Six Core Gene Markers for Colorectal Cancer Diagnosis

Six core genes (EIF2S3, GTF3A, HMGA1, HSP90AB1, PABPC1, S100A11) were identified as malignant transformation markers in colorectal polyps. The diagnostic model was validated using the TCGA-COADREAD cohort and an external independent cohort GSE41258. The dataset is a 13.4 KB XLSX file containing the identified gene markers and associated analysis results.

Use Cases

• Validate the six-gene diagnostic model (EIF2S3, GTF3A, HMGA1, HSP90AB1, PABPC1, S100A11) on new patient RNA-seq data for colorectal cancer risk assessment.
• Investigate the correlation between high expression of EIF2S3 and S100A11 genes and poor prognosis in colorectal cancer patients using survival analysis.
• Compare mRNA expression levels of the six core genes between colorectal cancer cell lines (SW480, HCT116) and normal epithelial cell line NCM460 via qRT-PCR data.
• Use the identified gene markers to explore molecular mechanisms of colorectal tumorigenesis through functional enrichment analysis of associated pathways.

Strengths

• Model validation was performed on an external independent cohort (GSE41258), supporting generalizability.
• Findings are supported by multiple analytical methods including WGCNA, Boruta algorithm, LASSO regression, and XGBoost.
• Protein expression validation was conducted using the UALCAN database, adding a multi-omics layer to the results.

Limitations

• The dataset is very small at 13.4 KB, indicating it contains summary results rather than raw, large-scale genomic data.
• The specific row count and column structure of the provided XLSX file are unknown, limiting understanding of its direct analytical utility.
• The data is derived from specific cohorts (GSE209741, TCGA-COADREAD) and cell lines, which may limit applicability to other populations.

Provenance

Source

Analysis derived from public datasets GSE209741, GSE161277, TCGA-COADREAD, and GSE41258.

Collection Method

Bioinformatics analysis using edgeR, WGCNA, Boruta, LASSO, XGBoost, and ridge regression on gene expression data.

Freshness

Last updated March 24, 2026.