LncRNA DIRC1 is a novel prognostic biomarker and correlated with immune infiltrates in Stomach adenocarcinoma

LncRNA DIRC1 is a novel prognostic biomarker and correlated with immune infiltrates in Stomach adenocarcinoma | 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 LncRNA DIRC1 is a novel prognostic biomarker and correlated with immune infiltrates in Stomach adenocarcinoma Zhongying Zhang, Yuning Lin, Ying Li, Yongquan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1361821/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background :The potential application value of LncRNA DIRC1 has not yet been explored, and the purpose of this study was to explore the relationship between DIRC1 and Stomach adenocarcinoma (STAD) based on The Cancer Genome Atlas (TCGA) database. Methods: Wilcoxon rank sum test, Chi-square test, Fisher test and logistic regression were used to evaluate relationships between clinical-pathologic features and DIRC1 expression.Receiver operating characteristic(ROC) curves were used to describe binary classifier value of DIRC1 using area under curve (AUC) score.Kaplan Meier method was used to assess the impact of DIRC1 on prognosis and the impact of DIRC1-related hub genes on prognosis.GO and KEGG enrichment analysis were used to predict the function of differentially expressed genes(DEGs) associated with DIRC1.Gene set enrichment analysis (GSEA) was used to predict biological states or processes associated with DIRC1.Immune infiltration analysis was performed to identify the significantly involved functions of DIRC1.Protein-protein interaction (PPI) networks were established and 10 hub genes identified with Cytoscape software. Results Increased DIRC1 expression in STAD was associated with T stage (P=0.004),Race (P=0.045), Histologic grade(P=0.029) and Anatomic neoplasm subdivision(P=0.034).ROC curve suggested the significant diagnostic ability of DIRC1 (AUC=0.779). High DIRC1 expression predicted a poorer Overall survival (P=0.004,HR:1.63; 95% CI: 1.17-2.27; P=0.034).GO and KEGG analysis demonstrated that DIRC1 is related to epidermis,collagen-containing extracellular matrix,receptor-ligand activity,protein digestion and absorption,etc.GSEA demonstrated that E2F target, G2M checkpoint, Myc target, interferon γ reaction were differentially enriched in the high DIRC1 expression phenotype. SsGSEA and Spearman correlation revealed the relationships between DIRC1 and macrophages,DC, and Th1 cells were the strongest. Coregulatory proteins were included in the PPI network, higher expressions of 4 hub genes were associated with worse prognosis in STAD. Conclusions DIRC1 expression was significantly correlated with poor survival and immune infiltrations in STAD, and it may be a promising prognostic biomarker in STAD. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction STAD is a common malignant tumor in the world and poses a serious threat to human health[ 1 ],and STAD is one of the world's top five cancers and a leading cause of cancer-related deaths, regardless of country development [ 2 ]. Since STAD mostly exists as non-specific symptoms in the early stage of the disease, STAD is usually discovered after the disease progresses to a more serious state, and the high mortality and poor prognosis are due to metastasis, intra-tumor heterogeneity and chemotherapy resistance[ 3 , 4 ]. STAD has high mortality and poor prognosis,and the prognosis of STAD is closely related to clinical stage. The 5-year survival rate of early-stage STAD is 90%, while the 5-year survival rate of middle-advanced STAD is less than 30%[ 5 ].Due to China's dietary characteristics, lack of screening awareness and other reasons, the disease may have developed to an advanced stage by the time it is tested[ 6 ]. At present, the early diagnosis of gastric cancer mainly relies on the pathological diagnosis of gastroscopy and biopsy tissue. The procedure is complicated and expensive, and the invasive procedure brings great pain to the subject, thus limiting the application of gastric cancer screening and early diagnosis in large-scale community population. Previous chemotherapy-based treatments only extended the median overall survival of patients with advanced STAD by 7–11 months[ 7 ]. Patients with early STAD have a good prognosis, but patients with advanced STAD have a poor prognosis due to the lack of effective targeted drugs and the tendency to develop drug resistance.Currently, only trastuzumab, ramucirumab, apatinib and Papalizumab have been approved for the targeted treatment of advanced STAD; The clinical application of these targeted agents is challenging[ 8 ]. In recent years, with the development of molecular biology, genetics and other disciplines and high-throughput omics technology, it is possible to explore the biomarkers related to the early diagnosis of gastric cancer from the multi-omics level, providing important evidence for the prevention of gastric cancer. LncRNAs are ≥ 200 nucleotides in length and regulate a series of cell biological processes, including chromatin remodeling, transcription and post-transcriptional events[ 9 , 10 ].The most recognized molecular mechanism of lncRNAs is to act as a miRNA "sponge" to regulate downstream target genes[ 11 , 12 ]. LncRNAs is abnormally expressed in various types of cancer cells and plays an important role in several common hallmarks of cancer [ 13 ]. Up to now, there are few studies on DIRC1.This study aimed to elucidate the association between lncRNA DIRC1 and STAD using The Cancer Genome Atlas (TCGA) database. We found that the expression level of DIRC1 in STAD tissues was significantly higher than that in adjacent tissues. High expression of DIRC1 is associated with some clinicopathological features. Kaplan-Meier analysis showed that patients with high DIRC1 expression had lower overall survival than patients with low DIRC1 expression. These results suggest that lncRNA DIRC1 may be an independent biomarker for poor prognosis in STAD. Results DIRC1 expression is correlated with poor clinicopathological features of STAD Downloaded RNA-seq data in TPM format from TCGA and Genotype-Tissue Expression (GTEx) was processed uniformly using the Toil process from XENA (https://xenabrowser.net/datapages/) by the University of California, Santa Cruz[14].As shown in Figure 1A, tthe Wilcoxon rank sum test was used to compare the expression of DIRC1 in GTEx and normal TCGA samples with corresponding TCGA tumor samples.DIRC1 expression is significantly different in the following cancers:bladder urothelial carcinoma (BLCA), breast-infiltrating carcinoma (BRCA), colon cancer (STAD), diffuse large B-cell lymphoma (DLBC), esophageal cancer (ESCA), Glioblastoma multiforme (GBM),Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC),Acute Myeloid Leukemia (LAML),Brain Lower Grade Glioma (LGG),Liver hepatocellular carcinoma (LIHC),Lung adenocarcinoma (LUAD),lung squamous carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), pancreatic cancer (PAAD), rectal adenocarcinoma (READ), skin melanoma (SKCM), Stomach adenocarcinoma (STAD), Testicular Germ Cell Tumors (TGCT),Thyroid carcinoma (THCA), Thymoma (THYM), Uterine Carcinosarcoma (UCS). In order to identify the difference of DIRC1 expression between STAD and normal tissues, we analyzed the expression level of DIRC1 in 375 STAD tissues and 32 adjacent normal tissues, and found that DIRC1 was highly expressed in STAD tissues (P<0.001, Figure 1B).Meanwhile, we also analyzed the expression of DIRC1 in 27 STAD tissues and their matched adjacent tissues. The results indicated that STAD tissues highly expressed DIRC1 (P<0.001, Figure 1C).Moreover, The Wilcoxon rank sum test was used to compare the expression of DIRC1 in normal GTEx samples and TCGA STAD samples (Figure 1D). Identification of DEGs According to the expression of lncRNA DIRC1 in tumor samples, qualified HTSeq-counts format data were divided into high and low expression groups according to the median value. Then, 207 DEGs were obtained using the DESeq2 package. |log2FC| > 1.5 and adjusted p < 0.05 were used as the screening threshold for the DEGs. Among them, 75 were upregulated, and 132 were downregulated (Figure 1E). Clinical Characteristics The characteristics of patients with STAD in TCGA were collected. According to the median expression of lncRNA DIRC1,188 patients were assigned to the low-expression group,and 187 patients were assigned to the high-expression group. The Chisq.test or Fisher’s exact test determined that lncRNA DIRC1 expression was significantly associated with T stage (P=0.004), Race (P=0.045), Histologic grade(P=0.029) and Anatomic neoplasm subdivision(P=0.034). No correlation existed between lncRNA DIRC1 expression and the other clinicopathological features, as shown in Table 1. Univariate logistic regression revealed that the increased lncRNA DIRC1 expression was related to poor prognostic clinicopathological characteristics, including a greater primary tumor extent [odds ratio (OR) = 1.734; 95% CI, 1.088–2.787) for T3 and T4 Stages vs. T1 and T2 Stages (p = 0.021),Histological type (OR = 2.187; 95% CI, 1.124-4.321) for Diffuse type&Signet Ring Type vs. Tubular type (p = 0.022),Histologic grade (OR = 1.677; 95% CI,1.102-2.563) for G3 vs. G1&G2 (p = 0.016),as shown in Table 2.The results showed that Stomach adenocarcinoma with increased expression of lncRNA DIRC1 is prone to adverse clinicopathological factors. ROC Differentiates Normal Tissue From Tumor Tissue The data from para-carcinoma tissue of patients and carcinoma tissue of patients were applied to draw the ROC curve and evaluate the diagnostic value of lncRNA DIRC1. Its AUC was 0.779, predicting a very efficient discrimination value for Stomach adenocarcinoma (Figure 2A). Role