Original Research | Published: 10 April 2024

PFDN6 contributes to colorectal cancer progression via transcriptional regulation


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Objective Colorectal cancer (CRC) is a common cancer worldwide. Although there are several treatments for cancer, the therapeutic effect on CRC remains unsatisfactory, and it is imperative to identify new therapeutic targets.

Design Prefoldin (PFDN) is mainly used in the cytoskeleton assembly during the folding of actin and tubulin monomers. However, whether PFDN subunits are involved in regulating the development of CRC remains to be elucidated. In this study, molecular biology, cell culture, transcriptome sequencing and other experimental techniques, combined with bioinformatics, were used to verify the regulatory effects of PFDN6 on CRC.

Results PFDN6 expression is elevated in patients with CRC and is closely associated with the development of CRC. Knockdown of PFDN6 reduced the tumour cell number, promoted apoptosis, and inhibited the migration and invasion of CRC cells in HCT-116 and RKO cell lines. Mechanistically, differentially expressed genes and related signalling pathways in RKO cells after PFDN6 knockdown were analysed by transcriptome sequencing.

Conclusion PFDN6 was found to regulate the generation and development of CRC by targeting ZNF575. These results open new avenues for therapeutic interventions for patients with CRC.

What is already known on this topic

  • Colorectal cancer (CRC) demonstrates a high metastatic potential and low survival rate. Current treatments, including surgery and chemotherapy, are limited, emphasising the urgent need for a a deeper understanding of the cellular and molecular mechanisms to improve diagnosis and treatment outcomes.

What this study adds

  • PFDN6 was implicated in the occurrence and development of CRC.

  • PFDN6 promoted the migration and metastasis of CRC through its interaction with ZNF575.

How this study might affect research, practice or policy

  • This study provided the initial evidence indicating that PFDN6 had the potential to be a novel target for regulating CRC, as it interacted with ZNF575. These findings introduced a promising therapeutic strategy for the treatment of CRC.


Colorectal cancer (CRC) is the third most widespread malignancy worldwide, and is characterised by a substantial propensity for metastasis.1 The survival rate of patients with metastatic CRC has been documented to be below 10% within a 5-year time frame.2 Projections indicate an anticipated surge of 2.2 million fresh CRC cases and 1.1 million fatalities by the year 2030, signifying a notable 60% escalation in the global burden.3 4 Surgical intervention, radiotherapy, chemotherapy and targeted therapy are viable modalities for managing CRC.5 Furthermore, the efficacy of gefitinib and erlotinib, which are inhibitors of the vascular endothelial growth factor and epidermal growth factor receptor, as primary chemotherapeutic interventions for CRC, has been substantiated.6 Numerous studies have documented the significance of molecular markers and biomarkers, including APC, β-catenin (CTNNB1), KRAS, BRAF, SMAD4, transforming growth factor-β receptor 2 and TP53, in the early detection and management of CRC.7 Various therapeutic modalities are used to treat this type of malignancy. Nevertheless, the treatment outcomes for CRC are unsatisfactory owing to recurrence and metastasis, especially in advanced cases. Taken together, a deeper understanding of the cellular and molecular mechanisms underlying CRC tumourigenesis will provide insights into the diagnosis and treatment of CRC.

The prefoldin subunit (PFDN) consists of six different subunits. PFDN is a co-chaperone that plays an important role in the folding of actin and tubulin monomers during cytoskeletal assembly.8–10 The current studies on PFDN6 are limited; the characteristics of PFDN6 are not fully understood, and its potential use in CRC remains to be confirmed.11 Previous studies have demonstrated the potential diagnostic and prognostic value of PFDN6 in dexamethasone-resistant childhood acute lymphoblastic leukaemia.12 Zheng et al showed that PFDN6 could be used as a marker of osteoporosis risk.13 A study by Li Gao et al showed that PFDN6 could act as a pivotal susceptibility gene for COVID-19 in patients with lung adenocarcinoma and was significantly associated with multiple immune cell infiltrations.14 Additionally, it has also been shown that PFDN6 expression is significantly associated with the prognosis of patients with ovarian cancer.15 However, studies investigating the role of PFDN6 in CRC are limited. Therefore, an in-depth investigation of CRC will provide new insights into its clinical diagnosis and treatment.

