSCLC,LUAD,LUSC). (A) Boxplot of differential immune cells. (B) Heat maps of biomarker correlation with differential immune cells. (C) Biomarker risk score - differential immune cell correlation network. groupGene1=TFPI2+FGA+HABP2+F5, groupGene2=F2RL1+MMP9+CSN1S1+F9. *p \u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7175734/v1/612e1148ba7c8842191641f7.png"},{"id":90314574,"identity":"599d470e-7790-4b15-a715-1c2830152b3f","added_by":"auto","created_at":"2025-09-01 10:09:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3670913,"visible":true,"origin":"","legend":"\u003cp\u003eImmune checkpoint analysis, immunotherapy efficacy prediction and drug-gene networks. (A) Expression of immune checkpoints in high-risk and low-risk groups. (B) Expression of immune checkpoints in LUAD tumor samples and LUSC tumor samples. (C) Expression of immune checkpoints in LUAD tumor samples and normal control samples. (D) Expression of immune checkpoints in LUSC tumor samples and normal control samples. (E) TIDE scores between high and low risk groups. (F) Drug gene network based on 8 CFRGs. Red nodes represent CFRGS in the risk model, and gray nodes represent drugs that target biomarkers. *p \u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7175734/v1/fbdaeae54e676b3fb40749a7.png"},{"id":90314571,"identity":"cb3acb6f-be02-42bc-8e0a-9df815995066","added_by":"auto","created_at":"2025-09-01 10:09:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5187971,"visible":true,"origin":"","legend":"\u003cp\u003eF9 gene is a potential target of LUAD. (A) Immunohistochemical staining of F9 in patients with LUAD. (B) The positive expression of F9 gene in 92 pairs of patients. (C) Kaplan-Meier survival curve of LUAD patients. (D) Kaplan-Meier survival curve of F9 gene based on Kaplan-Meier Plotter database. (E) ROC curve of F9 gene. (F) GSEA mutation analysis of F9 gene in TCGA-LUAD dataset. (G-H) Immune cell infiltration and immune checkpoint analysis of F9 gene in TCGA-LUAD dataset. (I) Correlation analysis. *p \u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7175734/v1/84af67fae8625fcab4f80a55.png"},{"id":94066034,"identity":"f5b270c6-60f3-4120-9fae-cc738765005e","added_by":"auto","created_at":"2025-10-22 08:02:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28685022,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7175734/v1/e9c38ed2-de57-455f-ab29-9c26ecf42168.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel risk prediction models for prognosis and immunotherapies of NSCLC based on coagulation and fibrinolysis-related genes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer has become the leading cause of cancer death worldwide and is expected to overtake ischaemic heart disease (IHD) as the leading cause of death by 2060\u0026nbsp;(1, 2). Non-small cell lung cancer (NSCLC) accounts for 80-85% of all lung cancer diagnoses, and 70% of patients have metastases at the time of diagnosis, leading to a poor prognosis. The median survival is usually less than 12 months, 5-year survival is only 10-15% (3-5). The histological types of NSCLC are diverse, including lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), large cell carcinoma, sarcomatoid carcinoma and carcinoid carcinoma. Among these, LUAD and LUSC are the most prevalent subtypes, with LUAD now surpassing LUSC as the predominant form of NSCLC (6, 7).\u003c/p\u003e\n\u003cp\u003eThe treatment and prognosis of NSCLC are highly dependent on the stage of the disease at diagnosis (8). Unfortunately, due to the lack of clearly specific symptoms early on, many patients have advanced disease with metastasis by the time they are diagnosed (9, 10). Despite this challenge, we have been able to significantly improve the detection rate of early lung cancer and thus the survival rate of patients with NSCLC through low-dose CT scans, liquid biopsies, and serum tumor marker detection (2, 11-13). At present, the therapeutic drugs for NSCLC are mainly divided into three categories: cytotoxic drugs, molecular targeted drugs and immunotherapy drugs (14). In recent years, advances in targeted therapies and immunotherapy have significantly extended the survival of patients with NSCLC (15, 16). Still, these treatments face a number of challenges, including the emergence of drug resistance, the lack of precise biomarkers to predict response to immunotherapy, and the limitations of the patient population that will benefit (less than 20%) (13, 17). Tumor biomarkers that can predict treatment response and prognosis hold substantial potential in the management of NSCLC (5). It is an important component of precision medicine, which is essential for early diagnosis and effective treatment. This helps to optimize clinical decision making, improve diagnostic accuracy, determine prognosis and predict efficacy, and ultimately improve patient survival and quality of life (18-20).\u003c/p\u003e\n\u003cp\u003eActivation of coagulation and fibrinolysis systems is a common phenomenon in the occurrence and development of malignant tumors\u0026nbsp;(21). Since Trousseau first described the link between malignant cell growth and the coagulation and fibrinolytic systems in 1865, numerous clinical and in vitro studies have further revealed the close relationship between them (22-24). Coagulation and fibrinolysis systems are associated with several key processes of malignant tumors (such as metastasis, invasion, poor prognosis, angiogenesis) and their response to immunotherapy (22, 25). Coagulation and fibrinolysis related genes (CFRGs) play an important role in a variety of cancers, including promoting tumor growth, invasion, migration, and angiogenesis (25-29). These genes have been identified as potential targets in the diagnosis and treatment of a variety of cancers (25). However, in NSCLC, the role of CFRGs has not been fully studied. Tumor microenvironment (TME) and immune infiltration play an important role in tumor initiation, development and drug efficacy (30). TME is a key agent of cancer occurrence, progression, and treatment outcome, and is closely related to immunotherapy outcomes (29, 31). \u003cem\u003eMeng Fan et al.\u003c/em\u003e found a significant association between TME and coagulation and fibrinolysis-associated genes (CFRGs) in hepatocellular carcinoma (HCC), a finding that highlights the important role of TME in tumor development (29). In light of this, in-depth studies of the interactions between CFRGs and TME and immune cell infiltration are particularly critical when exploring potential therapeutic targets for NSCLC. This relationship may reveal new therapeutic opportunities, particularly in terms of improving immunotherapy response rates and efficacy. Therefore, this study aimed to explore the potential value of CFRGs in NSCLC and analyze how they interact with TME and immune cell infiltration, thereby providing new molecular markers and therapeutic strategies for the precision treatment of NSCLC.\u003c/p\u003e\n\u003cp\u003eBy analyzing transcriptome data from NSCLC, we identified eight CFRGs associated with survival in NSCLC patients and explored the potential clinical value of these genes in NSCLC through a range of bioinformatics approaches. In addition, the prognosis and immune infiltration of F9 gene in patients with lung adenocarcinoma were further investigated by means of lung adenocarcinoma tissue microarray (containing 92 lung adenocarcinoma tissue samples and 92 corresponding paracancer tissue samples) combined with a public database. In summary, this study provides a new molecular target for the prognosis of NSCLC based on coagulation and fibrinolysis related genes, and fills a gap in the study of F9 gene in LUAD.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData on transcriptome sequencing for lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database, including a total of 110 control samples (59 LUAD and 51 LUSC) and 1043 tumor samples (541 LUAD and 502 LUSC). We combined the TCGA-LUAD and TCGA-LUSC data sets to construct a non-small cell lung cancer (NSCLC) dataset and randomly divided it into a training set (702 sample) and a test set (300 samples) at a ratio of 7:3. In addition, we downloaded the external validation set GSE50081 (181 NSCLC tumor tissue samples using the Affymetrix platform) from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo). In the GeneCards database, 136 genes related to coagulation and fibrinolysis (CFRGs) were identified by the keyword \u0026quot;coagulation and fibrinolysis\u0026quot; and the related score bbb50 threshold (\u003cstrong\u003eTable S1\u003c/strong\u003e). Furthermore, the effect of F9 gene on prognosis of lung adenocarcinoma patients was also searched in Kaplan-Meier Plotter (K-M Plotter, https://kmplot.com/analysis/index.php?p=service\u0026amp;cancer=lung) online database. Gene correlation was analyzed through the GEPIA2 database (http://gepia2.cancer-pku.cn/#correlation) (32).