• non-small-cell lung cancer
• prognostic markers
• predictive markers
• machine learning
• translational research

• Epithelial-Mesenchymal Transition/drug effects
• Humans
• Lung Neoplasms/drug therapy
• Lung Neoplasms/pathology
• Lung Neoplasms/genetics
• Lung Neoplasms/metabolism
• Drug Resistance, Neoplasm/drug effects
• Drug Resistance, Neoplasm/genetics
• Transforming Growth Factor beta/metabolism
• Transforming Growth Factor beta/antagonists & inhibitors
• Animals
• Mice
• Cell Line, Tumor
• Carcinoma, Squamous Cell/drug therapy
• Carcinoma, Squamous Cell/genetics
• Carcinoma, Squamous Cell/pathology
• Carcinoma, Squamous Cell/metabolism
• Xenograft Model Antitumor Assays
• Prognosis
• Female
• Vinorelbine/pharmacology
• Male

• 82201948 National Natural Science Foundation of China (National Science Foundation of China)

• computer tomography
• diffusion-weighted imaging
• esophageal cancer
• esophagography
• radiotherapy

• COL5A1
• GATA6
• LINC00173
• OSCC
• transcription

• Biomarker
• Cancer
• LINC00173

• clinical biomarkers
• esophageal cancer
• metastasis
• molecular markers
• postoperative recurrence

• Humans
• Esophageal Squamous Cell Carcinoma/pathology
• Carcinoma, Squamous Cell/pathology
• Neoplasm Staging
• Neoplasm Recurrence, Local/pathology
• Esophageal Neoplasms/diagnosis
• Esophageal Neoplasms/surgery
• Esophageal Neoplasms/pathology
• Adaptation, Psychological

• LINC00173
• Oxford Nanopore
• PacBio
• RNA sequencing
• RNA-seq
• alternative splicing
• bioinformatics
• computational genomics
• long-read sequencing
• next-generation sequencing
• software

• RNA/genetics
• Cloud Computing
• Gene Expression Profiling/methods
• Computational Biology/methods
• Software
• Sequence Analysis, RNA
• High-Throughput Nucleotide Sequencing/methods

• R21 AI156616 NIAID NIH HHS
• R21AI156616 National Institute of Allergy and Infectious Diseases

• LINC00173
• cancer
• ceRNAs
• chemoresistance
• target

• PRCC
• TCGA
• bioinformatics
• immunity
• prognosis
• risk model
• ubiquitin-proteasome system genes

Esophageal squamous cell carcinoma (ESCC) represents roughly 85–90% of all esophageal carcinoma patients in China. Immunotherapy is used to treat an increasing number of ESCC patients in clinical practice. This study aims to understand the molecular heterogeneity and the tumor immune microenvironment of ESCC for designing novel immunotherapies to improve response and outcomes. We identified two molecular subtypes associated with prognosis, immune-related pathways, and tumor microenvironment. In an independent cohort of Chinese ESCC patients treated with immunotherapy, the response rate of the S1 subtype is significantly higher than the S2 subtype. These findings provide a new perspective on the molecular subtyping for ESCC and a biological rationale for novel therapeutic intervention in a specific subgroup of ESCC that could potentially be translated into clinical practice both diagnostically and therapeutically to benefit ESCC patients.

Background: Immunotherapy is recently being used to treat esophageal squamous cell carcinoma (ESCC); however, response and survival benefits are limited to a subset of patients. A better understanding of the molecular heterogeneity and tumor immune microenvironment in ESCC is needed for improving disease management. Methods: Based on the DNA methylation and gene expression profiles of ESCC patients, we identify molecular subtypes of patients and construct a predictive model for subtype classification. The clinical value of molecular subtypes for the prediction of immunotherapy efficacy is assessed in an independent validation cohort of Chinese ESCC patients who receive immunotherapy. Results: We identify two molecular subtypes of ESCC (S1 and S2) that are associated with distinct immune-related pathways, tumor microenvironment and clinical outcomes. Accordingly, S2 subtype patients had a poorer prognosis. A 15-gene expression signature is developed to classify molecular subtypes with an overall accuracy of 94.7% (89/94, 95% CI: 0.880–0.983). The response rate of immunotherapy is significantly higher in the S1 subtype than in the S2 subtype patients (68.75% vs. 25%, p = 0.028). Finally, potential target drugs, including mitoxantrone, are identified for treating patients of the S2 subtype. Conclusions: Our findings demonstrated that the identified molecular subtypes constitute a promising prognostic and predictive biomarker to guide the clinical care of ESCC patients.

