scholarly journals Identification of Cell cycle Gene Signatures Predicting Survival in Patients with Lung Squamous Cell Carcinoma

2019 ◽  
Author(s):  
Lei Zhang ◽  
Zhe Zhang ◽  
Zhenglun Yu
Keyword(s):  
Cell Cycle ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Test Series ◽  
Gene Signatures ◽  
Gene Set

Abstract Background:Lung cancer (LC) is one of the most important and common malignant tumors, and its incidence and mortality are increasing annually. Lung squamous cell carcinoma (LUSC) is the common pathological type of lung cancer. A small part of biomarkers have been confirmed to be related to the prognosis and survival by data excavation. However, the moderate forecast effect of a single gene biomarker is not accurate. Thus, we aimed to identify new gene signatures to better predict Lung squamous cell carcinoma ( LU SC). Methods : Using the mRNA-mining approach, we performed mRNA expression profiling in large lung squamous cell carcinoma cohorts (n= from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis(GSVA) were accomplished, and connections between genes and cell cycle were found in the Cox proportional regression model. Results : We have confirmed a set of four genes (CDKN1A, CHEK2, E2F4 and RAD21) that were importantly associated with overall survival (OS) in the test series. Based on the research of the four-gene signature, the patients in the test series could be divided into high-risk and low-risk teams. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the four-gene signature is independent of the clinical factors. Conclusion : Our study demonstrated the connections between the four-gene signature and cell cycle. Novel insights into the research mechanisms of cell cycle was also revealed regarding the biomarkers of a poor prognosis for lung squamous cell carcinoma patients.

scholarly journals Identification of a Cell Cycle Gene Signature Predicting Survival in Patients with Lung Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Lei Zhang ◽  
Shize Yang ◽  
Zhenglun Yu
Keyword(s):  
Cell Cycle ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Gene Set ◽  
Incidence And Mortality

Abstract Purpose: Lung cancer (LC) is one of the most important and common malignant tumours, and its incidence and mortality are increasing annually. Lung squamous cell carcinoma (LUSC) is the most common pathological type of LC. A small number of biomarkers have been certified to be consistent with its survival and prognosis by data excavation. However, the moderate forecast effect of a single gene biomarker is not accurate. Thus, we planned to find new gene signatures to preferably predict LUSC. Methods: Using the mRNA mining method, we enforced mRNA expression analyzing in big LUSC cohorts (n=504) from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were enforced, and relations between genes and the cell cycle were got with the Cox proportional hazards regression model. Results: We confirmed a set of four genes (CDKN1A, CHEK2, E2F4 and RAD21) that was importantly related to overall survival (OS) in the test succession. Based on the four-gene signature, the patients were separated into high-risk and low-risk teams. Moreover ,multivariate Cox regression analysis showed that the prognostic value of the four-gene signature and clinical factors were mutual independent.Conclusion: Our research demonstrated connections between the four-gene signature and LUSC. Novel insights into mechanisms of the cell cycle were also revealed after determining that the biomarkers were related to a poor prognosis in LUSC patients.

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 Identification and Validation of HOTAIRM1 as a Novel Biomarker for Oral Squamous Cell Carcinoma

2022 ◽  
Vol 9 ◽  
Author(s):  
Yixiu Yu ◽  
Jiamei Niu ◽  
Xingwei Zhang ◽  
Xue Wang ◽  
Hongquan Song ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Gene Set Enrichment Analysis ◽  
Bioinformatics Analyses ◽  
Gene Set

ORAL squamous cell carcinoma (OSCC) is a malignant tumor with the highest incidence among tumors involving the oral cavity maxillofacial region, and is notorious for its high recurrence and metastasis potential. Long non-coding RNAs (lncRNAs), which regulate the genesis and evolution of cancers, are potential prognostic biomarkers. This study identified HOTAIRM1 as a novel significantly upregulated lncRNA in OSCC, which is strongly associated with unfavorable prognosis of OSCC. Systematic bioinformatics analyses demonstrated that HOTAIRM1 was closely related to tumor stage, overall survival, genome instability, the tumor cell stemness, the tumor microenvironment, and immunocyte infiltration. Using biological function prediction methods, including Weighted gene co-expression network analysis (WGCNA), Gene set enrichment analysis (GSEA), and Gene set variation analysis (GSVA), HOTAIRM1 plays a pivotal role in OSCC cell proliferation, and is mainly involved in the regulation of the cell cycle. In vitro, cell loss-functional experiments confirmed that HOTAIRM1 knockdown significantly inhibited the proliferation of OSCC cells, and arrested the cell cycle in G1 phase. At the molecular level, PCNA and CyclinD1 were obviously reduced after HOTAIRM1 knockdown. The expression of p53 and p21 was upregulated while CDK4 and CDK6 expression was decreased by HOTAIRM1 knockdown. In vivo, knocking down HOTAIRM1 significantly inhibited tumor growth, including the tumor size, weight, volume, angiogenesis, and hardness, monitored by ultrasonic imaging and magnetic resonance imaging In summary, our study reports that HOTAIRM1 is closely associated with tumorigenesis of OSCC and promotes cell proliferation by regulating cell cycle. HOTAIRM1 could be a potential prognostic biomarker and a therapeutic target for OSCC.

