scholarly journals A Novel Signature Derived from Metabolism-Related Genes GPT and SMS to Predict Prognosis of Laryngeal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Yujie Shen ◽  
Qiang Huang ◽  
Yifan Zhang ◽  
Chi-Yao Hsueh ◽  
Liang Zhou
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Cox Regression ◽  
External Validation ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Clinical Samples ◽  
Prognostic Signature

Abstract Background A growing body of evidence has suggested the involvement of metabolism in the occurrence and development of tumors. But the link between metabolism and laryngeal squamous cell carcinoma (LSCC) has rarely been reported. This study seeks to understand and explain the role of metabolic biomarkers in predicting the prognosis of LSCC. Methods We identified the differentially expressed metabolism-related genes (MRGs) through RNA-seq data of TCGA (The Cancer Genome Atlas) and GSEA (Gene set enrichment analysis). After the screening of protein-protein interaction (PPI), hub MRGs were analyzed by least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a prognostic signature. Kaplan–Meier survival analysis and the receiver operating characteristic (ROC) was applied to verify the effectiveness of the prognostic signature in four cohorts (TCGA cohort, GSE27020 cohort, TCGA-sub1 cohort and TCGA-sub2 cohort). The expressions of the hub MRGs in cell lines and clinical samples were verified by quantitative reverse transcriptase PCR (qRT-PCR). The immunofluorescence staining of the tissue microarray (TMA) was carried out to further verify the reliability and validity of the prognostic signature. Cox regression analysis was then used to screen for independent prognostic factors of LSCC and a nomogram was constructed based on the results. Results Among the 180 differentially expressed MRGs, 14 prognostic MRGs were identified. A prognostic signature based on two MRGs (GPT and SMS) was then constructed and verified via internal and external validation cohorts. Compared to the adjacent normal tissues, SMS expression was higher while GPT expression was lower in LSCC tissues, indicating poorer outcomes. The risk score proved the prognostic signature as an independent risk factor for LSCC in both internal and external validation cohorts. A nomogram based on these results was developed for clinical application. Conclusions Differentially expressed MRGs were found and proven to be related to the prognosis of LSCC. We constructed a novel prognostic signature based on MRGs in LSCC for the first time and verified via different cohorts from both databases and clinical samples. A nomogram based on this prognostic signature was developed.

scholarly journals Prognostic Value of Immune Associated Long Non-Coding RNAs In Laryngeal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Weixing Liu ◽  
Pei Li ◽  
Yue Liu ◽  
Zhiyuan Wang ◽  
Jiamin Liu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Molecular Mechanisms ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Cox Regression ◽  
Critical Role ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Sequencing Data

Abstract Background: Increasing studies have demonstrated that immune associated lncRNAs (IALs) take an important part in the occurrence and development of multiple cancers. However, the prognosis value of IALs in laryngeal squamous cell carcinoma (LSCC) remains unexplored. This study aimed to evaluate the importance of IALs in LSCC. Methods: RNA sequencing data profiles of LSCC and clinical information of patients were obtained from TCGA dataset. Correlation analysis was performed to screen IALs. Then, a IALs based prognostic signature was constructed through univariate and multivariate Cox regression. The uncover molecular mechanisms of these selected IALs were explored by the bioinformatics analyses.Results: a total of seven differentially expressed survival-associated IALs were found in LSCC patients. a six IALs (LINC02154, SNHG12, CHKB-DT, AL158166.1, AC027307.2 and AL121899.1) based prognostic signature was established, which was a reliable tool to predict the prognosis of LSCC. The area under the curve (AUC) were 0.817 (one-year), 0.847 (three-year) and 0.895 (five-year). Further analysis, there were different infiltration of immune cells between low-risk and high-risk group patients. Additionally, a lncRNA-miRNA-mRNA regulatory network basted on six IALs, 75 miRNAs, and 156 differentially expressed mRNAs was constructed.Conclusions: IALs may play critical role in the occurrence and progression of LSCC, and the IALs based prognostic signature can predict the overall survival rate of LSCC.

Development of an immunogenomics-based prognostic index model of laryngeal squamous cell carcinoma

2020 ◽  
Author(s):  
Yujie Shen ◽  
Han Zhou ◽  
Shikun Dong ◽  
Meiping Lu ◽  
Weida Dong ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Cox Regression ◽  
Prognostic Index ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Cox Regression Analysis

Abstract Background: The immune system greatly affects the prognosis of various malignancies. Studies on differentially expressed immune-related genes (IRGs) in the immune microenvironment of laryngeal squamous cell carcinoma (LSCC) have rarely been reported.Methods: In this paper, the prognostic potentials of IRGs in LSCC were explored. The RNAseq dataset containing differentially expressed IRGs and corresponding clinical information of LSCC patients was obtained from The Cancer Genome Atlas (TCGA). A total of 371 up-regulated and 61 down-regulated IRGs were identified. Subsequent functional enrichment analysis revealed that the pathway of IRGs was mainly enriched in the cytokine-cytokine receptor interaction. Then, 30 IRGs with prognostic potentials in LSCC were screened out, and the regulatory network induced by relevant transcription factors (TFs) were constructed.Results: Finally, multivariate Cox regression analysis was conducted to assess the prognostic potential of 15 IRGs after adjustment of clinical factors and LSCC patients were classified into 2 subgroups based on different outcomes. The gene expression of the model was verified by other independent databases. Nomogram including the 15 IRGs signature was established and shown some clinical net beneft. Intriguingly, B cells were significantly enriched in the low-risk group. Conclusion:These findings may contribute to the development of potential therapeutic targets and biomarkers for the new-immunotherapy of LSCC.

