scholarly journals Identification of a prognostic immune signature for esophageal squamous cell carcinoma to predict survival and inflammatory landscapes

2020 ◽  
Author(s):  
Jie He ◽  
Chaoqi Zhang ◽  
Yuejun Luo ◽  
Zhen Zhang ◽  
Zhihui Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cox Regression ◽  
Lung Squamous Cell Carcinoma ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Related Risk ◽  
Public Data

Abstract BackgroundImmunotherapy has achieved surprising success in the treatment of esophageal squamous cell carcinoma (ESCC). However, studies concerning immune phenotypes within the ESCC microenvironment and their relationship with prognostic outcomes are limited. Therefore, we aim to construct and validate an individual immune-related risk signature for patients with ESCC.MethodsWe collected 196 ESCC cases, including 119 samples from our previous public data (GSE53624) to use as a training set. We additionally collected an independent validation cohort consisting of 77 frozen tumor tissues with qPCR data. Head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LUSC) cohorts were also collected for validation. A least absolute shrinkage and selection operator (LASSO) model and a stepwise Cox proportional hazards regression model were used to construct the immune-specific signature. The potential mechanism and inflammatory landscapes of the signature were explored by using bioinformatics and immunofluorescence assay methods.ResultsImmune-related genes were significantly altered in ESCC tissues, and 16 differentially expressed immune-related genes with the most prognostic value were filtered out (P<0.01). Then a six-gene-based signature (TSPAN2, AMBP, ITLN1, C6, PRLR, and MADCAM1) was generated from the training set. This signature classified the patients into two groups with significantly different overall and relapse-free survival. Furthermore, the signature showed similar prognostic values in different clinical subgroups and in the independent cohort, as well as in patients with HNSCC and LUSC. Multivariable Cox regression analysis confirmed that the signature was an independent prognostic factor for patients with ESCC in different cohorts. Pathway analysis showed that genes related to the signature were mostly involved in cell adhesion, leukocyte transendothelial migration, and cancer progression. Further analysis revealed that the signature was closely associated with specific inflammatory activities (activation of macrophages and T cells signaling transduction). Additionally, high-risk patients were found characterized by distinctive immune checkpoints panel and higher filtration of Tregs and fibroblasts. ConclusionWe constructed the first comprehensive immune-related risk signature for ESCC and furnished new hints of immune profiling of ESCC. Future prospective studies are needed to test the clinical utility of this signature in the individualized management of patients with ESCC.

scholarly journals Identification of a Prognostic Immune Signature for Esophageal Squamous Cell Carcinoma to Predict Survival and Inflammatory Landscapes

2020 ◽  
Vol 8 ◽  
Author(s):  
Chaoqi Zhang ◽  
Yuejun Luo ◽  
Zhen Zhang ◽  
Zhihui Zhang ◽  
Guochao Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
T Cell ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Immune Checkpoints ◽  
Related Risk ◽  
Public Data ◽  
Independent Cohort

Immunotherapy has achieved success in the treatment of esophageal squamous cell carcinoma (ESCC). However, studies concerning immune phenotypes within the ESCC microenvironment and their relationship with prognostic outcomes are limited. We constructed and validated an individual immune-related risk signature for patients with ESCC. We collected 196 ESCC cases, including 119 samples from our previous public data (GSE53624) to use as a training set and an independent cohort with 77 quantitative real-time polymerase chain reaction (qRT-PCR) data, which we used for validation. Head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LUSC) cohorts were also collected for validation. A least absolute shrinkage and selection operator (LASSO) model and a stepwise Cox proportional hazards regression model were used to construct the immune-specific signature. The potential mechanism and inflammatory landscapes of the signature were explored using bioinformatics and immunofluorescence assay methods. This signature predicted different prognoses in clinical subgroups and the independent cohort, as well as in patients with HNSCC and LUSC. Further exploration revealed that the signature was associated with specific inflammatory activities (activation of macrophages and T-cell signaling transduction). Additionally, high-risk patients exhibited distinctive immune checkpoints panel and higher regulatory T cell and fibroblast infiltration. This signature served as an independent prognostic factor in ESCC. This was the first applicable immune-related risk signature for ESCC. Our results furnished new hints of immune profiling of ESCC, which may provide some clues to further optimize associated cancer immunotherapies.

scholarly journals A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guichuan Huang ◽  
Jing Zhang ◽  
Ling Gong ◽  
Yi Huang ◽  
Daishun Liu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

scholarly journals Development and Validation of a Ferroptosis-Related Gene Signature and Nomogram for Predicting the Prognosis of Esophageal Squamous Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiecheng Ye ◽  
Yining Wu ◽  
Heyuan Cai ◽  
Li Sun ◽  
Wanying Deng ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Group ◽  
Clinical Variable ◽  
Cox Regression Analysis

