PFDN6 contributes to colorectal cancer progression via transcriptional regulation

Cell culture

The CRC cell lines SW480, RKO and HCT-116, along the colorectal adenocarcinoma epithelial cell line DLD-1, were acquired from the Cell Resource Center at the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences. The cells were cultured in RPMI 1640 medium supplemented with 10% Fetal Bovine Serum (FBS) and maintained at 37°C in a 5% CO₂ incubator.

Immunohistochemistry

In this study, a total of 106 CRC tissues and 96 para-cancerous tissues were used. Informed consent was obtained from all patients, and the relevant clinical characteristics were obtained. Tissue slides were subjected to several steps, including placement in a 65°C oven for 30 min, xylene soaking, alcohol washing, 1× EDTA-based repair, and blocking with 3% H₂O₂ and serum. Primary and secondary antibodies were added and incubated overnight at 4°C. Subsequently, 3,3’-diaminobenzidine (DAB) and H&E staining were performed, followed by slide sealing with a neutral resin. The cells were observed under a microscope (IX73, Olympus, Tokyo, Japan). The criteria for determining the number of positive cells were as follows: 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%) and 4 (76–100%). Finally, the immunohistochemistry (IHC) scores of all tissues were analysed to obtain statistical data.

TCGA Database analysis

We analysed the transcriptome sequencing count data obtained from 635 tumours and 51 normal samples derived from TCGA-colon adenocarcinoma and TCGA-rectal adenocarcinoma. The DEseq2 package was employed for data normalisation using the ‘estimated dispersion’ method. mRNA transcripts with a reading count below 10 were prefiltered and removed. Quality control was performed using a principal component analysis (PCA). For multiple statistical analyses between different groups, we calculated the log2 ratio fold change (log2FC) and applied the selection criterion |logFC|>log2(1.5). Genes with a p value below 0.05, determined using the Benjamini-Hochberg (BH) correction method, were considered differentially expressed genes (DEGs). Genes with the highest FC and lowest p value were selected as target genes.16–19

Plasmid construction

RNA interference target sequences and overexpression plasmids for PFDN6 were designed by Sangon Biotech Co (Shanghai, China). The target sequence of PFDN6 was incorporated into the BR-V-108 vector using restriction sites at both ends, and subsequently transformed into TOP 10 Escherichia coli competent cells (Beijing Tiangen, China). Positive recombinants were identified by PCR screening. Plasmid extraction was performed using an EndoFree Maxi Plasmid Kit (Beijing Tiangen). A volume of 20 µL of the extracted plasmid was added to infect RKO or HCT-116 cells. The cells were cultured in RPMI 1640 medium supplemented with 10% FBS at a density of 2×10⁵ cells/well. The efficiency of infection and knockdown was assessed using fluorescence microscopy (Micropublisher 3.3RTV; Olympus, Tokyo, Japan), quantitative real-time (qRT)-PCR and western blot analysis.

Quantitative real-time PCR

qRT-PCR was conducted using the Applied Biosystems 7500 Real-Time Platform. Total RNA was extracted using a TRIzol kit (Sigma, St Louis, Missouri, USA) and cDNA was synthesised using a Promega M-MLV kit (Promega, Madison, Wisconsin, USA) through reverse transcription. Subsequently, a qRT-PCR reaction volume of 10 µL was prepared using the SYBR Green Mastermix kit (Vazyme, Nanjing, China). The reaction included an initial denaturation step at 95°C for 3 min, followed by 40 cycles of denaturation at 95°C for 3 s and annealing/extension at 60°C for 30 s. Fluorescence quantitative PCR was used to detect the relative mRNA expression.

Western blot assay

Following the knockdown of PFDN6, RKO and HCT-116 cells were collected and lysed using 1× lysis buffer (Cell Signal Technology, Danvers, Massachusetts, USA). Subsequently, the lysates were centrifuged at 12 000× g for 30 min and total protein was extracted. The protein concentration was determined using a BCA Protein Assay kit (KeyGEN, Nanjing, China). The total proteins were separated on a 10% SDS-PAGE gel (P0012AC, Beyotime, Shanghai, China), transferred onto PVDF (IPVH00010; Millipore, Billerica, MA, USA) membranes, and subsequently blocked with a blocking solution (Tris-buffered saline-Tween-20 buffer (TBST, PPB002, Sigma, St. Louis, MO, USA) solution containing 5% non-fat milk) for 1 hour at room temperature. The membranes were incubated with primary antibodies overnight at 4°C, followed by incubation with corresponding secondary antibodies for 2 hours at room temperature. Subsequently, the membranes were washed thrice with TBST solution (10 min/wash). Colour development was performed using a western blotting kit (Amersham, Chicago, Illinois, USA). GAPDH served as an internal control for PFDN6.