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Screening of DE-CFRGs and functional analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to identifying differentially expressed genes (DEGs) within NSCLC dataset, principal component analysis (PCA) was applied to examine potential batch effects. The DEGs were subsequently recognized via edgeR package (|log2FC| \u0026gt; 1, false discovery rate (FDR)-corrected p \u0026lt; 0.05) (33). Differentially expressed cancer-related genes (DE-CFRGs) were identified by intersecting the DEGs with cancer-related gene sets. Thereafter, the potential roles of DE-CFRGs in NSCLC were explored, with clusterProfiler package applied for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses (p \u0026lt; 0.05, q \u0026lt; 0.05, minimum gene set size (minGSSize) = 5). Additionally, a volcano plot generated by \u0026quot;ggplot2\u0026quot; package was used for visualization of DEGs, while the DE-CFRGs were visualized through a Venn diagram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Establishment and evaluation of prognostic models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the training set, assessment of the potential value of CFRGs for predicting overall survival (OS) of NSCLC patients was conducted. Specifically, the least absolute shrinkage and selection operator (LASSO) was conducted on DE-CFRGs by glmnet package, with family = \u0026ldquo;cox\u0026rdquo;, maxit = 10000 to obtain the feature genes. Then, biomarkers were obtained by multivariate cox regression analysis based on feature genes (direction = both). Based on the biomarkers related to coagulation and fibrinolysis, a prognostic risk model was constructed, and the risk scores related to coagulation and fibrinolysis were calculated according to the formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"223\" height=\"63\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eWe classified the 702 tumor samples in training set into high-risk group (HRG) and low-risk group (LRG) according to the median prognostic value. Afterwards, in order to further evaluate the effectiveness of the prognostic model, we plotted the OS survival curve to determine the survival difference between risk groups. At the same time, the AUC area of the model was calculated by ROC to evaluate the validity of the model. Finally, to further evaluate the accuracy and generalizability of the prognostic model, we validated the survival prognosis score model multiple times through test sets and external validation sets (GSE50081).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Clinicopathological characteristics and nomogram construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the influence of clinicopathological characteristics and risk models on NSCLC prognosis, we conducted a comprehensive analysis that incorporated age, gender, and tumor characteristics within the training cohort. Key clinicopathological variables, including tumor type, stage, size or extent of invasion (T), distant metastasis (M), and regional lymph node invasion and metastasis (N), were examined in conjunction with the risk model. Our objective was to assess whether significant differences in risk scores existed among various clinical information groups. Utilizing both univariate and multivariate Cox regression analyses, independent clinical factors impacting patient prognosis were recognized, including only those clinicopathological parameters with\u0026nbsp;p \u0026lt; 0.01. Furthermore, we employed the rms package to develop nomograms for predicting patient outcomes at 1-year, 2-year, and 3-year intervals\u0026nbsp;(34). The predictive accuracy of the nomogram model for NSCLC was subsequently assessed using calibration curves.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Somatic mutation analysis and GSEA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter obtaining somatic mutation information of NSCLC\u0026nbsp;samples in the training set, Maftools package\u0026nbsp;(35) was employed to evaluate the potential associations between genetic mutation profiles and risk scores related to coagulation and fibrinolysis, with the top 25 genes with the highest mutation rates in HRG and LRG were visualized, respectively. Moreover to elucidate differential biological pathways influenced by risk scores in NSCLC, Gene Set Enrichment Analysis (GSEA) was employed via clusterProfiler package\u0026nbsp;(36) (p \u0026lt; 0.05, FDR \u0026lt; 0.25, minimum gene set size (minGSSize) = 5). In detail, we utilized the dataset h.all.v7.4.symbols.gmt from the MSigDB database (https://www.gsea-msigdb.org/gsea) as the background set for enrichment analysis. Using the \u0026quot;edgeR\u0026quot; package, we calculated the differential expression (low vs. high) for all genes between HRG and LRG, ranking the genes in descending order based on these differential values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Immune landscape analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe altered immune landscape was further investigated. Specifically, CIBERSORT (37) was employed to conduct immunoinfiltration analysis on control and tumor samples derived from the NSCLC, LUAD, and LUSC datasets. After filtering samples with p \u0026lt; 0.05, the proportions of 22 distinct immune cell types within samples were ascertained. The Wilcoxon rank-sum test was utilized to evaluated the infiltration differences in 22 immune cell types between control and tumor samples. Furthermore, Spearman correlation analysis was performed to investigate relationships among differential immune cells, biomarkers, risk scores, and differential immune cells, utilizing \u0026quot;ggcor\u0026quot; package.\u003c/p\u003e\n\u003cp\u003eImmune checkpoints serve as critical regulators of immune evasion in cancer (38). Therefore, we selected routine immune checkpoints (PD_L1, CTLA_4, LAG_3, GAL9, HAVCR2, TIM_3, PD_1, PD_1LG2, TIGHT, ADORA2A, BTLA, CD160, CD274, CSF1R, IL10, KIR2DL1, KIR2DL3, LGALS9, TGFB1, CD96, PDCD1, PDCD1LG2, TGFBR1, TIGIT; some genes were not detected in the data matrix) to analyze its expression in HRG and LRG. The Tumor Immune Dysfunction and Exclusion (TIDE) platform (http://tide.dfci.harvard.edu/) represents a sophisticated computational framework engineered to model mechanisms of tumor immune evasion. It is extensively utilized to forecast the therapeutic response to immune checkpoint blockade therapies, including inhibitors directed against PD-1 (programmed cell death protein 1) and CTLA-4 (cytotoxic T lymphocyte-associated protein 4) (39, 40). An elevated TIDE predictive score correlates with a greater propensity for immune evasion, implying that such patients are more likely to demonstrate reduced responsiveness to immunotherapeutic interventions (41). Therefore, TIDE algorithm analysis was conducted on HRG and LRG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Expression analysis of biomarkers and the drug-gene network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed the Wilcoxon rank-sum test to assess the differential expression of biomarkers between tumor and normal control samples within both TCGA-LUAD and TCGA-LUSC datasets. Furthermore, for providing clues for treatment, we utilized the DGidb database (https://www.dgidb.org/) to identify potential targeted biomarkers and their associated drug formulations that exhibited consistent differential expression patterns across both LUAD and LUSC datasets. Subsequently, we constructed a gene-drug interaction network using Cytoscape software to visually represent and conduct an in-depth analysis of the potential connections between these biomarkers and drugs.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Tissue Microarray and Immunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the bioinformatics analysis results, F9 gene was selected as a target for clinical sample validation. Tissue microarray technology was used in this study. Lung adenocarcinoma tissue microarray included 92 cancer tissues and 92 paracancer tissues (Shanghai Xinchao Biotechnology Co., LTD.). The antigen was extracted by heating with citric acid after dewaxing and hydration. After antigen recovery, wash with PBS 3 times. The endogenous peroxidase activity was blocked with 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, incubated at room temperature for 10 minutes, and washed 3 times with PBS. Sections were then blocked with 5% BSA for 1 hour at room temperature. The F9 antibody (proteintech, 21481-1-AP) was diluted (1:1000) and incubated at 4℃ overnight. After reheating for 1 hour and washing with PBS 3 times, sections were incubated with biotin-labeled secondary antibodies at room temperature for 10 minutes, followed by 4 PBS washes. Horseradish peroxidase-conjugated streptavidin was applied, incubated for 10 minutes, and washed 4 times with PBS. Subsequently, DAB was used for chromogenic development, and hematoxylin was applied as a counterstain. Images were captured under a microscop, with PBS serving as a negative control. Nuclear brown-yellow granules indicated F9 positivity. The IRS was calculated as the product of staining intensity (SI) and percentage of positive cells (PP), i.e. IRS=SI\u0026times;PP. SI was graded as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong), while PP was categorized as 0 (negative), 1 (\u0026le;10%), 2 (11\u0026ndash;50%), 3 (51\u0026ndash;80%), or 4 (\u0026gt;80%). An IRS \u0026gt; 3 indicated positive immunoreactivity, with 3 \u0026lt; IRS \u0026le; 5 considered moderate and IRS \u0026gt; 5 strong. Immunohistochemical results were evaluated microscopically using the IRS scoring system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR language (version 4.1.3) was employed to conduct bioinformatics analyses. Besides, Wilcoxon rank-sum test and Chi-square test were employed in this study to assess differences between specific groups, setting significance threshold at p \u0026lt; 0.05. GraphPad Prism 9.5 software was applied for data visualization.Clinical trial registration number: Not applicable.