An eight-long noncoding RNA expression signature for colorectal cancer patients’ prognosis

Long noncoding RNAs (lncRNAs) have recently emerged as important biomarkers of cancer progression. Here, we proposed to develop a lncRNA-based signature with a prognostic value for colorectal cancer (CRC) overall survival (OS). Through mining microarray datasets, we analyzed the lncRNA expression profiles of 122 patients with CRC from Gene Expression Omnibus. Associations between lncRNA and CRC OS were firstly evaluated through univariate Cox regression analysis. A random survival forest method was applied for further screening of the lncRNA signature, which resulted in eight lncRNAs, including PEG3-AS1, LOC100505715, MINCR, DBH-AS1, LINC00664, FAM224A, LOC642852, and LINC00662. Combination of the eight lncRNAs weighted by their multivariate Cox regression coefficients formed a prognostic signature, through which, we could divide the 122 patients with CRC into two subgroups with significantly different OS. Good robustness of the lncRNA signature's prognostic value was verified through an independent data set consisting of 55 patients with CRC. In addition, gene set enrichment analysis indicated the potential association between high prognostic value and oxygen metabolism-related processes. This result should indicate that lncRNAs could be a useful signature for CRC prognosis.     

Development and validation of a five-lncRNA signature with prognostic value in colon cancer

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients’ overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.     

Identification of m6A methyltransferase-related lncRNA signature for predicting immunotherapy and prognosis in patients with hepatocellular carcinoma

N6-methyladenosine (m6A) methyltransferase has been shown to be an oncogene in a variety of cancers. Nevertheless, the relationship between the long non-coding RNAs (lncRNAs) and hepatocellular carcinoma (HCC) remains elusive. We integrated the gene expression data of 371 HCC and 50 normal tissues from The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression and Kaplan–Meier (K–M) analysis were performed to identify m6A methyltransferase-related lncRNAs. Three prognostic lncRNAs were selected by univariate and LASSO Cox regression analyses to construct the m6A methyltransferase-related lncRNA signature. Multivariate Cox regression analyses illustrated that this signature was an independent prognostic factor for overall survival (OS) prediction. The Gene Set Enrichment Analysis (GSEA) suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes (BPs) and pathways. Besides, we discovered that the lncRNAs signature was correlated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed that the lncRNAs could predict the clinical response to immunotherapy. Our study had originated a prognostic signature for HCC based on the potential prognostic m6A methyltransferase-related lncRNAs. The present study had deepened the understanding of the TME status of HCC patients and laid a theoretical foundation for the choice of immunotherapy.     

Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) show multiple functions, including immune response. Recently, the immune-related lncRNAs have been reported in some cancers. We first investigated the immune-related lncRNA signature as a potential target in hepatocellular carcinoma (HCC) survival. The training set (n = 368) and the independent external validation cohort (n = 115) were used. Immune genes and lncRNAs coexpression were constructed to identify immune-related lncRNAs. Cox regression analyses were perfumed to establish the immune-related lncRNA signature. Regulatory roles of this signature on cancer pathways and the immunologic features were investigated. The correlation between immune checkpoint inhibitors and this signature was examined. In this study, the immune-related lncRNA signature was identified in HCC, which could stratify patients into high- and low-risk groups. This immune-related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our immune-related lncRNA signature was still a powerful tool in predicting survival in each stratum of age, gender, and tumor stage. This signature mediated cell cycle, glycolysis, DNA repair, mammalian target of rapamycin signaling, and immunologic characteristics (i.e., natural killer cells vs. Th1 cells down, etc). This signature was associated with immune cell infiltration (i.e., macrophages M0, Tregs, CD4 memory T cells, and macrophages M1, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., PD-L1, PD-L2, and IDO1). Our findings suggested that the immune-related lncRNA signature had an important value for survival prediction and may have the potential to measure the response to ICB immunotherapy. This signature may guide the selection of the immunotherapy for HCC.     

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (−0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.     