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor with high mortality and poor prognosis. Ferroptosis is a newly discovered form of cell death induced by iron-catalyzed excessive peroxidation of polyunsaturated fatty acids (PUFAs). However, the prognostic value of ferroptosis-related genes (FRGs) for ESCC remains unclear. Based on the ESCC dataset from the Gene Expression Omnibus (GEO) database, we identified 39 prognostic FRGs through univariate Cox regression analysis. After LASSO regression and multivariate Cox regression analyses, a multigene signature based on 10 prognostic FRGs was constructed and successfully divided ESCC patients into two risk groups. Patients in the low-risk group showed a significantly better prognosis than patients in the high-risk group. In addition, we combined the risk score with clinical predictors to construct a nomogram for ESCC. The predictive ability of the nomogram was further verified by ROC curves and calibration plots in both the training and validation sets. The predictive power of the nomogram was demonstrated to be better than that of either the risk score or clinical variable alone. Furthermore, functional analysis revealed that the 10-FRG signature was mainly associated with ferroptosis, differentiation and immune response. Connectivity map analysis identified potential compounds capable of targeting FRGs in ESCC. Finally, we demonstrated the prognostic value of SRC gene in ESCC using the clinical samples and found that SRC inhibition sensitized ESCC cells to ferroptosis inducers by in vitro experiments. In conclusion, we identified and verified a 10-FRG prognostic signature and a nomogram, which provide individualized prognosis prediction and provide insight into potential therapeutic targets for ESCC.

scholarly journals Autophagy-Related Three-Gene Prognostic Signature for Predicting Survival in Esophageal Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Heyang Cui ◽  
Yongjia Weng ◽  
Ning Ding ◽  
Chen Cheng ◽  
Longlong Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Discovery Cohort ◽  
Cox Regression Analysis

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in China, and its prognosis remains poor. Autophagy is an evolutionarily conserved catabolic process involved in the occurrence and development of ESCC. In this study, we described the expression profile of autophagy-related genes (ARGs) in ESCC and developed a prognostic prediction model for ESCC patients based on the expression pattern of ARGs. We used four ESCC cohorts, GSE53624 (119 samples) set as the discovery cohort, The Cancer Genome Atlas (TCGA) ESCC set (95 samples) as the validation cohort, 155 ESCC cohort, and Oncomine cohort were used to screen and verify differentially expressed ARGs. We identified 34 differentially expressed genes out of 222 ARGs. In the discovery cohort, we divided ESCC patients into three groups that showed significant differences in prognosis. Then, we analyzed the prognosis of 34 differentially expressed ARGs. Three genes [poly (ADP-ribose) polymerase 1 (PARP1), integrin alpha-6 (ITGA6), and Fas-associated death domain (FADD)] were ultimately obtained through random forest feature selection and were constructed as an ARG-related prognostic model. This model was further validated in TCGA ESCC set. Cox regression analysis confirmed that the three-gene signature was an independent prognostic factor for ESCC patients. This signature effectively stratified patients in both discovery and validation cohorts by overall survival (P = 5.162E-8 and P = 0.052, respectively). We also constructed a clinical nomogram with a concordance index of 0.713 to predict the survival possibility of ESCC patients by integrating clinical characteristics and the ARG signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. In conclusion, we constructed a new ARG-related prognostic model, which shows the potential to improve the ability of individualized prognosis prediction in ESCC.

scholarly journals The Value of Complete Blood Count For The Prognosis Analysis of Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Xiang Lv ◽  
Songtao Han ◽  
Bin Xu ◽  
Yuqin Deng ◽  
Feng Yangchun
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Blood Count ◽  
Cox Regression ◽  
Complete Blood Count ◽  
Analysis Model ◽  
Cox Regression Analysis

Abstract Objective To investigate the value of complete blood count in predicting the survival rate of patients with esophageal squamous cell carcinoma. Methods A total of 3587 patients with esophageal squamous cell carcinoma who were initially admitted to the Affiliated Cancer Hospital of Xinjiang Medical University from January 2010 to December 2017 were collected by retrospective study. The relevant clinical data were collected by the medical record system, and the patients were followed up by the hospital medical record follow-up system. The follow-up outcome was death. The survival time of all patients was obtained. The survival curve was established using the cut-off value of each index obtained by ROC curve. The Cox proportional hazards regression analysis model and nomogram were established to predict the survival prognosis of esophageal squamous cell carcinoma. The role of each index in the prognosis of patients with esophageal squamous cell carcinoma was studied Results The cut-off values of NLR, NMR, LMR, RDW and PDW in blood routine were 3.52, 10.22, 2.25, 13.85% and 12.05%, respectively. Survival curve analysis showed that patients aged < 60 years and NLR < 3.52 had survival. All indicators were divided into high and low groups according to ROC curve. Univariate Cox regression analysis model showed that RDW (≥ 13.85) and NLR (≥ 3.52) groups were risk factors for the prognosis of ESCC, with HR values of 1.099 (1.015–1.191; p = 0.020) and 1.340 (1.231–1.458; p < 0.001) compared with RDW (< 13.85) and NLR (< 3.52), respectively; multivariate Cox regression analysis model showed that NLR was significantly associated with the prognosis of ESCC, with HR of 1.342 (1.232–1.461; p < 0.001) for NLR (≥ 3.52) NLR (< 3.52). Conclusion NLR results in blood count can be used to predict the survival of patients with esophageal squamous cell carcinoma.