of LncRNA DIRC1 in gastric adenocarcinoma survival (including clinicopathological subgroup analysis) Kaplan-Meier survival analysis showed that a high expression level of lncRNA DIRC1 was significantly correlated with a poorer overall survival(OS) of patients [hazard ratio (HR) = 1.63; 95% confidence interval(CI), 1.17-2.27; p = 0.004)(Figure 2B).In these subgroup analysis, the T3 subgroup of DIRC1 for the T stage was statistically significant (HR = 1.67; 95% CI, 1.04–2.68; p = 0.033) (Figure 2C), the N2 subgroup for the N stage was statistically significant (HR = 3.01; 95% CI, 1.33–6.83; p = 0.004) (Figure 2D), and the M0 subgroup for the M stage was statistically significant (HR = 1.79; 95% CI, 1.25–2.57; p = 0.002) (Figure 2E). Furthermore, the subgroups of G2 histologic grade had statistical significance (HR =1.95; 95% CI, 1.08–3.54; p = 0.028) (Figure 2F). LncRNA DIRC1 Related GSEA analysis GSEA was used to identify DIRC1-related signaling pathways. GSEA revealed significant differences (Padj<0.05,FDR<0.25) in enrichment of MSigDB Collection (h.all.v7.2.symbols.gmt [Hallmarks]) [15]. A total of 27 data sets met the requirements of FDR < 0.25 and adjusted p < 0.05. This analysis revealed that, in the lncRNA DIRC1 high-expression phenotype, 19 pathways were significantly differentially enriched.In addition, 8 pathways in the lncRNA DIRC1 low-expression phenotype were recognized(Supplementary 1).We selected the top 6 data sets with high value of normalized enrichment score (NES) in the lncRNA DIRC1 high-expression phenotype and top 3 data sets with high value of normalized enrichment score (NES) in the lncRNA DIRC1 low-expression phenotype (Figure 3 A-I). DIRC1 related GO and KEGG analysis To estimate the potential functions of DEGs in high-risk versus (vs.) low-risk groups, we identify DEGs of DIRC1 in TCGA-STAD data under cut-off criteria of adjusted P value 1.5.KEGG pathway and GO annotation were performed by R package clusterProfiler(3.14.3).GO reveals the catalogs of biological process (BP), cellular component (CC), and molecular function (MF).After multiple-test correction,KEGG pathways and GO terms with corrected P (P.adjust) value <0.05 were considered to be prominently enriched in DEGs.We selected top 9 of the lowest adj.p value of GO and KEGG pathway enrichment analysis of 207 DEGs related to DIRC1 in TCGA-STAD data(Figure 4). Immune infiltration analysis by ssGSEA The immune infiltration analysis of STAD was performed by single sample GSEA (ssGSEA) method from R (v.3.6.3) package GSVA (version 1.34.0) [16], and we quantified the infiltration levels of 24 immune cell types from gene expression profile in the literature [17]. In order to discover the correlation between DIRC1 and the infiltration levels of 24 immune cells(Figure 5 A), P values were determined by the Spearman and Wilcoxon rank sum test LncRNA DIRC1 expression was significantly positively correlated with Macrophages,DC,Th1 cells,Tem,iDC and so on.Helper T17 (Th17) cells and NK CD56bright cells were negatively correlated with lncRNA DIRC1 expression (p < 0.05). As show in Figure 5 B-G,Macrophages(r = 0.535,P＜0.001),DC(r = 0.392,P < 0.001 ),Th1 cells(r=0.367,P＜0.001),T effector memory cells(r=0.337,P＜0.001) showed positive association with DIRC1.However,NK CD56bright cells(r = −0.166, P=0.001) and Th17 cells(r = −0.126, P < 0.014) showed negative association with DIRC1. PPI Network and Hub Gene Identification The search tool (STRING, https://string-db.org/) is a database for finding interacting genes, with which we constructed a PPI network [18]. 145 intersection target genes were imported into the STRING database and the species were selected as “Homo sapiens” to obtain the interaction relationship between the targets.Genes with significant interactions were identified based on a confidence level ≥0.4, other irrelevant genes were excluded. The screening results were input into Cytoscape software 3.8.2[18] for network visualization(Figure 6). Verification of the Prognostic Value of 10 Hub Genes The MCC value of each node is calculated by the CytoHubba plugin in Cytoscape. In this study, the 10 genes with the highest MCC values were selected as hub genes.In co-expression networks, the maximum clique centrality (MCC) algorithm is considered to be the most efficient method to identify central nodes [19]. After the identification of 10 hub genes (ALB , ORM1 , APOH , FGA , ITIH2 , PAH , AHSG , FGG , TTR , ORM2) using the CytoHubba plug-in, we verified the prognostic value of these 10 hub genes with survival-related data in TCGA. The OS analysis of the 10 hub genes was performed with the Kaplan-Meier plotter using the R survival package. The analysis showed the expression level of ALB , ITIH1 , APOH and FGG were significantly correlated with OS in STAD patients (p < 0.05;Figures 7). Our results showed that the high expression of ALB,ITIH1,APOH and FGG indicate worse prognosis of patients with STAD. Discussion More recently, the understanding of lncRNAs has evolved to identify new insights into their involvement in disease pathogenesis. LncRNAs regulate gene expression through a variety of mechanisms, such as interactions with RNA or protein molecules. At present, many lncRNAs have been identified as important biomarkers of STAD.In general, lncRNAs exert regulatory functions at different levels of gene expression, including chromatin modification, transcription, and post-transcription [ 20 ].lncRNAs can interact with chromatin remodeling complexes to induce heterochromatin formation at specific genomic sites and reduce gene expression. In addition, lncRNAs interact with RNA-binding proteins and transcription factor co-activators, or regulate transcription by regulating the main promoters of their target genes.Mechanically,LncRNAs can communicate with DNA, mRNAs, ncRNAs and proteins and play cancer-related regulatory roles, such as, such as signals, decoys, scaffolds and guidelines [ 21 , 22 ]. In this study, we collected and organized STAD data using high-throughput RNA sequencing from TCGA database, and we verified that lncRNA DIRC1 was significantly upregulated in STAD tissues compared with in adjacent normal or normal tissues. Moreover, analyzing the relationship between the clinicopathological features of STAD and the dichotomy of high and low DIRC1 levels by using the logistic regression method, we showed that DIRC1 was also significantly correlated with T stage,histological grade,race and Anatomic neoplasm subdivision. Upregulated lncRNA DIRC1 in STAD tissues was positively correlated with higher T stage; advanced histological grade; and poorer overall survival.Elevated lncRNA DIRC1 was related to advanced clinicopathological features. These results suggest that lncRNA DIRC1 might be an independent biomarker of poor outcomes for STAD. We also used GSEA to study the function of lncRNA DIRC1 in STAD tissues, and the results showed that in the lncRNA DIRC1 high-expression phenotype,19 pathways were significantly differentially enriched, including the E2F target, G2M checkpoint, MYC Target, INF-α reaction, INF-γ reaction, MTORC1 signal and so on.In addition, 8 pathways in the lncRNA DIRC1 low-expression phenotype were recognized, including Epithelial mesenchymal transition, early response to estrogenas, down-regulated in response to ultraviolet (UV) radiation and so on. Epithelial mesenchymal transformation (EMT) is essential in development, wound healing, and stem cell behavior, and contributes pathologically to fibrosis and cancer progression [ 23 ].Entry into mitosis is regulated by checkpoints at the G2 and M (G2/M) boundaries of the cell cycle, and misregulation of entry into mitosis usually leads to tumorigenesis or cell death [ 24 ]. In this study, ssGSEA and Spearman correlation were used to reveal the relationship between lncRNA DIRC1 expression and immune infiltration level in STAD. We found the strongest relationship between lncRNA DIRC1 and Macrophages, DC, Th1 cells and Tem.In contrast, the levels of NK CD56 bright cells and Th17 cells were negatively correlated with lncRNA DIRC1 expression. Therefore, LncRNA DIRC1 may play a major role in immune cell infiltration and serve as a prognostic biomarker of STAD. To discover the molecular significance of DIRC1, coregulatory proteins were included in the PPI network analysis.According to the MCC scores of the CytoHubba plug-in in Cytoscape, the first 10 genes (ALB, ORM1, APOH, FGA, ITIH2, PAH, AHSG, FGG, TTR, ORM2) related to DIRC1 were screened.Among the 10 hub genes associated with DIRC1, the high expression of 4 genes(ALB,ITIH1,APOH and FGG) are related to THE OS of STAD. Therefore, we believe that DIRC1 has a good prognostic value for STAD. To confirm the relationship between lncRNA DIRC1 and overall STAD survival, kaplan-Meier survival analysis was performed for stratified clinicopathological features. Kaplan-meier survival analysis showed a significant correlation between lncRNA DIRC1 expression level and overall survival of T3, N2, M0 and G2 histological grade, suggesting that lncRNA DIRC1 expression level remains a strong predictor of prognosis in these subpopulations. Although this study improves our understanding of the association between lncRNA DIRC1 and STAD, some limitations remain.First, in order to fully clarify the special role of lncRNA DIRC1 in the development and progression of STAD, all clinical factors, such as details of the patient's treatment process, such as surgical treatment, chemotherapy or radiotherapy, should be included.However, such information is lacking or inconsistently processed in public databases. Second, this study only provides an analysis of biological information, not experimental verification.Quantitative polymerase chain reaction and immunohistochemical