Application of short hairpin RNA of knockdown CFDP1 in preparation of medicine for treating hepatocellular carcinoma

Abstract

The invention relates to the field of tumor treatment, in particular to application of short hairpin RNA with CFDP1 knocked down in preparation of hepatocellular carcinoma drugs. In the invention, the CFDP1 is found to be highly expressed in HCC tissues and cell lines. Overexpression of the CFDP1 is in positive correlation with bad clinical pathological characteristics. In addition, we have observed that CFDP1 overexpression is associated with low disease-free lifetime and overall lifetime. The CFDP1 can promote malignant development of liver cancer in vivo and in vitro. The result of a western blot technology shows that the CFDP1 enhances the malignancy degree of the HCC through an NEDD4-mediated PTEN / PI3K / AKT pathway. By knocking down the CFDP1 through the short hairpin RNA, the growth of the hepatocellular carcinoma can be remarkably inhibited, the apoptosis of the hepatocellular carcinoma can be remarkably induced, and the metastasis of the hepatocellular carcinoma can be remarkably inhibited.

Description

Technical Field

[0001] The present invention relates to the field of tumor treatment, and specifically refers to the application of short hairpin RNA for knocking down CFDP1 in the preparation of drugs for treating hepatocellular carcinoma. Background Art

[0002] Hepatocellular carcinoma (HCC) has been identified as one of the most lethal tumors, and the increase in the incidence of HCC is higher than that of all other tumors. Data obtained from the Global Cancer Monitoring Agency in 2018 showed that HCC is one of the top five cancers worldwide. Among them, the mortality rate caused by HCC is the highest, accounting for 8.2% of cancer-related deaths. In 2018, the global report estimated that there were approximately 841,000 new cases of HCC and approximately 782,000 deaths in HCC patients. Advances in surgical techniques have made effective partial liver resection and liver transplantation possible, which is considered to be the current mainstream treatment method. However, the high recurrence and metastasis rates after surgery seriously affect the survival of HCC patients. Therefore, understanding the occurrence and development mechanism of HCC is crucial to improve the survival rate of HCC patients.

[0003] Cranio Facial Development Protein 1 (CFDP1), a member of the Bucentaurr (BCNT) protein family, is evolutionarily conserved, 299 amino acids long, and contains a highly conserved C-terminal BCNT domain 1, 2. The biological function of CFDP1 is still unclear. Therefore, it is currently unknown whether knocking down CFDP1 can show an anti-cancer effect on HCC. The present invention will evaluate the therapeutic effect of short hairpin RNA that knocks down CFDP1 on HCC through in vivo and in vitro HCC models.

[0004] In this invention, we report that CFDP1 is closely associated with the prognosis of HCC and is upregulated in HCC tissues and cell lines. CFDP1 promotes the progression of HCC. CFPD1-mediated HCC progression involves migration, proliferation, epithelial-mesenchymal transition (EMT), invasion and metastasis processes observed in cell lines. Inhibition of apoptosis also contributes to this process. The results show that CFDP1 is a potential prognostic biomarker that can be used as an innovative therapeutic target for HCC, and CFDP1 upregulates the NEDD4 pathway. Summary of the invention

[0005] The Gene Expression Omnibus (GEO) database was analyzed to obtain gene expression profiles of HCC patients and matched clinical data. Gene co-expression networks were developed by weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers that can be used to predict prognosis. The Cancer Genome Atlas (TCGA) and Gene Expression Profile Interaction Analysis (GEPIA) databases were used to evaluate the relationship between gene expression and survival. To investigate the specific function of CFDP1 in liver cancer, we performed a series of in vivo and in vitro experiments. In addition, we explored the potential mechanism of CFDP1 associated with hepatocellular carcinoma by analyzing the KEGG pathway database and applying the GeneCards analysis method. Through the above studies, we first found that CFDP1 was highly expressed in HCC tissues and cell lines. We first found that the overexpression of CFDP1 was positively correlated with adverse clinicopathological features. In addition, we first found that CFDP1 overexpression was associated with poor disease-free survival and overall survival. We first found that CFDP1 promoted the malignant development of liver cancer both in vivo and in vitro. We first found through Western blotting that CFDP1 enhances the malignancy of HCC through the NEDD4-mediated PTEN / PI3K / AKT pathway. We first found that knocking down CFDP1 by short hairpin RNA can significantly inhibit the growth of hepatocellular carcinoma, significantly induce apoptosis of hepatocellular carcinoma, and significantly inhibit the metastasis of hepatocellular carcinoma. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 Dataset sample description and flow chart. (A) Clinicopathological characteristics of HCC patients. (B) Flow chart showing the target gene selection process we followed.