Cell migration assay

Cell migration was evaluated using a wound healing assay. The RKO and HCT-116 cells were seeded in 96-well plates at a density of 5×10⁴ cells/well. The cells were incubated in a 5% CO₂ incubator at 37°C for 48 hours and 72 hours, and their migration was observed under a microscope. The ImageJ software (https://imagej.nih.gov/ij/; Center for Information Technology, National Institutes of Health, Bethesda, Maryland, USA) was used to measure the area of the cell migration gap. The experiment was repeated three times, and cell migration rates were determined based on the analysis of the scratch images.

Transwell migration assay

RKO and HCT-116 cells (4–5×10⁴ cells/well) were seeded into Transwell chambers. The upper chamber was filled with serum-free medium supplemented with 0.1% bovine serum albumin, while the lower chamber contained 600 µL of medium with 10% FBS. After 24 hours, non-migrating and non-invasive cells were removed from the upper chamber using cotton swabs. The remaining cells were fixed with methanol for 30 min and stained with 0.1% crystal violet for an additional 20 min, and the area of the stained wells was calculated after magnification at 400× under a microscope. Random views were captured and analysed. Each measurement was repeated thrice.

Cell apoptosis assay

RKO and HCT-116 cells were seeded in 6-well plates at a volume of 2 mL/well and cultured for 5 days. Following the addition of 10 µL of Annexin V-APC (eBioscience, Thermo Fisher), the cells were incubated in the dark at room temperature for 10–15 min for staining. Apoptosis was measured using an FACS Calibur flow cytometer (BD Biosciences, San Jose, California, USA).

Celigo cell counting assay

Following the knockdown of PFDN6, RKO and HCT-116 cells were collected. Subsequently, they were seeded into 96-well plates at a density of 2000 cells/well and cultured in a 5% CO₂ incubator at 37°C until reaching a confluence of 70–90%. Cell images were captured and cell proliferation curves were generated using a Celigo image cytometer (Nexcelom Bioscience, Lawrence, Massachusetts, USA).

PrimeView human gene expression array

Gene expression in RKO cells following the knockdown of PFDN6 was examined using a gene chip (Shanghai Biotechnology Co, Shanghai, China). Briefly, total RNA was extracted using an RNeasy kit (Sigma, St Louis, Missouri, USA), and its quality and integrity were assessed using a Nanodrop 2000 spectrometer (Thermo Scientific, Waltham, Massachusetts, USA) and Agilent 2100 and Agilent RNA 6000 Nano kits (Agilent, Santa Clara, California, USA). Transcriptome sequencing was performed using the Affymetrix Human Gene Chip PrimeView, and the results were scanned using an Affymetrix Scanner 3000 (Affymetrix, Santa Clara, California, USA).

For the statistical analysis, a BH false discovery rate (FDR) with criteria of |fold change|≥2 and FDR <0.05 was considered significant. Ingenuity Pathway Analysis (Qiagen, Hilden, Germany) was used for significant differences and functional analyses, where a |Z-score|>2 was considered significant.

Immunofluorescence

The cells were fixed in cold methanol for 10 min and diluted with a solution of 0.4% Triton X-100 and 1% bovine serum albumin in phosphate buffered saline (PBS) for 1 hour at room temperature. Subsequently, the cells were then blocked with goat serum for 1 hour. Following blocking, the cells were incubated with a primary antibody against PFDN6. Immunofluorescence (IF) staining was performed using a fluorescence microscope (Nikon 80i; Tokyo, Japan).

Statistical analysis

All data obtained from this experiment were analysed using GraphPad Prism V.9 (San Diego, California, USA) and SPSS V.19.0 (IBM). The data are presented as mean±SD. The Student’s t-test and one-way analysis of variance were used for statistical analyses. The Mann-Whitney U test was used to assess the association between PFDN6 expression and the clinical characteristics of patients with CRC. Statistical significance was set at p<0.05. It is worth noting that all experiments were repeated three times to ensure reliability and reproducibility.