In this study, we analysed the clinical characteristics of patients with CRC from The Cancer Genome Atlas (TCGA) cohort. The further detection of PFDN6 expression in the tumour tissues of patients with CRC revealed that PFDN6 expression was significantly upregulated in tumour tissues compared with para-carcinoma tissues. The regulatory effects of PFDN6 on CRC were verified in vivo. The results showed that PFDN6 knockdown promoted CRC cell apoptosis and facilitated the migration and invasion of CRC cells. Finally, transcriptome sequencing was used to analyse the differences in genes and signalling pathways. We found that PFDN6 acts directly on ZNF575 to regulate CRC progression. Thus, PFDN6 has the potential to become a novel therapeutic target for CRC.

Materials and methods

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.


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.


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.

Figure 1
Figure 1

Expression of PFDN6 in patients with CRC. (A) Principal component (PC) analysis of normal and cancer samples. (B) Differentially expressed genes in normal and cancer samples. (C) Data mining of TCGA Database showed the expression of PFDN6. (D, E) Χ2 test for correlation between PFDN6 expression and different CRC stages (stage I, stage II, stage III and stage IV) and TNM stages. Statistical differences were analysed using Spearman’s test. (F) H&E staining of mice in control (Ctrl), tumour (T), tumour+lymph node metastasis (T+LNM) groups. Scale bar, 100 µm. Magnification times: 200×, 400×. (G) The expression levels of PFDN6 in CRC tumour tissues with or without lymph node metastasis and para-carcinoma tissues were determined by immunohistochemical staining. Magnification times: 200×, 400×. (H) The statistics of (G). The data are expressed as mean±SEM; **p<0.01 vs T+LNM, ***p<0.001 vs Ctrl. CRC, colorectal cancer; FC, fold change; TCGA, The Cancer Genome Atlas; TNM, tumour–node–metastasis.

Table 1
Expression patterns in colorectal cancer tissues and para-carcinoma tissues revealed in immunohistochemistry analysis
Table 2
Relationship between PFDN6 expression and tumour characteristics in patients with colorectal cancer
Table 3
Correlation between PFDN6 expression and clinicopathological parameters

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.

Figure 2
Figure 2

Construction of cell model for knockdown of PFDN6. (A) The mRNA expression of PFDN6 in CRC cell lines was evaluated by qRT-PCR. (B) The knockdown efficiencies of shPFDN6-1, shPFDN6-2 and shPFDN6-3 were detected by qRT-PCR. (C, D) The mRNA and protein expression of PFDN6 in CRC cell lines (HCT-116 and RKO cell lines) was evaluated by qRT-PCR (C) and western blot (D), the relative expression was quantified by normalising to GAPDH. (E) Immunofluorescence detection of PFDN6 expression in CRC cell lines. GFP: PFDN 6, magnification: 400. The data are expressed as mean±SEM; *p<0.05, **p<0.01, ***p<0.001 vs shCtrl. CRC, colorectal cancer; GFP, green fluorescent protein; qRT-PCR, quantitative real-time PCR.

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.

Figure 3
Figure 3

Effect of knockdown of PFDN6 on CRC cell lines. (A) PFDN6 expression after knockdown of PFDN6 was evaluated by immunofluorescence assay. Magnification: 400. (B) The cell proliferation rate was evaluated in CRC cell lines after infection by Celigo cell counting assay. (C–G) The effects of RFWD3 knockdown on cell apoptosis (C,D) and cell cycle (E–G) were examined by flow cytometry. (H–L) The migration rate of cells was detected in CRC cell lines after infection by transwell assay (H–J) and wound healing assay (K,L). Magnification times: 200×. The data are expressed as mean±SEM; *p<0.05, **p<0.01, ***p<0.001 vs shCtrl. CRC, colorectal cancer.

Figure 4
Figure 4

Screening results and visualisation of significantly differentially expressed genes (DEGs). (A) Principal component analysis (PCA) scatter plots were performed with all identified features to explore the largest sources of variance within each omics dataset. (B) Venn diagrams showed the overlapped features, including 14 046 genes among samples collected from shPFDN6 cells. (C) Statistical analysis of differential expression. (D) Volcano plot shows DEGs from shPFDN6 cells (compared with shCtrl cells), and significantly altered genes are shown in red (upregulation) or green (downregulation). (E–G) The scatter plot showed DEGs among the six groups (shCtrl-1, shCtrl-2, shCtrl-3, shPFDN6-1, shPFDN6-2 and shPFDN6-3). (H) Line plots depicting the expression trends of differential transcripts in various modules. Each subplot within the figure represents the expression trend of transcripts within a specific submodule. (I,J) The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses to visualise associated functional pathways. (K) Significantly enriched functional-gene interaction network diagram, where square nodes represent functional information, circular nodes represent genes, and edges represent the associations between genes and functions.