analysis are required to further study the function and mechanism of lncRNA DIRC1.Third, the understanding of gene function by single omics is not comprehensive, so it should be extended to multi-omics research, especially the study of protein level and its functional mechanism.Fourth, the lack of external data set validation can lead to bias. Finally, retrospective study has its limitations. Prospective studies must be conducted in the future.In this study, we found that lncRNA DIRC1 was an independent predictor of lower overall STAD survival. Conclusion LncRNA DIRC1 expression was significantly correlated with poor survival and immune infiltrations in STAD, and it may be a promising prognostic biomarker in STAD.Furthermore, these findings provide clues to further explore the possible role of lncRNA DIRC1 in STAD. Methods RNA-sequencing data and bioinformatics analysis We used TCGA database (https://portal.gdc.cancer.gov/) to collect RNA-seq data and clinical information from 407 cases of STAD projects, including 27 cases with matched adjacent tissues.The downloaded data format was level 3 HTSeq- fragments per kilobase per million (FPKM) and then was converted into transcripts per million (TPM) format for subsequent analysis.We also download TPM format RNA-seq data in TCGA and Genotype-Tissue Expression (GTEx) database that uniformly processed by Toil process from UCSC Xena (https://xenabrowser.net/datapages/)[14].All procedures performed in this study were in accordance with the Declaration of Helsinki (as revised in 2013). We used R package(DESeq2) to go differential analysis of DIRC1 expression,adjusted P value 1.5 were consider as cut off criteria,the DEGs obtained were used for GO,KEGG analysis.And adjusted P 1.0 were consider as another cut off criteria,the DEGs obtained were used for Gene set enrichment analysis (GSEA).According to the default statistical method, the process was repeated 1,000 times for each analysis and selected h.all.v7.2.symbols.gmt in MSigDB Collections as the reference gene collection, false discovery rate (FDR) q-value <0.25 and adjusted P adjust <0.05 were considered to be significantly enriched. Immune infiltration analysis by ssGSEA The immune infiltration analysis of STAD was performed by single sample GSEA (ssGSEA) method from R (v.3.6.3) package GSVA (version 1.34.0), and we quantified the infiltration levels of 24 immune cell types from gene expression profile in the literature. In order to discover the correlation between DIRC1 and the infiltration levels of 24 immune cells, P values were determined by the Pearson and Wilcoxon rank sum test. Protein-Protein Interaction Analysis Search Tool for the Retrieval of Interacting Genes (STRING) is an online database that searches for known proteins and predicts protein interaction relationships, including direct physical interactions between proteins and indirect functional correlations. The STRING database collects,evaluates, and integrates all publicly available protein-protein interaction information and complements this information with computational predictions to build a protein-protein interaction network. The software analyzes all DEGs; the interaction score threshold was set at 0.400. Statistical analysis All statistical analyses were performed using R(v.3.6.3).Wilcoxon rank sum test, chi-square test, Fisher exact test and logistic regression were used to analyze the relationship between clinical pathologic features and DIRC1.Kaplan Meier method was used to calculate the overall survival rate and progression free interval of STAD patients from TCGA.Univariate and multivariate analysis were performed to estimate the association between clinical and genetic clinical characteristics.Overall Survival(OS) were analysed by Cox proportional hazard models.P values less than 0.05 were considered statistically significant.An area under the curve (AUC) value between 0.5 and 0.7 was of low accuracy; between 0.7 and 0.9, of medium accuracy; and above 0.9, of high accuracy. Declarations Acknowledgments Funding Program:This study was supported by Xiamen medical and health guidance project(No.3502Z20209072). Xiantao Academic provides technical support for R analysis. Ethical Approval and Consent to participate Not applicable. Availability of data and materials The data were downloaded from the TCGA database ( https://portal.gdc.cancer.gov/). Competing interests All authors declare no conflict of interest. Authors' contributions (I)Conception and design:Yuning Lin,Ying Li; (II) Administrative support:Yongquan Chen; (III) Provision of study materials or patients:Ying Lin; (IV) Collection and assembly of data:Yuning Lin,Yongquan Chen; (V)Data analysis and interpretation:Yuning Lin,Zhongying Zhang; (VI) Manuscript writing:Yuning Lin,Ying Li,Zhongying Zhang; (VII)Final approval of manuscript: All authors. References Sung H, Ferlay J, Siegel RL et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021; 71: 209-249. Pearce A, Sharp L, Hanly P et al. 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Tables TABLE 1 Association between lncRNA DIRC1 expression and clinicopathological features in the validation cohort. Characteristic Low expression of DIRC1 High expression of DIRC1 p statistic method n 187 188 T stage, n (%) 0.004 13.43 Chisq.test T1 17 (4.6%) 2 (0.5%) T2 43 (11.7%) 37 (10.1%) T3 77 (21%) 91 (24.8%) T4 49 (13.4%) 51 (13.9%) N stage, n (%) 0.83 0.88 Chisq.test N0 58 (16.2%) 53 (14.8%) N1 45 (12.6%) 52 (14.6%) N2 39 (10.9%) 36 (10.1%) N3 38 (10.6%) 36 (10.1%) M stage, n (%) 1 0 Chisq.test M0 163 (45.9%) 167 (47%) M1 12 (3.4%) 13 (3.7%) Pathologic stage, n (%) 0.073 6.97 Chisq.test Stage I 35 (9.9%) 18 (5.1%) Stage II 50 (14.2%) 61 (17.3%) Stage III 77 (21.9%) 73 (20.7%) Stage IV 17 (4.8%) 21 (6%) Primary therapy outcome, n (%) 0.132 Fisher.test PD 40 (12.6%) 25 (7.9%) SD 11 (3.5%) 6 (1.9%) PR 1 (0.3%) 3 (0.9%) CR 113 (35.6%) 118 (37.2%) Gender, n (%) 0.884 0.02 Chisq.test Female 68 (18.1%) 66 (17.6%) Male 119 (31.7%) 122 (32.5%) Race, n (%) 0.045 6.2 Chisq.test Asian 37 (11.5%) 37 (11.5%) Black or African American 9 (2.8%) 2 (0.6%) White 106 (32.8%) 132 (40.9%) Age, n (%) 0.899 0.02 Chisq.test 65 101 (27.2%) 106 (28.6%) Histological type, n (%) 0.109 9 Chisq.test Diffuse Type 27 (7.2%) 36 (9.6%) Mucinous Type 8 (2.1%) 11 (2.9%) Not Otherwise Specified 101 (27%) 106 (28.3%) Papillary Type 1 (0.3%) 4 (1.1%) Signet Ring Type 6 (1.6%) 5 (1.3%) Tubular Type 44 (11.8%) 25 (6.7%) Residual tumor, n (%) 0.913 0.18 Chisq.test R0 155 (47.1%) 143 (43.5%) R1 7 (2.1%) 8 (2.4%) R2 8 (2.4%) 8 (2.4%) Histologic grade, n (%) 0.029 Fisher.test G1 4 (1.1%) 6 (1.6%) G2 80 (21.9%) 57 (15.6%) G3 97 (26.5%) 122 (33.3%) Anatomic neoplasm subdivision, n (%) 0.034 10.43 Chisq.test Antrum/Distal 61 (16.9%) 77 (21.3%) Cardia/Proximal 26 (7.2%) 22 (6.1%) Fundus/Body 63 (17.5%) 67 (18.6%) Gastroesophageal Junction 27 (7.5%) 14 (3.9%) Other 4 (1.1%) 0 (0%) Reflux history, n (%) 0.334 0.93 Chisq.test No 90 (42.1%) 85 (39.7%) Yes 24 (11.2%) 15 (7%) Antireflux treatment, n (%) 0.584 0.3 Chisq.test No 71 (39.7%) 71 (39.7%) Yes 21 (11.7%) 16 (8.9%) H pylori infection, n (%) 0.464 0.54 Chisq.test No 90 (55.2%) 55 (33.7%) Yes 9 (5.5%) 9 (5.5%) Barretts esophagus, n (%) 0.933 0.01 Chisq.test No 112 (53.8%) 81 (38.9%) Yes 8 (3.8%) 7 (3.4%) Table 2 Association of lncRNA DIRC1 expression with clinical pathological characteristics by logistic regression. Characteristics Total(N) Odds Ratio(OR) P value T stage (T3&T4 vs. T1&T2) 367 1.734 (1.088-2.787) 0.021 N stage (N1&N2&N3 vs. N0) 357 1.112 (0.710-1.744) 0.642 M stage (M1 vs. M0) 355 1.057 (0.466-2.417) 0.893 Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II) 352 1.076 (0.708-1.637) 0.732 Primary therapy outcome (PD&SD&PR vs. CR) 317 0.626 (0.376-1.032) 0.068 Histological type (Diffuse Type&Signet Ring Type vs. Tubular Type) 143 2.187 (1.124-4.321) 0.022 Anatomic neoplasm subdivision (Fundus/Body&Gastroesophageal Junction&Other vs. Antrum/Distal&Cardia/Proximal) 361 0.757 (0.500-1.145) 0.188 Antireflux treatment (Yes vs. No) 179 0.762 (0.363-1.574) 0.465 Hpylori infection (Yes vs. No) 163 1.636 (0.604-4.436) 0.326 Barretts esophagus (Yes vs. No) 208 1.210 (0.409-3.502) 0.723 Histologic grade (G3 vs. G1&G2) 366 1.677 (1.102-2.563) 0.016 Reflux history (Yes vs. No) 214 0.662 (0.320-1.334) 0.254 Residual tumor (R1&R2 vs. R0) 329 1.156 (0.549-2.445) 0.701 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-1361821","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":84734220,"identity":"454b858b-33f3-457d-9af2-52481c0a6c41","order_by":0,"name":"Zhongying Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACCQjFw8be2PgAwk4gTosMH8/hZgOStNjISaS3SRClRX5287OHX/4c5mFjSGyr5s05zMDPnmPA8HMHbi2Mc46ZG8vwgLQcbLs5c9thBsmeNwaMvWdwa2GWSDCTlpAAamFsbLvxEajF4EaOATNjG24tbBLp36QlDIBagMoKEoFa7Alp4ZHIMZP8kADUwgZUBrZFgoAWCYmcMmmGA+k8bDyMzZIzt6XzSJx5VnCwF48W+Rnp2yR//LG2l5///OFn3m3WcvztyRsf/MSjBRwEPAzNCJeCiAP4NQAD+gdDHSE1o2AUjIJRMJIBAMPeS4sTBcVVAAAAAElFTkSuQmCC","orcid":"","institution":"Xiamen Humanity Hospital Fujian Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhongying","middleName":"","lastName":"Zhang","suffix":""},{"id":84734217,"identity":"36cd6a36-99a5-439c-b2fc-ec28a39dbc48","order_by":1,"name":"Yuning Lin","email":"","orcid":"","institution":"Xiamen Humanity Hospital Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuning","middleName":"","lastName":"Lin","suffix":""},{"id":84734218,"identity":"9ff32aeb-fc1a-44ac-b8f2-5d2c9138e2c9","order_by":2,"name":"Ying Li","email":"","orcid":"","institution":"Women and Children's Hospital,School of Medicine,Xiamen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""},{"id":84734219,"identity":"828713b9-22c6-4ecb-a9ff-6e91af36dc97","order_by":3,"name":"Yongquan Chen","email":"","orcid":"","institution":"Xiamen Humanity Hospital Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongquan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2022-02-15 11:59:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1361821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1361821/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18585715,"identity":"2c0ffe5f-3f67-440a-bd16-6c6937ed849f","added_by":"auto","created_at":"2022-02-24 20:59:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130167,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of lncRNA DIRC1 in Genotype-Tissue Expression (GTEx) and normal The Cancer Genome Atlas (TCGA) samples with corresponding TCGA tumor samples(A);in 375 gastric cancer tissues and 32 normal tissues in TCGA database(B);in 27 pairs of gastric cancer tissues and non-cancerous adjacent tissues in TCGA database(C);in GTEx and normal TCGA samples with TCGA STAD(D);Volcano plot of differentially expressed genes,normalized expression levels are shown in descending order from green to red. There 75 differential molecules had log\u003csub\u003e2\u003c/sub\u003eFC \u0026gt;1.5 and adjusted p \u0026lt; 0.05, and 132 differential molecules had log\u003csub\u003e2\u003c/sub\u003eFC \u0026lt; −1.5 and adjusted p \u0026lt; 0.05(E).