Integrated analysis of a competing endogenous RNA network reveals key long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

Growing evidence has revealed that long noncoding RNAs (lncRNAs) have an important impact on tumorigenesis and tumor progression via a mechanism involving competing endogenous RNAs (ceRNAs). However, their use in predicting the survival of a patient with hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to develop a novel lncRNA expression–based risk score system to accurately predict the survival of patients with HCC. In our study, using expression profiles downloaded from The Cancer Genome Atlas database, the differentially expressed messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) were explored in patients with HCC and normal liver tissues, and then a ceRNA network constructed. A risk score system was established between lncRNA expression of the ceRNA network and overall survival (OS) or recurrence-free survival (RFS); it was further analyzed for associations with the clinical features of patients with HCC. In HCC, 473 differentially expressed lncRNAs, 63 differentially expressed miRNAs, and 1417 differentially expressed mRNAs were detected. The ceRNA network comprised 41 lncRNA nodes, 12 miRNA nodes, 24 mRNA nodes, and 172 edges. The lncRNA expression–based risk score system for OS was constructed based on six lncRNAs (MYLK-AS1, AL359878.1, PART1, TSPEAR-AS1, C10orf91, and LINC00501), while the risk score system for RFS was based on four lncRNAs (WARS2-IT1, AL359878.1, AL357060.1, and PART1). Univariate and multivariate Cox analyses showed the risk score systems for OS or RFS were significant independent factors adjusted for clinical factors. Receiver operating characteristic curve analysis showed the area under the curve for the risk score system was 0.704 for OS, and 0.71 for RFS. Our result revealed a lncRNA expression–based risk score system for OS or RFS can effectively predict the survival of patients with HCC and aid in good clinical decision-making.     

缺氧相关长链非编码RNA作为肝癌预后预测标志物的潜在价值

肝细胞癌(hepatocellular carcinoma,简称肝癌)是一种常见的恶性肿瘤。缺氧是肝癌等实体肿瘤的一个重要特征,同时也是诱导肿瘤恶性进展的重要因素。然而,肝癌缺氧相关的长链非编码RNA(long non-coding RNA,lncRNA)的鉴定及其在临床生存预后等方面的价值仍未得到系统的研究。本研究旨在通过肝癌转录组的整合分析鉴定肝癌缺氧相关的lncRNA,并评估其在肝癌预后中的价值。基于癌症基因组图谱(The Cancer Genome Atlas,TCGA)计划的肝癌转录组数据的整合分析,初步鉴定到233个缺氧相关的候选lncRNA。进一步筛选具有预后价值的候选者,基于其中12个缺氧相关lncRNA(AC012676.1、PRR7-AS1、AC020915.2、AC008622.2、AC026401.3、MAPKAPK5-AS1、MYG1-AS1、AC015908.3、AC009275.1、MIR210HG、CYTOR和SNHG3)建立了肝癌预后风险模型。Cox比例风险回归分析显示,基于该模型计算的缺氧风险评分作为肝癌患者新的独立预后预测指标,优于传统的临床病理因...     

Long noncoding RNA MAGI1-IT1 regulates cardiac hypertrophy by modulating miR-302e/DKK1/Wnt/beta-catenin signaling pathway

Cardiac hypertrophy (CH) is an adaptive cardiac response to overload whose decompensation eventually leads to heart failure or sudden death. Recently, accumulating studies have indicated the implication of long noncoding RNAs (lncRNAs) in CH progression. MAGI1-IT1 is a newly-identified lncRNA that is highly associated with CH, while its specific role in CH progression remains masked. In this study, we uncovered that MAGI1-IT1 was distinctly downregulated in angiotensin (Ang) II-induced hypertrophic H9c2 cells. Also, MAGI1-IT1 overexpression in Ang II-treated H9c2 cells strikingly abolished the enlarged surface area and the enhanced levels of hypertrophic markers such as ANP, BNP, and β-MHC. Mechanically, we found MAGI1-IT1 sponged miR-302e which was identified as a hypertrophy-facilitator here, and that miR-302e upregulation countervailed the inhibition of MAGI1-IT1 overexpression on hypertrophic cells. Moreover, it was confirmed that MAGI1-IT1 boosted DKK1 expression by absorbing miR-302e. Subsequently, we also illustrated that MAGI1-IT1 inactivated Wnt/beta-catenin signaling through a DKK1-dependent pathway. Finally, both the DKK1 inhibition and LiCI (Wnt activator) supplement abrogated the hypertrophy-suppressive impact of MAGI1-IT1 on Ang II-simulated hypertrophic H9c2 cells. Jointly, our findings disclosed that MAGI1-IT1 functioned as a negative regulator in CH through inactivating Wnt/beta-catenin pathway via targeting miR-302e/DKK1 axis, revealing a novel road for CH treatment.     