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Recognition and multiple functions of DE-CFRGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe general workflow of this current study is illustrated in \u003cstrong\u003eFigure 1\u003c/strong\u003e. We combined both TCGA-LUAD and TCGA-LUSC data sets to construct the NSCLC dataset. PCA showed that there was no significant batch effect in the dataset (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). We then screened 19,123 DEGs based on tumor and control samples from the NSCLC dataset, which exhibited markedly different expression levels in tumor and normal tissues (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). Cross-testing of DEG between NSCLC and CFRGs produced a total of 73 DE-CFRGs, including 43 up-regulated and 30 down-regulated genes (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). GO and KEGG analyses revealed the crucial function of DE-CFRGs in NSCLC and the important pathways involved. The results show that DE-CFRGs may be involved in biological processes like blood coagulation, hemostasis, wound healing, etc. And in HIF-1 signaling pathway, Malaria, complement and cogulation cascades and other important pathways play a role (\u003cstrong\u003eFigure 2D-E\u003c/strong\u003e). These results aid in understanding the multiple roles of coagulation and fibrinolysis in NSCLC progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Strong predictive power of CFRGs for NSCLC prognosis demonstrated by a risk model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter LASSO and Cox regression analyses, eight key biomarkers were identified, including CSN1S1, F2RL1, F5, F9, FGA, HABP2, MMP9 and TFPI2, from 73 candidate CFRGs (\u003cstrong\u003eFigure 3A-C, Table 1\u003c/strong\u003e). In the training set, in-depth analysis was conducted on the risk model constructed by these 8 genes, including survival state heat map, risk score distribution, and gene expression heat map (\u003cstrong\u003eFigure 3D\u003c/strong\u003e). Kaplan-Meier survival curve analysis showed that OS was significantly lower in HRG than in LRG (p\u0026lt;0.0001) (\u003cstrong\u003eFigure 3G\u003c/strong\u003e). For 1-year, 2-year, and 3-year OS, corresponding ROC curves were plotted and the resulting AUC values were 0.65, 0.66, and 0.63, respectively (\u003cstrong\u003eFigure 3J\u003c/strong\u003e). These results suggest that our risk model has good prognostic ability for patients with NSCLC. In addition, we also obtained consistent results on the test and validation sets (\u003cstrong\u003eFigure 3E-F,H-I,K-L\u003c/strong\u003e). The remarkable reliability and generalizability reveals the potential of this model as an invaluable instrument for tailored prognostic evaluation in clinical management of NSCLC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Expression trends of biomarkers and a nomogram integrating risk scores and clinicopathological features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt was found that CSN1S1, F2RL1, F5, F9, FGA, MMP9 and TFPI2 genes were significantly overexpressed in the HRG and HABP2 gene was significantly overexpressed in the LRG within training and validation sets (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). In the TCGA-LUAD dataset, CSN1S1, F2RL1, F5, HABP2, MMP9 and TFPI2 genes were significantly overexpressed in tumor samples. In TCGA-LUSC, CSN1S1, F2RL1, F5, F2RL1 and F5 genes were highly expressed in tumor samples, while FGA, HABP2 and TFPI2 genes were significantly low expressed in tumor samples. We further explored the correlation between clinicopathological features and risk model prognosis (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). The analysis showed that male patients showed a higher risk score. In terms of tumor staging, stage III patients exhibited markedly higher risk scores than stage I and II patients. In addition, in terms of tumor size (T), patients with stage T2 and T4 had higher risk scores than those with stage T1. However, there were no marked differences in risk scores between the age, lymph node metastasis and distant metastasis groups, as well as between the TCGA-LUAD and TCGA-LUSC datasets. Nomograms play an important role in individualized risk assessment, risk stratification, identification of high-risk patients, risk adjustment, and direct prediction of cancer patient survival (42, 43). This tool helps clinicians make personalized decisions (44, 45). Through Cox regression analyses, lymph node metastasis (pathologic_n), tumor stage (pathologic_stage), tumor size (pathologic_T), and risk score were finally identified as important prognostic indicators (p\u0026lt;0.01,\u0026nbsp;\u003cstrong\u003eFigure 4C\u003c/strong\u003e). The 1-year, 2-year and 3-year clinical multi-indicator charts were constructed (\u003cstrong\u003eFigure 4D\u003c/strong\u003e). The slope of corresponding calibration curve was observed to be close to 1, revealing the model calibration was excellent (\u003cstrong\u003eFigure 4E\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Differential somatic mutation profiles and functional pathways altered by risk scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSomatic mutation profiles of HRG and LRG were illustrated in the training set (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). Markedly, TP53 and TTN emerged as the genes with HIGHER mutation rates in HRG and LRG, with mutation rates of 68.6% and 60.9%, respectively. The predominant mutation types identified were missense mutation in both TP53 and TTN. These findings helped to uncover the potential impact of risk scores related to coagulation and fibrinolysis on mutation patterns of specific genes in NSCLC patients. The mutation gene correlation analysis showed (\u003cstrong\u003eFigure 5B\u003c/strong\u003e) that mutations in TP53 gene and FAT3,MUC17,NAV3 and SYNE1 genes were mutually exclusive. TTN gene mutation and TP53, ADAMTS12 COL11A1 gene mutation of mutually exclusive, such as with CSMD3 FAT3, PCDH15, RYR2, RYR3, SPTA1, SYNE1 such as gene mutations occur together. Through GSEA, pathways biologically linked to genes differing between HRG and LRG were revealed. Specifically, the differential genes were associated with the activities of pathways such as heme metabolism, protein_secretion, androgen response, apoptosis, and KRAS signal (\u003cstrong\u003eFigure 5C\u003c/strong\u003e). These findings suggest that CFRGs associated with NSCLC may play a potential role in biological processes like cell signaling, cell death, immune response, and metabolism. Coagulation and fibrinolysis may play critical roles in progression of NSCLC by influencing activities of these pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Close associations between CFRGs and immune microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune cell infiltration plays an crucial role in the occurrence and progression of NSCLC. We hypothesized that CFRGs, as a prognostic indicator of NSCLC. We hypothesized that CFRGs, as a prognostic indicator of NSCLC, is associated with immune invasion. Therefore, infiltration levels of 22 immune cell types in the NSCLC, LUAD and LUSC datasets were further investigated. It was found that their infiltration levels exhibited differences between normal and tumor samples (\u003cstrong\u003eFigure 6A\u003c/strong\u003e). In NSCLC, cell types like naive B cell and M1 macrophages were significantly overexpressed, whereas cell types like M0 macrophages and resting mast cells exhibited markedly lower expression. Notably, the correlation between biomarkers and differential immune cells were illustrated (\u003cstrong\u003eFigure 6B\u003c/strong\u003e), and it found that TFPI2, FGA, HABP2, F2RL1, F5 and MMP9 genes were strongly correlated with differential immune cells, while CSN1S1 and F9 genes were relatively weakly correlated with differential immune cells. In addition, we constructed a biomarker risk score-differential immune cell correlation network (\u003cstrong\u003eFigure 6C\u003c/strong\u003e), which divided the genes into two groups: groupGene1=TFPI2+FGA+HABP2+F5, groupGene2=F2RL1+MMP9+CSN1S1+F9. The results show that in the NSCLC dataset, groupGene1 is significantly correlated with T foliicular helper cells and M0 macrophages, while groupGene2 is significantly correlated with M0 macrophages. In the LUAD dataset, groupGene1 was significantly correlated with activated CD4 memory T cells. groupGene2 was significantly associated with naive B cells, and riskScore was significantly associated with naive B cells and T foliicular helper cells. However, in the LUSC dataset, groupGene1 and groupGene2 had no significant correlation with differential immune cells. Consequently, coagulation and fibrinolysis might influence the progression of NSCLC by altering the immune infiltration levels of specific immune cell types, which are closely associated with specific biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Evaluation of ICI treatment response of CFRGs and drug-gene network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmunotherapies, known as checkpoint inhibitors (CPIs), are currently the second most common cancer treatment for patients with advanced NSCLC (46). Our analysis showed no marked difference in immune checkpoints between HRG and LRG (\u003cstrong\u003eFigure 7A\u003c/strong\u003e). Therefore, we compared and analyzed the differences in immune checkpoint expression between tumor samples in the LUAD and LUSC datasets and between tumor samples and their respective control samples. It was showed that compared with LUAD tumor samples, immune checkpoints such as BTLA, CD160, CD244, and CTLA4 were significantly lower expressed in LUSC tumor samples, while CD274, TGFB1, and VTCN1 were significantly higher expressed (\u003cstrong\u003eFigure 7B\u003c/strong\u003e). In the LUAD dataset, immune checkpoints such as ADORA2A, CD160, KDR and TGFB1 were significantly underexpressed in tumor samples, while immune checkpoints such as BTLA, CTLA4, LAG3 and TIGIT were significantly overexpressed (\u003cstrong\u003eFigure 7C\u003c/strong\u003e). In the LUSC dataset, 18 immune checkpoints such as ADORA2A, BTLA and CD160 showed directional consistency with their expression in the LUAD dataset (\u003cstrong\u003eFigure 7D\u003c/strong\u003e). It is worth mentioning that there is no significant difference in the expression of IDO1, IL10 and IL10RB between tumor samples and control normal samples in LUAD dataset, while the expression of IDO1, IL10 and IL10RB is significantly low in LUSC dataset. In addition, there was no significant difference in the expression of PDCD1 in the LUSC dataset, but significantly high expression in the tumor samples in the LUAD dataset. The different expression patterns of these genes in LUAD and LUSC suggest that they may serve as potential biomarkers to distinguish the two lung cancer subtypes. To validate these findings, we utilized the TIDE algorithm to predict immunotherapy responses in LRG and HRG patients. The results showed no significant difference in TIDE scores between the HRG and LRG (\u003cstrong\u003eFigure 7E\u003c/strong\u003e), further confirming that risk scores may not have a direct impact on immunotherapy efficacy.