Searching the Resource Information Network

Our searching services are busy right now. Please try again later

  • Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes

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

Dito Anurogo | Chao-Lien Liu | Yu-Chu Chang | Yu-Hsiang Chang | J Timothy Qiu
Aging | 2024

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.

Pubmed ID: 39033780

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


DAVID (tool)

RRID:SCR_001881

Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis.

View all literature mentions

STRING (tool)

RRID:SCR_005223

Database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)

View all literature mentions

NCBI (tool)

RRID:SCR_006472

A portal to biomedical and genomic information. NCBI creates public databases, conducts research in computational biology, develops software tools for analyzing genome data, and disseminates biomedical information for the better understanding of molecular processes affecting human health and disease.

View all literature mentions

PROFILER (tool)

RRID:SCR_009339

A flexible software tool to generate the probability distribution of joint multilocus genotypes defined by sets of individuals within the pedigree and sets of markers within the framework map. (entry from Genetic Analysis Software)

View all literature mentions

Microsoft Excel (tool)

RRID:SCR_016137

Software application with data analysis tools and spreadsheet templates to track and visualize data. It is used to manage and process data.

View all literature mentions

Metascape (tool)

RRID:SCR_016620

Web service to analyze gene or protein lists. Provides automated meta analysis tools to understand pathways within a group of orthogonal target-discovery studies.

View all literature mentions

Gene Expression Profiling Interactive Analysis (tool)

RRID:SCR_018294

Web server for cancer and normal gene expression profiling and interactive analyses. Interactive web server for analyzing RNA sequencing expression data of tumors and normal samples from TCGA and GTEx projects, using standard processing pipeline. Provides customizable functions such as tumor or normal differential expression analysis, profiling according to cancer types or pathological stages, patient survival analysis, similar gene detection, correlation analysis and dimensionality reduction analysis.

View all literature mentions

Reactome Knowledgebase (tool)

RRID:SCR_023504

Open source relational database of signaling and metabolic molecules and their relations organized into biological pathways and processes. Core unit of Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes, vaccines, anti-cancer therapeutics and small molecules) participating in reactions form network of biological interactions and are grouped into pathways including classical intermediary metabolism, signaling, transcriptional regulation, apoptosis and disease. External domain expert provides expertise, curator formalizes it into database structure, and external domain expert reviews representation. System of evidence tracking ensures that all assertions are backed up by primary literature. Website is designed to give the user graphical map of known biological processes and pathways that is also an interface. Database and website enable to find, organize, and utilize biological information to support data visualization, integration and analysis.

View all literature mentions

GeneHancer (tool)

RRID:SCR_023953

Database of human regulatory elements like enhancers and promoters, and their inferred target genes which is embedded in GeneCards, human gene compendium. Associations between regulatory elements and target genes were based on multiple sources of linking molecular data, along with distance.

View all literature mentions