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1361821/v1/22a8de6efb84bcd990194f75.png"},{"id":18585713,"identity":"7f85cd36-4fee-44e8-aa1f-323818c67a1a","added_by":"auto","created_at":"2022-02-24 20:59:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147039,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Receiver operating characteristic (ROC) analysis of lncRNA DIRC1 expression showing promising discrimination power between non-tumor and tumor tissues.(B)Prognostic value of DIRC1 in Overall Survival of STAD patients.Statistically significant subgroups were (C) T3; (D) N2; (E) M0; and (F) Histologic grade G2.\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1361821/v1/3dfbe75adea1e1ba0464d0c9.png"},{"id":18585718,"identity":"eb1387e4-c014-40c9-b605-8f5595f59a80","added_by":"auto","created_at":"2022-02-24 20:59:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":647012,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of 207 differentially expressed genes (DEGs) between high and low expression of lncRNA DIRC1 in patients with STAD in TCGA.The data set was on the left significantly enriched in red area (A-F,DIRC1 high expression group).The data set was on the right significantly enriched in blue area (G-I,DIRC1 low expression group).NES, normalized NS; Padj, adjust P value; FDR, false discovery rate.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1361821/v1/d12926abbff95c041cb66acd.jpg"},{"id":18585716,"identity":"e892ff59-1295-47fa-a367-40a9923bc410","added_by":"auto","created_at":"2022-02-24 20:59:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":143012,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of 207 differentially expressed genes (DEGs) between high and low expression of lncRNA DIRC1 in patients with STAD in TCGA. (A) Enriched Gene Ontology (GO) terms in the biological process category.(B) Enriched GO terms in the molecular function category.(C) Enriched GO terms in the cellular component category. (D) Enriched GO terms in the Kyoto Encyclopedia of Genes and Genomes (KEGG) category.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1361821/v1/cda960d96a46140cd2ac580b.png"},{"id":18585792,"identity":"4d82ebcd-964b-4641-86ed-2d170a51a915","added_by":"auto","created_at":"2022-02-24 21:02:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":234037,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression level of lncRNA DIRC1 was associated with immune infiltration in the tumor microenvironment. (A) Correlation between the relative abundances of 24 immune cells and lncRNA DIRC1 expression level. The size of dots shows the absolute value of Spearman. Correlation between the relative enrichment score of Macrophages (B), DC (C), Th1 cells (D), Tem (E), NK CD56bright cells (F), Th17 cells (G) and the expression level (TPM) of lncRNA DIRC1.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1361821/v1/7e499c65d1b1d5d9a39bb9a4.png"},{"id":18585720,"identity":"2d31f483-b407-44c5-ba8d-0f69795ad9ef","added_by":"auto","created_at":"2022-02-24 20:59:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":271174,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network constructed by the STRING database.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1361821/v1/d6735450e8583ec552b26157.png"},{"id":18585791,"identity":"5cea9302-e3d3-4203-ab72-77ef22c1e0bf","added_by":"auto","created_at":"2022-02-24 21:02:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":136478,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Identification of the hub genes from the PPI network by the maximum clique centrality (MCC) algorithm,the red nodes represent genes with the highest MCC sores.(B) Overall survival of ALB associated with STAD. (C) Overall survival of ITIH1 associated with STAD.(D) Overall survival of APOH associated with STAD.(E) Overall survival of FGG associated with STAD.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-1361821/v1/88b2c72d568e69b586ebe773.png"},{"id":18585793,"identity":"7fdc070a-a28f-42ea-8fdb-c273d9754553","added_by":"auto","created_at":"2022-02-24 21:02:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1590656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1361821/v1/fe88347c-f88a-4a6b-b761-2d518ea254c0.pdf"},{"id":18585790,"identity":"a8676a07-e7ac-4ed4-a6a1-6fab10071fa7","added_by":"auto","created_at":"2022-02-24 21:02:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15137,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.docx","url":"https://assets-eu.researchsquare.com/files/rs-1361821/v1/f3172861f92b110e1c7743ac.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"LncRNA DIRC1 is a novel prognostic biomarker and correlated with immune infiltrates in Stomach adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSTAD is a common malignant tumor in the world and poses a serious threat to human health[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e],and STAD is one of the world's top five cancers and a leading cause of cancer-related deaths, regardless of country development [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince STAD mostly exists as non-specific symptoms in the early stage of the disease, STAD is usually discovered after the disease progresses to a more serious state, and the high mortality and poor prognosis are due to metastasis, intra-tumor heterogeneity and chemotherapy resistance[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSTAD has high mortality and poor prognosis,and the prognosis of STAD is closely related to clinical stage. The 5-year survival rate of early-stage STAD is 90%, while the 5-year survival rate of middle-advanced STAD is less than 30%[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].Due to China's dietary characteristics, lack of screening awareness and other reasons, the disease may have developed to an advanced stage by the time it is tested[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt present, the early diagnosis of gastric cancer mainly relies on the pathological diagnosis of gastroscopy and biopsy tissue. The procedure is complicated and expensive, and the invasive procedure brings great pain to the subject, thus limiting the application of gastric cancer screening and early diagnosis in large-scale community population.\u003c/p\u003e \u003cp\u003ePrevious chemotherapy-based treatments only extended the median overall survival of patients with advanced STAD by 7\u0026ndash;11 months[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Patients with early STAD have a good prognosis, but patients with advanced STAD have a poor prognosis due to the lack of effective targeted drugs and the tendency to develop drug resistance.Currently, only trastuzumab, ramucirumab, apatinib and Papalizumab have been approved for the targeted treatment of advanced STAD; The clinical application of these targeted agents is challenging[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, with the development of molecular biology, genetics and other disciplines and high-throughput omics technology, it is possible to explore the biomarkers related to the early diagnosis of gastric cancer from the multi-omics level, providing important evidence for the prevention of gastric cancer.\u003c/p\u003e \u003cp\u003eLncRNAs are \u0026ge;\u0026thinsp;200 nucleotides in length and regulate a series of cell biological processes, including chromatin remodeling, transcription and post-transcriptional events[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].The most recognized molecular mechanism of lncRNAs is to act as a miRNA \"sponge\" to regulate downstream target genes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. LncRNAs is abnormally expressed in various types of cancer cells and plays an important role in several common hallmarks of cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUp to now, there are few studies on DIRC1.This study aimed to elucidate the association between lncRNA DIRC1 and STAD using The Cancer Genome Atlas (TCGA) database. We found that the expression level of DIRC1 in STAD tissues was significantly higher than that in adjacent tissues. High expression of DIRC1 is associated with some clinicopathological features. Kaplan-Meier analysis showed that patients with high DIRC1 expression had lower overall survival than patients with low DIRC1 expression. These results suggest that lncRNA DIRC1 may be an independent biomarker for poor prognosis in STAD.