\u003c/p\u003e\n\u003cp\u003eFurthermore, utilizing eight CFRGs within the risk model, the GDidb database was employed to identify drugs that target biomarkers exhibiting similar trends of variation between control and tumor samples in both the LUAD and LUSC datasets. A total of 19 gene-drug pairs were discovered, with five approved drugs targeting F2RL1, six approved drugs targeting F9, seven approved drugs targeting FGA, and one drug targeting MMP9 (comprising four target genes and 19 drugs). Subsequently, the complex drug-gene interaction networks were constructed (\u003cstrong\u003eFigure 7F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Validation and immune infiltration of F9 gene in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor further verifying the prognostic value of F9 gene in LUAD patients, we analyzed 92 pairs of tumor and para-cancer samples and related clinical data. Tissue samples were stained with immunohistochemistry, and F9 clinical data were analyzed for survival and correlation. The expression of F9 gene in cancer tissue and adjacent normal lung tissue is shown in \u003cstrong\u003eFigure 8A\u003c/strong\u003e. The positive expression of F9 gene in cancer tissues (78.4%) was markedly higher than that in non-cancer tissues (19.8%) (p\u0026lt;0.0001), and was not correlated with other clinicopathological parameters (p\u0026gt;0.05) (\u003cstrong\u003eTable 2\u003c/strong\u003e). Importantly, it was showed that F9 gene was an independent adverse prognostic factor in patients with lung adenocarcinoma (p =0.03) (\u003cstrong\u003eTable3\u003c/strong\u003e). Moreover, F9 gene was significantly higher expressed in tumor tissues than in adjacent normal tissues (\u003cstrong\u003eFigure 8B\u003c/strong\u003e). Furthermore, patients with positive expression of F9 had worse survival (\u003cstrong\u003eFigure 8C\u003c/strong\u003e). The Kaplan-meier plotter database also showed consistent results (\u003cstrong\u003eFigure 8D\u003c/strong\u003e). The ROC curve showed that the area under the curve (AUC) was 0.7622, p\u0026lt;0.0001 (\u003cstrong\u003eFigure 8E\u003c/strong\u003e), further confirming the prognostic value of F9 in LUAD patients. In addition, we explored the relationship between F9 gene and immune infiltration in the TCGA-LUAD dataset. According to the median value of F9 gene in the LUAD dataset, they were divided into high- and low-expression groups. GSEA showed the differential pathways of F9 gene between high-low expression groups, such as apical surface, IL6 JAK STAT3 signaling, and TGF beta signaling (\u003cstrong\u003eFigure 8F\u003c/strong\u003e). As shown in \u003cstrong\u003eFigure 8G,\u003c/strong\u003e in the LUAD data set, resting dendritic cells and activated natural killer (NK) cells are higher expressed in the low-expression group, while M0 macrophaged is higher expressed in the high-expression group. The expression of LAG3, CTLA4, PDCD1, TIGIT and other immune checkpoints was different between high- and low-expression groups (\u003cstrong\u003eFigure 8H\u003c/strong\u003e). We also verified the expression of ALK, EGFE, PDL1 and other three genes in the tissue chip samples, and analyzed their correlation with F9 gene. The results showed that there was no significant correlation between them, and the results retrieved in the GEPIA2 database further confirmed our conclusion (\u003cstrong\u003eFigure 8I\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAdenosine receptor A2A (ADORA2A), a a member of the G-protein-coupled receptor (GPCR) family, mediates intracellular signaling cascades,including the AKT and ERK pathways, through its interaction with adenosine. This receptor exhibits selective upregulation in neuroendocrine lung cancer (47). Existing studies have shown that adenosine A2A receptors (ADORA2A) in myeloid cells can inhibit T cell and NK cell responses in the solid tumor microenvironment (48), and can also inhibit macrophage-apoptosis by inhibiting the expression of tumor necrosis factor-\u0026alpha; (TNF-\u0026alpha;) (49). The significantly differential expression of ADORA2A1 may indicate that it is a potential immunotherapeutic target. Therapeutic strategies targeting ADORA2A1 may help modulate the immune response in the tumor microenvironment, thereby improving the effectiveness of immunotherapy.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eNSCLC is a heterogeneous disease with multiple histopathological and clinical features\u0026nbsp;(50, 51). It is characterized by high morbidity, mortality, metastasis and recurrence rates, and low five-year survival rates (4, 5, 52). Therefore, the discovery of new molecular markers and therapeutic targets is critical to improving treatment outcomes in patients with NSCLC (53). Hypercoagulation and hyperfibrinolysis of blood are common in NSCLC patients, and these pathological states not only disturb the balance of the blood circulatory system, but also may aggravate the aggressiveness and migration ability of tumor cells (54). Clinical studies have shown that elevated coagulation and fibrinolysis levels are strongly associated with poor patient outcomes in a variety of cancers (55). In addition, cancer development and the immune system are closely related, especially in the tumor microenvironment, where tumor cells have developed multiple mechanisms to evade immune surveillance, resulting in impaired immune cell function and weakened anti-tumor immune response (25, 56). Based on this background, we explored prognostic CFRGs as potential biomarkers of NSCLC and analyzed their role in the NSCLC immune microenvironment, immune efficacy, and prognosis. This study is dedicated to exploring new research directions in the field of non-small cell lung cancer (NSCLC), aiming to provide innovative perspectives for clinical treatment strategies and promote the development of precision medicine, thereby providing patients with more personalized and effective treatment options.\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to explore the genes associated with coagulation and fibrinolysis that are associated with the prognosis of NSCLC. First, we selected 73 differentially expressed genes (DE-CFRGs) at the intersection of TCGA_LUAD dataset, TCGA_LUSC dataset and CFRGs dataset, among which 43 up-regulated genes and 30 down-regulated genes. Functional enrichment analysis revealed the relationship between these DE-CFRGs and blood coagulation, coagulation, coagulation, hemostasis,wound healing, and regulation of body fluid levels are closely related, suggesting that they may play a key role in tumor growth, angiogenesis, and metastasis in NSCLC. In addition, KEGG enrichment analysis showed that the signaling pathways involved in these genes include Complement and coagulation cascades, Malaria, African trypanosomiasis, HIF-1 signaling pathways, suggesting that they may play an important role in tumor immune escape, hypoxia response, and energy metabolism. Subsequently, by applying LASSO algorithm and multivariate COX proportional risk model, eight biomarker genes (CSN1S1, F2RL1, F5, F9, FGA, HABP2, MMP9, TFPI2) that are closely related to the prognosis of NSCLC were screened. Higher expression of the CSN1S1, F2RL1, F9, FGA, \u0026nbsp; and TFPI2 genes is linked to a higher mortality risk in NSCLC patients, with CSN1S1, F2RL1, and F9 having the most impact. Conversely, increased HABP2 expression is linked to a lower mortality risk, \u0026nbsp; while F5 and MMP9 expression levels show no significant impact on mortality risk.\u003c/p\u003e\n\u003cp\u003eCasein \u0026alpha;-s1 (CSN1S1) gene is a tumor suppressor with immunomodulatory function, which plays an important role in the growth and metastasis of cancer (57, 58). CSN1S1 expression can be up-regulated or down-regulated in different types of cancer (lung cancer) and can be evaluated by monitoring promoter methylation, gene mutations, and chromosome copy number variation (CNAs) (58, 59). CSN1S1 has been studied as a biomarker in various cancers (breast cancer, epithelial ovarian cancer, hepatocellular carcinoma, and lung squamous cell carcinoma, etc.) (58-61). It is important to note that there are different opinions about the expression of CSN1S1 in lung cancer. \u003cem\u003eMohsina Akter Mou et al.\u003c/em\u003e explored the expression level of CSN1S1 mRNA in normal and cancerous tissues based on public databases, and found that the expression level of CSN1S1 transcription was generally low in lung cancer tissues (58). However,\u003cem\u003e\u0026nbsp;Feng Xu et al\u003c/em\u003e. \u0026apos;s study indicated that CSN1S1, as a gene related to tumor suppressor gene p53 (TP53), was significantly overexpressed in lung squamous cell carcinoma tumor tissues and significantly correlated with PD-L1 expression(59).