血清外泌体lncRNA PCGEM1、miR-129-5p与非小细胞肺癌患者临床病理特征及预后的关系

摘要 目的：研究血清外泌体长链非编码核糖核酸（lncRNA）前列腺癌基因表达标记1（PCGEM1）、微小核糖核酸（miR）-129-5p与非小细胞肺癌（NSCLC）患者临床病理特征及预后的关系。方法：选取2016年2月-2018年1月南京脑科医院收治的125例NSCLC患者作为NSCLC组,同期选取体检的70例健康人群作为健康组。采集两组静脉血,提取血清外泌体;采用实时定量聚合酶链式反应（qRT-PCR）检测血清外泌体lncRNA PCGEM1、miR-129-5p表达情况;采用Pearson相关性分析lncRNA PCGEM1与miR-129-5p的关系。并分析血清外泌体lncRNA PCGEM1、miR-129-5p与NSCLC患者临床病理特征的关系。对NSCLC患者行5年随访,绘制Kaplan-Meier曲线分析预后情况,多因素Cox比例风险回归模型分析预后不良危险因素,受试者工作特征（ROC）曲线分析lncRNA PCGEM1、miR-129-5p对NSCLC预后的预测价值。结果：：NSCLC组lncRNA PCGEM1相对表达量高于健康组,miR-129-5p相对表达量低于健康组（P＜0.05）。血清外泌体lncRNA PCGEM1相对表达量与miR-129-5p表达呈负相关（r= -0.420,P＜0.05）。血清外泌体lncRNA PCGEM1、miR-129-5p表达与患者TNM分期、分化程度、淋巴结转移有关（P＜0.05）。Kplan-Meier生存曲线显示,lncRNA PCGEM1低表达组5年生存率69.05%高于lncRNA PCGEM1高表达组35.53%,miR-129-5p高表达组5年生存率68.09%高于miR-129-5p低表达组33.80%。多因素Cox比例风险回归显示,TNM分期III期、有淋巴结转移、lncRNA PCGEM1高表达、miR-129-5p低表达为NSCLC患者预后不良的独立危险因素（P＜0.05）。ROC曲线显示,lncRNA PCGEM1、miR-129-5p联合检测对NSCLC预后的预测曲线下面积（AUC）为0.865,预测价值高于两者单独预测。结论：NSCLC患者血清外泌体lncRNA PCGEM1表达上调、miR-129-5p表达下调,二者表达与NSCLC患者TNM分期、分化程度、淋巴结转移有关,且与患者预后密切相关,对NSCLC预后不良具有较好预测价值。     

A 7-lncRNA signature predict prognosis of Uterine corpus endometrial carcinoma

Current research indicate that long noncoding RNAs (lncRNAs) are associated with the progression of various cancers and can be used as prognostic biomarkers. This study aims to construct a prognostic lncRNA signature for the risk assessment of Uterine corpus endometrial carcinoma (UCEC). The RNA-Seq expression profile and corresponding clinical data of UCEC patients obtained from The Cancer Genome Atlas database. First, some prognosis-related lncRNAs were obtained by univariate Cox analysis. The minimum absolute contraction and selection operator (LASSO) regression and the Cox proportional hazard regression method were used to further identify the lncRNA prognostic model. Finally, seven lncRNAs (AC110491.1, AL451137.1, AC005381.1, AC103563.2, AC007422.2, AC108025.2, and MIR7-3HG) were identified as potential prognostic factors. According to the model constructed by the above analysis, the risk score of each UCEC patient was calculated, and the patients were classified into high and low-risk groups. The low-risk group had significant survival benefits. Moreover, we constructed a nomogram that incorporated independent prognostic factors (age, tumor stage, tumor grade, and risk score). The c-index value for evaluating the predictive nomogram model was 0.801. The area under the curve was 0.797 (3-year survival). The calibration curve also showed that there was a satisfactory agreement between the predicted and observed values in the probability of 1-, 3-, and 5-year overall survival. On the basis of the coexpression relationship, we established a coexpression network of lncRNA-messenger RNA (mRNA) of the 7-lncRNA. The Kyoto Encyclopedia of Genes and Genomes analysis of the coexpressing mRNAs showed that the main pathways related to the 7-lncRNA signature were neuroactive ligand-receptor interaction, serotonergic synapse, and gastric cancer pathway. Therefore, our study revealed that the 7-lncRNA could be used to predict the prognosis of UCEC and for postoperative treatment and follow-up.     

