Esculetin inhibited PI3K/Akt/mTOR pathway and enhances anti-colorectal cancer activity via binding to ENO1
Data files
Jul 10, 2025 version files 11.52 MB
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Data_4A.xlsx
9.32 MB
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Data_4B.xlsx
449.31 KB
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Data_4C.xlsx
1.71 MB
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Data_4D.xlsx
23.64 KB
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Data_4E.xlsx
14.80 KB
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README.md
8.24 KB
Abstract
Colorectal cancer (CRC) is the third most common malignant tumor globally and the second leading cause of cancer-related deaths. Currently, although the standard treatment for CRC is a combination of surgery and chemotherapy, metastasis and recurrence are often associated with a poor prognosis. In our preliminary research, we found that Esc has anti-colon cancer effects both in vivo and in vitro. However, its specific target sites and molecular mechanisms still lack in-depth exploration and remain unclear. Therefore, we employed transcriptomics technology to analyze the complex interactions between the drug and its targets, thereby conducting a comprehensive evaluation. To elucidate the molecular mechanisms underlying the anti-tumor properties of Esc in colorectal cancer, we performed RNA sequencing for whole-genome expression analysis on HCT116 cell samples treated with Esc. Through enrichment analysis of differentially expressed genes, we revealed that Esc is likely to intervene in the development of colorectal cancer by regulating these signaling pathway processes. This finding provides a more solid evidence base for the development of new anti-tumor drugs with enhanced efficacy and reduced toxicity.
Dataset DOI: 10.5061/dryad.d7wm37qd2
Description of the data and file structure
Illumina, Library preparation, PubChem, Transcriptome Sequencing data
Files and variables
File: Data_4B.xlsx
Description: Differential gene expression heatmap
This table presents the similarities and differences in gene expression patterns between the Esc and Ctrl groups. The data are standardized using Z-score normalization, where positive Z-scores (indicated in red) represent high expression levels, and negative Z-scores (indicated in blue) represent low expression levels.
File: Data_4A.xlsx
Description: Principal Component Analysis (PCA) Data among Samples
This table describes the similarity among samples in the Esc and Ctrl groups.
Variables
geneID: A unique identifier assigned to a gene in the database.
gene_name: The commonly used symbol for the gene.
Ctrl-1, Ctrl-2, trl-3_fpkm: The FPKM expression levels of genes in each sample of the three Ctrl groups. FPKM (Fragments Per Kilobase of transcript per Million fragments mapped) is used as an indicator to measure the expression levels of transcripts or genes.
Esc-1, Esc-2, Esc-3_fpkm: The FPKM expression levels of genes in each sample of the three Esc groups.
Ctrl-1, Ctrl-2, Ctrl-3_count: The raw read counts of genes in the samples of the three Ctrl groups.
Esc-1, Esc-2, Esc-3_count: The raw read counts of genes in the samples of the three Esc groups.
chr: The chromosome number where the gene is located.
start: The start coordinate of the gene.
end: The end coordinate of the gene.
strand: Information indicating whether the sequence is on the positive or negative strand.
gene_biotype: The biological type of the gene.
gene_description: A brief description of the gene’s function, typically including known functions of the gene, biological processes it is involved in, and associated diseases.
The data in this table indicate that the samples within the Ctrl and Esc groups show high similarity and good reproducibility, which is suitable for the analysis of differentially expressed genes between the groups.
File: Data_4D.xlsx
Description: Enrichment Analysis of Differentially Expressed Genes in Disease Ontology
Disease Ontology (DO) is a database that describes the relationships between human gene functions and diseases. This table presents the results of mapping differentially expressed genes to disease entries in DO and assessing the significance of these genes in specific disease categories, thereby revealing potential biological significance and disease associations.
Variables
ID: Disease ID, from the Disease Ontology (DO) database, used to uniquely identify diseases.
Description: Disease name.
DiffRatio: Proportion of differentially expressed genes, for example: 114/1781 (6.4%). This value indicates that among the differentially expressed genes, 114 genes are associated with a specific disease, accounting for 6.4% of all differentially expressed genes.
BgRatio: Proportion of background genes, for example: 441/10312 (4.28%). This value indicates that within the entire genomic background, 441 genes are associated with a specific disease, accounting for 4.28% of all genes.