\u003c/p\u003e\n\u003cp\u003eCoagulation factor II(thrombin) receptor-like 1 (F2RL1), also known as protease-activated receptor 2 (PAR-2), is a transmembrane G-protein-coupled receptor belonging to the PAR family, whose activation affects the cellular molecules and microenvironment of tumor cells during tumourgenesis (62-64). In patients with lung adenocarcinoma, high mRNA expression of F2RL1 is strongly associated with poor overall survival (OS) and relapse-free survival (RFS), suggesting that high expression of F2RL1 is associated with adverse prognosis (65, 66). Previous research has demonstrated that the inhibition of PAR2 can reverse non-small cell lung cancer (NSCLC) resistance to gefitinib through the \u0026beta;-arrestin-EGFR-ERK signaling pathway (67). Additionally, the suppression of PAR2 promoter methylation has been shown to upregulate PAR2 gene expression in lung adenocarcinoma (LUAD) cells, thereby influencing the regulation of cellular proliferation, migration, apoptosis, and invasion (62). Furthermore, inhibiting the promoter methylation of PAR2 can enhance the expression of PAR2 gene in lung adenocarcinoma (LUAD) cells, thereby participating in the regulation of the proliferation, migration, apoptosis and invasion of cancer cells (65).\u003c/p\u003e\n\u003cp\u003eFibrinogen \u0026alpha; chain (FGA), also known as Fib2, is a plasma glycoprotein that affects the clotting cascade (68-70). FGA is not only involved in blood clotting, but it plays a role in a variety of cellular and physiological functions, including tumor cell apoptosis, tumor angiogenesis, and tumor development processes (69, 71). In the field of cancer research, the role and related mechanisms of FGA have been extensively studied, with low expression in lung cancer, stomach cancer, hepatocellular carcinoma, cholangiocarcinoma, cervical cancer, breast cancer and other cancers (69, 72-76),and high expression in ovarian cancer, gallbladder cancer, colorectal cancer and other cancers (77-79). Existing studies have shown that highly expressed FGA can inhibit the proliferation and metastasis of lung cancer cells by inhibiting the expression of integrin receptors and Akt /mTOR pathway, and FGA and ITGA5 can inhibit the mTOR pathway by reducing AKT phosphorylation (72). Existing studies have shown that highly expressed FGA can inhibit the proliferation and metastasis of lung cancer cells by inhibiting the expression of integrin receptors and Akt /mTOR pathway, and FGA and ITGA5 can inhibit the mTOR pathway by reducing AKT phosphorylation (71).\u003c/p\u003e\n\u003cp\u003eHyaluronan-binding protein 2 (HABP2), factor VIIactivating protease (FSAP), codes for a hyaluronic acid binding protein (80). Current studies on HABP2 have focused on thyroid cancer, especially familial non-medullary thyroid cancer (80-82). In addition, HABP2 is highly expressed in endometrial and colorectal cancer tumors and is associated with poor prognosis (80, 83). Existing studies have shown that HABP2 is elevated in different NSCLC tissues, and its expression is more specific in lung adenocarcinoma, and it can promote lung cancer progression through direct activation of uPA (84, 85).\u003c/p\u003e\n\u003cp\u003eTissue factor pathway inhibitor 2 (TFPI2), also known as stroma-associated serine protease inhibitor (MSPI), is a potent inhibitor of plasminase in ECM (86). TFPI2 belongs to the kunitz type serine protease inhibitor family, which plays an important role in cancer progression by regulating cell adhesion, proliferation, and migration, and has been shown to be a tumor suppressor gene in a variety of malignant tumors, including NSCLC (87, 88). The low expression of TFPI-2 gene in NSCLC is associated with increased tumor aggressiveness, and its gene methylation is an independent factor affecting the prognosis of patients with non-metastatic NSCLC (89, 90). In addition, TFPI-2 inhibits NSCLC angiogenesis by decreasing the expression of vascular endothelial growth factor and inhibits the activity of multiple matrix metalloproteinases (MMPs) to reduce tumor cell invasion and metastasis (91).\u003c/p\u003e\n\u003cp\u003eThe F9 gene, localized to chromosome Xq27 and approximately 34kb in length, encodes Human coagulation factor IX (FIX), a serine protease that plays an important role in the coagulation cascade (92, 93). Currently, the F9 gene is widely studied as a hemophilia defect factor, and its deficiency can cause hemophilia type B (also known as Christmas disease) (94, 95). In addition, \u003cem\u003eXiaoge Gao et al\u003c/em\u003e. found that the expression of F9 gene was related to the prognosis of hepatocellular carcinoma through bioinformatics studies (96), \u003cem\u003ePaula Carpintero-Fernandez et al\u003c/em\u003e. identified clotting factor IX (F9) as a regulator of aging through genome-wide CRISPR/Cas9 screening (97), Proteomic studies by \u003cem\u003eZhao Xu et al\u003c/em\u003e have revealed that F9 may be a potential biomarker for pseudoexfoliative syndrome (PEX) and pseudoexfoliative glaucoma (PEXG) (98). However, no studies have investigated the link between the F9 gene and non-small cell lung cancer. Through public database screening and tissue chip studies, we found that F9 gene, as a key gene of coagulation and fibrinolysis, may be a potential prognostic biomarker of LUAD. Moreover, F9 gene was associated with immune infiltration of lung adenocarcinoma.\u003c/p\u003e\n\u003cp\u003eIn this study, we constructed a prognostic gene-associated risk model based on eight coagulation and fibrinolysis related genes (CFRGs) and rigorously evaluated and validated it with an internal validation set and an external GSE50081 dataset. The results showed that the model was able to significantly distinguish between high-risk group (HRG) and low-risk group (LRG) non-small cell lung cancer (NSCLC) patients, with the HRG having a significantly lower overall survival rate than the LRG, a finding that highlights the potential of the model in identifying patients with poorer prognosis. Further, the area under ROC curve (AUC) values of the model at 1 year, 2 years and 3 years were 0.65, 0.66 and 0.63, respectively, and these values were close to 0.7, indicating that our prognostic risk model had good predictive ability. In addition, by combining prognostic risk model with clinicopathological features, the predictive power and clinical usability of the model can be improved. It was found that there were statistically significant differences in risk scores for clinicopathological factors such as gender, Stage, N, and riskScore, and pathologic_t and riskScore were independent prognostic factors for NSCLC patients . In order to further verify the predictive performance of the model, we drew 1-year, 2-year, 3-year clinical multi-indicator columns and their correction curves. The calibration curve is close to a straight line with a slope of 1, which indicates that the prediction results of the model are highly reliable and provide a solid basis for clinical decision making. Somatic mutation analysis revealed that TP53 and TTN were the most commonly mutated genes in the non-small cell lung cancer (NSCLC) training dataset. The tumor suppressor gene TP53 is the most commonly mutated gene in human cancer cells, affecting about 50% of patients with non-small cell lung cancer, and TP53 mutation is associated with poor prognosis in advanced NSCLC (99, 100). The TTN gene encodes the largest human protein, titin. In non-small cell lung cancer (NSCLC), TTN gene mutation is strongly associated with patient responsiveness to immune checkpoint blockade (ICB) therapy, and TTN mutation status in Circulatory tumor DNA (ctDNA) predicts treatment effect. Indicating its potential as a biomarker for predicting therapeutic response (101). In lung squamous cell carcinoma, TTN gene mutation is positively associated with tumor mutation load (TMB) and is associated with better prognosis, suggesting that TTN mutation may be a potential prognostic indicator of LUSC\u0026nbsp;(102). In lung adenocarcinoma, reduced expression of the TTN gene is associated with poor prognosis in patients and may serve as a prognostic biomarker and potential immunotherapeutic target (103).\u003c/p\u003e\n\u003cp\u003eThrough GSEA analysis, 16 biological pathways of differentially expressed genes (DEGs) were found to be significantly enriched between HRG and LRG, such as heme metabolism, androgen response and other pathways. The enrichment of these pathways indicates that these genes play a complex role in tumor development, involving several key biological processes such as blood metabolism, protein synthesis and release, androgen response, cell apoptosis, and cell signaling. These findings provide new insights into the complex biological behavior of tumors and may help in the discovery of new therapeutic targets. We also performed tumor immune microenvironment and immune checkpoint analyses. Immune cells and molecules in the tumor microenvironment are strongly associated with tumor prognosis in NSCLC (104). We evaluated 22 immune cell types in NSCLC, LUAD, and LUSC3 datasets, and only retained statistically significant trusted samples. The results showed that there were significant differences in the expression of these 22 immune cells in different datasets. This reflects the diversity and variability of immune cell composition in the tumor microenvironment. This difference may be related to the biology of different lung cancer subtypes, the stage of progression, or the response to treatment. The correlation between biomarkers and immune cells showed that seven biomarkers, including FGA, HABP2, F2RL1, F5, MMP9 and CSN1S1, were correlated with differential immune cells to varying degrees in the three datasets, while only F9 gene was not significantly correlated in the three datasets. This may imply that F9 has a different role in the tumor immune microenvironment than other screened biomarkers and that F9 may influence tumor development and prognosis through non-immune cell-dependent pathways. There was no statistical difference in immune checkpoints between HRG and LRG, and TIDE analysis showed no significant difference between HRG and LRG. It is suggested that this risk model can effectively distinguish prognosis, but it may not directly affect the immune escape mechanism of tumors.