• Apoptosis
• Epithelial–mesenchymal transition
• Esophageal cancer
• SLC2A3
• lncRNA
• miR-200b-3p

• LINC00173
• TCF3/PBX1
• acute lymphoblastic leukemia
• biomarker
• cancer
• relapse

Esophageal squamous cell carcinoma (ESCC) is correlated with worse clinical prognosis and lacks available targeted therapy. Thus, identification of reliable biomarkers is required for the diagnosis and treatment of ESCC.

We downloaded the GSE53625 dataset as a training dataset to screen differentially expressed RNAs (DERs) with the criterion of false discovery rate (FDR) < 0.05 and |log₂fold change (FC)| > 1. A support vector machine classifier was used to find the optimal feature gene set that could conclusively distinguish different samples. An eight-lncRNA signature was identified by random survival forest algorithm and multivariate Cox regression analysis. The RNA sequencing data from The Cancer Genome Atlas (TCGA) database were used for external validation. The predictive value of the signature was assessed using Kaplan–Meier test, time-dependent receiver operating characteristic (ROC) curves, and dynamic area under the curve (AUC). Furthermore, a nomogram to predict patients’ 3-year and 5-year prognosis was constructed. CCK-8 assay, flow cytometry, and transwell assay were conducted in ESCC cells.

A total of 1136 DERs, including 689 downregulated mRNAs, 318 upregulated mRNAs, 74 downregulated lncRNAs and 55 upregulated lncRNAs, were obtained in the GES53625 dataset. From the training dataset, we identified an eight-lncRNA signature, (ADAMTS9-AS1, DLX6-AS1, LINC00470, LINC00520, LINC01497, LINC01749, MAMDC2-AS1, and SSTR5-AS1). A nomogram based on the eight-lncRNA signature, age, and pathologic stage was developed and showed good accuracy for predicting 3-year and 5-year survival probability of patients with ESCC. Functionally, knockdown of LINC00470 significantly suppressed cell proliferation, G1/S transition, and migration in two ESCC cell lines (EC9706 and TE-9). Moreover, knockdown of LINC00470 downregulated the protein levels of PCNA, CDK4, and N-cadherin, while upregulating E-cadherin protein level in EC9706 and TE-9 cells.

Our eight-lncRNA signature and nomogram can provide theoretical guidance for further research on the molecular mechanism of ESCC and the screening of molecular markers.

The online version contains supplementary material available at 10.1186/s11658-022-00331-x.

• Esophageal squamous cell carcinoma
• Long noncoding RNA
• Nomogram
• Signature

• ALL, Acute lymphblastic leukemia
• B-ALL, B cell acute lymphoblastic leukemia
• Bioinformatics
• COG, Children's Oncology Group
• Clinical oncology
• EFS, Event-free survival
• Genomics
• MRD, Minimal residual disease
• Machine learning
• Random survival forest
• WBC, White blood cell count

• R01 LM013236 NLM NIH HHS

• Esophageal squamous cell carcinoma (ESCC)
• nomogram
• prognosis
• risk score
• weighted gene co-expression network analysis (WGCNA)

Esophageal cancer (EC) is one of the deadliest solid malignancies, mainly consisting of esophageal squamous cell carcinoma (ESCC) and adenocarcinoma (EAC). Robust biomarkers that can improve patient risk stratification are needed to optimize cancer management. We sought to establish potent prognostic signatures with immune-related gene (IRG) pairs for ESCC and EAC.

