Deciphering the divergent transcriptomic landscapes of cervical cancer cells grown in 3D and 2D cell culture systems

Abstract

Cervical cancer remains a significant health challenge for women worldwide, with a disproportionate impact on developing regions like sub-Saharan Africa. Taking advantage of recent advancements in developing suitable preclinical models to study cell proliferation, differentiation, and gene expression, we used RNA sequencing to compare the transcriptomic profiles of SiHa cervical cancer cells grown in 3D versus 2D culture systems. Pathway analysis of 3D cultures revealed upregulation of immune activation, angiogenesis, and tissue remodeling pathways. The high expression of cytokines, chemokines, matrix metalloproteinases, and immediate early genes, suggests that 3D cultures replicate the tumor microenvironment better than 2D monolayer cultures. HPV gene expression analysis further demonstrated higher expression levels of HPV16 E1, E2, E6, and E7 genes in 3D versus 2D cultures. Further, by using a set of linear models, we identified 79 significantly differentially expressed genes in 3D culture compared to 2D culture conditions, independent of HPV16 viral gene effects. We subsequently validated five of these genes at the protein level in both the SiHa cell line and a newly developed, patient-derived cervical cancer cell line. In addition, correlation analysis identified 26 human genes positively correlated with viral genes across 2D and 3D culture conditions. The top five 3D versus 2D differentially expressed and HPV-correlated genes were validated via qRT-PCR in our patient derived cell line. Altogether, these findings suggest that 3D cultures provide superior model systems to explore mechanisms of immune evasion, cancer progression and antiviral therapeutics.

Keywords: 2 dimensional; 3 dimensional; HPV; SiHa cell line; cervical cancer.

FIGURE 1

Overview of RNAseq analysis. (A) A heatmap depicting similarities and dissimilarities between samples, calculated using the Poisson distance method. (B) PCA plot illustrating group differences, with data transformed using the rlog method. (C) Depiction of the number of DEGs, both upregulated (L2FC > 1.5, FDR ≤ 0.05) and downregulated (L2FC < −1.5, FDR ≤ 0.05), at different log2 fold change values. (D) Volcano plot showing results of DE analysis in 3D versus 2D culture conditions.

FIGURE 2

Analysis of HPV-independent DEGs in 3D versus 2D SiHa cultures. (A) UpSet plot depicting number of key DEGs between 3D and 2D cell culture conditions across five linear models (M1: Y ∼ culture_condition, M2: Y ∼ HPV16_E6 + culture_condition, M3: Y ∼ HPV16_E7 + culture_condition, M4: Y ∼ HPV16_E2 + culture_condition, M5: Y ∼ HPV16_E1 + culture_condition). The top panel displays the number of DEGs in various model combinations (intersections). The bottom panel summarizes the relationships among the models by highlighting shared and unique DEGs identified by each model. (B) Heatmap depicting 79 DEGs across models suggesting their expression changes are likely specific to the cell culture condition (M1) and independent of HPV16 viral gene expression.

FIGURE 3

HPV gene and correlated host gene expression in 3D vs. 2D SiHa cultures (A) Boxplots showing distribution of 26 host genes and HPV16 gene expression across 3D and 2D SiHa cultures. (B) Barplot comparing L2FC in expression of the 26 DEGs correlated with HPV gene expression. (C) Venn diagram showing overlap of the 26 human genes identified by Pearson correlation and hierarchical clustering (n = 1,156) and literature-curated HPV-associated genes (n = 636). (D) Correlation plot depicting higher correlation of the 26 selected host genes with HPV16 gene expression.

FIGURE 4

Validation of key host gene expression in 3D vs. 2D cultures. (A) RT-qPCR validation of top DEGs and HPV45 genes in HPV45+ primary cervical cancer cells (MCW-3) grown in 3D vs. 2D culture. Results shown as mean fold change in 3D relative to 2D (error bars: SE, n = 3 biological x 3 technical replicates). p-value < 0.01*, <0.0001***, <0.00001 ****; (B) Western blot analysis of HPV-independent genes, CASP14, S100A9, ALPP, CLDN1, and IL18, with differential expression in 2D-3D cultures.