\u003c/p\u003e\n\u003cp\u003eWe selected F9 gene as a target for further study to explore its prognostic and immune value in LUAD patients. In lung adenocarcinoma microarray samples, we observed that the positive expression rate of F9 gene in tumor samples was significantly higher than that in neighboring normal samples, indicating that the expression of F9 gene in lung adenocarcinoma was significantly up-regulated. In addition, high expression of F9 gene was associated with poor prognosis in LUAD patients, suggesting that F9 gene may be a marker of poor prognosis. The AUC value of the ROC curve was 0.7622 , indicating that the model based on F9 gene has good stability and predictive ability, and can be used as an effective tool to predict the prognosis of LUAD patients. Multivariate Cox analysis further confirmed that F9 gene was an independent prognostic risk factor in LUAD patients, emphasizing the importance of F9 gene in prognostic evaluation. Our study also found that F9 gene had no significant correlation with three genes, including ALK, EGFR and PDL1, which was further confirmed in the GEPIA2 database, suggesting that F9 gene may influence the prognosis of LUAD through a pathway independent of these known genes. Based on the analysis of TCGA-LUAD dataset, the differential expression pathway between the high expression group and the low expression group of F9 gene is closely related to immune response and tumor microenvironment. These results suggest that F9 gene may be involved in tumor immune escape and immune regulation by affecting APICAL_SURFACE, IL6_JAK_STAT3_SIGNALING pathways. Immune cell analysis showed that resting dendritic cells and activated NK cells were higher expressed in the group with low F9 expression, while M0 macrophageswas higher in the group with high F9 expression. This may indicate that F9 gene expression levels are related to the infiltration and functional status of specific immune cell types. The expression of immune checkpoint LAG3, CTLA4, PDCD1, TIGIT and ADORA2A was different between high- and low- expression groups of F9, which may affect the response of LUAD patients to immune checkpoint inhibitor treatment, providing potential biomarkers for the individualization of immunotherapy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAt present, a large number of biomarkers related to the coagulation system in cancers have been reported in the literature, but the role of coagulation and fibrinolysis related genes in NSCLC has not been systematically studied. This study aims to fill this gap by analyzing the transcriptomic data of NSCLC, using bioinformatics methods to screen out coagulation and fibrinolytic genes closely related to the prognosis of NSCLC, and explore their potential clinical value in NSCLC. In addition, our study focused specifically on the F9 gene and found that it may be a potential prognostic biomarker for patients with LUAD. The expression level of F9 gene is closely related to the prognosis of LUAD patients, and there is a certain correlation with the immune microenvironment, especially the immune checkpoint ADORA2A, which provides new evidence for the role of F9 gene in NSCLC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 82160529), the Yunnan Revitalization Talent Support Program (No. RLQB20220014), Yunnan Health Training Project of High Level Talents (No. H-2024021) and the 535 Talent Project of the First Affiliated Hospital of Kunming Medical University (No. 2023535D17).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used GSE50081 data sets can be found in the GEO database (https://www.ncbi.nlm.nih.gov/geo), TCGA can be found in the data set (https://portal.gdc.cancer.gov/). Survival curve from K-M Plotter (https://kmplot.com/analysis/index.php?p=service\u0026amp;cancer=lung) and GEPIA2 (http://gepia2.cancer-pku.cn/#correlation) Available in an online database. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research was carried out in accordance with the Declaration of Helsinki. This study was approved by the Ethics Committee of the First Affiliated Hospital of Kunming Medical University with the informed consent of all participants.Clinical trial registration number: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY. Z. and H.T.J. analyzed the data and wrote the manuscript. Z.L.W, C.L, and F.Z take on the task of data collection and visualization. Q.F.F led the planning and design of the whole project, and gave professional guidance and supervision in the whole process of project promotion. All the authors have reviewed and approved the manuscript\u0026apos;s final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO | world health organization 2022 [Accessed February 25,2022:[Available from: https://www.who.int/.\u003c/li\u003e\n\u003cli\u003ePadinharayil H, Varghese J, John MC, Rajanikant GK, Wilson CM, Al-Yozbaki M, et al. Non-small cell lung carcinoma (NSCLC): Implications on molecular pathology and advances in early diagnostics and therapeutics. Genes \u0026amp; Diseases. 2023;10(3):960-89.\u003c/li\u003e\n\u003cli\u003eChen P, Liu Y, Wen Y, Zhou C. Non‐small cell lung cancer in China. 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Chemotherapy. 2023;68(2):73-86.\u003c/li\u003e\n\u003cli\u003eKatneni UK, Liss A, Holcomb D, Katagiri NH, Hunt R, Bar H, et al. Splicing dysregulation contributes to the pathogenicity of several F9 exonic point variants. Mol Genet Genomic Med. 2019;7(8):e840.\u003c/li\u003e\n\u003cli\u003eXie C, Wang Z, Su Y, Wang J, Shen WC. Discovery of An Orally Effective Factor IX-Transferrin Fusion Protein for Hemophilia B. Int J Mol Sci. 2019;21(1).\u003c/li\u003e\n\u003cli\u003eChernyi N, Gavrilova D, Saruhanyan M, Oloruntimehin ES, Karabelsky A, Bezsonov E, et al. Recent Advances in Gene Therapy for Hemophilia: Projecting the Perspectives. Biomolecules. 2024;14(7).\u003c/li\u003e\n\u003cli\u003eBatty P, Lillicrap D. Advances and challenges for hemophilia gene therapy. Hum Mol Genet. 2019;28(R1):R95-r101.\u003c/li\u003e\n\u003cli\u003eGao X, Ren X, Wang F, Ren X, Liu M, Cui G, et al. Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma. Sci Rep. 2024;14(1):9509.\u003c/li\u003e\n\u003cli\u003eCarpintero-Fern\u0026aacute;ndez P, Borghesan M, Eleftheriadou O, Pan-Castillo B, Fafi\u0026aacute;n-Labora JA, Mitchell TP, et al. Genome wide CRISPR/Cas9 screen identifies the coagulation factor IX (F9) as a regulator of senescence. Cell Death Dis. 2022;13(2):163.\u003c/li\u003e\n\u003cli\u003eXu Z, Ke Y, Feng Q, Tuerdimaimaiti A, Zhang D, Dong L, et al. Proteomic characteristics of the aqueous humor in Uyghur patients with pseudoexfoliation syndrome and pseudoexfoliative glaucoma. Exp Eye Res. 2024;243:109903.\u003c/li\u003e\n\u003cli\u003eCole AJ, Zhu Y, Dwight T, Yu B, Dickson KA, Gard GB, et al. Comprehensive analyses of somatic TP53 mutation in tumors with variable mutant allele frequency. Sci Data. 2017;4:170120.\u003c/li\u003e\n\u003cli\u003eJiang W, Cheng H, Yu L, Zhang J, Wang Y, Liang Y, et al. Mutation patterns and evolutionary action score of TP53 enable identification of a patient population with poor prognosis in advanced non-small cell lung cancer. Cancer Med. 2023;12(6):6649-58.\u003c/li\u003e\n\u003cli\u003eSu C, Wang X, Zhou J, Zhao J, Zhou F, Zhao G, et al. Titin mutation in circulatory tumor DNA is associated with efficacy to immune checkpoint blockade in advanced non-small cell lung cancer. Transl Lung Cancer Res. 2021;10(3):1256-65.\u003c/li\u003e\n\u003cli\u003eZou S, Ye J, Hu S, Wei Y, Xu J. Mutations in the TTN Gene are a Prognostic Factor for Patients with Lung Squamous Cell Carcinomas. Int J Gen Med. 2022;15:19-31.\u003c/li\u003e\n\u003cli\u003eChen J, Wen Y, Su H, Yu X, Hong R, Chen C, et al. Deciphering Prognostic Value of TTN and Its Correlation With Immune Infiltration in Lung Adenocarcinoma. Front Oncol. 2022;12:877878.\u003c/li\u003e\n\u003cli\u003eYao Y, Yang G, Lu G, Ye J, Cui L, Zeng Z, et al. Th22 Cells/IL-22 Serves as a Protumor Regulator to Drive Poor Prognosis through the JAK-STAT3/MAPK/AKT Signaling Pathway in Non-Small-Cell Lung Cancer. J Immunol Res. 2022;2022:8071234.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. The information of 8 prognosis-related genes.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"973\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 219px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 431px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGene symbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGene ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFull name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFunction of the encoded protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCSN1S1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ecasein alpha s1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCytoplasm、membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePredicted to be involved in response to dehydroepiandrosterone, response to estradiol, and response to steroid hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF2RL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF2R like trypsin receptor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG protein-coupled receptors encoded by F2RL1 promote vasodilation and blood pressure reduction, and are involved in inflammatory responses and immune regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ecoagulation factor V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGolgi apparatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThe FV gene encodes an important cofactor in the clotting cascade, which is involved in the activation of clotting factor X, thereby promoting the conversion of prothrombin to thrombin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ecoagulation factor