We obtained differentially expressed IRGs by intersecting the Immunology Database and Analysis Portal (ImmPort) with the transcriptome data set of The Cancer Genome Atlas (TCGA)-ESCC and EAC cohorts. A novel rank-based pairwise comparison algorithm was applied to select effective IRG pairs (IRGPs), followed by constructing a prognostic IRGP signature via the least absolute shrinkage and selection operator (LASSO) regression model. We assessed the predictive power of the IRGP signatures on prognosis, tumor-infiltrating immune cells, and immune checkpoint inhibitor (ICI) efficacy in EC. Kaplan-Meier survival analysis and receiver operating characteristic curves (ROC) were used to evaluate the clinical significance of IRGPs. Univariate and multivariate Cox regression analyses were performed to investigate the association of overall survival (OS) with IRGPs and clinical characteristics.

We built a 19-IRGP signature for ESCC (n=75) and a 17-IRGP signature for EAC (n=78), with an area under the ROC curve (AUC) of 0.931 and 0.803, respectively. IRGP signature-derived risk scores stratified patients into low- and high-risk groups with significantly different OS in ESCC and EAC (P<0.001). Nomogram and decision curve analysis were used to evaluate the clinical relevance of the prognostic signatures, achieving a C-index of 0.973 in ESCC and 0.880 in EAC. The risk scores were associated with immune and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) scores and the composition of immune cells in the tumor microenvironment. The association between risk score and human leukocyte antigens (HLAs), mismatch repair (MMR) genes, and immune checkpoint molecules demonstrated its predictive value for ICI response. Differential immune characteristics and predictive value of the risk score were observed in EAC.

The established immune signatures showed great promise in predicting prognosis, tumor immunogenicity, and immunotherapy response in ESCC and EAC.

• Esophageal cancer (EC)
• immune-related gene pair
• prognosis
• signature

Gastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC.

Ferroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model.

We constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410–2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR.

This risk model was shown to be a novel method for predicting prognosis for GC patients.

The online version contains supplementary material available at 10.1186/s12885-021-08975-2.

• Bioinformatics
• Ferroptosis
• Gastric cancer
• Long non-coding RNA
• Prognosis

• LINC00173
• Non-small-cell lung cancer
• biomarker
• diagnosis

• Autophagy
• Bioinformatics analysis
• Esophageal carcinoma

• Biomarkers, Tumor/genetics
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Humans
• Kaplan-Meier Estimate
• Neoplasms/diagnosis
• Neoplasms/genetics
• Prognosis
• RNA, Long Noncoding/genetics

Long non-coding RNAs (lncRNAs) have emerged as useful prognostic markers in many tumors. In this study, we investigated the potential application of lncRNA markers for the prognostic prediction of esophageal squamous cell carcinoma (ESCC). We identified ESCC-associated lncRNAs by comparing ESCC tissues with normal tissues. Subsequently, Kaplan–Meier (KM) method in combination with the univariate Cox proportional hazards regression (UniCox) method was used to screen prognostic lncRNAs. By combining the differential and prognostic lncRNAs, we developed a prognostic model using cox stepwise regression analysis. The obtained prognostic prediction model could effectively predict the 3- and 5-year prognosis and survival of ESCC patients by time-dependent receiver operating characteristic (ROC) curves (area under curve = 0.87 and 0.89, respectively). Besides, a lncRNA-based classification of ESCC was generated using k-mean clustering method and we obtained two clusters of ESCC patients with association with race and Barrett’s esophagus (BE) (both P < 0.001). Finally, we found that lncRNA AC007128.1 was upregulated in both ESCC cells and tissues and associated with poor prognosis of ESCC patients. Furthermore, AC007128.1 could promote epithelial-mesenchymal transition (EMT) of ESCC cells by increasing the activation of MAPK/ERK and MAPK/p38 signaling pathways. Collectively, our findings indicated the potentials of lncRNA markers in the prognosis, molecular subtyping, and EMT of ESCC.

