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.

scholarly journals Competitive Endogenous RNA Network Construction and Comparison of Lung Squamous Cell Carcinoma in Smokers and Nonsmokers

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yan Yao ◽  
Tingting Zhang ◽  
Lingyu Qi ◽  
Ruijuan Liu ◽  
Gongxi Liu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Molecular Mechanisms ◽  
Differential Expression Analysis ◽  
Lung Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cerna Network

Background. Lung squamous cell carcinoma (LUSC) is a subtype of highly malignant lung cancer with poor prognosis, for which smoking is the main risk factor. However, the underlying genetic and molecular mechanisms of smoking-related LUSC remain largely unknown. Methods. We mined existing LUSC-related mRNA, miRNA, and lncRNA transcriptome data and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and divided them into smoking and nonsmoking groups, followed by differential expression analysis. Functional enrichment analysis of the unique differentially expressed mRNAs of the two groups was performed using the DAVID database. Subsequently, the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network of LUSC in smoking and nonsmoking groups was constructed. Finally, survival analyses were performed to determine the effects of differentially expressed lncRNAs/mRNAs/miRNAs that were involved in the ceRNA network on overall survival and to discover the hub genes. Results. A total of 1696 lncRNAs, 125 miRNAs, and 3246 mRNAs and 1784 lncRNAs, 96 miRNAs, and 3229 mRNAs with differentially expressed profiles were identified in the smoking and nonsmoking groups, respectively. The ceRNA network and survival analysis revealed four lncRNAs (LINC00466, DLX6-AS1, LINC00261, and AGBL1), one miRNA (hsa-mir-210), and two mRNAs (CITED2 and ENPP4), with the potential as biomarkers for smoking-related LUSC diagnosis and prognosis. Conclusion. Taken together, our research has identified the differences in the ceRNA regulatory networks between smoking and nonsmoking LUSC, which could lay the foundation for future clinical research.

scholarly journals Identification of lncRNAs associated with lung squamous cell carcinoma prognosis in the competitive endogenous RNA network

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7727 ◽  
Author(s):  
Lingyu Qi ◽  
Tingting Zhang ◽  
Yan Yao ◽  
Jing Zhuang ◽  
Cun Liu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Molecular Mechanisms ◽  
Lung Squamous Cell Carcinoma ◽  
Differentially Expressed ◽  
Cerna Network ◽  
Verification Methods ◽  
Competitive Endogenous Rna

Background Long noncoding RNAs (lncRNAs) play a role in the formation, development, and prognosis of various cancers. Our study aimed to identify prognostic-related lncRNAs in lung squamous cell carcinoma (LUSC), which may provide new perspectives for individualized treatment of patients. Materials and Methods The RNA sequencing (lncRNA, microRNA (miRNA), mRNA) data and clinical information related to LUSC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA sequences were used to construct the competitive endogenous RNA (ceRNA) network. In present study, we mainly used two prognostic verification methods, Cox analysis and survival analysis, to identify the prognostic relevance of specific lncRNAs and construct prognostic model of lncRNA. Results Datasets on 551 samples of lncRNA and mRNA and 523 miRNA samples were retrieved from the TCGA database. Analysis of the normal and LUSC samples identified 170 DElncRNAs, 331 DEmiRNAs, and 417 DEmRNAs differentially expressed RNAs. The ceRNA network contained 27 lncRNAs, 43 miRNAs, and 11 mRNAs. Furthermore, we identified seven specific lncRNAs (ERVH48-1, HCG9, SEC62-AS1, AC022148.1, LINC00460, C5orf17, LINC00261) as potential prognostic factors after correlation analysis, and five of the seven lncRNAs (AC022148.1, HCG9, LINC00460, C5orf17, LINC00261) constructed a prognostic model of LUSC. Conclusion In present study, we identified seven lncRNAs in the ceRNA network that are associated with potential prognosis in LUSC patients, and constructed a prognostic model of LUSC which can be used to assess the prognosis risk of clinical patients. Further biological experiments are needed to elucidate the specific molecular mechanisms underlying them.

scholarly journals Identifying the p65-Dependent Effect of Sulforaphene on Esophageal Squamous Cell Carcinoma Progression via Bioinformatics Analysis

2020 ◽  
Vol 22 (1) ◽  
pp. 60
Author(s):  
Sichong Han ◽  
Zhe Wang ◽  
Jining Liu ◽  
Qipeng Yuan
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Squamous Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Dependent Manner

