Enterotoxin‐related genes PPFIA4 and SCN3B promote colorectal cancer development and progression

To identify the role of enterotoxin‐related genes in colorectal cancer (CRC) development and progression. Upregulated differentially expressed genes shared by three out of five Gene Expression Omnibus (GEO) data sets were included to screen the key enterotoxin‐induced oncogenes (EIOGs) according to criteria oncogene definition, enrichment, and protein–protein interaction (PPI) network analysis, followed by prognosis survival, immune infiltration, and protential drugs analyses was performed via integration of RNA‐sequencing data and The Cancer Genome Atlas‐derived clinical profiles. We screened nine common key EIOGs from at least three GEO data sets. A Cox proportional hazards regression models verified that more alive cases, decreased overall survival, and highest 4‐year survival prediction in CRC patients with high‐risk score. Protein tyrosine phosphatase receptor type F polypeptide‐interacting protein alpha‐4 (PPFIA4), STY11, SCN3B, and SPTBN5 were shared in the same PPI network. Immune infiltration results showed that SCN3B and synaptotagmin 11 expression were obviously associated with B cell, macrophage, myeloid dendritic cell, neutrophils, and T cell CD4+ and CD8+ in both colon adenocarcinoma and rectal adenocarcinoma. CHIR‐99021, MLN4924, and YK4‐279 were identified as the potential drugs for treatment. Finally, upregulated EIOGs genes PPFIA4 and SCN3B were found in colon adenocarcinoma and PPFIA4 and SCN3B were proved to promote cell proliferation and migration in vitro. We demonstrated here that EIOGs promoting a malignancy phenotype was related with poor survival and prognosis in CRC, which might be served as novel therapeutic targets in CRC management.


| INTRODUCTION
Colorectal cancer (CRC), with the typical symptoms such as hematochezia or melena, abdominal pain, [1,2] is one of the most common malignancies, accounting for approximately 10% of all new diagnosed cancer cases worldwide. [3]According to previous studies, multiple risk factors, including nutrition, lifestyle, smoking, obesity, and so on, could induce the development of CRC. [4]The statistical data have shown that in the United States, approximately 153,000 new diagnosed cases of CRC annually, [5] while in China at 2020, about 555,477 newly diagnosed CRC and 286,162 CRC-related deaths occurred. [6]Considering these serious situation, effective diagnosis and management CRC patient is urgently needed, therefore, deep mining the disease mechanisms involved in the pathophysiological processes involved in the CRC is main methodology to intervention.
Studies have shown that microbe released toxin could result in the intestinal and colon epithelium damage, thereby promoting the development and progression of CRC. [7,8]With the advancement of gut microbiome technology, it has been possible for identification of microbe strains and corresponding metabolites or toxin that functions in CRC generation and development.In a early study by Toprak et al. showed that an increased prevalence of enterotoxigenic Bacteroides fragilis (ETBF) was found in CRC patients compared to the normal controls. [9]However, until now, little is known about the detailed role and specific molecular mechanisms of enterotoxins in the development of CRC.
Theoretically, the analysis of global gene expression changes in CRC cell line or intestinal epithelial cells stimulated with enterotoxin may help explore the role of enterotoxin in CRC carcinogenesis.Since the rapid development of multiomics technologies, including RNA-sequencing (RNA-seq), [10][11][12] proteomic, [13][14][15] and epigenetic ATAC-seq, [16][17][18][19][20][21] and maturation of bacterial protein purification technologies, [22,23] related data sets are available for possible study about the role of enterotoxin in CRC.Therefore, rational utilization and effective integration of these data sets provides us a window to solve these concerns.
Here, we aimed to identify the role of enterotoxin-related genes in CRC development and progression using both bioinformatic and experimental study.

| Data sets acquisition and enterotoxin-related genes identification
Through searching the Gene Expression Omnibus database using the terms as follows: "Enterotoxin" AND "Intestine"/"Intestinal" AND "Homo sapiens"[porgn:_txid9606], the data sets containing the information about enterotoxin-treated CRC cell lines at 24 h were employed in our study.Finally, five data sets were selected and they were GSE104492, GSE144633, GSE162393, GSE115081, and GSE164334.The detailed information about these data sets were listed in Table 1.
The differentially expressed genes (DEGs) of above data sets were screened using the DESeq. 2 package in R software according to |log 2 FC| >1 and p < 0.05, and visualized by volcano plots.Then, the intersection of the DEGs from these five data sets were performed and the upregulated DEGs with oncogenes feature shared in at least three data sets (defined as key enterotoxin-induced oncogenes, key EIOGs) were selected for the following analysis.