• EMT
• ESCC
• lncRNAs
• molecular subtyping
• prognosis

• LINC00173
• SPHK1
• TSV1
• TSV2
• pancreatic cancer

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality, with poor prognosis worldwide. DNA damage repair is one of the hallmarks of cancer. Loss of genomic integrity owing to inactivation of DNA repair genes can increase the risk of cancer progression and lead to poor prognosis. We aimed to identify a novel gene signature related to DNA repair to predict the prognosis of ESCA patients. Based on gene expression profiles of ESCA patients from The Cancer Genome Atlas and gene set enrichment analysis, 102 genes related to DNA repair were identified as candidates. After stepwise Cox regression analysis, we established a five-gene prognostic model comprising DGCR8, POM121, TAF9, UPF3B, and BCAP31. Kaplan-Meier survival analysis confirmed a strong correlation between the prognostic model and survival. Moreover, we verified the clinical value of the prognostic signature under the influence of different clinical parameters. We found that small-molecule drugs (trametinib, selumetinib, and refametinib) could help to improve patient survival. In summary, our study provides a novel and promising prognostic signature based on DNA-repair-related genes to predict survival of patients with ESCA. Systematic data mining provides a theoretical basis for further exploring the molecular pathogenesis of ESCA and identifying therapeutic targets.

• DNA repair
• esophageal cancer
• overall survival
• prognostic biomarkers
• small molecular drugs
• targeted therapy

Esophageal cancer (EC) is one of the most common malignant tumors of the digestive system with high incidence and mortality rate worldwide. Therefore, exploring the pathogenesis of EC and searching for new targeted therapies are the current research hotspot for EC treatment. Long non‐coding RNAs (lncRNAs) are endogenous RNAs with more than 200 nucleotides, but without protein‐coding function. In recent years, lncRNAs have gradually become the focuses in the field of non‐coding RNA. Some lncRNAs have been proved to be closely related to the pathogenesis of EC. Many lncRNAs are abnormally expressed in EC and participate in many biological processes including cell proliferation, apoptosis, and metastasis by inhibiting or promoting target gene expression. LncRNAs can also regulate the progression of EC through epithelial‐mesenchymal transformation (EMT), which is closely related to the occurrence, development, and prognosis of EC. In this article, we review and discuss the involvement of lncRNAs in the progression of EC.

• apoptosis
• biomarker
• diagnosis
• drug resistance
• esophageal cancer
• long non-coding RNA
• metastasis
• prognosis
• proliferation

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide. ESCC has a generally poor prognosis and there is a lack of available biomarkers for diagnosis and prognosis. The aim of the study was to identify novel biomarkers for ESCC. We screened the overlapping differentially expressed genes (DEGs) acquired from six Gene Expression Omnibus (GEO) ESCC datasets and The Cancer Genome Atlas (TCGA) ESCC datasets. Subsequently, protein-protein interaction network analysis was performed to identify the key hub genes. Then, Kaplan Meier survival and receiver operating curve (ROC) analysis were utilized to clarify the diagnostic and prognostic role of these hub genes. The UALCAN database, single cell RNA sequencing (scRNA-seq) and real-time quantitative PCR (qPCR) were performed to confirm the expression levels of identified hub genes. Finally, immune infiltration analysis was conducted to investigate the role of these genes in the pathogenesis of ESCC. The results showed that PBK, KIF2C, NUF2, KIF20A, RAD51AP1, and DEPDC1 effectively distinguish ESCC tissues from normal samples, and all of them were significantly correlated with overall survival. The results of scRNA-seq and qPCR indicated that the expression levels of hub genes in ESCC were significantly higher than in normal cells or tissues. Further immune infiltration analysis showed that infiltration of dendritic cells was significantly negatively correlated with PBK, KIF2C, NUF2, RAD51AP1, and DEPDC1 expression levels. In conclusion, our results suggest that PBK, KIF2C, NUF2, KIF20A, RAD51AP1 and DEPDC1 are all potential biomarkers for ESCC diagnosis and prognosis may also be potential therapeutic targets for ESCC.

