Data from: NEK4 suppresses cell proliferation in BT20 triple-negative breast cancer cells by diminishing expression of cell cycle genes, while its depletion mitigates proliferation in other cell lines
Data files
Sep 08, 2025 version files 35.48 KB
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README.md
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UniPvsNEK4P.xlsx
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Abstract
BT20 cells were exposed to a non-targeting siRNA (Uni) or siRNA targeting NEK4. RNA was harvested, and RNASeq followed by GSEA was completed. We note that depleting NEK4 alone caused enhancement in cell cycle genes that was consistent with the increase in cell proliferation observed in the BT20 cells following knockdown.
Dataset DOI: 10.5061/dryad.jm63xsjps
Description of the data and file structure
Cells were exposed to siRNA targeting NEK4 (NEK4) or a scrambled negative control (UNI), and RNASeq was completed followed by GSEA (Gene Set Enrichment Analysis). The results are shown here.
Files and variables
File: UniPvsNEK4P.xlsx
Description:
Variables
- Uni/NT: Cells exposed to scrambled siRNA control; universal negative control
- NEK4: Cells treated with siRNA targeting NEK4
- Gene name (Abbrv): HUGO gene name
- Fold change: Fold change in cells depleted of NEK4 versus scrambled siRNA (Uni)
- Gene Set: Set of genes enriched in our NEK4 siRNA-treated cells versus Uni control
- Size: Number of genes in the gene set identified
- ES: Enrichment score
- NES: Normalized Enrichment score
- NOM p-val: Nominal p value
- FDR q-val: False discover rate p value
- FWER p-val: Family wise error rate p value
- Rank at Max: The position (rank) in the ordered list of genes where the maximum enrichment score for a gene set occurs
- Leading Edge: The leading edge subset is the core set of genes in a gene set that contribute the most to the enrichment signal
- REACTOME_MRNA_SPLICING_signal: Genes identified as enriched in our dataset regulating mRNA splicing
- REACTOME_CELL_CYCLE_MITOTIC_signal: : Genes identified as enriched in our dataset regulating the mitotic cell cycle
- REACTOME_CELL_CYCLE_signal: : Genes identified as enriched in our dataset regulating the cell cycle in general
- REACTOME_GPCR_LIGAND_BINDING_signal: : Genes identified as enriched in our dataset regulating binding of G-protein coupled receptor
- Sheet names: FDR<0.25: All gene identified with a FDR<0.25; All Reactome: all genes input into Reactome; Reactome summary: a summarized version of our Reactome output; mRNA splicing: genes identified as enriched in mRNA splicing regulation; Cell cycle: genes identified as enriched in regards to cell cycle regulation; GPCR: genes enriched that regulated GPCR signaling; MET signaling: genes enriched that regulated MET-related signaling; neg NES: negative enrichment scores; pos NES: positive enrichment score
RNA from BT20 cells, transfected with NT or NEK4 siRNA, was isolated and quantified as above. RNA was sequenced using Illumina HiSeq 2000 at the National Center for Genome Resources (NCGR) in Santa Fe, NM. The samples were sequenced using paired-end reads with a read length of 150 base pairs and a read depth of 20 million reads per sample. Quality control of sample reads was performed using the FastQC tool. Raw reads from sequencing were processed to remove adapter and primer sequences. Processed sequences were aligned to the Human reference genome using HISAT2. Read counts were generated using the featureCounts software. Differential gene expression was assessed using the DeSEQ2 tool. Significantly differentially expressed Q24 genes, i.e., genes with an FDR < 0.05 and fold change of > 0 for upregulated genes and < 0 for downregulated genes, were included for pathway enrichment analysis using Broad Institute’s Gene Set Enrichment Analysis (GSEA) software (http://www.broadinstitute.org/gsea/). Genes were pre-ranked based on the Wald statistic, which is represented by log2 fold change/ standard error of log2 fold change. Change in gene expression was considered significant when the Padj value was < 0.05. Log2 fold change of > 0 was noted as upregulation, while < 0 was noted as downregulation. Genes were interrogated against the Reactome database provided in the GSEA tool. Leading edge analysis was carried out on significantly enriched pathways (FDR < 0.05) to reveal a subset of genes contributing the most toward the enrichment score for each of the enriched pathways. The STRING database was used to prioritize genes with known interactors that promote cell cycle.