RNA Methylation Genes Predicting Cervical Cancer Prognosis and Immunotherapy Response

Yan Wang from Wannan Medical College created a dataset for investigating RNA methylation-related genes in cervical cancer. The data includes mRNA expression profiles, clinical data, and genes related to m6A, m5C, and m1A methylation modifications. Differential analysis identified 106 methylation-related differential genes, and a prognostic model was built using 10 genes validated with TCGA and GSE39001 data.

Use Cases

• Building prognostic risk models based on the 10 identified methylation-related genes.
• Analyzing associations between gene expression and immune cell infiltration based on the six immune cells mentioned.
• Predicting patient response to immunotherapy, particularly anti-CTLA-4 therapy, using the described risk groups.
• Screening for potential drug treatments based on the nine drugs identified in the study.

Strengths

• The model is validated internally with TCGA data and externally with GSE39001 data, achieving an AUC of 0.729.
• Key findings include specific high-risk (e.g., IQGAP3, SLC2A1) and low-risk (e.g., IGBP1, DUOX1) genes with hazard ratios.
• Experimental validation was performed on three key genes (SLC2A1, CUX1, CA2) using immunohistochemistry and RT-qPCR.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Public databases including TCGA and GSE39001.

Collection Method

Downloaded mRNA expression profiles and clinical data, followed by serial bioinformatics analysis and clinical sample validation.