• ESCC
• biomarkers
• diagnostic
• poor prognosis
• therapeutic targets

Long noncoding RNAs (lncRNAs) have been shown to play an important role in the development and progression of esophageal carcinoma (EC). Recently, lncRNA LOC441178 was shown to be dysregulated in many cancer types; however, the role of LOC441178 in EC remains unclear.

Flow cytometry, transwell and wound healing assays were used to measure the apoptosis and migration in esophageal squamous cell carcinoma (ESCC) cells. RT-qPCR was used to detect the level of miR-182 in LOC441178-overexpressed EC cells. In addition, DNA methylation status of miR-182 promoter in LOC441178-overexpressed ESCC cells was detected by methylation-specific PCR (MSP) and bisulfite sequencing PCR.

In this study, we found that LOC441178 negatively regulated miR-182 expression in ESCC cells. In addition, overexpression of LOC441178 inhibited the proliferation and migration and induced apoptosis in ESCC cells via downregulation of miR-182. Moreover, overexpression of LOC441178 markedly inhibited the phosphorylation of Akt and phosphorylation FOXO3a and increased the expression of FOXO3a in ESCC cells via downregulation of miR-182. Mechanistically, LOC441178 overexpression epigenetically suppressed miR-182 expression via DNA methylation. In vivo experiments revealed that overexpression of LOC441178 inhibited ESCC tumor growth in mouse xenograft model.

Collectively, our data suggested that LOC441178 overexpression epigenetically inhibited tumorigenesis of ESCC via DNA methylation of miR-182. These data indicated that the LOC441178/miR-182 axis might represent a novel therapeutic option for the treatment of ESCC.

• DNA methylation
• esophageal carcinoma
• lncRNA LOC441178
• miR-182

• Apoptosis
• Hypertrophic scar
• LINC00173
• β-catenin

• Cancer survival analysis
• lincRNA
• non-coding RNA
• pseudogene

• Biomarkers, Tumor
• Computational Biology/methods
• Databases, Genetic
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Humans
• Neoplasms/genetics
• Neoplasms/mortality
• Prognosis
• RNA, Long Noncoding/genetics
• RNA, Untranslated/genetics

• P30 CA118100 NCI NIH HHS
• R01 DE023222 NIDCR NIH HHS

The aim of this study was to identify prognostic long non-coding RNAs (lncRNAs) and develop a multi-lncRNA signature for suvival prediction in esophageal squamous cell carcinoma (ESCC).

The clinical and gene expression data from Gene Expression Omnibus database (GSE53624, n = 119) were obtianed as training set. A total of 98 paired ESCC tumor and normal tissues were detected by RNA sequencing and used as test set. Another 84 ESCC tissues were used for real-time quantitative PCR(qRT-PCR) and as an independent validation cohort. Survival analysis, Cox regression and Kaplan–Meier analysis were performed.

We screened a prognostic marker of ESCC from the GSE53624 dataset and named it as the five-lncRNA signature including AC007179.1, MORF4L2-AS1, RP11-488I20.9, RP13-30A9.2, RP4-735C1.6, which could classify patients into high- and low-risk groups with significantly different survival(median survival: 1.75 years vs. 4.01 years, log rank P < 0.05). Then test dataset and validation dataset confirmed that the five-lncRNA signature can determine the prognosis of ESCC patients. Predictive independence of the prognostic marker was proved by multivariable Cox regression analyses in the three datasets (P < 0.05). In addition, the signature was found to be better than TNM stage in terms of prognosis.

The five-lncRNA signature could be a good prognostic biomarker for ESCC patients and has important clinical value.