Prognostic value of programmed death-ligand 1 expression in patients with esophageal squamous cell carcinoma

2020 ◽  
Author(s):  
Qun Zhang ◽  
Feng Li ◽  
Peng Cai ◽  
Hongwei Li ◽  
Hongmei Yin ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cox Regression ◽  
Tumor Resection ◽  
Programmed Death Ligand 1 ◽  
Cox Regression Analysis ◽  
Programmed Death ◽  
L1 Protein

Abstract Background To investigate the expression of PD-L1(programmed death-ligand 1)in patients with esophageal squamous cell carcinoma (ESCC) and its clinical significance. Methods The tissue expression of PD-L1 protein in 139 cases of ESCC and 50 adjacent non-malignant epithelial tissues (> 5 cm from the tumor resection margins) were identified by immunohistochemical staining. Subsequently, the relationship between expression and the observed clinical characteristics was analyzed. Results The positive expression rate of PD-L1 protein was increased in tumor tissues compared to that of adjacent noncancerous mucosa tissues (40.3% vs. 22.0%, P < 0.05). The findings also indicated that PD-L1 protein expression had no significant correlation with age, gender, tumor location, differentiation and lymph node (N) status (P > 0.05). The 91 months follow-up Kaplan-Meier survival analysis showed that patients in positively expressed PD-L1 group experienced a lower survival rate compared to their negatively expressed PD-L1 counterparts (32.1% vs. 48.2%, P < 0.05). The COX regression analysis results suggested that PD-L1 represented an independent prognosis factor for ESCC. Conclusions The findings indicated that PD-L1 plays an important role in the progression of ESCC and might represent a potential therapeutic and prognostic target for ESCC patients.

scholarly journals Predictive Value of LINC01133 for Unfavorable Prognosis was Impacted by Alcohol in Esophageal Squamous Cell Carcinoma

2018 ◽  
Vol 48 (1) ◽  
pp. 251-262 ◽  
Author(s):  
Xian-Zi Yang ◽  
Qing-Jun He ◽  
Tian-Tian Cheng ◽  
Jun Chi ◽  
Zi-Ying Lei ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Protective Factor ◽  
Cox Regression ◽  
Tnm Stage ◽  
Rank Test ◽  
Cox Regression Analysis ◽  
Drinking Status

Background/Aims: Considerable evidence indicates that long noncoding RNAs (lncRNAs) exert importantly regulatory functions during human cancer initiation and progression and are promising biotargets in the flight against cancer. Methods: In this study, we evaluated the role of the lncRNA LINC01133 in esophageal squamous cell carcinoma (ESCC). LINC01133 expression in ESCC was examined by quantitative real-time PCR. The correlations between LINC01133 expression and clinicopathological variables and survival were examined by the χ2 test, Kaplan–Meier method, log-rank test, and univariate Cox regression analysis. Results: LINC01133 expression levels were frequently lower in ESCC tissues and cell lines than in paired normal tissues and an immortalized esophageal epithelial cell line, respectively. The expression of LINC01133 decreased in a TNM stage- and lifestyle-independent manner. LINC01133 was an independent protective factor and had an anti-tumor effect in the early stage of ESCC development. More importantly, we discovered that drinking status in our cohort impaired the predictive accuracy of LINC01133 for patients with ESCC. Furthermore, a new risk model combining LINC01133 expression, drinking status, and TNM stage provided better survival discrimination compared with three other predictors. Conclusions: Our data indicate that a loss of LINC01133 expression is a potential poor prognostic biomarker and therapeutic target for ESCC and provide additional prognostic information to improve the outcomes of ESCC patients.

