Dataset Information

Identification of Novel Biomarkers Related to Lung Squamous Cell Carcinoma Using Integrated Bioinformatics Analysis.

ABSTRACT:

Background

Lung squamous cell carcinoma (LUSC) is one of the most common types of lung carcinoma and has specific clinicopathologic characteristics. In this study, we screened novel molecular biomarkers relevant to the prognosis of LUSC to explore new diagnostic and treatment approaches for this disease.

Methods

We downloaded GSE73402 from the Gene Expression Omnibus (GEO) database. GSE73402 contains 62 samples, which could be classified as four subtypes according to their pathology and stages. Via weighted gene coexpression network analysis (WGCNA), the main module was identified and was further analyzed using differentially expressed genes (DEGs) analysis. Then, by protein-protein interaction (PPI) network and Gene Expression Profiling Interactive Analysis (GEPIA), hub genes were screened for potential biomarkers of LUSC.

Results

Via WGCNA, the yellow module containing 349 genes was identified, and it is strongly related to the subtype of CIS (carcinoma in situ). DEGs analysis detected 180 genes that expressed differentially between the subtype of CIS and subtype of early-stage carcinoma (Stage I and Stage II). A PPI network of DEGs was constructed, and the top 20 genes with the highest correlations were selected for GEPIA database to explore their effect on LUSC survival prognosis. Finally, ITGA5, TUBB3, SCNN1B, and SERPINE1 were screened as hub genes in LUSC.

Conclusions

ITGA5, TUBB3, SCNN1B, and SERPINE1 may have great diagnostic and prognostic significance for LUSC and have great potential to be new treatment targets for LUSC.

SUBMITTER: Wang H

PROVIDER: S-EPMC8519687 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Identification of Novel Biomarkers Related to Lung Squamous Cell Carcinoma Using Integrated Bioinformatics Analysis.

Wang Haiyan H Huang Lizhi L Chen Li L Ji Jing J Zheng Yuanyuan Y Wang Zhen Z

Computational and mathematical methods in medicine 20211008

<h4>Background</h4>Lung squamous cell carcinoma (LUSC) is one of the most common types of lung carcinoma and has specific clinicopathologic characteristics. In this study, we screened novel molecular biomarkers relevant to the prognosis of LUSC to explore new diagnostic and treatment approaches for this disease.<h4>Methods</h4>We downloaded GSE73402 from the Gene Expression Omnibus (GEO) database. GSE73402 contains 62 samples, which could be classified as four subtypes according to their patholo ...[more]

PMID: 34659450

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Project description:Lung cancer is the leading cause of cancer-related mortality worldwide. Despite progress in the treatment of non-small-cell lung cancer, there are limited treatment options for lung squamous cell carcinoma (LUSC), compared with lung adenocarcinoma. The present study investigated the disease mechanism of LUSC in order to identify key candidate genes for diagnosis and therapy. A total of three gene expression profiles (GSE19188, GSE21933 and GSE74706) were analyzed using GEO2R to identify common differentially expressed genes (DEGs). The DEGs were then investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes/Proteins, and visualized using Cytoscape software. The expression levels of the hub genes identified using CytoHubba were validated using the University of California, Santa Cruz (UCSC) database and the Human Protein Atlas. A Kaplan-Meier curve and Gene Expression Profiling Interactive Analysis were then employed to evaluate the associated prognosis and clinical pathological stage of the hub genes. Furthermore, non-coding RNA regulatory networks were constructed using the Gene-Cloud Biotechnology information website. A total of 359 common DEGs (155 upregulated and 204 downregulated) were identified, which were predominantly enriched in 'mitotic nuclear division', 'cell division', 'cell cycle' and 'p53 signaling pathway'. The PPI network consisted of 257 nodes and 2,772 edges, and the most significant module consisted of 66 upregulated genes. A total of 19 hub genes exhibited elevated RNA levels, and 10 hub genes had elevated protein levels compared with normal lung tissues. The upregulation of five hub genes (CCNB1, CEP55, FOXM1, MKI67 and TYMS; defined in Table I) were significantly associated with poor overall survival and unfavorable clinical pathological stages. Various ncRNAs, such as C1orf220, LINC01561 and MGC39584, may also play important roles in hub-gene regulation. In conclusion, the present study provides further understanding of the pathogenesis of LUSC, and reveals CCNB1, CEP55, FOXM1, MKI67 and TYMS as potential biomarkers or therapeutic targets.

Project description:BackgroundInvasion and metastasis of cervical cancer are the main factors affecting the prognosis of patients with cervical squamous cell carcinoma (CESC). Therefore, it is of vital importance to find novel biomarkers that are associated with CESC invasion and metastasis, which will aid in the amelioration of individualized therapeutic methods for advanced patients.MethodsThe gene expression profiles of 10 metastatic and 116 non-metastatic samples were downloaded from The Cancer Genome Atlas (TCGA), where differentially expressed genes (DEGs) were defined. Weighted gene correlation network analysis (WGCNA) was employed to identify the stemness-related genes (SRGs). Univariate and multivariate regression analyses were used to identify the most significant prognostic key genes. Differential expression analysis of transcription factor (TF) and Gene Set Variation Analysis (GSVA) were utilized to explore the potential upstream regulation of TFs and downstream signaling pathways, respectively. Co-expression analysis was performed among significantly enriched TFs, key SRGs, and signaling pathways to construct a metastasis-specific regulation network in CESC. Connectivity Map (CMap) analysis was performed to identify bioactive small molecules which might be potential inhibitors for the network. Additionally, direct regulatory patterns of key genes were validated by ChIP-seq and ATAC-seq data.ResultsDEGs in yellow module acquired via WGCNA were defined as key genes which were most significantly related to mRNAsi. A multivariate Cox regression model was constructed and then utilized to explore the prognostic value of key SRGs by risk score. Area under curve (AUC) of the receiver operating characteristic (ROC) curve was 0.842. There was an obvious co expression pattern between the TF NR5A2 and the key gene VIM (R = 0.843, p < 0.001), while VIM was also significantly co-expressed with hallmark epithelial mesenchymal transition (EMT) signaling pathway (R = 0.318, p < 0.001). Naringenin was selected as the potential bioactive small molecule inhibitor for metastatic CESC based on CMap analysis.ConclusionsVIM positively regulated by NR5A2 affected EMT signaling pathways in metastatic CESC, and naringenin was the inhibitor for the treatment of metastatic CESC via suppressing cancer stemness. This hypothetical signaling axis and potential inhibitors provide biomarkers and novel therapeutic targets for metastatic CESC.

Dataset Information

Identification of Novel Biomarkers Related to Lung Squamous Cell Carcinoma Using Integrated Bioinformatics Analysis.

Background

Methods

Results

Conclusions

Publications

Identification of Novel Biomarkers Related to Lung Squamous Cell Carcinoma Using Integrated Bioinformatics Analysis.

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