Prediction of head and neck squamous cell carcinoma survival based on the expression of 15 lncRNAs

Recent evidence suggests that long noncoding RNAs (lncRNAs) are essential regulators of many cancer-related processes, including cancer cell proliferation, invasion, and migration. There is thus a reason to believe that the detection of lncRNAs may be useful as a diagnostic and prognostic strategy for cancer detection, however, at present no effective genome-wide tests are available for clinical use, constraining the use of such a strategy. In this study, we performed a comprehensive assessment of lncRNAs expressed in samples in the head and neck squamous cell carcinoma (HNSCC) cohort available in The Cancer Genome Atlas database. A risk score (RS) model was constructed based on the expression data of these 15 lncRNAs in the validation data set of HNSCC patients and was subsequently validated in validation data set and the entire data set. We were able to stratify patients into high- and low-risk categories, using our lncRNA expression panel to determine an RS, with significant differences in overall survival (OS) between these two groups in our test set (median survival, 1.863 vs. 5.484 years; log-rank test, p < 0.001). We were able to confirm the predictive value of our 15-lncRNA signature using both a validation data set and a full data set, finding our signature to be reproducible and effective as a means of predicting HNSCC patient OS. Through the multivariate Cox regression and stratified analyses, we were further able to confirm that the predictive value of this RS was independent of other predictive factors such as clinicopathological parameters. The Gene set enrichment analysis revealed potential functional roles for these 15 lncRNAs in tumor progression. Our findings indicate that an RS established based on a panel of lncRNA expression signatures can effectively predict OS and facilitate patient stratification in HNSCC.     

Long noncoding RNA CPS1-IT1 suppresses melanoma cell metastasis through inhibiting Cyr61 via competitively binding to BRG1

Long noncoding RNA CPS1-IT1 is recently recognized as a tumor suppressor in several cancers. Here, we investigate the role of CPS1-IT1 in human melanoma. Presently, our study reveals the low expression of CPS1-IT1 in human melanoma tissues and cell lines, which is significantly associated with metastasis and tumor stage. Besides, the potential of CPS1-IT1 as a prognosis-predictor is strongly indicated. Functionally, CPS1-IT1 overexpression inhibits cell migration, invasion, epithelial–mesenchymal transition, and angiogenesis in melanoma cells. CYR61, an angiogenic factor that participates in tumor metastasis as well as a recognized oncogene in melanoma, is shown to be confined under CPS1-IT1 overexpression in melanoma cells. Furthermore, enforced expression of Cyr61 in CPS1-IT1-silenced melanoma cells dramatically normalized the protein level of Cyr61 and that of its downstream targets vascular endothelial growth factor and matrix metalloproteinase-9, as well as the repressive effect of CPS1-IT1 overexpression on melanoma cell metastasis. BRG1, a core component of SWI/SNF complex, is implied to interact with both CPS1-IT1 and Cyr61 in melanoma cells. Moreover, CPS1-IT1 negatively regulates Cyr61 expression by blocking the binding of BRG1 to Cyr61 promoter. Jointly, CPS1-IT1 controls melanoma metastasis through impairing Cyr61 expression via competitively binding with BRG1, uncovering a novel potential therapeutic and prognostic biomarker for patients with melanoma.     