[0007] Figure 2.Dendrogram for clustering and evaluation of soft thresholding power (WGCNA); identification of modules associated with HCC clinical features; ten-fold cross validation for selection of tuning parameters and survival correlation. (A) Cluster dendrogram generated for 52 samples. (B) Average connectivity analysis at different soft threshold powers. (C) Clustering of differentially expressed genes with the help of dendrogram created using dissimilarity measure (1-TOM). (D) Relationship between module characteristic genes and clinical features, visualized as a heat map. € Average gene significance and error distribution in modules associated with HCC tumor grade. (F) Ten-fold cross validation for selection of tuning parameters (Lasso model). (G) LASSO coefficient curves for 327 prognostic genes were recorded. (H) Relationship between survival and expression of differentially expressed genes.

[0008] Figure 3 .CFDP1 is overexpressed in HCC. (A) Analysis of CFDP1 expression using TCGA data in HCC and adjacent normal samples. (B) Quantitative RT-PCR analysis was performed to determine the expression of CFDP1 in HCC tissues and corresponding normal samples. (C) The level of CFDP1 in 10 random HCC tissue pairs was evaluated by Western blot analysis. (D,E) Quantitative analysis of the relative levels of protein and mRNA for CFDP1 in L02 cells and six human HCC cell lines using RT-PCR and Western blot analysis. (F) Expression of CFDP1 in tumor and adjacent normal samples examined by IHC. (G) Analysis of the relationship between CFDP1 expression and HCC stage by analyzing the GEPIA database. (H,I) Analysis of overall survival and recurrence-free survival of CFDP1 using the GEPIA database. *P<.05, **P<.01, and ***P<.001.

[0009] Figure 4 CFDP1 affects the ability of HCC cells to proliferate, migrate, and invade. (A, B) The effectiveness of CFDP1 silencing and overexpression determined by qRT-PCR and Western blotting techniques (C) The relationship between BUB1B and PCNA examined using the GEPIA database. (DH) Colony formation, CCK-8, and EDU assays to evaluate cell proliferation in HCC cell lines with CFDP1 knockdown or overexpression. (I, J) Wound healing assays performed to detect cell migration in cell lines characterized by overexpression of CFDP1 (K, L) Transwell assays performed to determine the migration and invasion abilities of HCC cell lines under conditions of CFDP1 knockdown or overexpression. *P < .05, **P < .01, and ***P < .001.

[0010] Figure 5CFDP1 overexpression inhibits apoptosis and promotes cell migration and invasion through EMT. (AD) Apoptosis of HCC cell lines under CFDP1 overexpression and silencing conditions Western blotting and flow cytometry were used to detect apoptosis-related proteins in HCC cell lines characterized by CFDP1 knockdown or overexpression. (EH) Immunofluorescence and Western blotting were used to examine the expression of CFDP1, vimentin, and E-cadherin in HCC cell lines with stable CFDP1 knockdown or overexpression. *P<.05, **P<.01, and ***P<.001.

[0011] Figure 6 .CFDP1 promotes the progression and metastasis of HCC in vivo. (A,B) Representative images of transplanted HCC tumors taken from mice treated with injection of Hep3B-SH1 and Hep3B-shNC or HepG2-CFDP1 and HepG2 control. (C,D) Calculation of tumor volume growth curves for each group. (E,F) Tumor weight of HCC in each group. (G) IHC images of KI-67 and TUNEL expression in xenograft HCC tumors in each group. Scale: 50 μm. (H) Bioluminescence imaging. (I) The table shows the proportion of mice that developed lung metastasis. (I) Data are expressed as mean ± SD of at least 3 independent experiments. *P < .05, **P < .01, and ***P < .001.