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eDIRC1 expression is correlated with poor clinicopathological features of STAD\u003c/h2\u003e\n\u003cp\u003eDownloaded RNA-seq data in TPM format from TCGA and Genotype-Tissue Expression (GTEx) was processed uniformly using the Toil process from XENA (https://xenabrowser.net/datapages/) by the University of California, Santa Cruz[14].As shown in Figure 1A, tthe Wilcoxon rank sum test was used to compare the expression of DIRC1 in GTEx and normal TCGA samples with corresponding TCGA tumor samples.DIRC1 expression is significantly different in the following cancers:bladder urothelial carcinoma (BLCA), breast-infiltrating carcinoma (BRCA), colon cancer (STAD), diffuse large B-cell lymphoma (DLBC), esophageal cancer (ESCA), Glioblastoma multiforme (GBM),Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC),Acute Myeloid Leukemia (LAML),Brain Lower Grade Glioma (LGG),Liver hepatocellular carcinoma (LIHC),Lung adenocarcinoma (LUAD),lung squamous carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), pancreatic cancer (PAAD), rectal adenocarcinoma (READ), skin melanoma (SKCM), Stomach adenocarcinoma (STAD), Testicular Germ Cell Tumors (TGCT),Thyroid carcinoma (THCA), Thymoma (THYM), Uterine Carcinosarcoma (UCS).\u003c/p\u003e\n\u003cp\u003eIn order to identify the difference of DIRC1 expression between STAD and normal tissues, we analyzed the expression level of DIRC1 in 375 STAD tissues and 32 adjacent normal tissues, and found that DIRC1 was highly expressed in STAD tissues (P\u0026lt;0.001, Figure 1B).Meanwhile, we also analyzed the expression of DIRC1 in 27 STAD tissues and their matched adjacent tissues. The results indicated that STAD tissues highly expressed DIRC1 (P\u0026lt;0.001, Figure 1C).Moreover, The Wilcoxon rank sum test was used to compare the expression of DIRC1 in normal GTEx samples and TCGA STAD samples (Figure 1D).\u003c/p\u003e\n\u003ch2\u003eIdentification of DEGs\u003c/h2\u003e\n\u003cp\u003eAccording to the expression of lncRNA DIRC1 in tumor samples, qualified HTSeq-counts format data were divided into high and low expression groups according to the median value. Then, 207 DEGs were obtained using the DESeq2 package. |log2FC| \u0026gt; 1.5 and adjusted p \u0026lt; 0.05 were used as the screening threshold for the DEGs. Among them, 75 were upregulated, and 132 were downregulated (Figure 1E).\u003c/p\u003e\n\u003ch2\u003eClinical Characteristics\u003c/h2\u003e\n\u003cp\u003eThe characteristics of patients with STAD in TCGA were collected. According to the median expression of lncRNA DIRC1,188 patients were assigned to the low-expression group,and 187 patients were assigned to the high-expression group. The Chisq.test or Fisher\u0026rsquo;s exact test determined that lncRNA DIRC1 expression was significantly associated with T stage (P=0.004), Race (P=0.045), Histologic grade(P=0.029) and Anatomic neoplasm subdivision(P=0.034). No correlation existed between lncRNA DIRC1 expression and the other clinicopathological features, as shown in Table 1.\u003c/p\u003e\n\u003cp\u003eUnivariate logistic regression revealed that the increased lncRNA DIRC1 expression was related to poor prognostic clinicopathological characteristics, including a greater primary tumor extent [odds ratio (OR) = 1.734; 95% CI, 1.088\u0026ndash;2.787) for T3 and T4 Stages vs. T1 and T2 Stages (p = 0.021),Histological type (OR = 2.187; 95% CI, 1.124-4.321) for Diffuse type\u0026amp;Signet Ring Type vs. Tubular type (p = 0.022),Histologic grade (OR = 1.677; 95% CI,1.102-2.563) for G3 vs. G1\u0026amp;G2 (p = 0.016),as shown in Table 2.The results showed that Stomach adenocarcinoma with increased expression of lncRNA DIRC1 is prone to adverse clinicopathological factors.\u003c/p\u003e\n\u003ch2\u003eROC Differentiates Normal Tissue From Tumor Tissue\u003c/h2\u003e\n\u003cp\u003eThe data from para-carcinoma tissue of patients and carcinoma tissue of patients were applied to draw the ROC curve and evaluate the diagnostic value of lncRNA DIRC1. Its AUC was 0.779, predicting a very efficient discrimination value for Stomach adenocarcinoma (Figure 2A).\u003c/p\u003e\n\u003ch2\u003eRole of LncRNA DIRC1 in gastric adenocarcinoma survival (including clinicopathological subgroup analysis)\u003c/h2\u003e\n\u003cp\u003eKaplan-Meier survival analysis showed that a high expression level of lncRNA DIRC1 was significantly correlated with a poorer overall survival(OS) of patients [hazard ratio (HR) = 1.63; 95% confidence interval(CI), 1.17-2.27; p = 0.004)(Figure 2B).In these subgroup analysis, the T3 subgroup of DIRC1 for the T stage was statistically significant (HR = 1.67; 95% CI, 1.04\u0026ndash;2.68; p = 0.033) (Figure 2C), the N2 subgroup for the N stage was statistically significant (HR = 3.01; 95% CI, 1.33\u0026ndash;6.83; p = 0.004) (Figure 2D), and the M0 subgroup for the M stage was statistically significant (HR = 1.79; 95% CI, 1.25\u0026ndash;2.57; p = 0.002) (Figure 2E). Furthermore, the subgroups of G2 histologic grade had statistical significance (HR =1.95; 95% CI, 1.08\u0026ndash;3.54; p = 0.028) (Figure 2F).\u003c/p\u003e\n\u003ch2\u003eLncRNA DIRC1 Related GSEA analysis\u003c/h2\u003e\n\u003cp\u003eGSEA was used to identify DIRC1-related signaling pathways. GSEA revealed significant differences (Padj\u0026lt;0.05,FDR\u0026lt;0.25) in enrichment of MSigDB Collection (h.all.v7.2.symbols.gmt [Hallmarks])\u0026nbsp;[15]. A total of 27 data sets met the requirements of FDR \u0026lt; 0.25 and adjusted p \u0026lt; 0.05. This analysis revealed that, in the lncRNA DIRC1 high-expression phenotype, 19 pathways were significantly differentially enriched.In addition, 8 pathways in the lncRNA DIRC1 low-expression phenotype were recognized(Supplementary 1).We selected the top 6 data sets with high value of normalized enrichment score (NES) in the lncRNA DIRC1 high-expression phenotype and top 3 data sets with high value of normalized enrichment score (NES) in the lncRNA DIRC1 low-expression phenotype (Figure 3 A-I).\u003c/p\u003e\n\u003ch2\u003eDIRC1 related GO and KEGG analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTo estimate the potential functions of DEGs in high-risk versus (vs.) low-risk groups, we identify DEGs of DIRC1 in TCGA-STAD data under cut-off criteria of adjusted P value \u0026lt; 0.05 and |logFC|\u0026gt;1.5.KEGG pathway and GO annotation were performed by R package clusterProfiler(3.14.3).GO reveals the catalogs of biological process (BP), cellular component (CC), and molecular function (MF).After multiple-test correction,KEGG pathways and GO terms with corrected P (P.adjust) value \u0026lt;0.05 were considered to be prominently enriched in DEGs.We selected top 9 of the lowest adj.p value of GO and KEGG pathway enrichment analysis of 207 DEGs related to DIRC1 in TCGA-STAD data(Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eImmune infiltration analysis by ssGSEA\u003c/h2\u003e\n\u003cp\u003eThe immune infiltration analysis of STAD was performed by single sample GSEA (ssGSEA) method from R (v.3.6.3) package GSVA (version 1.34.0) [16], and we quantified the infiltration levels of 24 immune cell types from gene expression profile in the literature [17]. In order to discover the correlation between DIRC1 and the infiltration levels of 24 immune cells(Figure 5 A), P values were determined by the Spearman and Wilcoxon rank sum test LncRNA DIRC1 expression was significantly positively correlated with Macrophages,DC,Th1 cells,Tem,iDC and so on.Helper T17 (Th17) cells and NK CD56bright cells were negatively correlated with lncRNA DIRC1 expression (p \u0026lt; 0.05). As show in Figure 5 B-G,Macrophages(r = 0.535,P＜0.001),DC(r = 0.392,P \u0026lt; 0.001 ),Th1 cells(r=0.367,P＜0.001),T effector memory cells(r=0.337,P＜0.001) showed positive association with DIRC1.However,NK CD56bright cells(r = \u0026minus;0.166, P=0.001) and Th17 cells(r = \u0026minus;0.126, P \u0026lt; 0.014) showed negative association with DIRC1. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePPI Network and Hub Gene Identification\u003c/h2\u003e\n\u003cp\u003eThe search tool (STRING, https://string-db.org/) is a database for finding interacting genes, with which we constructed a PPI network\u0026nbsp;[18]. 145 intersection target genes were imported into the STRING database and the species were selected as \u0026ldquo;Homo sapiens\u0026rdquo; to obtain the interaction relationship between the targets.Genes with significant interactions were identified based on a confidence level \u0026ge;0.4, other irrelevant genes were excluded. The screening results were input into Cytoscape software 3.8.2[18]\u0026nbsp;for network visualization(Figure 6).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eVerification of the Prognostic Value of 10 Hub Genes\u003c/h2\u003e\n\u003cp\u003eThe MCC value of each node is calculated by the CytoHubba plugin in Cytoscape. In this study, the 10 genes with the highest MCC values were selected as hub genes.In co-expression networks, the maximum clique centrality (MCC) algorithm is considered to be the most efficient method to identify central nodes\u0026nbsp;[19].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter the identification of 10 hub genes (ALB , ORM1 , APOH , FGA , ITIH2 , PAH , AHSG , FGG , TTR , ORM2) using the CytoHubba plug-in, we verified the prognostic value of these 10 hub genes with survival-related data in TCGA. The OS analysis of the 10 hub genes was performed with the Kaplan-Meier plotter using the R survival package. The analysis showed the expression level of ALB , ITIH1 , APOH and FGG were significantly correlated with OS in STAD patients (p \u0026lt; 0.05;Figures 7). Our results showed that the high expression of ALB,ITIH1,APOH and FGG indicate worse prognosis of patients with STAD.