Welcome to ImmCeRNA

ImmCeRNA (Prediction and Evaluation of Immune-related ceRNA for Personalized Therapy and Prognosis in Pan-cancer), which offers an in-depth identification and exploration of immune-related ceRNAs and their close relationship with immune cell infiltration, immunotherapy, drug sensitivity, and cancer prognosis across 27 human cancer types in a cancer-specific, pan-cancer comparative, and patient individual-specific manner. Through this, it facilitates a better comprehension of the intricate interactions within the tumor microenvironment and contributes to the advancement of personalized medicine and improved patient outcomes.

e.g. ceRNA:" HOTAIR-MIR221-ACTG1 " Cancer: " BRCA "
LAML KICH LUAD LGG THCA HNSC SKCM SARC LIHC MESO COAD READ UCEC TGCT PAAD ESCA BRCA STAD THYM PCPG PRAD BLCA LUSC KIRC OV CESC UCS

ImmCeRNA-Differential expression

A tool to identify differentially expressed immune-related ceRNAs in pan-cancer.

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ImmCeRNA-Immune infiltration

A tool for immune cell infiltration analysis of immune-related ceRNAs in pan-cancer.

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ImmCeRNA-Function

A tool to identify dysregulated functions of immune-related ceRNA in pan-cancer based on Gene Ontology and biological pathways.

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ImmCeRNA-Drug sensitivity

A tool to identify immune-related ceRNAs associated with drug sensitivity in pan-cancer.

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ImmCeRNA-Survival

A tool to perform COX regression analysis and survival curves for ceRNAs across more than 20 types of malignant cancers.

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ImmCeRNA-Immune therapy

A tool to identify immunotherapy-associated
ceRNAs in bulk datasets and single-cell datasets.

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ImmCeRNA-Pseudotime Trajectory

A tool to analysis ceRNAs in Pseudotime Trajectory.

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ImmCeRNA-Patient-specific

A tool to identify patient-specific
ceRNA and clinical drug responses.

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ImmCeRNA-Transcription Factors

A tool to identify associated transcription factors (TFs) by ceRNA.

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ImmCeRNA-Spatial ceRNA

A tool to identify ceRNA in Spatial Transcriptomic Data

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ImmCeRNA-Crisper Impact

A tool to analyze CRISPR gene knockouts in ceRNA.

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ImmCeRNA-Predictive Model

We developed an XGBoost-based prediction model for pan-cancer immunotherapy response.

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Visits/Contact Us

Contact Us

For questions, problems, comments on the database, please send a mail to wangli@hrbmu.edu.cn