scholarly journals A novel pyroptosis-related gene signature to predict outcomes in laryngeal squamous cell carcinoma

Aging ◽  
2021 ◽  
Author(s):  
Chongchang Zhou ◽  
Guowen Zhan ◽  
Yangli Jin ◽  
Jianneng Chen ◽  
Zhisen Shen ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Related Gene

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 A Novel Ferroptosis-Related Biomarker Signature to Predict Overall Survival of Esophageal Squamous Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiahang Song ◽  
Yanhu Liu ◽  
Xiang Guan ◽  
Xun Zhang ◽  
Wenda Yu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Risk Model ◽  
Expression Profiles ◽  
Gene Signature ◽  
Immune Checkpoints ◽  
Related Gene

Esophageal squamous cell carcinoma (ESCC) accounts for the main esophageal cancer (ESCA) type, which is also associated with the greatest malignant grade and low survival rates worldwide. Ferroptosis is recently discovered as a kind of programmed cell death, which is indicated in various reports to be involved in the regulation of tumor biological behaviors. This work focused on the comprehensive evaluation of the association between ferroptosis-related gene (FRG) expression profiles and prognosis in ESCC patients based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). ALOX12, ALOX12B, ANGPTL7, DRD4, MAPK9, SLC38A1, and ZNF419 were selected to develop a novel ferroptosis-related gene signature for GEO and TCGA cohorts. The prognostic risk model exactly classified patients who had diverse survival outcomes. In addition, this study identified the ferroptosis-related signature as a factor to independently predict the risk of ESCC. Thereafter, we also constructed the prognosis nomogram by incorporating clinical factors and risk score, and the calibration plots illustrated good prognostic performance. Moreover, the association of the risk score with immune checkpoints was observed. Collectively, the proposed ferroptosis-related gene signature in our study is effective and has a potential clinical application to predict the prognosis of ESCC.

scholarly journals A novel immune-related gene signature predicts survival in esophageal squamous cell carcinoma

2021 ◽  
Vol 10 (5) ◽  
pp. 2354-2367
Author(s):  
Tao Xu ◽  
Tianyang Dai ◽  
Peiyuan Zeng ◽  
Yunfen Guo ◽  
Kaiming He
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Gene Signature ◽  
Related Gene ◽  
Immune Related Gene

scholarly journals Identification of a Hypoxia-Related Gene Signature and Establishment of a Nomogram for Predicting Prognosis in Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Wanyi Xiao ◽  
Peng Tang ◽  
Zhilin Sui ◽  
Xianxian Wu ◽  
Yueyang Yang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Gene Signature ◽  
Related Gene

scholarly journals A Novel Glycolysis-related Gene Signature for Survival Prediction in Patients with Head and Neck Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Haimei Qin ◽  
Junli Wang ◽  
Biyun Liao ◽  
Zhonglin Liu ◽  
Rong Wang
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Poor Prognosis ◽  
Risk Group ◽  
Gene Signature ◽  
Neck Squamous Cell Carcinoma ◽  
Related Gene ◽  
Cancer Subtypes

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is most diagnosed at an advanced stage with poor prognosis. Single gene biomarkers cannot have enough predictive ability in HNSCC. Glycolysis participating in cancers was verified. Thus, this study aimed to identify glycolysis-related gene signature predict the outcome of HNSCC. Methods: The mRNA expression data of HNSCC downloaded The Cancer Genome Atlas (TCGA) project was analyzed by Gene Set Enrichment Analysis (GSEA). We use the Cox proportional regression model to construct a prognostic model. Kaplan–Meier and receiver operating characteristic (ROC) curves were employed to estimate the signature. We also analyzed the relationship of the signature and cancer subtypes. Results: We identified nine glycolysis-related genes including G6PD, EGFR, ALDH2, GPR87, STC2, PDK3, ELF3, STC1 and GNPDA1 as prognosis-related genes signature in HNSCC. HNSCC patients were divided into high and low risk group according to the signature. High risk group showed more poor prognosis and the risk score can precisely predict the prognosis of HNSCC. Additionally, the signature also can be used in cancer subtypes. Conclusion: This study established the 9-mRNA glycolysis signature which may serve as a prospective biomarker for prognosis and novel treatment target in HNSCC.

scholarly journals Glycolysis related gene expression signature in predicting prognosis of laryngeal squamous cell carcinoma

Bioengineered ◽  
2021 ◽  
Author(s):  
Hui-Ching Lau ◽  
Yujie Shen ◽  
Qiang Huang ◽  
Hui-Ying Huang ◽  
Liang Zhou
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Gene Expression Signature ◽  
Related Gene ◽  
Expression Signature

scholarly journals Development and Validation of an E2F-Related Gene Signature to Predict Prognosis of Patients With Lung Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Cailian Wang ◽  
Xuyu Gu ◽  
Xiuxiu Zhang ◽  
Min Zhou ◽  
Yan Chen
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Decision Tree ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Related Gene ◽  
Risk Patients

