Dataset Information

Integrative Bioinformatics Approaches to Screen Potential Prognostic Immune-Related Genes and Drugs in the Cervical Cancer Microenvironment.

ABSTRACT: In developing countries, cervical cancer is still the major cause of cancer-related death among women. To better understand the correlation between tumor microenvironment (TME) and prognosis of cervical cancer, we screened 1367 differentially expressed genes (DEGs) of cervical cancer samples in The Cancer Genome Atlas (TCGA) database using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm-derived immune scores. Then, we extracted 401 tumor immune microenvironment (TIME)-related DEGs that related to patients' survival outcomes. Protein-protein interaction (PPI) network and functional enrichment analysis revealed that the prognostic genes mainly participated in myeloid leukocyte activation, adaptive immune response regulation, and receptor signaling pathways. A total of 79 key prognostic DEGs were obtained through PPI network. A TF-lncRNA-miRNA-mRNA regulatory network was constructed to explore the potential regulatory mechanism. 4 genes (CCR7, PD-1, ZAP70, and CD28) were validated in another independent cohort of cervical cancer from the Gene Expression Omnibus (GEO) database. Finally, potential drugs for key prognostics DEGs were predicted using DrugBank. In conclusion, we obtained a list of potential prognostic TIME-related genes and potential predicted drugs by integrative bioinformatics approaches. A comprehensive understanding of prognostic genes within the TIME may provide new strategies for cervical cancer treatment.

SUBMITTER: Zhao Z

PROVIDER: S-EPMC7359727 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrative Bioinformatics Approaches to Screen Potential Prognostic Immune-Related Genes and Drugs in the Cervical Cancer Microenvironment.

Zhao Zitong Z Li Jigang J Li He H Yuan Wu Na-Yi NY Ou-Yang Peilin P Liu Shan S Cai Jingting J Wang Jing J

Frontiers in genetics 20200707

In developing countries, cervical cancer is still the major cause of cancer-related death among women. To better understand the correlation between tumor microenvironment (TME) and prognosis of cervical cancer, we screened 1367 differentially expressed genes (DEGs) of cervical cancer samples in The Cancer Genome Atlas (TCGA) database using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm-derived immune scores. Then, we extracted 401 tum ...[more]

PMID: 32733542

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Project description:Among gynecological cancers, cervical cancer is a common malignancy and remains the leading cause of cancer-related death for women. However, the exact molecular pathogenesis of cervical cancer is not known. Hence, understanding the molecular mechanisms underlying cervical cancer pathogenesis will aid in the development of effective treatment modalities. In this research, we attempted to discern candidate biomarkers for cervical cancer by using multiple bioinformatics approaches. First, we performed differential expression analysis based on cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas database, then used differentially expressed genes for weighted gene co-expression network construction to find the most relevant gene module for cervical cancer. Next, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the module genes, followed by using protein-protein interaction network analysis and Cytoscape to find the key gene. Finally, we validated the key gene by using multiple online sites and experimental methods. Through weighted gene co-expression network analysis, we found the turquoise module was the highest correlated module with cervical cancer diagnosis. The biological process of the module genes focused on cell proliferation, cell adhesion, and protein binding processes, while the Kyoto Encyclopedia of Genes and Genomes pathway of the module significantly enriched pathways related to cancer and cell circle. Among the module genes, SOX9 was identified as the hub gene, and its expression was associated with cervical cancer prognosis. We found the expression of SOX9 correlates with cancer-associated fibroblast immune infiltration in immune cells by Timer2.0. Furthermore, cancer-associated fibroblast infiltration is linked to cervical cancer patients' prognosis. Compared to those in normal adjacent, immunohistochemical and real-time quantitative polymerase chain reaction (qPCR) showed that the protein and mRNA expression of SOX9 in cervical cancer were higher. Therefore, the SOX9 gene acts as an oncogene in cervical cancer, interactive with immune infiltration of cancer-associated fibroblasts, thereby affecting the prognosis of patients with cervical cancer.

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Project description:BackgroundThere is currently a lack of clinical models to accurately predict the prognosis of cervical adenocarcinoma. This study aimed to explore the correlation between immune genes and the prognosis of cervical adenocarcinoma patients, and establish a prognostic model.MethodsTranscriptome sequencing data sets and clinical data sets of cervical adenocarcinoma samples were downloaded from the Gene Expression Omnibus (GEO) database. Information about the immune gene was obtained from the ImmPort database. Differentially expressed genes and differentially expressed immune genes were screened in cervical adenocarcinoma tissue and normal cervical group by edgeR package. Differentially expressed immune genes were screened for prognosis-related immune genes by Cox analysis. Taking the immune genes related to prognosis as variables, a prognosis prediction model was established by multivariate Cox regression analysis. Kaplan-Meier analysis and a receiver operating characteristic (ROC) curve were used to test the effectiveness of the model. According to the clinical information and risk score, univariate multivariate Cox analyses were used to screen the independent prognostic risk factors of cervical adenocarcinoma.ResultsCXCL9 was an independent prognostic factor of cervical adenocarcinoma [hazard ratio (HR) =0.63; P=0.025]. CGB5 (HR =1.22; P=0.034), CXCL12 (HR =1.33; P=0.023), PTX3 (HR =1.53; P=0.024), and CXCL10 (HR =2.31; P=0.031) were prognostic risk factors for cervical adenocarcinoma. The risk score was calculated as follows: risk score = (0.005 × CXCL10) + (0.076 × CGB5) + (0.061 × CXCL12) + (0.034 × PTX3) + (-0.004 × CXCL9). The prognosis of the low-risk score group was better than that of the high-risk score group (P=0.035). The area under the ROC curve (AUC) of the risk score was 0.713, and the predictive power was good. Multivariate Cox analysis showed that N stage (HR =1.34; P=0.035) and risk score (HR =1.37; P<0.001) were independent risk factors for the prognosis of cervical adenocarcinoma (HR >1; P<0.001).ConclusionsIn this study, an immune gene prognosis prediction model for cervical adenocarcinoma was established based on the GEO and ImmPort databases. The prediction performance of the model is good, and the prognosis of patients can be evaluated according to the gene expression, which has clinical practicability.

Dataset Information

Integrative Bioinformatics Approaches to Screen Potential Prognostic Immune-Related Genes and Drugs in the Cervical Cancer Microenvironment.

Publications

Integrative Bioinformatics Approaches to Screen Potential Prognostic Immune-Related Genes and Drugs in the Cervical Cancer Microenvironment.

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