scholarly journals A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

2020 ◽  
Author(s):  
Guichuan Huang ◽  
Jing Zhang ◽  
Ling Gong ◽  
Yi Huang ◽  
Daishun Liu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background: Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of major histological subtypes. Although, numerous biomarkers were found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is not sufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival of patients with LUSC.Methods: The mRNA expression files and clinical information of LUSC were obtained from The Cancer Genome Atlas (TCGA) dataset.Results: Based on Gene set enrichment analysis (GSEA), we found 5 glycolysis-related gene sets were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were conducted to choose prognostic-related gene signature. Based on Cox proportional regression model, a risk score of three-gene signature (including HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. We found that a risk score of three-gene signature was an independent of prognostic indicator in LUSC using multivariate Cox regression analysis. Additionally, based on the cBioPortal database, the rate of alterations in HKDC1, ALDH7A1, and MDH1 genes were 1.9%, 1.1%, and 5% in LUSC patients, respectively. Conclusion: In conclusion, a glycolysis-based three-gene signature could serve as a novel biomarker in predicting prognosis of patients with LUSC, which provided more gene targets to cure LUSC patients.

scholarly journals Identification of the Prognostic Value of Tumor Microenvironment-Related Genes in Esophageal Squamous Cell Carcinoma

2020 ◽  
Vol 7 ◽  
Author(s):  
Donglei Zhang ◽  
Changlin Qian ◽  
Huabing Wei ◽  
Xiaozhe Qian
Keyword(s):  
Squamous Cell Carcinoma ◽  
Tumor Microenvironment ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Prognostic Value ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cox Regression Analysis

Background: Esophageal squamous cell carcinoma (ESCC) is the most prevalent histological type of esophageal cancer, but there is a lack of definite prognostic markers for this cancer.Methods: We used the ESTIMATE algorithm to access the tumor microenvironment (TME) of ESCC cases deposited in the TCGA database, and identified TME-related prognostic genes using Cox regression analysis. A least absolute shrinkage and selector operation or LASSO algorithm was used to identify key prognostic genes. Risk scores were calculated, and a clinical predictive model was constructed to evaluate the prognostic value of TME-related genes.Results: We found that high immune and stromal scores were significantly associated with poor overall survival (p &lt; 0.05). We identified a total of 1,151 TME-related differently expression genes, among which 67 were prognosis-related genes. Through the LASSO method, 13 key prognostic genes were selected, namely, ADAMTS16, LOC51089, CH25H, CORO2B, DLGAP1, GYS2, HAL, MXRA8, NPTX1, OTX1, RET, SLC24A2, and SPI1, and a 13-gene risk score was constructed. A higher score was indicative of a poorer prognosis than a lower risk score (hazard ratio = 8.21, 95% confidence interval: 2.56–26.31; P &lt; 0.001). The risk score was significantly correlated with immune/stromal scores and various types of infiltrating immune cells, including CD8 cells, regulatory T cells, and resting macrophages.Conclusion: We characterized the tumor microenvironment in ESCC, and identified the key prognosis genes. The risk score based on the expression profiles of these genes is proposed as an indicator of TME status and is instrumental in predicting patient prognosis.

scholarly journals RNA sequencing reveals the expression profiles of circRNA and identifies a four-circRNA signature acts as a prognostic marker in esophageal squamous cell carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weiwei Wang ◽  
Di Zhu ◽  
Zhihua Zhao ◽  
Miaomiao Sun ◽  
Feng Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Squamous Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Characteristic Curve ◽  
Cox Regression Analysis ◽  
Normal Tissues

Abstract Background CircRNAs with tissue-specific expression and stable structure may be good tumor prognostic markers. However, the expression of circRNAs in esophageal squamous cell carcinoma (ESCC) remain unknown. We aim to identify prognostic circRNAs and construct a circRNA-related signature in ESCC. Methods RNA sequencing was used to test the circRNA expression profiles of 73 paired ESCC tumor and normal tissues after RNase R enrichment. Bioinformatics methods, such as principal component analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm, unsupervised clustering and hierarchical clustering were performed to analyze the circRNA expression characteristics. Univariate cox regression analysis, random survival forests-variable hunting (RSFVH), Kaplan–Meier analysis, multivariable Cox regression and ROC (receiver operating characteristic) curve analysis were used to screen the prognostic circRNA signature. Real-time quantitative PCR (qPCR) and fluorescence in situ hybridization(FISH) in 125 ESCC tissues were performed. Results Compared with normal tissues, there were 11651 differentially expressed circRNAs in cancer tissues. A total of 1202 circRNAs associated with ESCC prognosis (P < 0.05) were identified. Through bioinformatics analysis, we screened a circRNA signature including four circRNAs (hsa_circ_0000005, hsa_circ_0007541, hsa_circ_0008199, hsa_circ_0077536) which can classify the ESCC patients into two groups with significantly different survival (log rank P < 0.001), and found its predictive performance was better than that of the TNM stage(0.84 vs. 0.66; 0.65 vs. 0.62). Through qPCR and FISH experiment, we validated the existence of the screened circRNAs and the predictive power of the circRNA signature. Conclusion The prognostic four-circRNA signature could be a new prognostic biomarker for ESCC, which has high clinical application value.

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