• Esophageal squamous cell carcinoma
• Overall survival
• Prognosis
• Signature
• lncRNA

• Gastric neuroendocrine neoplasms
• Marriage
• Propensity score matching

• Adult
• Aged
• Aged, 80 and over
• Databases, Factual
• Female
• Humans
• Male
• Marital Status/statistics & numerical data
• Middle Aged
• Multivariate Analysis
• Neuroendocrine Tumors/diagnosis
• Neuroendocrine Tumors/mortality
• Prognosis
• Propensity Score
• Retrospective Studies
• Risk Factors
• SEER Program
• Stomach Neoplasms/diagnosis
• Stomach Neoplasms/mortality
• Survival Analysis

• Cancer survival
• Esophageal cancer
• Infiltrating immune cells
• Molecular drugs
• Prognostic signature
• Targeted therapy

Hepatocellular carcinoma (HCC) is a common neoplasm located in the liver. Accumulating evidence has highlighted that long noncoding RNAs (lncRNAs) are correlated with the survival of HCC patients. This study focuses on finding a lncRNA signature to predict the prognostic risk of HCC patients.

Statistical and machine learning analyses were conducted to analyze the lncRNA expression data and corresponding clinical data of 180 HCC patients collected from the public online Tanric and The Cancer Genome Atlas (TCGA) databases.

From the training dataset, we obtained the four‐lncRNA model comprising RP11‐495K9.6, RP11‐96O20.2, RP11‐359K18.3, and LINC00556 which can divide HCC patients into two different groups with significantly different prognosis (n = 90, median 1.81, 95% confidence interval [CI]: 1.50‐4.91 vs 8.56 years, 95% CI: 6.96‐9.97, log‐rank test P < .001). The test dataset confirmed the prognostic ability of the signature (n = 90, median 1.95, 95% CI: 1.14‐4.08 vs 5.80 years, 95% CI: 3.11‐6.82, log‐rank test P = .007). Receiver operating characteristic curve displayed the better prediction efficiency of the four‐lncRNA signature than the tumor/node/metastasis stage. Cox analysis showed the four‐lncRNA signature was an independent predictor of HCC prognosis.

The four‐lncRNA signature can be used as an independent biomarker for HCC patients to predict the prognostic risk.

• biomarker
• hepatocellular carcinoma
• lncRNA
• prognostic
• signature

• Cervical cancer
• FBXW7
• Long noncoding RNA
• Tumor progression
• microRNA

Colorectal cancer (CRC) is one of the most prevalent tumors worldwide. Recently, long noncoding RNAs (lncRNAs) have been shown to influence tumorigenesis and tumor progression by acting as competing endogenous RNAs (ceRNAs). It is difficult to extract prognostic lncRNAs and useful bioinformation from most ceRNA networks constructed previously.

To construct a prognostic related ceRNA regulatory network and lncRNA related signature based on risk score in CRC.

RNA transcriptome profile and clinical information of 506 CRC patients were downloaded from the Cancer Genome Atlas database. R packages and Perl program were used for data processing. Cox regression analysis was used for prognostic model construction. Quantitative real-time polymerase chain reaction was used to detect the expression of lncRNAs.

A prognostic-related ceRNA network was constructed, including 9 lncRNAs, 44 mRNAs, and 30 miRNAs. In addition, a four-lncRNA model was constructed using multivariate Cox regression analysis, which could be an independent prognostic model in CRC. The risk score for each patient was calculated, and the 506 patients were divided into high and low-risk groups (253 for each group) based on the median risk score. The results of the survival analysis showed that patients with a high-risk score had a poor survival rate. Furthermore, the predictive value of the four-lncRNA model was evaluated in GSE38832. Patient survival probabilities could be better predicted when combing the risk score and clinical features. Gene Set Enrichment Analysis results verified that a number of cancer-related signaling pathways were enriched with a high-risk score in CRC. Finally, we validated a novel lncRNA (LINC00488) using quantitative real-time polymerase chain reaction in 22 paired CRC patient tumor tissues compared to adjacent non-tumor tissues.