Understanding the mechanism by which sulforaphene (SFE) affects esophageal squamous cell carcinoma (ESCC) contributes to the application of this isothiocyanate as a chemotherapeutic agent. Thus, we attempted to investigate SFE regulation of ESCC characteristics more deeply. We performed gene set enrichment analysis (GSEA) on microarray data of SFE-treated ESCC cells and found that differentially expressed genes are enriched in TNFα_Signaling_via_the_NFκB_Pathway. Coupled with the expression profile data from the GSE20347 and GSE75241 datasets, we narrowed the set to 8 genes, 4 of which (C-X-C motif chemokine ligand 10 (CXCL10), TNF alpha induced protein 3 (TNFAIP3), inhibin subunit beta A (INHBA), and plasminogen activator, urokinase (PLAU)) were verified as the targets of SFE. RNA-sequence (RNA-seq) data of 182 ESCC samples from The Cancer Genome Atlas (TCGA) were grouped into two phenotypes for GSEA according to the expression of CXCL10, TNFAIP3, INHBA, and PLAU. The enrichment results proved that they were all involved in the NFκB pathway. ChIP-seq analyses obtained from the Cistrome database indicated that NFκB-p65 is likely to control the transcription of CXCL10, TNFAIP3, INHBA, and PLAU, and considering TNFAIP3 and PLAU are the most significantly differentially expressed genes, we used chromatin immunoprecipitation-polymerase chain reaction (ChIP-PCR) to verify the regulation of p65 on their expression. The results demonstrated that SFE suppresses ESCC progression by down-regulating TNFAIP3 and PLAU expression in a p65-dependent manner.

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 Identifying an lncRNA-Related ceRNA Network to Reveal Novel Targets for a Cutaneous Squamous Cell Carcinoma

Biology ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 432
Author(s):  
Yaqin Xu ◽  
Yingying Dong ◽  
Yunhua Deng ◽  
Qianrong Qi ◽  
Mi Wu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Biological Process ◽  
Normal Skin ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Cutaneous Squamous Cell Carcinoma ◽  
Differentially Expressed ◽  
Cerna Network

A cutaneous squamous cell carcinoma (cSCC) derived from keratinocytes is the second most common cause of non-melanoma skin cancer. The accumulation of the mutational burden of genes and cellular DNA damage caused by the risk factors (e.g., exposure to ultraviolet radiation) contribute to the aberrant proliferation of keratinocytes and the formation of a cSCC. A cSCC encompasses a spectrum of diseases that range from recursor actinic keratosis (AK) and squamous cell carcinoma (SCC) in situ (SCCIS) to invasive cSCCs and further metastatic SCCs. Emerging evidence has revealed that lncRNAs are involved in the biological process of a cSCC. According to the ceRNA regulatory theory, lncRNAs act as natural miRNA sponges and interact with miRNA response elements, thereby regulating the mRNA expression of their down-stream targets. This study was designed to search for the potential lncRNAs that may become potential therapeutic targets or biomarkers of a cSCC. Considering the spirit of the study to be adequately justified, we collected microarray-based datasets of 19 cSCC tissues and 12 normal skin samples from the GEO database (GSE42677 and GSE45164). After screening the differentially expressed genes via a limma package, we identified 24 differentially expressed lncRNAs (DElncRNAs) and 3221 differentially expressed mRNAs (DEmRNAs). The miRcode, miRTarBase, miRDB and TargetScan databases were used to predict miRNAs that could interact with DElncRNAs and DEmRNAs. A total of 137 miRNA-lncRNA and 221 miRNA-mRNA pairs were retained in the ceRNA network, consisting of 31 miRNAs, 11 DElncRNAs and 155 DEmRNAs. For the functional analysis, the top enriched biological process was enhancer sequence-specific DNA binding in Gene Ontology (GO) terms. The FoxO signaling pathway, autophagy and cellular senescence were the top enrichment terms based on a Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The combination of a STRING tool and Cytoscape software (plug-in MCODE) identified five core mRNAs and built a core mRNA-associated ceRNA network. The expression for five identified core mRNAs and their related nine lncRNAs was validated using the external dataset GSE7553. Finally, one lncRNA HLA-F-AS1 and three mRNAs named AGO4, E2F1 and CCND1 were validated with the same expression patterns. We speculate that lncRNA HLA-F-AS1 may sponge miR-17-5p or miR-20b-5p to regulate the expression of CCND1 and E2F1 in the cSCC. The present study may provide potential diagnostic and therapeutic targets for cSCC patients.

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