PFDN6 could be involved in the regulation of CRC

TCGA is one of the most ambitious and successful cancer genomic databases available to date. TCGA Programme has generated, analysed and made available the genomic sequences, expression, methylation and copy number variation data for over 11 000 individuals, representing over 30 different types of cancer.20 To investigate the role of PFDN6 in the regulation of CRC, we analysed the expression of PFDN6 in the transcriptome sequencing count data of 635 tumour and 51 normal specimens of CRC in the TCGA Database. A PCA revealed separate clusters for the CRC and normal groups, which indicated that compared with the intergroup differences, the intragroup differences were significantly smaller than those between groups (figure 1A). Figure 1B shows a volcanic map of DEGs. The red spots represent upregulated genes, and green spots represent downregulated genes. PFDN6 expression was upregulated in CRC specimens (figure 1C and table 1). We further analysed the correlation between PFDN6 expression and the CRC stage. These results indicated that there were significant differences in the expression of PFDN6 and tumour–node–metastasis stages (table 2), pathological stages (figure 1D and table 3) and different CRC stages (stage I, stage II, stage III and stage IV) (figure 1E and table 3). A Spearman’s rank correlation analysis showed that the expression of PFDN6 was positively correlated with the stage of CRC, suggesting that the expression level of PFDN6 increases with disease aggravation. In summary, PFDN6 participates in the progression of CRC, suggesting that PFDN6 may affect CRC development.

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The expression of PFDN6 is upregulated in CRC

By statistically analysing the clinical data of patients with CRC from the TCGA cohorts, we initially determined the regulatory role of PFDN6 on CRC. Therefore, we examined the PFDN6 expression in patients with CRC and lymph node metastasis. We mainly focused on the differences in histological staining between tumour and para-carcinoma tissues. H&E staining was used to observe the morphology of the tumour and normal tissues (figure 1F). The differential expression of PFDN6 in colorectal tumours and adjacent normal tissues was investigated using a immunohistochemical analysis. IHC results showed that PFDN6 expression was upregulated in 55 tumour tissues, but was upregulated in only one para-carcinoma tissue (figure 1G and table 3). Moreover, PFDN6 expression was upregulated in tumour tissues with lymph node metastasis compared with those without lymph node metastasis (figure 1H). These results suggest that PFDN6 expression in CRC tissues is associated with lymph node metastasis and the depth of tumour invasion in patients with CRC.

Expression of PFDN6 in vitro

Except for detecting the PFDN6 level in CRC tumour tissues, the PFDN6 expression levels in human colorectal adenocarcinoma epithelial cell lines (RKO, HCT-116 and DLD-1) were determined by qRT-PCR to determine the potential roles of PFDN6 in these cell lines. PFDN6 expression was upregulated in the three cell lines (figure 2A). Then, we constructed knockdown cell models of PFDN6 to investigate the biological role it plays in vitro. Short hairpin RNA (shRNA) was used to mediate the knockdown of PFDN, and RKO cells were infected with three lentiviral plasmids: shPFDN6-1, shPFDN6-2 and shPFDN6-3. Immediately afterwards, shPFDN6-3 achieved optimum knockdown efficiency and was used for subsequent experiments (p<0.01, figure 2B). Moreover, qRT-PCR and western blot analyses suggested that shPFDN6 significantly reduced the mRNA and protein levels of PFDN6 in both HCT-116 and RKO cell lines (figure 2C,D). The IF results showed that shPFDN6 significantly reduced the PFDN6 levels in both cell lines (figure 2E). The above experimental results indicated that PFDN6 knockdown cell lines were successfully established using shRNA.

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Knockdown of PFDN6-attenuated CRC invasion and migration

These findings provide evidence that PFDN6 was upregulated in CRC tissues, which suggested that PFDN6 was involved in the regulation of CRC. To confirm that PFDN6 influenced CRC cells, we established stable cell lines from HCT-116 cells and RKO cells by the knockdown of CDX2. Successful CDX2 knockdown was demonstrated using IF and western blotting. The number of tumour cells was determined using the green fluorescent protein signal, and our results showed that the number of HCT-116 cells and RKO cells gradually decreased over time after the addition of shPFDN6 (figure 3A,B). These results suggest that the knockdown of PFDN6 inhibits the proliferation of HCT-116 cells and RKO cells. Moreover, both HCT-116 cells and RKO cells with PFDN6 disruption showed enhanced apoptosis (figure 3C,D). Cell cycle arrest was observed in CRC cells (figure 3E-G). Finally, we performed migration and invasion assays to determine whether PFDN6 increased the metastatic potential of CRC cells. As illustrated in figure 4, the knockdown of PFDN6 ameliorated the migratory ability of both HCT-116 and RKO cells (figure 3H, J and L). Wound healing assays were performed to determine the migratory ability of PFDN6-knockdown HCT-116 and RKO cells. Similar results were observed in the migration assay (figure 3I and K). In summary, these findings provide evidence that PFDN6 can aggravate the progression of CRC, and that the knockdown of PFDN6 plays a role in protecting against CRC.