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 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 Purpose: 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. Material and 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.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 PPARG Could Work as a Valid Therapeutic Strategy for the Treatment of Lung Squamous Cell Carcinoma

PPAR Research ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shunbin Shi ◽  
Guiping Yu ◽  
Bin Huang ◽  
Yedong Mi ◽  
Yan Kang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Nonsmall Cell Lung Cancer ◽  
Ppar Gamma ◽  
Gene Set Enrichment Analysis ◽  
Marker Genes ◽  
Molecular Pathways ◽  
Regulatory Pathways

Previous studies showed that PPAR-gamma (PPARG) ligands might serve as potential therapeutic agents for nonsmall cell lung cancer (NSCLC). However, a few studies reported the specific relationship between PPARG and lung squamous cell carcinoma (LSCC). Here, we made an effort to explore the relationship between PPARG and LSCC. First, we used mega-analysis and partial mega-analysis to analyze the effects of PPARG on LSCC by using 12 independent LSCC expression datasets (285 healthy controls and 375 LSCC cases). Then, literature-based molecular pathways between PPARG and LSCC were established. After that, a gene set enrichment analysis (GSEA) was conducted to study the functionalities of PPARG and PPARG-driven triggers within the molecular pathways. Finally, another mega-analysis was constructed to test the expression changes of PPARG and its driven targets. The partial mega-analysis showed a significant downregulated expression of PPARG in LSCC (LFC=−1.08, p value=0.00073). Twelve diagnostic markers and four prognostic markers were identified within multiple PPARG-LSCC regulatory pathways. Our results suggested that the activation of PPARG expression may inhibit the development and progression of LSCC through the regulation of LSCC upstream regulators and downstream marker genes, which were involved in tumor cell proliferation and protein polyubiquitination/ubiquitination.

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 PTBP3 regulates proliferation of lung squamous cell carcinoma cells via CDC25A‐mediated cell cycle progression

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yingji Chen ◽  
Ying Ji ◽  
Suo Liu ◽  
Yicai Liu ◽  
Wei Feng ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Squamous Cell Carcinoma ◽  
Western Blot ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cell Cycle Progression ◽  
Lung Squamous Cell Carcinoma ◽  
Molecular Therapy ◽  
Cycle Progression

Abstract Background The roles of Polypyrimidine tract-binding protein 3 (PTBP3) in regulating lung squamous cell carcinoma (LUSC) cells progression is unclear. The aim of this study was to investigate the role of PTBP3 in LUSC. Methods Expression and survival analysis of PTBP3 was firstly investigated using TCGA datasets. Quantitative reverse transcription PCR and Western blot were performed to detect PTBP3 expression in clinical samples. Moreover, cell counting kit 8 (CCK-8) assays, colony formation assays and in vivo tumor formation assays were used to examine the effects of PTBP3 on LUSC cell proliferation. RNA-sequence and analysis explores pathways regulated by PTBP3.Flow cytology was used analyzed cell cycle. Cell cycle-related markers were analyzed by Western blot. Results PTBP3 was found to be overexpressed in LUSC tissues compared with normal tissues. High PTBP3 expression was significantly correlated with poor prognosis. In vitro and vivo experiments demonstrated that PTBP3 knockdown caused a significant decrease in the proliferation rate of cells. Bioinformatics analysis showed that PTBP3 involved in cell cycle pathway regulation in LUSC. Furthermore, PTBP3 knockdown arrested cell cycle progression at S phase via decreasing CDK2/Cyclin A2 complex. In addition, downregulation of PTBP3 significantly decreased the expression of CDC25A. Conclusions Our results suggest that PTBP3 regulated LUSC cell proliferation via cell cycle and might be a potential target for molecular therapy of LUSC.

scholarly journals Immune Infiltration Landscape in Lung Squamous Cell Carcinoma Implications

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Jungang Zhao ◽  
Wenming Bao ◽  
Weiyang Cai
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Clustering Analysis ◽  
Immune Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Immune Microenvironment

Intrinsic cancer cells and the tumor-infiltrating immune cells (TIICs) recruited to the immune microenvironment define the malignant phenotype of lung squamous cell carcinoma (LUSC). Understanding more about the immune microenvironment of LUSC enables the selection of high-risk patients who would derive benefit from immunotherapy. Based on large public LUSC cohorts obtained from TCGA and GEO datasets, 22 types of infiltrating immune cell subgroups were evaluated by CIBERSORT. Meta-analysis, principal component analysis (PCA), single-sample gene set enrichment analysis (ssGSEA), and hierarchical clustering analysis were used to evaluate specific immune responses of LUSC. The distribution of TIICs of LUSC was entirely different from normal. TIIC subpopulations were also found to be closely associated with clinical features and molecular subtypes. Unsupervised clustering analysis revealed that three distinct TIIC subgroups existed with different survival patterns. TIICs are extensively implicated in the pathogenesis and development of LUSC. Characterizing the composition of TIICs influences the metabolism, pathological stage, and survival of tumor patients. It is hoped that this immune landscape could provide a more accurate understanding of the development and immunotherapy of LUSC.

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