[0012] Figure 7 .Pathway enrichment analysis and GeneCards analysis of Brown module genes. (A) KEGG pathway analysis. (B) Association of CFDP1 with potential differentially expressed genes. (C) Association of NEDD4 with other differentially expressed genes.

[0013] Figure 8 CFDP1 activates NEDD4 expression via the PTEN / PI3K / AKT pathway. (A,B) Western blots were performed to examine a number of key components, including CFDP1, NEDD4, and the PTEN / PI3K / AKT pathway. (C,D) Western blot analysis of the levels of various key components involved in the NEDD4 pathway in Hep3B-shNC and Hep3B-shCFDP1 in the presence or absence of NEDD4 overexpression. (EI) CCK-8, colony formation, EdU, Transwell, and flow cytometry were used to examine proliferation, invasion, migration, and apoptosis of Hep3B-shNC and Hep3B-shCFDP1 in the presence or absence of NEDD4 overexpression. *P<0.05, **P<0.01, and ***P<0.001. DETAILED DESCRIPTION

[0014] Experimental materials and methods involved in the examples

[0015] 1 Materials

[0016] Six human HCC cell lines (SMMC7721, Focus, Huh7, HepG2, LM3, and Hep3B) and the normal hepatocyte line L02 were provided by American Type Culture Collection (Manassas, VA, USA).

[0017] 2 Data processing

[0018] GSE148355 was downloaded from the GEO database (Submitted by: Nan Jinwu). We selected the 5000 most variable genes with the goal of creating a co-expression network. Human malignant tumor specimens with a survival time of less than 30 days were not considered. Subsequently, we extracted genes from the expression profile, averaged the expression values ​​of genes with the same sign, and deleted genes with expression levels less than 30%. We selected the intersection of expression and mutation data with the goal of obtaining sample information of the intersection of expression and mutation data. We used the Limma software package (R software) to obtain differentially expressed genes with a threshold of |logfc|>=|log1.3|&P<0.05.

[0019] 3 Establishment of co-expression module

[0020] The availability of the selected 5000 genes was studied, some cases for which complete clinical data were not available were excluded, and the gene co-expression network was constructed using the WGCNA software package (R software package). The formula of the adjacency matrix AMN is as follows:

[0021] A mn =|Smn| β

[0022] Where Aij represents the adjacency of gene n and gene m, and Smn represents the Pearson correlation between gene n and gene m. The β value is set to 6 (soft threshold parameter; scale-free R2 = 0.89). The adjacency matrix is ​​converted into a topological overlap matrix (TOM). In order to classify genes with large absolute correlation, they are divided into gene modules using the TOM-derived difference measure.

[0023] 4 Module Identification

[0024] Determine the correlation between module characteristic genes and clinical characteristic genes, so as to understand the relationship between HCC modules and clinical characteristics. Determine the module significance of each module. It is generally believed that the higher the absolute significance of the module, the greater its biological significance.

[0025] 5 Key genes: identification and validation

[0026] The least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to identify candidate genes. The penalized parameter adjustment process (performed by 10-fold cross validation with the “survival” and “glmnet” packages) was used to conduct the sample analysis. The LASSO Cox regression method was used to determine the variables for constructing the signature and the distribution coefficient. The risk score is a measure of the prognosis of HCC patients and is calculated as follows:

[0027] Risk score = gene 1 expression level * β1 + gene 2 expression level * β2 + gene 3 expression level * β3 + gene 4 expression level * β4, where β is the regression coefficient of each variable.

[0028] Subsequently, we validated their effects on survival (TCGA database) and selected genes with an effect on survival. The ONCOMINE microarray database (https: / / www.oncomine.org) was used to examine the mRNA expression levels of the screened hub genes. Comparisons were made between HCC and non-tumor liver tissues. Of note, gene rankings represent the median ranking of a single target gene. Rankings were determined by performing multiple studies and analyzing all results (thresholds: P = 0.05; 2-fold change; top 10% gene ranking). The UCSC Xena browser was used to obtain hierarchical clusters of hub genes present in primary HCC (TCGA HCC, n = 438). Data corresponding to 9,736 tumors and 8,587 controls were included in the GEPIA database. By analyzing this comprehensive database, the mRNA expression of each hub gene present in liver hepatocellular carcinoma (LIHC) and non-cancerous liver samples was determined. The Human Protein Atlas (HPA) website was used to delve into the expression levels of proteins.