The four-lncRNA model could give better predictive value for CRC patients. Our understanding of the lncRNA-related ceRNA regulatory mechanism could provide a potential diagnostic indicator for CRC patients.

• Colorectal cancer
• Long noncoding RNA
• miRNA
• mRNA
• Transcription factor
• Competing endogenous RNA
• Survival

• Aged
• Biomarkers, Tumor/genetics
• Colorectal Neoplasms/genetics
• Colorectal Neoplasms/mortality
• Databases, Genetic
• Female
• Gene Regulatory Networks/genetics
• Humans
• Male
• Middle Aged
• Predictive Value of Tests
• Prognosis
• Proportional Hazards Models
• RNA, Long Noncoding/analysis
• Risk Assessment
• Survival Analysis
• Survival Rate
• Transcriptome

Background: Metabolic syndrome (MetS) is associated with the development of esophageal squamous cell carcinoma (ESCC), and long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms of MetS and tumor. This study will explore the prognostic effect of MetS and the associated lncRNA signature on ESCC.

Methods: Our previous RNA-chip data (GSE53624, GSE53622) for 179 ESCC patients were reanalyzed according to MetS. The recurrence-free survival (RFS) was collected for these patients. The status of the MetS-related tumor microenvironment was analyzed with the CIBERSORT and ESTIMATE algorithms. A lncRNA signature was established with univariate and multivariate Cox proportional hazards regression (PHR) analysis and verified using the Kaplan–Meier survival curve analysis and time-dependent receiver operating characteristic (ROC) curves. A clinical predictive model was constructed based on multiple risk factors, evaluated using C-indexes and calibration curves, and verified using data from the GEO and TCGA databases.

Results: The results showed that MetS was an independent risk factor for ESCC patients conferring low OS and RFS. Tumor microenvironment analysis indicated that patients with MetS have high stromal scores and M2 macrophage infiltration. A six-lncRNA signature was established by 60 ESCC patients randomly selected from GSE53624 and identified with an effective predictive ability in validation cohorts (59 patients from GSE53624 and 60 patients from GSE53622), subgroup analysis, and ESCC patients from TCGA. MetS and the six-lncRNA signature could be regarded as independent risk factors and enhanced predictive ability in the clinical predictive model.

Conclusions: Our results indicated that MetS was associated with poor prognosis in ESCC patients, and the possible mechanism was related to changes in the tumor microenvironment. MetS and the six-lncRNA signature could also serve as independent risk factors with available clinical application value.

• clinical predictive model
• esophageal squamous cell carcinoma
• lncRNA signature
• metabolic syndrome
• prognosis

• Cox regression model
• Esophageal cancer
• Head and neck squamous cell carcinoma (HNSCC)
• Long non-coding RNA (lncRNA)
• Prognostic signature

• Carcinoma, Squamous Cell/genetics
• Carcinoma, Squamous Cell/metabolism
• Carcinoma, Squamous Cell/mortality
• Databases, Genetic
• Disease-Free Survival
• Esophageal Neoplasms/genetics
• Esophageal Neoplasms/metabolism
• Esophageal Neoplasms/mortality
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Head and Neck Neoplasms/genetics
• Head and Neck Neoplasms/metabolism
• Head and Neck Neoplasms/mortality
• Humans
• Kaplan-Meier Estimate
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• RNA, Neoplasm/genetics
• RNA, Neoplasm/metabolism
• Squamous Cell Carcinoma of Head and Neck/genetics
• Squamous Cell Carcinoma of Head and Neck/metabolism
• Squamous Cell Carcinoma of Head and Neck/mortality
• Survival Rate

• P30 CA177558 NCI NIH HHS
• Education Department of Jilin Province

• esophageal squamous cell carcinoma
• long non-coding RNA
• nomogram
• signature
• weighted gene co-expression network analysis