| Gene enrichment analysis and protein-protein interaction (PPI) network of EIOGs
Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and reactome gene enrichment analyses were performed using WebGestalt (http://www.webgestalt.org/)based on the key EIOGs genes according to the following criteria: (1) terms with a p < 0.05, (2) a minimum overlap of two genes, and (3) top 10 were selected.The PPI enrichment analysis was conducted based on STRING (https://string-db.org/)using a confidence score > 0.14 based on the key EIOGs genes.

| Correlation analyses of the key EIOGs and pathway score
Bulk RNA-seq data of 620 CRC cases were downloaded from the The Cancer Genome Atlas (TCGA) data set (https://portal.gdc.com), while T A B L E 1 Information about the GEO data sets included in this study.

GSE104492
Caco- pathway scores were calculated using gene set variation analysis package in R. The correlation analyses were carried out using Spearman method and scatterplots were employed for illustration.

| Expression and survival analyses using the key EIOGs
The survival analyses including the overall survival (OS) and progression-free survival (PFS) of these key EIOGs in CRC were performed using GEPIA2 online tool [doi: 10.1093/nar/gkx247], and the receiver operating characteristic (ROC) curves with p value and hazard ratio (HR) was obtained for comparison according to the highlow expression of the key EIOGs.Moreover, the expression of these key EIOGs between tumor and normal tissues was also compared.

| Multivariate Cox regression prognostic analysis
According to the bulk RNA-seq data and corresponding clinical data obtained from TCGA colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) data sets, multivariate cox regression was used to establish the prognostic risk model.The risk scores of individual samples were calculated using the coefficient value multiply the expression level of each EIOGs.The efficacy of the prognostic risk model was evaluated using area under curve (AUC) of the corresponding ROC curve.

| Correlation between tumor immune infiltration levels and EIOGs
The immune infiltration analyses were performed using the Tumor IMmune Estimation Resource (TIMER) databases.Based on RNAsequencing data from the COAD and READ TCGA data set, the correlations between EIOG expression and immune score were analyzed using Spearman's correlation.

| Potential therapeutic drugs analysis based on the EIOGs
The

| Western blot assay
The cells were lysed in radioimmunoprecipitation assay lysis buffer (BL651A; Invitrogen) to extract protein.Sodium dodecyl sulfate polyacrylamide gel electropheresis gel was used to isolate total proteins from cells, and then the protein was transferred to polyvinylidene fluoride membranes.The membranes were then blocked with 3% bovine serum albumin for 2 h at room temperature and then incubated with primary antibodies (anti-PPFIA4 and anti-SCN3B) overnight at 4°C.Then, the membranes were incubated with specific secondary antibodies for 2 h at room temperature.Signals were detected via electrochemiluminescence Kit (WP20005; Invitrogen) was used to detect the protein bands.

| Wound healing assay
Transfected or untransfected cells were seeded in 12 well plate.
Wound healing assay was performed when the confluency reached over 90%, and then a 200-μL tips made scratch, and the migration range was quantified through photographing at 0 and 48 h.

| Transwell
Transwell assay was performed to evaluate cell migration.5 × 10 3 transfected or untransfected cells in DMEM without FBS were added onto the upper chamber and 600 μL DMEM containing 10% FBS was added into the lower chamber.Then, the cells were cultured in incubator.The cells stained by crystal violet at 48 h followed by imaging.

| Statistical analysis
R software (version 4.3) was employed for the statistical analyses.
Quantitative data was expressed as mean ± standard deviation (SD).
Comparison of quantitative data between two groups was analyzed using t test.High-low expression of the genes was categorized according to the mean value.Kaplan-Meier method was used to analyze the relationship between gene expression and OS and PFS in cancer patients, and log-rank test was used for comparison.ROC curves were also used to determine the prognostic value of genes.
Correlation analyses were carried out using Pearson or Spearman method according to the data distribution property.p < 0.05 was considered as statistical significance.

