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Discovery of differentially expressed proteins for CAR-T therapy of ovarian cancers with a bioinformatics analysis.

ABSTRACT: Target antigens are crucial for developing chimeric antigen receptor (CAR)-T cells, but their application to ovarian cancers is limited. This study aimed to identify potential genes as CAR-T-cell antigen candidates for ovarian cancers. A differential gene expression analysis was performed on ovarian cancer samples from four datasets obtained from the GEO datasets. Functional annotation, pathway analysis, protein localization, and gene expression analysis were conducted using various datasets and tools. An oncogenicity analysis and network analysis were also performed. In total, 153 differentially expressed genes were identified in ovarian cancer samples, with 60 differentially expressed genes expressing plasma membrane proteins suitable for CAR-T-cell antigens. Among them, 21 plasma membrane proteins were predicted to be oncogenes in ovarian cancers, with nine proteins playing crucial roles in the network. Key genes identified in the oncogenic pathways of ovarian cancers included MUC1, CXCR4, EPCAM, RACGAP1, UBE2C, PRAME, SORT1, JUP, and CLDN3, suggesting them as recommended antigens for CAR-T-cell therapy for ovarian cancers. This study sheds light on potential targets for immunotherapy in ovarian cancers.

SUBMITTER: Anurogo D

PROVIDER: S-EPMC11315388 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Discovery of differentially expressed proteins for CAR-T therapy of ovarian cancers with a bioinformatics analysis.

Anurogo Dito D Liu Chao-Lien CL Chang Yu-Chu YC Chang Yu-Hsiang YH Qiu J Timothy JT

Aging 20240718 14

Target antigens are crucial for developing chimeric antigen receptor (CAR)-T cells, but their application to ovarian cancers is limited. This study aimed to identify potential genes as CAR-T-cell antigen candidates for ovarian cancers. A differential gene expression analysis was performed on ovarian cancer samples from four datasets obtained from the GEO datasets. Functional annotation, pathway analysis, protein localization, and gene expression analysis were conducted using various datasets and ...[more]

PMID: 39033780

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Project description:BackgroundThe mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms.ResultsThe expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network.ConclusionThis study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.

Project description:Osteoarthritis (OA), also known as degenerative arthritis, affects millions of people all over the world. OA occurs when the cartilage wears down over time, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental chondrogenesis as well as to explore potential molecular mechanisms.The expression profiles of GSE51812 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 9 samples, including 6-week pre-chondrocytes (PC, 6 independent specimens) and 17-week fetal periarticular resting chondrocytes (RC, 3 independent specimens). The raw data were integrated to obtain differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. The Gene Ontology (GO) and pathway enrichment of DEGs were conducted via Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the search tool for the retrieval of interacting genes (STRING) database. An intersection figure was provided to show the relationship between the DEGs identified in this study and genes from any existed related studies.A total of 9486 DEGs, including 4821 upregulated genes and 4665 downregulated genes were observed. The top 30 developmental chondrogenesis associated genes were identified, including matrix metalloproteinase (MMP)1, MMP3, MMP13, prostaglandin-endoperoxide synthase 2 (PTGS2), and so on. The majority of DEGs, including PTGS2, CCL20, CHI3L1, LIF, CXCL8, and CXCL12 were intensively enriched in immune-associated biological process terms, including inflammatory, and immune responses. Additionally, the majority of DEGs were mainly enriched in NF-kappa β (NF-kβ) signaling pathway and tumor necrosis factor (TNF) signaling pathway. The hub genes identified in STRING and Cytoscape databases included MMP1, MMP3, MMP13, PTGS2 and so on. Among the top 30 upregulated and downregulated DEGs, there were 15 genes have been reported to be associated with OA or developmental chondrogenesis.This large scale gene expression study observed genes associated with human developmental chondrogenesis and their relative GO function, which may offer opportunities for the research for cartilage tissue engineering and novel insights into the prevention of OA in the near future.

Project description:Stroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine - cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

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Discovery of differentially expressed proteins for CAR-T therapy of ovarian cancers with a bioinformatics analysis.

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Discovery of differentially expressed proteins for CAR-T therapy of ovarian cancers with a bioinformatics analysis.

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