• Biomarkers, Tumor
• Computational Biology/methods
• Esophageal Neoplasms/genetics
• Esophageal Neoplasms/mortality
• Esophageal Neoplasms/pathology
• Esophageal Squamous Cell Carcinoma/genetics
• Esophageal Squamous Cell Carcinoma/mortality
• Esophageal Squamous Cell Carcinoma/pathology
• Female
• Gene Expression Profiling
• Humans
• Kaplan-Meier Estimate
• Male
• Neoplasm Staging
• Nomograms
• Prognosis
• Proportional Hazards Models
• RNA, Long Noncoding/genetics
• ROC Curve
• Transcriptome

• differentially expressed genes
• metastasis
• osteosarcoma
• risk signature
• survival analysis

• Lipids
• nomogram
• non-esophageal squamous cell carcinoma (non-ESCC)
• prognosis

In cancer progression, hypoxia, or low oxygen tension, is a major regulator of tumor aggressiveness and metastasis. However, how cancer cells adapt to the hypoxia and communicate with other mesenchymal cells in microenvironment during tumor development remains to be elucidated. Here, we investigated the involvement of exosomes in modulating angiogenesis and enhancing metastasis in esophageal squamous cell carcinoma (ESCC).

Differential centrifugation, transmission electron microscopy and nanoparticle tracking analysis were used to isolate and characterize exosomes. Colony formation and transwell assay were performed to assess the proliferation, migration and invasion of human umbilical vein endothelial cells (HUVECs). The tube formation assay and matrigel plug assay were used to evaluate the vascular formation ability of HUVECs in vitro and in vivo respectively. An in vivo nude mice model was established to detect the regulatory role of exosomes in ESCC progression. Microarray analysis was performed to analyze the transcriptome profiles in HUVECs.

Exosomes derived from ESCC cells cultured under hypoxia played a better role in promoting proliferation, migration, invasion and tube formation of HUVECs in vitro and in vivo than exosomes from ESCC cells cultured under normoxia. Moreover, hypoxic exosomes significantly enhanced the tumor growth and lung metastasis compared with normoxic exosomes in nude mice models. Interestingly, endothelial cells were programmed by hypoxic and normoxic exosomes from ESCC cells which altered the transcriptome profile of HUVECs.

Taken together, our data identified an angiogenic role of exosomes from ESCC cells which shed light on the further application of exosomes as valuable therapeutic target for ESCC.

The online version of this article (10.1186/s13046-019-1384-8) contains supplementary material, which is available to authorized users.

• Angiogensis
• ESCC
• Exosomes
• Metastasis

As one of the most frequently diagnosed cancers, esophageal squamous cell carcinoma (ESCC) remains the leading cause of malignancy‐related death worldwide. Many studies have focused on the potential role of cancer cells in educating B cells during cancer progression. Here, we aim to explore the role of circulating exosomes from ESCC in the generation of two main regulatory B (Breg) subsets, including interleukin‐10⁺ Bregs (B10) and programmed cell death (PD)‐1^high Bregs. Firstly, we observed an elevated percentage of B10 cells in peripheral blood of ESCC patients compared with healthy controls. Then we isolated and characterized exosomes from the peripheral blood of ESCC patients and an ESCC cell line. Exosomes from ESCC patients and the ESCC cell line suppressed the proliferation of B cells and induced the augmentation of B10 and PD‐1^high Breg cells. By comparing the long non‐coding RNA and mRNA expression profiles in exosomes from ESCC patients or healthy controls, we identified a series of differentially expressed genes. Finally, we undertook gene annotation and pathway enrichment analyses on differentially expressed genes to explore the potential mechanism underlying the modulatory role of cancer exosomes in B cells. Our findings contribute to the study on B cell‐mediated ESCC immunosuppression and shed light on the possible application of exosomes in anticancer therapies.

• Breg
• IL-10
• PD-1
• esophageal squamous cell carcinoma
• exosome