MYCT1 Inhibits the Adhesion and Migration of Laryngeal Cancer Cells Potentially Through Repressing Collagen VI

Abstract

MYCT1, a target of c-Myc, inhibits laryngeal cancer cell migration, but the underlying mechanism remains unclear. In the study, we detected differentially expressed genes (DEGs) from laryngeal cancer cells transfected by MYCT1 using RNA-seq (GSE123275). DEGs from head and neck squamous cell carcinoma (HNSCC) were first screened by comparison of transcription data from the Gene Expression Omnibus (GSE6631) and the Cancer Genome Atlas (TCGA) datasets using weighted gene co-expression network analysis (WGCNA). GO and KEGG pathway analysis explained the functions of the DEGs. The DEGs overlapped between GSE6631and TCGA datasets were then compared with ours to find the key DEGs downstream of MYCT1 related to the adhesion and migration of laryngeal cancer cells. qRT-PCR and Western blot were applied to validate gene expression at mRNA and protein levels, respectively. Finally, the cell adhesion, migration, and wound healing assays were to check cell adhesion and migration abilities, respectively. As results, 39 overlapping genes were enriched in the GSE6631 and TCGA datasets, and most of them revealed adhesion function. Thirteen of 39 genes including COL6 members COL6A1, COL6A2, and COL6A3 were overlapped in GSE6631, TCGA, and GSE123275 datasets. Similar to our RNA-seq results, we confirmed that COL6 is a target of MYCT1 in laryngeal cancer cells. We also found that MYCT1 inhibited the adhesion and migration of laryngeal cancer cells via COL6. These indicate that COL6 is a potential target of MYCT1 and participates the adhesion and migration of laryngeal cancer cells, which provides an important clue for further study on how MYCT1 regulating COL6 in laryngeal cancer progression.

Keywords: Collagen Ⅵ; MYCT1; gene interaction network; laryngeal cancer; progression.

Figure 1

Construction of GSE6631- and TCGA-HNSCC-specific modules using WGCNA. (A) Network topology for different soft-thresholding powers of GSE6631. Left: the scale-free topology fit index (y-axis) in the GSE6631 dataset; right: the mean connectivity (y-axis) as a function of the soft-thresholding power (x-axis) in the GSE6631 dataset. (B) Network topology for different soft-thresholding powers of TCGA-HNSCC. Left: the scale-free topology fit index (y-axis) in the TCGA-HNSCC dataset; right: the mean connectivity (y-axis) as a function of the soft-thresholding power (x-axis) in the TCGA-HNSCC dataset. (C) Branches of the clustering dendrogram from different modules based on differentially expressed genes (DEGs) in GSE6631. Based on the unsupervised hierarchical clustering, GSE6631 DEGs were clustered into seven modules (yellow, blue, green, red, brown, turquoise, and gray). (D) Branches of the clustering dendrogram from different modules based on DEGs in TCGA-HNSCC. Based on the unsupervised hierarchical clustering, TCGA-HNSCC DEGs were clustered into nine modules (black, red, pink, blue, brown, turquoise, green, yellow, and gray). (E) Module-clinical feature relationships in the GSE6631 modules. In the heatmap, red represents a positive correlation, whereas blue represents a negative correlation. The numbers in each block are the related-value and P-value of the correlation. (F) Module-clinical feature relationships in TCGA-HNSCC modules. In the heatmap, red represents a positive correlation, and blue represents a negative correlation. The numbers in each block are the related-value and P-value of the correlation.

Figure 2

Identification of a functional network and laryngeal cancer-associated DEGs from the red-green overlapping DEGs. (A) GO term interactions among overlapping DEGs. (B) KEGG pathway interactions among overlapping DEGs. (C) Venn diagram of laryngeal cancer associated DEGs downstream of MYCT1 from the GSE123275, GSE6631, and TCGA datasets. The blue circle represents the unique DEGs in the GSE6631 green module. The yellow circle represents the DEGs in the TCGA-HNSCC red modules. Large nodes represent GO terms (A) or KEGG pathways (B), whereas small nodes represent the DEGs. The colors of the nodes represent different GO terms or KEGG pathways in each network diagram. The red circle represents the DEGs downstream of MYCT1 in Hep2 cells. In the middle area, 13 genes were distinguished out of three datasets.

Figure 3

MYCT1 downregulates COL6A1, COL6A2, and COL6A3 gene expression. (A) MYCT1 mRNA level in Hep2 cells transfected with MYCT1 and siMYCT1. (B) Effect of MYCT1 on the levels of COL6A1, COL6A2, and COL6A3 mRNA. The levels of COL6A1, COL6A2, and COL6A3 mRNA were detected by qRT-PCR in Hep2 cells transfected with control, siMYCT1 RNA, or MYCT1. (C) Effect of MYCT1 on the Collagen VI protein level. MYCT1 and Collagen VI protein level in Hep2 cells transfected with MYCT1 and siMYCT1. The Collagen VI protein level was examined by Western blot in Hep2 cells transfected with control, siMYCT1 RNA, or MYCT1. (D) Effects of MYCT1 knockdown on COL6A2 and COL6A3 mRNA expression levels in Hep2 cells by qRT-PCR (E) Effects of MYCT1 knockdown on COL6A2 and COL6A3 protein levels in Hep2 cells by western bolt. Symbols *, ** and *** indicate P < 0.05, P < 0.01, and P < 0.001, respectively.

Figure 4

MYCT1 inhibits laryngeal cancer cell migration and adhesion. (A) Effect of MYCT1 on laryngeal cancer cell adhesion by cell adhesion assay. (B) Effect of MYCT1 on laryngeal cancer cell migration according to a Transwell experiment. (C) Effect of MYCT1 on laryngeal cancer cell migration according to a wound healing assay. (D) Effect of MYCT1 knockdown on COL6A2 and COL6A3 in laryngeal cancer cell adhesion by cell adhesion assay. (E) Effect of MYCT1 knockdown on COL6A2 and COL6A3 in laryngeal cancer cell migration according to a Transwell experiment. (F) Effect of MYCT1 knockdown on COL6A2 and COL6A3 in laryngeal cancer cell migration according to a wound healing assay. Symbols *, ** and *** indicate P < 0.05, P < 0.01, and P < 0.001, respectively.