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 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 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 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.

scholarly journals Human sperm-associated antigen 4 as a potential prognostic biomarker of lung squamous cell carcinoma

2021 ◽  
Vol 49 (7) ◽  
pp. 030006052110328
Author(s):  
Yongheng Wang ◽  
Yao Tang ◽  
Jianhui Li ◽  
Danfang Wang ◽  
Wenhan Li
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Malignant Tumors ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Tissue Samples

Objective Lung cancer (LC) is one of the most prevalent malignant tumors worldwide. As a subtype of LC, lung squamous cell carcinoma (LUSC) has a 5-year survival rate of less than 15%. In this study, we aimed to evaluate the prognostic value of a glycolysis-related gene signature in LUSC patients. Methods We obtained RNA-Seq data from The Cancer Genome Atlas (TCGA) database. Prognosis-related genes were screened out by Gene Set Enrichment Analysis (GSEA) and Cox proportional regression models. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the mRNA expression levels in relevant tissues. Results We found that sperm-associated antigen 4 (SPAG4) overexpression was an independent risk factor for overall survival (OS) in LUSC. Patients with high-risk scores had higher mortality rates than those with low-risk scores. Moreover, by using RT-qPCR, we validated that SPAG4 mRNA was overexpressed in LUSC tissue samples compared with their paired para-cancerous histological normal tissues. Conclusions Analysis of aberrantly overexpressed SPAG4 may provide a further useful approach to complement existing methods and predict prognosis 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 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 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 SLC17A9 Is a Novel Prognostic Biomarker Correlated With Immune Infiltrates in Human Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Yan Gao ◽  
Yi-Jia Chen ◽  
Fuyan Li ◽  
Ruimin Wu ◽  
Daobing Zeng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Lung Adenocarcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Human Lung ◽  
Prognostic Biomarker ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
Human Lung Adenocarcinoma

Abstract Background Overexpression of vesicular nucleotide transporter (SLC17A9) has been found in different types of cancers. Nonetheless, little is known about its influence on lung cancers including human lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Methods Integrative analysis of SLC17A9 and other solute carrier family 17 genes (SLC17A1-8) was performed in patients with LUAD and LUSC based on The Cancer Genome Atlas database. Real-time PCR, western blots, MTS assay, EdU assay, ATP production assays and cell cycle analysis were applied to determine the effect and mechanism of SLC17A9 knockdown in LUAD cells. Results Compared with normal tissue, two SLC17 genes (SLC17A5 and SLC17A9) exhibited a distinctly different expression pattern in LUAD and LUSC. The expression of SLC17A3/7/8/9 expression was significantly correlated with worse overall survival (p < 0.05) in LUAD. Conversely, SLC17A1/2/4/6/9 expression was correlated with poorer OS (p < 0.05) in LUSC. ROC analysis suggested that the area under the curve of most SLC17 family genes was higher than 0.5. Meanwhile, multiple types of genetic alterations in SLC17 family genes were observed in tumor samples. Gene set enrichment analysis GSEA and protein-protein interaction results revealed that oncogenic signaling pathways and biological regulation, metabolic process, hallmark of myc targets, DNA repair, coagulation and complement were linked to SLC17A9 upregulation. Moreover, SLC17A9 knockdown significantly inhibited cell proliferation and ATP levels by affecting Myc, MFN2, STAT3, Cytochrome C and P2X1 expression in A549 cells. Specifically, SLC17A9 expression correlated negatively with overall survival and positively with most LUSC immune cells. SLC17A9 expression has correlations with infiltrating levels of B cells, CD4 + T cells, M1 macrophages, natural killer cells, Th1, Th2, Tfh, Th17 and Treg cells, as well as PD-1, CTLA4, and LAG3 of T cell exhaustion in LUAD. Conclusions Together, SLC17A9 could potentially serve as a prognostic biomarker correlated with immune infiltrates in LUAD and LUSC.

scholarly journals Multiomics Differences in Lung Squamous Cell Carcinoma Patients with High Radiosensitivity Index Compared with Those with Low Radiosensitivity Index

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yajing Du ◽  
Sujuan Yuan ◽  
Xibing Zhuang ◽  
Qi Zhang ◽  
Tiankui Qiao
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Pair Interaction ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Cerna Network ◽  
Gene Sets

Objectives. Radiosensitivity Index (RSI) can predict intrinsic radiotherapy sensitivity. We analyzed multiomics characteristics in lung squamous cell carcinoma between high and low RSI groups, which may help understand the underlying molecular mechanism of radiosensitivity and guide optional treatment for patients in the future. Methods. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) data were used to download clinical data, mRNA, microRNA, and lncRNA expression. Differential analyses, including mRNA, miRNA, lncRNA, and G.O. and KEGG, and GSVA analyses, were performed with R. Gene set enrichment analysis was done by GSEA. miRNA-differentially expressed gene network and ceRNA network were analyzed and graphed by the Cytoscape software. Results. In TCGA data, 542 patients were obtained, including 171 in the low RSI group (LRSI) and 371 in the high RSI group (HRSI). In RNAseq, 558 significantly differentially expressed genes (DEGs) were obtained. KRT6A was the most significantly upregulated gene and IDO1 was the most significantly downregulated gene. In miRNAseq, miR-1269a was the most significantly upregulated. In lncRNAseq, LINC01871 was the most upregulated. A 66-pair interaction between differentially expressed genes and miRNAs and an 11-pair interaction between differential lncRNAs and miRNAs consisted of a ceRNA network, of which miR-184 and miR-490-3p were located in the center. In the GEO data, there were 40 DEGs. A total of 17 genes were founded in both databases, such as ADAM23, AHNAK2, BST2, COL11A1, CXCL13, FBN2, IFI27, IFI44L, MAGEA6, and PTGR1. GSVA analysis revealed 31 significant pathways. GSEA found 87 gene sets enriched in HRSI and 91 gene sets in LRSI. G.O. and KEGG of RNA expression levels revealed that these genes were most enriched in T cell activation and cytokine−cytokine receptor interaction. Conclusions. Patients with lung squamous cell carcinoma have different multiomics characteristics between two groups. These differences may have an essential significance with radiotherapy effect.

Sign in / Sign up

Export Citation Format

Share Document