Sign in to view source links and access this dataset
Description
A bioinformatics study identifies six core genes (EIF2S3, GTF3A, HMGA1, HSP90AB1, PABPC1, S100A11) as markers for malignant transformation in colorectal polyps. The research constructs a ridge regression diagnostic model, validated using the TCGA-COADREAD cohort and external dataset GSE41258. Analysis includes validation of protein expression via the UALCAN database and mRNA expression in CRC cell lines.
Use Cases
Validate the diagnostic model's performance using the six core genes (EIF2S3, GTF3A, HMGA1, HSP90AB1, PABPC1, S100A11) on independent cohorts like GSE41258.
Assess the correlation between high expression of EIF2S3 and S100A11 genes and poor prognosis in colorectal cancer patients via survival analysis.
Compare mRNA expression levels of the six core genes between CRC cell lines (SW480, HCT116) and normal epithelial cell line NCM460.
Verify upregulated protein expression of the six identified genes in CRC tissues using data from the UALCAN database.
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 and mRNA expression of the six core genes were validated through the UALCAN database and qRT-PCR experiments.
Limitations
The dataset is a 147.0 KB Excel file, indicating it contains processed results or summary data, not the underlying raw sequencing data.
Specific row counts, column features, and sample-level data for the model are not provided in the input.
The study's primary data sources (GSE209741, GSE161277, TCGA) are external; this file likely contains derived analytical outputs.
Provenance
Source
Analysis derived from public datasets GSE209741, GSE161277, and the TCGA-COADREAD cohort.
Collection Method
Bioinformatics analysis using edgeR, WGCNA, Boruta, LASSO, XGBoost, and ridge regression modeling.
Freshness
Last updated March 24, 2026.
File is a 147.0 KB XLSX containing study results; users seeking raw RNA-seq or single-cell data must access the original GEO and TCGA entries. License is CC BY 4.0.