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.

Figure 5
Figure 5

Enrichment analysis of upregulated and downregulated genes, respectively. (A) Venn diagrams about upregulated genes among the six groups (shCtrl-1, shCtrl-2, shCtrl-3, shPFDN6-1, shPFDN6-2 and shPFDN6-3). (B) Heatmap of the expression level of DEGs after PFDN6 knockdown. (C) CoIP to detect the interaction of ZNF575 and PFDN6. (D,E) KEGG enrichment analysis about downregulated genes among the above six groups. CoIP, co-immunoprecipitation; DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.


CRC, a cancer of the gastrointestinal tract, is ranked second for causing cancer-related deaths worldwide. It has been shown that the number of newly diagnosed patients with CRC was more than 1.9 million, and the number of deaths was 935 000 in 2020.21 The cancer stage at the time of diagnosis has a major impact on survival. The 5-year survival rate is approximately 90% for localised disease, 70% for regional diseas and 13% for distant metastatic CRC.22 CRC is currently treated with surgery, radiotherapy and chemotherapy, and is often associated with a large number of side-effects in patients with advanced tumours due to its low specificity and cytotoxicity to growing and dividing cells.23–25 However, the efficacy of CRC treatments remains poor. Therefore, the aetiology and pathogenesis of CRC remain unclear (figure 6).

Figure 6
Figure 6

Our analysis of publicly available clinical datasets (TCGA) confirmed the correlation between PFDN6 and CRC, and demonstrated that the depletion of PFDN6 inhibited the migration and invasion of CRC cells. Furthermore, we revealed that PFDN6 regulates the progression of CRC by modulating the expression of ZNF575. CRC, colorectal cancer; TCGA, The Cancer Genome Atlas.

An increasing number of studies have shown that the PFDN is composed of six distinct subunits (PFDN1–6) that are responsible for protein delivery to the class II eukaryotic cytosolic chaperone c-cpn.8 Among these, PFDN1 expression was positively correlated with tumour size and invasiveness.11 Currently, the research on PFDN is limited. Nonetheless, the optimal properties of each gene have not been fully recognised and their corresponding applications are yet to be demonstrated. However, the predictive value of PFDN in tumours, especially in CRC, remains largely unknown. The link between PFDN and tumours may change our understanding of PFDN, further extending its potential regulatory role in cancer research and clinical practice.

This study aimed to investigate whether PFDN6 is involved in the regulation of CRC. First, we screened genes significantly associated with CRC from the CRC transcriptome sequencing data in TCGA Database. Our results showed that the expression of PFDN6 was elevated in CRC tissues and that the expression level of PFDN6 was positively correlated with the disease stage, suggesting that PFDN6 has a potential predictive role in the development of CRC. Second, the expression level of PFDN6 was detected in tumour tissues of patients with CRC and tumour tissues with lymph node metastasis. Consistent with the TCGA Database, the expression level of PFDN6 was upregulated in the tumour tissues of patients with CRC, and this upregulation was more significant in tissues with lymph node metastasis. Next, two human CRC cell lines were used to verify the protective effects of PFDN6 knockdown on CRC. The results showed that PFDN6 knockdown promoted the apoptosis of CRC cells, inhibited the invasion and migration of CRC cells, and played a protective role in CRC. Finally, we performed heatmap and Venn diagram analyses to identify the genes and targets in shPFDN6 to treat CRC. GO and KEGG enrichment analyses were performed to visualise the associated functional pathways. Our results indicate that ZNF575 was upregulated after silencing PFDN6.

However, the current study has a few limitations. First, the sample size was small. Next, we performed in vitro functional studies on PFDN6. However, further in vivo experiments are required to confirm our findings. However, the regulatory mechanisms of PFDN6 are poorly understood. Further experiments are needed to investigate the role and related molecular mechanisms of PFDN6 in regulating other characteristics of cancer, such as metastasis, therapeutic responses and the immune environment.

In this study, we identified a novel role of PFDN6 in CRC. We found that PFDN6 regulates CRC progression by regulating the expression of ZNF575. Our analysis of publicly available clinical datasets (TCGA) confirmed the association between PFDN6 and CRC stages. Furthermore, we confirmed that knockdown of PFDN6 inhibited the migration and invasion of CRC cells. Thus, the findings of our study emphasise the role of PFDN6 in CRC progression and suggest that the development of therapeutics for patients with CRC with high PFDN6 expression could enhance treatment efficacy.