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMore recently, the understanding of lncRNAs has evolved to identify new insights into their involvement in disease pathogenesis. LncRNAs regulate gene expression through a variety of mechanisms, such as interactions with RNA or protein molecules. At present, many lncRNAs have been identified as important biomarkers of STAD.In general, lncRNAs exert regulatory functions at different levels of gene expression, including chromatin modification, transcription, and post-transcription [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].lncRNAs can interact with chromatin remodeling complexes to induce heterochromatin formation at specific genomic sites and reduce gene expression. In addition, lncRNAs interact with RNA-binding proteins and transcription factor co-activators, or regulate transcription by regulating the main promoters of their target genes.Mechanically,LncRNAs can communicate with DNA, mRNAs, ncRNAs and proteins and play cancer-related regulatory roles, such as, such as signals, decoys, scaffolds and guidelines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we collected and organized STAD data using high-throughput RNA sequencing from TCGA database, and we verified that lncRNA DIRC1 was significantly upregulated in STAD tissues compared with in adjacent normal or normal tissues.\u003c/p\u003e \u003cp\u003eMoreover, analyzing the relationship between the clinicopathological features of STAD and the dichotomy of high and low DIRC1 levels by using the logistic regression method, we showed that DIRC1 was also significantly correlated with T stage,histological grade,race and Anatomic neoplasm subdivision.\u003c/p\u003e \u003cp\u003eUpregulated lncRNA DIRC1 in STAD tissues was positively correlated with higher T stage; advanced histological grade; and poorer overall survival.Elevated lncRNA DIRC1 was related to advanced clinicopathological features. These results suggest that lncRNA DIRC1 might be an independent biomarker of poor outcomes for STAD.\u003c/p\u003e \u003cp\u003eWe also used GSEA to study the function of lncRNA DIRC1 in STAD tissues, and the results showed that in the lncRNA DIRC1 high-expression phenotype,19 pathways were significantly differentially enriched, including the E2F target, G2M checkpoint, MYC Target, INF-α reaction, INF-γ reaction, MTORC1 signal and so on.In addition, 8 pathways in the lncRNA DIRC1 low-expression phenotype were recognized, including Epithelial mesenchymal transition, early response to estrogenas, down-regulated in response to ultraviolet (UV) radiation and so on. Epithelial mesenchymal transformation (EMT) is essential in development, wound healing, and stem cell behavior, and contributes pathologically to fibrosis and cancer progression [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].Entry into mitosis is regulated by checkpoints at the G2 and M (G2/M) boundaries of the cell cycle, and misregulation of entry into mitosis usually leads to tumorigenesis or cell death [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, ssGSEA and Spearman correlation were used to reveal the relationship between lncRNA DIRC1 expression and immune infiltration level in STAD. We found the strongest relationship between lncRNA DIRC1 and Macrophages, DC, Th1 cells and Tem.In contrast, the levels of NK CD56 bright cells and Th17 cells were negatively correlated with lncRNA DIRC1 expression. Therefore, LncRNA DIRC1 may play a major role in immune cell infiltration and serve as a prognostic biomarker of STAD.\u003c/p\u003e \u003cp\u003eTo discover the molecular significance of DIRC1, coregulatory proteins were included in the PPI network analysis.According to the MCC scores of the CytoHubba plug-in in Cytoscape, the first 10 genes (ALB, ORM1, APOH, FGA, ITIH2, PAH, AHSG, FGG, TTR, ORM2) related to DIRC1 were screened.Among the 10 hub genes associated with DIRC1, the high expression of 4 genes(ALB,ITIH1,APOH and FGG) are related to THE OS of STAD. Therefore, we believe that DIRC1 has a good prognostic value for STAD.\u003c/p\u003e \u003cp\u003eTo confirm the relationship between lncRNA DIRC1 and overall STAD survival, kaplan-Meier survival analysis was performed for stratified clinicopathological features. Kaplan-meier survival analysis showed a significant correlation between lncRNA DIRC1 expression level and overall survival of T3, N2, M0 and G2 histological grade, suggesting that lncRNA DIRC1 expression level remains a strong predictor of prognosis in these subpopulations.\u003c/p\u003e \u003cp\u003eAlthough this study improves our understanding of the association between lncRNA DIRC1 and STAD, some limitations remain.First, in order to fully clarify the special role of lncRNA DIRC1 in the development and progression of STAD, all clinical factors, such as details of the patient's treatment process, such as surgical treatment, chemotherapy or radiotherapy, should be included.However, such information is lacking or inconsistently processed in public databases. Second, this study only provides an analysis of biological information, not experimental verification.Quantitative polymerase chain reaction and immunohistochemical analysis are required to further study the function and mechanism of lncRNA DIRC1.Third, the understanding of gene function by single omics is not comprehensive, so it should be extended to multi-omics research, especially the study of protein level and its functional mechanism.Fourth, the lack of external data set validation can lead to bias. Finally, retrospective study has its limitations. Prospective studies must be conducted in the future.In this study, we found that lncRNA DIRC1 was an independent predictor of lower overall STAD survival.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLncRNA DIRC1 expression was significantly correlated with poor survival and immune infiltrations in STAD, and it may be a promising prognostic biomarker in STAD.Furthermore, these findings provide clues to further explore the possible role of lncRNA DIRC1 in STAD.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eRNA-sequencing data and bioinformatics analysis\u003c/h2\u003e\n\u003cp\u003eWe used TCGA database (https://portal.gdc.cancer.gov/) to collect RNA-seq data and clinical information from 407 cases of STAD projects, \u0026nbsp;including 27 cases with matched adjacent tissues.The downloaded data format was level 3 HTSeq- fragments per kilobase per million (FPKM) and then was converted into transcripts per million (TPM) format for subsequent analysis.We also download TPM format RNA-seq data in TCGA and Genotype-Tissue Expression (GTEx) database that uniformly processed by Toil process from UCSC Xena (https://xenabrowser.net/datapages/)[14].All procedures performed in this study were in accordance with the Declaration of Helsinki (as revised in 2013). We used R package(DESeq2) to go differential analysis of DIRC1 expression,adjusted P value \u0026lt; 0.05 and |logFC| \u0026gt; 1.5 were consider as cut off criteria,the DEGs obtained were used for GO,KEGG analysis.And adjusted P\u0026lt; 0.05 and |logFC| \u0026gt; 1.0 were consider as another cut off criteria,the DEGs obtained were used for Gene set enrichment analysis (GSEA).According to the default statistical method, the process was repeated 1,000 times for each analysis and selected h.all.v7.2.symbols.gmt in MSigDB Collections as the reference gene collection, false discovery rate (FDR) q-value \u0026lt;0.25 and adjusted P adjust \u0026lt;0.05 were considered to be significantly enriched.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eImmune infiltration analysis by ssGSEA\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe immune infiltration analysis of STAD was performed by single sample GSEA (ssGSEA) method from R (v.3.6.3) package GSVA (version 1.34.0), and we quantified the infiltration levels of 24 immune cell types from gene expression profile in the literature. In order to discover the correlation between DIRC1 and the infiltration levels of 24 immune cells, P values were determined by the Pearson and Wilcoxon rank sum test.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eProtein-Protein Interaction Analysis\u003c/h2\u003e\n\u003cp\u003eSearch Tool for the Retrieval of Interacting Genes (STRING) is an online database that searches for known proteins and predicts protein interaction relationships, including direct physical interactions between proteins and indirect functional correlations. The STRING database collects,evaluates, and integrates all publicly available protein-protein interaction information and complements this information with computational predictions to build a protein-protein interaction network. The software analyzes all DEGs; the interaction score threshold was set at 0.400.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were performed using R(v.3.6.3).Wilcoxon rank sum test, chi-square test, Fisher exact test and logistic regression were used to analyze the relationship between clinical pathologic features and DIRC1.Kaplan Meier method was used to calculate the overall survival rate and progression free interval of STAD patients from TCGA.Univariate and multivariate analysis were performed to estimate the association between clinical and genetic clinical characteristics.Overall Survival(OS) were analysed by Cox proportional hazard models.P values less than 0.05 were considered statistically significant.An area under the curve (AUC) value between 0.5 and 0.7 was of low accuracy; between 0.7 and 0.9, of medium accuracy; and above 0.9, of high accuracy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFunding Program:This study was supported by Xiamen medical and health guidance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eproject(No.3502Z20209072).