| Enterotoxin-related genes in CRC cell lines and key EIOGs
The DEGs from five included data sets were illustrated by volcano in data sets, respectively.Among these DEGs, seven DEGs defined as core enterotoxin-associated genes were shared by four data sets, including six were coding genes: CCN1, PPFIA4, DUSP2, BTBD19, EPHA2, and CXCL2.Moreover, 89 DEGs defined as important enterotoxin-associated genes were shared by three data sets (Figure 1F).Furthermore, we selected the upregulated DEG shared by at least three data sets and defined them as the key EIOGs.Finally, nine key EIOGs were obtained and they were PPFIA4, synaptotagmin 11 (SYT11), SCN3B, SLC46A2, TAS2R5, SPTBN5, FBP1, BEST4, and MIA (Figure 1G).

| Enrichment analysis and PPI network of EIOGs
The GO, KEGG, and reactome analyses using nine EIOGs were also

| Immune infiltration levels associated with key EIOGs
According to the TIMER database, the correlations between EIOG

| Potential drugs for key EIOGs
As shown in Figure 5A, key EIOGs might be sensitized by CHIR-99021, MLN4924, and YK4-279, as supported by the GDSC database.No ideal drugs were found (positively correlated with the majority of the nine key EIOGs) in the CTRP database (Figure 5B).

| Survival analysis using the key EIOGs: PPFIA4 and SCN3B
We further selected two key EIOGs PPFIA4 and SCN3B based on their good performance in PPI network and immune infiltration for the survival analyses using the data from TCGA data set.The results showed that both PPFIA4 (OS: HR = 2.5, p = 0.00027; PFS: HR = 2.5, p = 0.00019) and SCN3B (OS: HR = 1.9, p = 0.0079; PFS: HR = 1.6, p = 0.045) were associated with poor OS and PFS in COAD patients (Figure 6A-D).

| Correlation of the core EIOG PPFIA4 and pathways in CRC
PPFIA4 was then selected for the pathway correlation analyses based on its presence in the PPI network and correlation with patient survival in CRC.Further PPFIA4 expression and pathway score correlation analyses were performed and the results showed that PPFIA4 was positively correlated with Angiogenesis and Collagen formation, and was negatively correlated with biotin metabolism, butanoate metabolism, citrate cycle, D-arginine and D-ornithinemetabolism, glycosylphosphatidylinositol anchor biosynthesis and lipoic acid metabolism (Supporting Information S1: Figure 1A-H).

| PPFIA4 and SCN3B promoted cell proliferation, wound healing, and migration in vitro
To investigate the function of EIOG in colon cancer cell lines, we selected two of them (PPFIA4 and SCN3B) for in vitro experiments.
The expression of PPFIA4 and SCN3B was examined in CRC tissues.
The results showed that increased expression of PPFIA4 and SCN3B with the higher stage of the colon cancer tissue (Figure 7A,B).These results indicated that PPFIA4 and SCN3B may be oncogenic genes of CRC.Next, lentiviral techniques were used to overexpress and knockdown PPFIA4 and SCN3B in CRC cells, which were verified by qRT-PCR and Western blot.The expression of PPFIA4 or SCN3B in CRC cells of si-PPFIA4 and si-SCN3B groups was significantly lower than that of si-NC group, and the expression of PPFIA4 or SCN3B in CRC cells of OV-PPFIA4 and OV-SCN3B groups was significantly higher than that of OV-NC group (Figure 8A-C).CCK-8 (Figure 8D,E), wound healing (Figure 8F), and transwell assay (Figure 8G) revealed that overexpression and knockdown of PPFIA4 and SCN3B could promote and inhibit the cell proliferation and cell migration, respectively (Figure 8D-G).These results suggested that PPFIA4 and SCN3B can promote the proliferation and migration of CRC cells.