IX\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSecreted to blood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor IX is a vitamin K-dependent plasma protein involved in the intrinsic pathway of blood clotting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;fibrinogen alpha chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEndoplasmic reticulum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFGA plays a key role in blood clotting and wound healing, and is involved in hemostasis and early wound repair, pregnancy success, immune response, etc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHABP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ehyaluronan binding protein 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSecreted to blood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHABP2 activates coagulation factor VII and may negatively regulate cell proliferation and cell migration as a tumor suppressor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMMP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ematrix metallopeptidase 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCytoplasm、membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMMP9 is involved in local proteolysis and leukocyte migration of extracellular matrix, osteoclasts, degradation of fibronectin, but not laminatin or Pz peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTFPI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003etissue factor pathway inhibitor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTFPI2 regulates plasminase-mediated matrix remodeling. Inhibition of trypsin, plasminase, VIIa factor/tissue factor and weak Xa facton. There was no effect on thrombin.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Information in a table from The \u003cem\u003eNCBI\u0026nbsp;\u003c/em\u003edatabase (https://www.ncbi.nlm.nih.gov/gene/), and \u003cem\u003eTHE HUMAN PROTEIN ATLAS\u003c/em\u003e database (https://www.proteinatlas.org/).\u003c/p\u003e\n\u003cp\u003eTable 2. Association of F9 expression with clinicopathological features of lung adenocarcinoma.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 239px;\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCases (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 196px;\"\u003e\n \u003cp\u003eF9 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e19(21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e69(78.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Non-cancerous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e69(80.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e17(19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.138\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt; 60 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e12(36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e21(63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge; 60 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e12(21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e43(78.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.430\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e15(30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e34(69.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e9(23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e30(76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eT_stage①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.623\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e14(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8(22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e28(77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e9(81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e5(62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eT_stage②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; T1+T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e15(26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e42(73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; T3+T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5(26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e14(73.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eTNM stage①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.732\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; I \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7(28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e18(72.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; II \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3(21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e11(78.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e18(78.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; IV \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e16(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.766\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eTNM stage②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.882\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; I-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e10(25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e29(74.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; III-IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e6(24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e19(76.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e16(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.729\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.759\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8(24.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e25(75.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e9(26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e25(73.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003ePositive lymph node ratio (LNR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.194\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026le; 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e20(45.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e24(54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt; 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e6(28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e15(71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePathological grade①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.429\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18(32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37(67.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26(81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePathological grade②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.175\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;I-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18(32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38(67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26(81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMaximum tumor diameter①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.655\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 3cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9(29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22(71.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt; 3cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11(24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34(75.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMaximum tumor diameter②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 3cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9(29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22(71.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.506\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;3cm \u0026lt; and \u0026le; 5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26(81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt; 5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMaximum tumor diameter③\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.547\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48(75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt; 5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMaximum tumor diameter④\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 7cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19(27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51(72.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt; 7cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5(83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eTumor site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.991\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; left lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8(26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e22(73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; right lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e15(26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e41(73.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eALK expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.893\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Negative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e18(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e54(75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Positive \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e6(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eEGFR expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.877\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14(26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39(73.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4(21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e15(78.