P-value: Indicates the significance of enrichment of the disease among differentially expressed genes.
Adjusted P-value: Corrected p-value used to control the false discovery rate in multiple hypothesis testing.
All_index: List of Ensembl IDs for all associated genes, including both upregulated and downregulated genes.
All_GeneName: List of names for all associated genes.
Count_all: Total number of all associated genes.
Up_index: List of Ensembl IDs for upregulated genes.
Up_GeneName: List of names for upregulated genes.
Count_up: Number of upregulated genes.
Down_index: List of Ensembl IDs for downregulated genes.
Down_GeneName: List of names for downregulated genes.
Count_down: Number of downregulated genes.
File: Data_4E.xlsx
Description: Reactome Enrichment Analysis of Differentially Expressed Genes
Reactome enrichment analysis is a statistical method used to evaluate the enrichment of differentially expressed genes in specific signaling pathways. The data in this table are intended to identify the key signaling pathways involved in differentially expressed genes, thereby providing important clues for subsequent mechanistic studies.
Variables
ID: Pathway ID, indicating a specific pathway in the Reactome database (e.g., R-HSA-190840).
Description: Description of various cellular biological processes.
DiffRatio: Proportion of differentially expressed genes. For example, 114/1781 (6.4%) indicates that among the differentially expressed genes, 114 genes belong to this pathway, accounting for 6.4% of all differentially expressed genes.
BgRatio: Proportion of background genes. For example, 18/10899 (0.17%) indicates that within the entire genomic background, 18 genes belong to this pathway, accounting for 0.17% of all genes.
P-value: Indicates the significance of enrichment of this pathway among differentially expressed genes.
Adjusted P-value: Corrected p-value used to control the false discovery rate in multiple hypothesis testing.
All_index: List of Ensembl IDs for all associated genes, including both upregulated and downregulated genes.
All_GeneName: List of names for all associated genes.
Count_all: Total number of all associated genes.
Up_index: List of Ensembl IDs for upregulated genes.
Up_GeneName: List of names for upregulated genes.
Count_up: Number of upregulated genes.
Down_index: List of Ensembl IDs for downregulated genes.
Down_GeneName: List of names for downregulated genes.
Count_down: Number of downregulated genes.
File: Data_4C.xlsx
Description: Volcano plot showing differential genes
This table is used to compare the significance levels of differentially expressed genes between the Esc and Ctrl groups.
Variables
Ctrl-1, Ctrl-2, Ctrl-3_fpkm: The FPKM expression levels of genes in each sample of the three Ctrl groups.
Esc-1, Esc-2, Esc-3_fpkm: The FPKM expression levels of genes in each sample of the three Esc groups.
Ctrl-1, Ctrl-2, Ctrl-3_count: The raw read counts of genes in the samples of the three Ctrl groups.
Esc-1, Esc-2, Esc-3_count: The raw read counts of genes in the samples of the three Esc groups.
log2FoldChange: The log2-transformed fold change value of gene expression, indicating the expression change ratio of the Esc group relative to the Ctrl group.
pvalue: The significance p-value for differential expression, used to determine whether there is a statistically significant difference in gene expression between the two groups.
padj: The adjusted p-value, used to control the false discovery rate.
regulated: Indicates whether the gene is up-regulated or down-regulated, typically determined based on the sign of log2FoldChange.
chr: The chromosome number where the gene is located.
start: The start position of the gene on the chromosome.
end: The end position of the gene on the chromosome.
strand: The orientation of the gene on the DNA strand, typically indicated as “+” (positive strand) or “–” (negative strand).
gene_biotype: The biological type of the gene.
gene_description: A brief description of the gene’s function
KEGG: The identifier of the gene in the KEGG database, used for linking to metabolic pathways and other related information.
KEGG_pathway: The name of the KEGG pathway in which the gene is involved.
GO: Gene Ontology (GO) annotations, including Molecular Function (MF), Biological Process (BP), and Cellular Component (CC).
TF_Family: If the gene is a transcription factor (TF), this field indicates the transcription factor family to which it belongs.
All missing data or data not available is represented as NA.
Code/software
Microsoft excel can be used to view these files.
Access information
Other publicly accessible locations of the data:
- NA
Data was derived from the following sources:
- NA