| DISCUSSION
More and more recent evidence have supported the role of enterotoxins as an important risk factor driving the development of CRC.An early study by Qin et al. revealed that Bacteroides fragilis was one of the major carcinogenic bacteria contributing the CRC growth. [24]As a secretion product, Bacteroides fragilis toxin is considered as the main virulence factor during the CRC carcinogenesis. [25,26]It is reported that ETBF can promote CRC proliferation via CCL3-related pathways, [27] moreover, lower-dose recombinant Bacteroides fragilis enterotoxin-2 was found to generate CRC inhibition effects in a azoxymethane/dextran sulfate sodium-induced mouse model, [28] furthermore, ETBF was reported to be detected in 90% of CRC patients (vs.only 50% in healthy individuals), which is significantly associated with CRC. [29,30]In addition, some enterotoxin receptors (e.g., guanylyl cyclase C), have been regarded as a potential pharmacological target in CRC immunotherapy. [31]Taken together, besides the role in CRC development and progression, enterotoxins might also provide therapeutic intervention target for CRC treatment.
In the present study, we have uncovered several oncogenes that mediates enterotoxin-induced CRC development.Until a recent study by Fu et al., [32] the role of PPFIA family member in human CRC has rarely been reported.PPFIA4 was previously shown to promote COAD proliferation and migration by enhancing tumor glycolysis. [33]SYT11 could promote stem-like molecular subtype in gastric cancer oncogenesis. [34]However, it is unclear its role in CRC.
Our results suggested that SYT11 might be involved in enterotoxininduced CRC progression; however, due to its negative correlation with survival of CRC patients, SYT11 might serve as a concomitantly elevated factor in CRC progression.Among the nine key EIOGs, SCN3B may play an important in CRC progression based on its correlation with poor OS and PFS in COAD, and its hub position in the PPI network.An early study by Adachi et al. identified that SCN3B as p53-inducible proapoptotic gene, indicating its possible proapoptotic role in CRC. [35]Here, we also identified that SLC46A2 is a highly risk factor in both COAD and READ according to the results of our Cox regression models.Cordova et al. have shown that it functions as the dominant cGAMP importer in primary human monocytes; monocytes and M1-polarized macrophages directly sense tumor-derived extracellular cGAMP in murine tumors. [36]ENG ET AL.Combining the immune infiltration data, our results also indicated that the PPFIA4-STY11-SCN3B-SPTBN5 network may serve as an important molecular mechanism mediating the procarcinogenesis of enterotoxins via their impacts on immune cell expression.In both COAD and READ, STY11 and SCN3B were positively correlated with all immune cell expression; PPFIA4 was correlated with the expression scores of myeloid dendritic cells, neutrophils, and CD4+ T cells in COAD, as well as neutrophils and CD4+ T cells in READ.
These findings suggested that the above procancer mechanisms may be achieved through amplified inflammatory signaling and immune infiltration.
Previous studies have established several prognostic model with a ranged AUCs from 0.643 to 0.83 for CRC prognosis prediction. [37,38]The genes included in their models were as follows: PTGER2, FGF2, IGFBP3, ANGPTL4, DKK1, WNT16, SPP1, ZNF532, COLEC12, DPP7/2, YWHAB, MCM4, FBXO46, GLP2R, VSTM2A, and so on. [37,39,40]In our model, we included directly nine key EIOGs, and finally got a mode with the highest AUC reached 0.788, which indicated that EIOS alone play an important role in the CRC progression.We also proposed that a better prognostic model could be established based on a comprehensive understanding all these genes.
According to the potential drug database searching, we found that CHIR-99021, MLN4924, and YK4-279 could serve as the candidate medication based on the enterotoxin strategies.[43] Interestingly, low dose of CHIR 99021 could increase cell survival, progenitor cell proliferation, and migration, while a higher dose increased cell apoptosis and arrested cell growth. [44]Therefore, the specific effect of CHIR-99021 on CRC inhibition remains to be investigated.
Similarly, MLN4924 was reported to promote self-renewal of limbal stem cells and ocular surface restoration. [45]Meanwhile, it can inhibit both the tumor stroma and angiogenesis in pancreatic cancer via Gli1 and REDD1 related pathways. [46]Moreover, MLN4924 was found to inhibit cell proliferation by targeting the activated neddylation pathway in endometrial carcinoma. [47]It sensitizes apoptosis and necroptosis in human cancer cells with different origins. [48]Based on our results, we proposed that MLN4924 could be one of the potential drugs for CRC treatment. of noncoding RNAs in present study has not been explored between the limitation of the RNA-seq data.Finally, although some of the in vitro functional studies have been employed, possible the mechanistic role of these EIOGs has not been determined.Therefore, certain study with detailed mechanistic studies should be performed in future studies.

| CONCLUSION
In conclusion, we demonstrated here that PPFIA4 and SCN3B promoted cell proliferation and migration, and were related with poor prognosis in CRC, which might be served as novel therapeutic targets in CRC management.