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXiantao Academic provides technical support for R analysis.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthical Approval and Consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data were downloaded from the TCGA database ( https://portal.gdc.cancer.gov/).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003e(I)Conception and design:Yuning Lin,Ying Li;\u003c/p\u003e\n\u003cp\u003e(II) Administrative support:Yongquan Chen;\u003c/p\u003e\n\u003cp\u003e(III) Provision of study materials or patients:Ying Lin;\u003c/p\u003e\n\u003cp\u003e(IV) Collection and assembly of data:Yuning Lin,Yongquan Chen;\u003c/p\u003e\n\u003cp\u003e(V)Data analysis and interpretation:Yuning Lin,Zhongying Zhang;\u003c/p\u003e\n\u003cp\u003e(VI) Manuscript writing:Yuning Lin,Ying Li,Zhongying Zhang;\u003c/p\u003e\n\u003cp\u003e(VII)Final approval of manuscript: All authors. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e Sung H, Ferlay J, Siegel RL et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021; 71: 209-249.\u003c/li\u003e\n \u003cli\u003e Pearce A, Sharp L, Hanly P et al. Productivity losses due to premature mortality from cancer in Brazil, Russia, India, China, and South Africa (BRICS): A population-based comparison. Cancer Epidemiol 2018; 53: 27-34.\u003c/li\u003e\n \u003cli\u003e Allemani C, Matsuda T, Di Carlo V et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet 2018; 391: 1023-1075.\u003c/li\u003e\n \u003cli\u003e Petryszyn P, Chapelle N, Matysiak-Budnik T. Gastric Cancer: Where Are We Heading? Dig Dis 2020; 38: 280-285.\u003c/li\u003e\n \u003cli\u003e Isobe Y, Nashimoto A, Akazawa K et al. Gastric cancer treatment in Japan: 2008 annual report of the JGCA nationwide registry. Gastric Cancer 2011; 14: 301-316.\u003c/li\u003e\n \u003cli\u003e Ferlay J, Colombet M, Soerjomataram I et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer 2019; 144: 1941-1953.\u003c/li\u003e\n \u003cli\u003e Sano T. [Evaluation of the gastric cancer treatment guidelines of the Japanese Gastric Cancer Association]. Gan To Kagaku Ryoho 2010; 37: 582-586.\u003c/li\u003e\n \u003cli\u003e Catenacci DVT, Tebbutt NC, Davidenko I et al. Rilotumumab plus epirubicin, cisplatin, and capecitabine as first-line therapy in advanced MET-positive gastric or gastro-oesophageal junction cancer (RILOMET-1): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol 2017; 18: 1467-1482.\u003c/li\u003e\n \u003cli\u003e Li Y, Zhou L, Lu C et al. Long non-coding RNA FAL1 functions as a ceRNA to antagonize the effect of miR-637 on the down-regulation of AKT1 in Hirschsprung\u0026apos;s disease. Cell Prolif 2018; 51: e12489.\u003c/li\u003e\n \u003cli\u003e Raveendra BL, Swarnkar S, Avchalumov Y et al. Long noncoding RNA GM12371 acts as a transcriptional regulator of synapse function. Proc Natl Acad Sci U S A 2018; 115: E10197-e10205.\u003c/li\u003e\n \u003cli\u003e Dong Z, Zhang A, Liu S et al. Aberrant Methylation-Mediated Silencing of lncRNA MEG3 Functions as a ceRNA in Esophageal Cancer. Mol Cancer Res 2017; 15: 800-810.\u003c/li\u003e\n \u003cli\u003e Liu T, Chi H, Chen J et al. Curcumin suppresses proliferation and in vitro invasion of human prostate cancer stem cells by ceRNA effect of miR-145 and lncRNA-ROR. Gene 2017; 631: 29-38.\u003c/li\u003e\n \u003cli\u003e Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000; 100: 57-70.\u003c/li\u003e\n \u003cli\u003e Vivian J, Rao AA, Nothaft FA et al. Toil enables reproducible, open source, big biomedical data analyses. Nat Biotechnol 2017; 35: 314-316.\u003c/li\u003e\n \u003cli\u003e Liberzon A, Birger C, Thorvaldsd\u0026oacute;ttir H et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 2015; 1: 417-425.\u003c/li\u003e\n \u003cli\u003e H\u0026auml;nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14: 7.\u003c/li\u003e\n \u003cli\u003e Bindea G, Mlecnik B, Tosolini M et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 2013; 39: 782-795.\u003c/li\u003e\n \u003cli\u003e Szklarczyk D, Gable AL, Lyon D et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019; 47: D607-d613.\u003c/li\u003e\n \u003cli\u003e Chin CH, Chen SH, Wu HH et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 2014; 8 Suppl 4: S11.\u003c/li\u003e\n \u003cli\u003e Wang L, Cho KB, Li Y et al. Long Noncoding RNA (lncRNA)-Mediated Competing Endogenous RNA Networks Provide Novel Potential Biomarkers and Therapeutic Targets for Colorectal Cancer. Int J Mol Sci 2019; 20.\u003c/li\u003e\n \u003cli\u003e \u0026Oslash;rom UA, Derrien T, Beringer M et al. Long noncoding RNAs with enhancer-like function in human cells. Cell 2010; 143: 46-58.\u003c/li\u003e\n \u003cli\u003e Wang KC, Chang HY. Molecular mechanisms of long noncoding RNAs. Mol Cell 2011; 43: 904-914.\u003c/li\u003e\n \u003cli\u003e Lamouille S, Xu J, Derynck R. Molecular mechanisms of epithelial-mesenchymal transition. Nat Rev Mol Cell Biol 2014; 15: 178-196.\u003c/li\u003e\n \u003cli\u003e Yasutis KM, Kozminski KG. Cell cycle checkpoint regulators reach a zillion. Cell Cycle 2013; 12: 1501-1509.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" width=\"100%\"\u003e\n \u003cp style=\"text-align: center;\"\u003eTABLE 1\u0026nbsp;\u003c/p\u003e\n \u003cp style=\"text-align: center;\"\u003eAssociation between lncRNA DIRC1 expression and clinicopathological features in the validation cohort.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003eLow expression of DIRC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003eHigh expression of DIRC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eT stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e13.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e17 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e2 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e43 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e37 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e77 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e91 (24.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e49 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e51 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eN stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e58 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e53 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e45 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e52 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e39 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e36 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e38 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e36 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eM stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e163 (45.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e167 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e12 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e13 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003ePathologic stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e35 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e18 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e50 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e61 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e77 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e73 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e17 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e21 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003ePrimary therapy outcome, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eFisher.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e40 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e25 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e11 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e6 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e3 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e113 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e118 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e68 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e66 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e119 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e122 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e37 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e37 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e9 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e2 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e106 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e132 (40.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003e\u0026lt;=65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e82 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e82 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003e\u0026gt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e101 (27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e106 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eHistological type, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eDiffuse Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e27 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e36 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eMucinous Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e8 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e11 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eNot Otherwise Specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e101 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e106 (28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003ePapillary Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e4 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eSignet Ring Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e5 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eTubular Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e44 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e25 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eResidual tumor, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eR0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e155 (47.