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003eunknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e10(62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.540\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003ePDL1 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.243\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Negative \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7(38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e11(61.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Positive \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e17(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e51(75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEvaluation of PDL1 expression①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.631\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt;10% \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e14(31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e30(68.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; 10-50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8(25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e23(74.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e9(81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEvaluation of PDL1 expression②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.682\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026le;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e22(29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e53(70.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 239px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e9(81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEvaluation of PDL1 expression③\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.556\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22(29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52(70.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt; 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10(83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEvaluation of PDL1 expression④\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.434\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt; 1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7(38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11(61.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026ge;1% and \u0026lt;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15(26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42(73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026ge; 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9(81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEvaluation of PDL1 expression⑤\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.631\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt; 10% \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14(31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30(68.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;10% -50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8(25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23(74.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt; 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9(72.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable3. Univariate and multivariate analysis.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"799\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 269px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eF9 expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Negative vs Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.316(1.1118-4.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.596(1.094-6.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt; 60 years vs \u0026ge;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.185(0.649-2.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female vs Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.146(0.641-2.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT_stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;T1 vs T2 vs T3 vs T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.667(1.219-2.278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;T1-2 vs T3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.076(1.569-6.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.880(1.058-7.841)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTNM stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;I vs II vs III vs IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.711(1.189-2.461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;I-II vs III-IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.599(1.377-4.905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.354(0.770-7.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLymph node metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;NO vs YES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.565(0.832-2.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLNR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 20% vs \u0026gt; 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.102(1.104-4.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.821(0.273-2.471)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePathological grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;I vs II vs III\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.743(0.983-3.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;I-II vs III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.723(0.962-3.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum tumor diameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 3cm vs (3 \u0026lt; and \u0026le;5) vs \u0026gt; 5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.225(0.809-1.855)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 5cm vs \u0026gt; 5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.217(0.564-2.627)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 7cm vs \u0026gt; 7cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.344(1.288-8.685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.047(0.300-3.653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTumor site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Left lung \u0026nbsp;vs \u0026nbsp; \u0026nbsp; Right lung \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.069(0.591-1.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eALK expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Negative vs Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.225(0.542-2.763)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEGFR expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Negative vs Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.482(0.767-2.865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePDL1 expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Negative vs Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.214(0.599-2.457)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEvaluation of PDL1 expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt; 10% vs 10-50% vs \u0026gt; 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.388(0.966-1.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 50% vs \u0026gt; 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.476(0.712-3.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026le; 40% vs \u0026gt; 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.649(0.818-3.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt; 1% vs (1\u0026le; and \u0026gt; 50) vs \u0026ge; 50% \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.261(0.790-2.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt; 10% vs 10-50% vs \u0026gt; 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.246(0.872-1.779)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"coagulation and fibrinolysis, non-small cell lung cancer, immune, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7175734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7175734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study aimed to identify clotting and fibrinolysis related genes (CFRGs) influencing prognosis of non-small cell lung cancer (NSCLC) patients and explore their role in tumor immune microenvironment, with a focus on the F9 gene in lung adenocarcinoma (LUAD). Using TCGA and GEO databases, we screened differentially expressed CFRGs and constructed a risk model. Kaplan-Meier survival analysis and ROC curves evaluated the model's predictive efficiency. Cox regression models were applied to establish nomograms for 1, 2, and 3 years. We performed gene expression, somatic mutation, GSEA, immune microenvironment studies, and gene-drug interaction network analyses. The CFRGs-related risk model, including CSN1S1, F2RL1, F5, F9, FGA, HABP2, MMP9, and TFPI2, revealed that high-risk patients had worse survival outcomes. The expression levels of these genes and immune cell infiltration differed significantly across NSCLC, LUAD, and LUSC datasets. Tissue microarray analysis revealed higher F9 expression in LUAD tumor tissues, associated with poor prognosis. Differences in immune cell expression and immune checkpoints were observed between F9 high- and low-expression groups in LUAD. Our findings highlight CFRGs' role in NSCLC prognosis and immunity, identifying F9 as a LUAD prognostic indicator.","manuscriptTitle":"Novel risk prediction models for prognosis and immunotherapies of NSCLC based on coagulation and fibrinolysis-related genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:09:14","doi":"10.21203/rs.3.rs-7175734/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":"c5395589-532d-4be7-955f-9e77958e4763","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T07:53:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:09:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7175734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7175734","identity":"rs-7175734","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}