3. 3 |
Prognosis analysis using multivariate Cox regression modelBased on these nine key EIOGs, the nine-factor Cox proportional hazards regression model was established for COAD and READ prognosis prediction.F I G U R E 1 Enterotoxin-related genes in CRC cell lines and key EIOGs.(A-E) Volcano plots to illustrated DEGs from the five included GEO data sets (A) GES104492, (B) GSE115091, (C) GSE144633, (D) GSE162393, and (E) GSE164334.(F) Venn diagrams were used to illustrated the DEGs shared in five included GEO data sets with all the DEGs, core and important genes were, respectively, defined as the DEGs shared by four and three data sets.(G) Venn diagrams were used to illustrated the DEGs shared in five included GEO data sets with only the upregulated DEGs, nine key EIOGs were identified using the criteria of DEG with upregulated pattern and oncogene features.CRC, colorectal cancer; DEGs, differentially expressed genes; EIOGs, enterotoxin-induced oncogenes; GEO, Gene Expression Omnibus; PPI, protein-protein interaction.For COAD, risk score was calculated as follows: PPFIA4 (0.6628), SYT11: (−0.1074),SCN3B (0.1596), SLC46A2 (0.3949), TAS2R5 (0.1897), SPTBN5 (0.1531), FBP1 (0.0476), BEST4 (0.1348), MIA (0.1916).All COAD patients were divided into high risk and low risk groups according to the median risk score, and the results showed that the COAD patients in the high-risk group had significantly lower OS than the low-risk group (HR = 2.654; 95% confidence interval [CI]: 1.739-4.051;p = 6.05e−06).Moreover, ROC results indicated that this risk prognosis model had the highest predictive value for 4-year survival prognosis of COAD patients, with AUC = 0.722 (Figure 3A).For READ, risk score was calculated as follows: PPFIA4 (−0.4183),SYT11: (−0.0907),SCN3B (0.0497), SLC46A2 (0.7344), F I G U R E 2 Enrichment analysis and PPI network of EIOGs.(A) The top 10 enriched GO terms.(B) The top 10 enriched reactome terms.(C) The PPI network of EIOGs.EIOG, enterotoxin-induced oncogene; GO, Gene Ontology; PPFIA4, protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4; PPI, protein-protein interaction; SYT11, synaptotagmin 11.F I G U R E 3 Multivariate Cox regression models for prognosis prediction using COAD and READ data sets.(A)The nine key EIOGs Cox proportional hazards regression model for prognosis prediction using COAD data sets.The high and low risks were categorized using the risk score, and scatter plot, survival curves, and survival ratio were plotted according to the survival statue and time.Obviously, more alive cases, lower overall survival, and highest 4year survival prediction were found with high-risk scores.(B) The nine key EIOGs Cox proportional hazards regression model for prognosis prediction in READ.The high and low risks were categorized using the risk score, and scatter plot, survival curves, and survival ratio were plotted according to the survival statue and time.Obviously, more alive cases, lower overall survival, and highest 4-year survival prediction were found with high-risk score.AUC, area under the curve; CI, confidence interval; COAD, colon adenocarcinoma; EIOG, enterotoxin-induced oncogene; PPFIA4, protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4; READ, rectal adenocarcinoma; SYT11, synaptotagmin 11; TAS2R5 (−0.5937),SPTBN5 (0.1822), FBP1 (0.1675), BEST4 (−0.3475),MIA (−0.7574).The results of prognosis analysis revealed that lower OS was observed in READ patients in the high-risk group compared with these in the low-risk group (HR = 3.048; 95% CI: 1.271-7.31,p = 0.0125).Additionally, this risk prognosis model had the highest predictive value for 4-year survival prognosis of READ patients, with AUC = 0.788 (Figure3B).
expression and immune cell (B cell, macrophage, myeloid dendritic cell, neutrophils, T cell CD4+ and T cell CD8+) expression in CRC (COAD and READ) were analyzed with Spearman methods, and the results were shown in Figure 4A,B.SYT11, SCN3B were significantlyF I G U R E 4Correlation between immune infiltration levels and EIOGs.(A) According to the TIMER database, the correlations between EIOG expression and immune cell expression in COAD analyzed with Spearman methods.Significance was found on SYT11 and SCN3B with B cell, macrophage, myeloid dendritic cell, neutrophils, and T cell CD4+ and CD8+, while no correlation was found with MIA.(B) The correlations between EIOG expression and immune cell expression in READ.Significance was found on SYT11 and SCN3B with B cell, macrophage, myeloid dendritic cell, neutrophils, and T cell CD4+ and CD8+, while no correlation was found using FBP1 and BEST4.COAD, colon adenocarcinoma; EIOG, enterotoxin-induced oncogene; PPFIA4, protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4; READ, rectal adenocarcinoma; SYT11, synaptotagmin 11; TIMER, Tumor IMmune Estimation Resource.associated with all immune cell infiltrations in CRC (p < 0.05).In addition, TAS2R5 was associated with T cell CD4+ in CRC, SPTBN5 was associated with B cells, SLC46A2 was associated with myeloid dendritic cells, neutrophils, and T cell CD4+, and PPFIA4 was associated with neutrophils and T cell CD4+ (p < 0.05) (Figure4A,B).