Long non‐coding RNA MALAT1 mediates hypoxia‐induced pro‐survival autophagy of endometrial stromal cells in endometriosis

Endometriosis is a common gynecological disease characterized by diminished apoptosis, sustained ectopic survival of dysfunctional endometrial cells. Hypoxia has been implicated as a crucial microenvironmental factor that contributes to endometriosis. It has been reported that long non‐coding RNA MALAT1 (lncRNA‐MALAT1) highly expressed in endometriosis and up‐regulated by hypoxia. Hypoxia may also induce autophagy, which might act as cell protective mechanism. However, the relationship between lncRNA‐MALAT1 and autophagy under hypoxia conditions in endometriosis remains unknown. In the present study, we found that both lncRNA‐MALAT1 and autophagy level were up‐regulated in ectopic endometrium from patients with endometriosis, and its expression level correlates positively with that of hypoxia‐inducible factor‐1α (HIF‐1α). In cultured human endometrial stromal cells, both lncRNA‐MALAT1 and autophagy were induced by hypoxia in a time‐dependent manner and lncRNA‐MALAT1 up‐regulation was dependent on HIF‐1α signalling. Our analyses also show that knockdown of lncRNA‐MALAT1 suppressed hypoxia induced autophagy. Furthermore, inhibiting autophagy with specific inhibitor 3‐Methyladenine (3‐MA) and Beclin1 siRNA enhanced apoptosis of human endometrial stromal cells under hypoxia condition. Collectively, our findings identify that lncRNA‐MALAT1 mediates hypoxia‐induced pro‐survival autophagy of endometrial stromal cells in endometriosis.     

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.     

Hypoxia-related LncRNAs signature predicts prognosis and is associated with immune infiltration and progress of head and neck squamous cell carcinoma

BackgroundDisclosing prognostic information is necessary to enable good treatment selection and improve patient outcomes. Previous studies suggest that hypoxia is associated with an adverse prognosis in patients with HNSCC and that long non-coding RNAs (lncRNAs) show functions in hypoxia-associated cancer biology. Nevertheless, the understanding of lncRNAs in hypoxia related HNSCC progression remains confusing.MethodsData were downloaded from TCGA and GEO database. Bioinformatic tools including R packages GEOquery, limma, pheatmap, ggplot2, clusterProfiler, survivalROC and survcomp and LASSO cox analysis were utilized. Si-RNA transfection, CCK8 and real-time quantified PCR were used in functional study.ResultsGEO data (GSE182734) revealed that lncRNA regulation may be important in hypoxia related response of HNSCC cell lines. Further analysis in TCGA data identified 314 HRLs via coexpression analysis between differentially expressed lncRNAs and hypoxia-related mRNAs. 23 HRLs were selected to build the prognosis predicting model using lasso Cox regression analyses. Our model showed excellent performance in predicting survival outcomes among patients with HNSCC in both the training and validation sets. We also found that the risk scores were related to tumor stage and to tumor immune infiltration. Moreover, LINC01116 were selected as a functional study target. The knockdown of LINC01116 significantly inhibited the proliferation of HNSCC cells and effected the hypoxia induced immune and the NF-κB/AKT signaling.ConclusionsData analysis of large cohorts and functional experimental validation in our study suggest that hypoxia related lncRNAs play an important role in the progression of HNSCC, and its expression model can be used for prognostic prediction.     

Esophageal cancer lymph node metastasis–associated gene signature optimizes overall survival prediction of esophageal cancer

Esophageal cancer (EC) is characteristic of early regional lymph node metastasis (LNM) and most patients with metastasis have a poor prognosis. However, the current diagnostic techniques do not enable precise differentiation of EC LNM, prognostic stratification, and individual survival estimation. To identify potential molecular biomarkers for EC patients with LNM, we explored differently expressed genes in The Cancer Genome Atlas database between 77 non-LNM cases and 88 LNM cases by limma package R. Then, according to univariate and multivariate Cox regression analyses, we constructed an 8-messenger RNA (mRNA) prognostic signature model, which could predict the outcome in a more exact way. The area under the curve of the risk score is significantly higher than other clinical information, indicating that the 8-mRNA–based risk score is a good indicator for prognosis. Then, combined with other individual risk factors, such as age, sex, T stage, M stage, etc, we could precisely calculate the individual 1-, 3-, and 5-year survival rates. The Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis indicate that the risk model is mainly associated with cancer-related pathways, such as cell division, cellular meiosis, and cell cycle regulation. In summary, the 8-mRNA–based risk score model that we developed successfully predicts the survival of EC. It is independent of clinical information and performing better than other clinical information for prognosis.