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e143 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e7 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e8 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e8 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e8 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eHistologic grade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eFisher.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e4 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e80 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e57 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e97 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e122 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eAnatomic neoplasm subdivision, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e10.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eAntrum/Distal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e61 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e77 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eCardia/Proximal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e26 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e22 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eFundus/Body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e63 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e67 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eGastroesophageal Junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e27 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e14 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e4 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eReflux history, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e90 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e85 (39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e24 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e15 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eAntireflux treatment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e71 (39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e71 (39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e21 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e16 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eH pylori infection, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e90 (55.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e55 (33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e9 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e9 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eBarretts esophagus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e112 (53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e81 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.99644128113879%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.597864768683273%\"\u003e\n \u003cp\u003e8 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.622775800711743%\"\u003e\n \u003cp\u003e7 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540925266903914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.209964412811388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.03202846975089%\"\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\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" width=\"100%\"\u003e\n \u003cp style=\"text-align: center;\"\u003eTable 2\u0026nbsp;\u003c/p\u003e\n \u003cp style=\"text-align: center;\"\u003eAssociation of lncRNA DIRC1 expression with clinical pathological characteristics by logistic regression.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003eTotal(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003eOdds Ratio(OR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eT stage (T3\u0026amp;T4 vs. T1\u0026amp;T2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.734 (1.088-2.787)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eN stage (N1\u0026amp;N2\u0026amp;N3 vs. N0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.112 (0.710-1.744)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eM stage (M1 vs. M0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.057 (0.466-2.417)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003ePathologic stage (Stage III\u0026amp;Stage IV vs. Stage I\u0026amp;Stage II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.076 (0.708-1.637)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003ePrimary therapy outcome (PD\u0026amp;SD\u0026amp;PR vs. CR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.626 (0.376-1.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eHistological type (Diffuse Type\u0026amp;Signet Ring Type vs. Tubular Type)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e2.187 (1.124-4.321)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eAnatomic neoplasm subdivision (Fundus/Body\u0026amp;Gastroesophageal Junction\u0026amp;Other vs. Antrum/Distal\u0026amp;Cardia/Proximal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.757 (0.500-1.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eAntireflux treatment (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.762 (0.363-1.574)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eHpylori infection (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.636 (0.604-4.436)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eBarretts esophagus (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.210 (0.409-3.502)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eHistologic grade (G3 vs. G1\u0026amp;G2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.677 (1.102-2.563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eReflux history (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.662 (0.320-1.334)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.03866432337434%\"\u003e\n \u003cp\u003eResidual tumor (R1\u0026amp;R2 vs. R0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.666080843585238%\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.156 (0.549-2.445)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.490333919156415%\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"","lastPublishedDoi":"10.21203/rs.3.rs-1361821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1361821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:The potential application value of LncRNA DIRC1 has not yet been explored, and the purpose of this study was to explore the relationship between DIRC1 and Stomach adenocarcinoma (STAD) based on The Cancer Genome Atlas (TCGA) database.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eWilcoxon rank sum test, Chi-square test, Fisher test and logistic regression were used to evaluate relationships between clinical-pathologic features and DIRC1 expression.Receiver operating characteristic(ROC) curves were used to describe binary classifier value of DIRC1 using area under curve (AUC) score.Kaplan Meier method was used to assess the impact of DIRC1 on prognosis and the impact of DIRC1-related hub genes on prognosis.GO and KEGG enrichment analysis were used to predict the function of differentially expressed genes(DEGs) associated with DIRC1.Gene set enrichment analysis (GSEA) was used to predict biological states or processes associated with DIRC1.Immune infiltration analysis was performed to identify the significantly involved functions of DIRC1.Protein-protein interaction (PPI) networks were established and 10 hub genes identified with Cytoscape software. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIncreased DIRC1 expression in STAD was associated with T stage (P=0.004),Race (P=0.045), Histologic grade(P=0.029) and Anatomic neoplasm subdivision(P=0.034).ROC curve suggested the significant diagnostic ability of DIRC1 (AUC=0.779). High DIRC1 expression predicted a poorer Overall survival (P=0.004,HR:1.63; 95% CI: 1.17-2.27; P=0.034).GO and KEGG analysis demonstrated that DIRC1 is related to epidermis,collagen-containing extracellular matrix,receptor-ligand activity,protein digestion and absorption,etc.GSEA demonstrated that E2F target, G2M checkpoint, Myc target, interferon γ reaction were differentially enriched in the high DIRC1 expression phenotype. SsGSEA and Spearman correlation revealed the relationships between DIRC1 and macrophages,DC, and Th1 cells were the strongest. Coregulatory proteins were included in the PPI network, higher expressions of 4 hub genes were associated with worse prognosis in STAD.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eDIRC1 expression was significantly correlated with poor survival and immune infiltrations in STAD, and it may be a promising prognostic biomarker in STAD.\u003c/p\u003e","manuscriptTitle":"LncRNA DIRC1 is a novel prognostic biomarker and correlated with immune infiltrates in Stomach adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-24 20:59:22","doi":"10.21203/rs.3.rs-1361821/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":"94ad65d7-adc0-4835-9e9c-71e5a4949f10","owner":[],"postedDate":"February 24th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-02-24T20:59:24+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-24 20:59:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1361821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1361821","identity":"rs-1361821","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}