Transcriptome Profiling of HCT-116 Colorectal Cancer Cells with RNA Sequencing Reveals Novel Targets for Polyphenol Nano Curcumin

Abstract

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. The gemini nanoparticle formulation of polyphenolic curcumin significantly inhibits the viability of cancer cells. However, the molecular mechanisms and pathways underlying its toxicity in colon cancer are unclear. Here, we aimed to uncover the possible novel targets of gemini curcumin (Gemini-Cur) on colorectal cancer and related cellular pathways. After confirming the cytotoxic effect of Gemini-Cur by MTT and apoptotic assays, RNA sequencing was employed to identify differentially expressed genes (DEGs) in HCT-116 cells. On a total of 3892 DEGs (padj < 0.01), 442 genes showed a log2 FC >|2| (including 244 upregulated and 198 downregulated). Gene ontology (GO) enrichment analysis was performed. Protein−protein interaction (PPI) and gene-pathway networks were constructed by using STRING and Cytoscape. The pathway analysis showed that Gemini-Cur predominantly modulates pathways related to the cell cycle. The gene network analysis revealed five central genes, namely GADD45G, ATF3, BUB1B, CCNA2 and CDK1. Real-time PCR and Western blotting analysis confirmed the significant modulation of these genes in Gemini-Cur-treated compared to non-treated cells. In conclusion, RNA sequencing revealed novel potential targets of curcumin on cancer cells. Further studies are required to elucidate the molecular mechanism of action of Gemini-Cur regarding the modulation of the expression of hub genes.

Keywords: PPI network; RNA sequencing; colorectal cancer; differentially expressed genes; gemini curcumin.

Figure 1

Gemini-Cur affects the proliferation of HCT-116 cells. (A): Gemini-Cur suppresses HCT-116 cells proliferation in a time- and dose-dependent manner. (B): Acridine orange/ethidium bromide staining also illustrated cells with apoptotic characteristics including membrane shrinkage and nuclear fragmentation in different incubation times (24, 48 and 72 h, Magnification ×400). Gemini: gemini surfactant nanoparticles; Cur: curcumin; Gemini-Cur: gemini curcumin.

Figure 2

Distribution of gene counts. As shown in bar plot (A), more than 8 million reads are produced for each sample. According to density plot, raw read Counts (log2 (counts + 1)) are not non-normalized distributed (B). The density plot of normalized count (log2 (normalized counts)) per sample is shown in plot (C). C1-3: controls; T1-3: Treated cells with Gemini-Cur.

Figure 3

Normalization of raw read counts and Differential Expression Analysis (DEA). The volcano plot (A) of non-significant DEGs (colored in dark gray), significant DEGs (padj < 0.01) (colored in light blue), and up/downregulated genes (Colored in red). The x-axis represents the log2 FC, while the y-axis represents the statistical significance for each gene based on −log10 (p value). Heatmap of top 22 up/downregulated genes associated with non-normalized counts (B). Heatmap of top 22 up/downregulated DEGs associated with normalized counts (C). From the heatmap (downregulated genes are colored in light blue and upregulated genes are colored in orange in both plots). NS: non-significant; FC: fold change.

Figure 4

Overview of PPI network and subnetworks (modules). The PPI network (A); the yellow colors represent key nodes (highest BC and K) in PPI networks. Generally, 10 modules were obtained from the main network and module 1 (D), module 2 (B) and module 3 (C) are significant modules. Module 1 with the score of 47.057 and 54 nodes is the more significant module, covering three genes CDK1, CCNA2, and BUB1B with the highest BC and K in the PPI network and with the highest score in the MCODE plug-in.

Figure 5

Overview of the gene-pathway network (annotated network) created by cystoscope. The blue nodes represent the CRC candidate genes connected with the pathways and other nodes in the network. The orange nodes represent other pathways in connection with CRC genes. According to this network, it can be demonstrated that CDK1 is involved in all significant first-level pathways; it is associated with related genes in each of the pathways and with other most significant CRC-related genes (CCNA2, BUB1B, GADD45G, ATF3). The dominant pathway is cell cycle with padj 1.82 × 10⁻³⁴.

Figure 6

Validation of the expression of selected genes by real-time PCR and Western blotting. Nearly all genes except GADD45A validate findings from RNA seq. data. (A) The data show relative expression of genes compared to non-treated controls. (B) Blots illustrate protein expression in control and treated cells.