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 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 Identification of Potential Prognostic Biomarkers for Tongue Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Mi Zhang ◽  
Sihui Zhang ◽  
Ling Wu ◽  
Dexiong Li ◽  
Jiang Chen
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Ppi Network ◽  
Potential Biomarkers

Abstract Background: Tongue squamous cell carcinoma (TSCC) is one of the most common types of oral cancer and has a poor prognosis owing to a limited understanding of its pathogenetic mechanisms. The purpose of this study was to explore and identify potential biomarkers in TSCC by integrated bioinformatics analysis.Methods: The RNA sequencing data, methylation data, and clinical characteristics of TSCC patients were downloaded from The Cancer Genome Atlas (TCGA), and then differentially expressed RNAs (DERNAs), including differentially expressed long noncoding RNAs (DElncRNAs) and differentially expressed messenger RNAs (DEmRNAs), were identified in TSCC by bioinformatics analysis. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Hallmark pathway analyses were used to analyze the DERNAs. Univariate and multivariate Cox regression analyses were used to develop four-lncRNA and two-mRNA signatures and predict survival in TSCC patients. We established a risk model to predict the overall survival (OS) of TSCC patients based on the DERNAs with Kaplan–Meier analysis and the log-rank p test. Furthermore, weighted gene coexpression network analysis (WGCNA) was performed in Cytoscape, and a protein-protein interaction (PPI) network was established in the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database.Results: A total of 2,006 DEmRNAs and 1,001 DElncRNAs were found to be dysregulated in TSCC. A total of 417 DERNAs were used to construct the coexpression network, and the PPI network included 103 DEmRNAs. Univariate regression analysis of the DERNAs revealed 51 DElncRNAs and 90 DEmRNAs that were associated with OS in TSCC patients. Multivariate Cox regression analysis demonstrated that four of those lncRNAs (MGC32805, RP1-35C21.2, RP11-108K3.1, and RP11-109M17.2) and two mRNAs (CA9, GTSF1L) had significant prognostic value, and their cumulative risk score indicated that these four-lncRNA and two-mRNA signatures independently predicted OS in TSCC patients. Additionally, there was a positive correlation between the expression and methylation level of RP11-108K3.1, the OS significantly negatively correlated with hypermethylation and low expression of GTSF1L along with hypomethylation and high expression of CA9.Conclusions: The current findings provide novel insights into the molecular mechanisms of TSCC and identify four lncRNAs and two mRNAs that are potential biomarkers that may be independent prognostic signatures for TSCC diagnosis and treatment.

scholarly journals lncRNA TMEM51-AS1 and RUSC1-AS1 function as ceRNAs for induction of laryngeal squamous cell carcinoma and prediction of prognosis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7456 ◽  
Author(s):  
Lian Hui ◽  
Jing Wang ◽  
Jialiang Zhang ◽  
Jin Long
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Cox Regression ◽  
Negative Relationship ◽  
Prognostic Biomarkers ◽  
Cox Regression Analysis ◽  
Cerna Network

Background Long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) to interact with miRNAs to regulate target genes and promote cancer initiation and progression. The expression of lncRNAs and miRNAs can be epigenetically regulated. The goal of this study was to construct an lncRNA-miRNA-mRNA ceRNA network in laryngeal squamous cell carcinoma (LSCC) and reveal their methylation patterns, which was not investigated previously. Methods Microarray datasets available from the Gene Expression Omnibus database were used to identify differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) between LSCC and controls, which were then overlapped with differentially methylated regions (DMRs). The ceRNA network was established by screening the interaction relationships between miRNAs and lncRNAs/mRNAs by corresponding databases. TCGA database was used to identify prognostic biomarkers. Results Five DELs (downregulated: TMEM51-AS1, SND1-IT1; upregulated: HCP5, RUSC1-AS1, LINC00324) and no DEMs were overlapped with the DMRs, but only a negative relationship occurred in the expression and methylation level of TMEM51-AS1. Five DELs could interact with 11 DEMs to regulate 242 DEGs, which was used to construct the ceRNA network, including TMEM51-AS1-miR-106b-SNX21/ TRAPPC10, LINC00324/RUSC1-AS1-miR-16-SPRY4/MICAL2/ SLC39A14, RUSC1-AS1-miR-10-SCG5 and RUSC1-AS1-miR-7-ZFP1 ceRNAs axes. Univariate Cox regression analysis showed RUSC1-AS1 and SNX21 were associated with overall survival (OS); LINC00324, miR-7 and ZFP1 correlated with recurrence-free survival (RFS); miR-16, miR-10, SCG5, SPRY4, MICAL2 and SLC39A14 were both OS and RFS-related. Furthermore, TRAPPC10 and SLC39A14 were identified as independent OS prognostic factors by multivariate Cox regression analysis. Conclusion DNA methylation-mediated TMEM51-AS1 and non-methylation-mediated RUSC1-AS1 may function as ceRNAs for induction of LSCC. They and their ceRNA axis genes (particularly TMEM51-AS1-miR-106b-TRAPPC10; RUSC1-AS1-miR-16-SLC39A14) may be potentially important prognostic biomarkers for LSCC.

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.

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