BackgroundLung squamous cell carcinoma (LUSC) generally correlates with poor clinical prognoses due to the lack of available prognostic biomarkers. This study is designed to identify a potential biomarker significant for the prognosis and treatment of LUSC, so as to provide a scientific basis for clinical treatment decisions.MethodsGenomic changes in LUSC samples before and after radiation were firstly discussed to identify E2 factor (E2F) pathway of prognostic significance. A series of bioinformatics analyses and statistical methods were combined to construct a robust E2F-related prognostic gene signature. Furthermore, a decision tree and a nomogram were established according to the gene signature and multiple clinicopathological characteristics to improve risk stratification and quantify risk assessment for individual patients.ResultsIn our investigated cohorts, the E2F-related gene signature we identified was capable of predicting clinical outcomes and therapeutic responses in LUSC patients, besides, discriminative to identify high-risk patients. Survival analysis suggested that the gene signature was independently prognostic for adverse overall survival of LUSC patients. The decision tree identified the strong discriminative performance of the gene signature in risk stractification for overall survival while the nomogram demonstrated a high accuracy.ConclusionThe E2F-related gene signature may help distinguish high-risk patients so as to formulate personalized treatment strategy in LUSC 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 Establishment of a Prognostic Stemness Signature For Cervical Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Xiwen Sun ◽  
Fang Wang ◽  
Jiayu Shen ◽  
Jianwei Zhou
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Univariate Analysis ◽  
Therapeutic Targets ◽  
Cervical Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Related Gene ◽  
Prognosis Prediction ◽  
Tcga Dataset

Abstract Background: The aim of this study was to construct a robust stemness-related gene signature for predicting prognosis of cervical squamous cell carcinoma (CSCC).Methods: Expression data for the PCBC database-derived pluripotent stem cell (PSC) samples were collected using the one-class logistic regression (OCLR) method to calculate stemness indexes (mRNAsi) of samples derived from the TCGA dataset. Functions of possible mRNAsi-related stemness genes extracted through WGCNA were then examined by enrichment analysis. Most representative stemness genes for prognosis prediction were screened to construct a stemness-related gene signature by shrinkage estimate and univariate analysis. Next, the TCGA dataset and the GSE44001 external dataset were incorporated into that model and classified to evaluate the model efficiency and stability in patient prognosis prediction and classification according to the Riskscore. The associations between the Riskscore and clinical characteristics as well as relevant signaling pathways were also explored. Moreover, the prognosis predicting efficiency of the stemness-related gene signature was compared with those of CSCC prognostic signatures reported in other studies.Results: According to the findings, mRNAsi showed significant correlation with key oncogene mutation degrees (including DMD, KMT2C, EP300 and MUC4), infiltrating stroma cells and the CIMP classification for CSCC cases. The 8-stemness gene signature in this study achieved high stability and accuracy in prognosis prediction for CSCC cases. In the meanwhile, the model provided diverse therapeutic targets to precisely treat CSCC in clinical practice based on various subtype-specific stemness genes.Conclusion: Our present study suggested that the 8 stemness gene signature can help to screen out novel stem-related diagnostic indicators, therapeutic targets and prognostic predictors in CSCC.

scholarly journals Development of a Prognostic Pyroptosis-Related Gene Signature for Head and Neck Squamous Cell Carcinoma Patient

2021 ◽  
Author(s):  
Weiwen Zhu ◽  
Jiayi Zhang ◽  
Mengyao Wang ◽  
Rundong Zhai ◽  
Yanbin Xu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Death ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Patient Outcomes ◽  
Squamous Cell ◽  
Gene Signature ◽  
Related Gene ◽  
Patient Prognosis ◽  
Hnscc Patient

Abstract Objective: Head and neck squamous cell carcinoma (HNSCC) is a major threat to public health. Pyroptosis is a form of inflammatory programmed cell death that is still incompletely understood. The role of pyroptotic cell death in HNSCC remains to be fully defined. As such, the present study was developed to explore the potential prognostic utility of a pyroptosis-related gene (PRG) signature in HNSCC.Methods: PRG expression patterns and the associated mutational landscape in HNSCC were analyzed, after which a 6-gene prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) and Cox regression analyses using the TCGA dataset, followed by validation with two GEO datasets (GSE41643 and GSE65858). Potential predictors of patient outcomes associated with this 6-gene model were identified through topological degree analyses of a protein-protein interaction network. Lastly, the prognostic value of NLRP3 as a predictor of HNSCC patient prognosis was established through immunohistochemical (IHC) analyses of samples from 176 HNSCC patients.Results: Differentially expressed PRGs were able to readily differentiate between HNSCC tumors and normal tissues. Risk scores derived from the 6-gene PRG model were independent predictors of HNSCC patient prognosis, and genes that were differentially expressed between low- and high-risk groups were associated with tumor immunity. IHC analyses further supported the value of NLRP3 as a predictor of HNSCC patient outcomes. Conclusions: Overall, these results highlight a novel prognostic gene signature that offers value in the context of HNSCC patient evaluation, although additional research will be essential to elucidate the mechanisms linking these PRGs to HNSCC outcomes.

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