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Enrichment analysis of DEGs after PFDN6 knockdown

Our findings showed that PFDN6 plays a critical role in CRC. We attempted to determine the mechanism by which PFDN6 is overexpressed in colorectal carcinoma. Transcriptome sequencing was performed on RKO cells following PFDN6 knockdown. A PCA demonstrated the independence of CRC samples from the controls (figure 4A). Next, we analysed the co-expressed transcripts in the shPFDN6 and control groups (figure 4B). We observed a substantial overlap in the expression of transcripts between the shPFDN6 and control groups. Consequently, we conducted pairwise and global transcript expression comparisons with the control group. Red indicates upregulated transcripts and green indicates downregulated transcripts (figure 4C). Pairwise or global comparisons indicated that both groups exhibited a significant number of upregulated and downregulated transcripts. The results of the volcano plot indicate the fold change (log2FC) in the expression difference of DEGs between the two groups; each point in the plot represents a gene, where red indicates upregulated genes and green indicates downregulated genes (figure 4D–G). Next, we conducted a detailed analysis of the trends in differential transcript expression between shPFDN6 and control group (figure 4H). We selected subcluster30 transcript in the line plots, and the results indicated significant differences in this transcript between the two groups. Therefore, we performed a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway classification analysis to predict the cells in the shPFDN6 and control groups. The results suggested significant differences between the shPFDN6 and control groups in the signalling pathways related to cellular processes, genetic information processing and metabolism. Target genes were significantly enriched in the Gene Ontology (GO) terms intracellular, protein-containing complex and organelle part (figure 4J). Finally, we constructed a network of shPFDN6 therapeutic targets and created an interaction network (protein protein interaction networks (PPI)) (figure 4K). The square nodes represent functional information, the circular nodes represent genes, and the edges represent associations between genes and functions. The size of a node is proportional to its connectivity (degree), indicating that the nodes with more connections are larger. The colours of the square nodes represent the enrichment levels of the functions, with darker colours indicating higher enrichment. The larger the area of a square node, the more differential genes are involved, making a greater contribution to the biological phenomena. In summary, the sequencing results indicate that PFDN6 may activate a series of downstream oncogenic signalling pathways by promoting a specific transcript, thereby accelerating the onset and progression of cancer.

PFDN6 silencing upregulated the ZNF575 expression in CRC cells

To determine how PFDN6 regulates CRC progression, we used transcriptome sequencing to identify the candidate genes. Therefore, we conducted a thorough selection and comparative analysis of genes that were upregulated after PFDN6 silencing. Differential gene expression (log2FC) between the shPFDN6 and shCtrl groups was examined to identify significant differences in gene expression (figure 5A). Among the upregulated genes, ZNF575 was most significantly upregulated when PFDN6 was silenced (figure 5B). Moreover, some studies have demonstrated that ZNF575 impairs CRC growth by targeting the p53 promoter and that the downregulation of ZNF575 is positively associated with the prognosis of patients with CRC. These results suggest that PFDN6 regulates CRC invasion and migration by targeting ZNF575. Therefore, we used co-immunoprecipitation (CoIP) to detect the interaction between PFDN6 and ZNF575. CoIP results demonstrated the binding of PFDN6 to ZNF575 (figure 5C). KEGG pathway classification and KEGG enrichment analysis were also performed based on the KEGG Database. Most proteins were involved in mRNA surveillance, RNA transport and RNA degradation pathway (figure 5B,C). In summary, the regulation of PFDN6 and ZNF575 is closely associated with CRC progression. Our mechanistic studies confirmed that the silencing of PFDN6 can effectively alleviate the invasion and metastasis of CRC, making it a promising research target for the treatment of CRC and other related diseases.

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