F I G U R E 5
Potential drugs based on the IC 50 correlation using the nine EIOGs.(A) The analysis of drug sensitivity based on the GDSC database.The results revealed that key EIOGs might be sensitized by CHIR-99021, MLN4924, and YK4-279.(B) The analysis of drug sensitivity was also performed based on the CTRP database.No ideal drugs were found (positively correlated with the majority of the nine key EIOGs) in the CTRP database.Blue bubbles represent negative correlations, red bubbles represent positive correlations, the deeper of color, the higher of the correlation.For bubble plots, the bubble size was positively correlated with the FDR significance.The black outline border indicates FDR ≤ 0. 05.The drugs are ranked by the integrated level of correlation coefficient and FDR values, and only the top 30 ranked drugs were presented.CTRP, Cancer Therapeutics Response Portal; EIOG, enterotoxin-induced oncogene; FDR, false discovery rate; GDSC, Genomics of Drug Sensitivity in Cancer; IC 50 , half-maximal inhibitory concentration; mRNA, messenger RNA; PPFIA4, protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4; SYT11, synaptotagmin 11.

F I G U R E 6
Survival analysis using key EIOGs derived genes, PPFIA4 and SCN3B using COAD data sets.(A) High PPFIA4 expression indicated a poor OS. (B) High PPFIA4 expression indicated a poor PFS.(C) High SCN3B expression indicated a poor OS. (D) High SCN3B expression indicated a poor PFS.COAD, colon adenocarcinoma; EIOG, enterotoxin-induced oncogene; HR, hazard ratio; OS, overall survival; PFS, progression-free survival; PPFIA4, protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4.
The significance of present study was as follows.Identification of EIOGs as possible therapeutic targets for CRC; establishment a prognostic model containing key EIOGs through Cox regression models.In addition, we provided a pioneering insight into the impact of key EIOGs on immune cells in the CRC microenvironment and deepens the understanding of the mechanisms underlying the development of enterotoxin-induced CRC.Based on the key EIOGs, we also found several potential drugs (e.g., CHIR-99021, MLN4924, and YK4-279) that might provide help in CRC treatment.Limitations are presented in present study.First, CRC cell lines rather than normal colorectal cells were employed to DEG discovery after enterotoxin exposure, the finding from the present study could not employed for the condition of CRC occurrence.Second, the role F I G U R E 7 Increased expression of EIOGs genes PPFIA4 and SCN3B in colon adenocarcinoma.(A, B) Increased expression of PPFIA4 and SCN3B with the higher stage of the colon cancer tissue.EIOG, enterotoxin-induced oncogene; PPFIA4, protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4.*p < 0.05; **p < 0.01.F I G U R E 8 PPFIA4 and SCN3B promoted cell proliferation, wound healing, and migration in vitro.(A-C) Overexpression and knockdown of PPFIA4 and SCN3B in colon cancer cell lines were realized by lentivirus and verified by qPCR and Western blotting.(D, E) Cell proliferation assay by CCK-8 revealed that overexpression and knockdown of PPFIA4 and SCN3B could, respectively, promote and inhibit the cell proliferation.(F) Wound healing assay at 48 h showed that overexpression and knockdown of PPFIA4 and FSCN3B could, respectively, promote and inhibit cell migration at 2D level.(G) Transwell cell migration assay revealed that overexpression and knockdown of PPFIA4 and SCN3B could, respectively, promote and inhibit cell migration.2D, two-dimensional; CCK-8, Cell Counting Kit-8; OD, optical density; PPFIA4, protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4; qPCR, quantitative polymerase chain reaction.*p < 0.05; **p < 0.01.