[0029] 6. Gene enrichment and gene card analysis

[0030] The Gene Ontology (GO) project website http: / / www.geneontology.org was visited to obtain information about the functions of gene products. This resource is used to study ontologies and obtain biological knowledge. We used this database to analyze gene GO and KEGG pathways. The GO project can be effectively used to obtain comprehensive knowledge about functional genomics. Evidence-supported annotations can be obtained to define the biological functions of different genomic products (such as proteins, genes, various complexes, and ncRNAs). The results can be determined by classification using the ontology we obtained. GO terms can be divided into three parts: cellular component (CC), molecular function (MF), and biological process (BP). P < 0.05 was considered statistically significant.

[0031] The GeneCards (https: / / www.genecards.org / ) website can be visited to obtain detailed information about annotated and predicted human genes. Relevant data from approximately 150 Internet resources were collated to develop this comprehensive repository. Information on genomics, transcriptomics, proteomics, genetics, clinical methods, and functional information are integrated into this database. We used its online analysis capabilities to obtain its association with other differentially expressed genes.

[0032] 7 Tissue samples and cell lines

[0033] Liver tissues (tumor and adjacent normal samples) were collected from 60 patients who underwent partial hepatectomy at the Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital. A portion of the tissue samples was processed and stored in formaldehyde solution before the experiments were performed. Liquid nitrogen was used to freeze the rest of the tissue. This study was approved by the Ethics Committee of Nanjing Medical University Drum Tower Clinical College and was conducted in accordance with the Declaration of Helsinki and government policies. All participants signed written informed consent. All cells were treated with Dulbecco's modified Eagle's medium (DMEM; Gibco, USA) containing 10% fetal bovine serum (FBS) and 1% antibiotics (streptomycin / penicillin; Gibco, USA). The samples were processed at 37°C. A humidified incubator (5% CO2) was used for sample processing.

[0034] 8 Lentiviral transfection

[0035] We purchased lentiviral vectors encoding CFDP1 (LV-CFDP1), short hairpin RNA targeting CFDP1 (LV-shCFDP1), and matching empty vectors (LV-control / LV-shNC) from GenePharma Biotech. Polyamine (Genepharma; 5 μg / mL) was added to the cell lines to improve infection efficiency. Stably transfected cell lines were selected and 6 μg / mL puromycin was added for 7 consecutive days.

[0036] 9 Western blotting

[0037] RIPA buffer containing 1% protease inhibitor cocktail (Cwbio, China) and 1% PMSF was used to extract total protein from tissues and cell lines. Samples were incubated on ice (time: 30 min). Subsequently, the samples were centrifuged at 14000×g for 15 min. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis technique was used to separate proteins. Then, they were loaded onto polyvinylidene difluoride (PVDF) membranes (Millipore, USA). Fast limiting solution (Beyotime) was used for blocking. The blocking time was 30 min. Subsequently, the PVDF membrane was incubated overnight at 4°C with specific primary antibodies. Horseradish peroxidase-conjugated secondary antibodies (1:2000, Abcam, USA) were used for incubation (2 h; ambient temperature). In addition, to detect the samples, enhanced chemiluminescence (ECL) reagents (Engreen, Beijing, China) were used. The optical density of protein bands was detected with the help of ECL Western Blot Detection Kit (Bio-Rad, USA).

[0038] 10 Cell Counting Kit-8

[0039] Cell proliferation potential was examined with the help of a cell counting kit (CCK-8) (Vazyme Biotech Co., Ltd). The study was conducted following the guidelines specified by the manufacturer. Cells were transferred to a 96-well plate (500 cells / well) and CCK-8 solution (10 μL / well) was added to the wells. The samples were incubated for 2 hours and then the absorbance (at 450 nm) of each well was measured.

[0040] 11. Colony formation assay

[0041] First, extract the cells in the logarithmic growth phase of each group to prepare the cell suspension. Then prepare the samples according to the process of gradient dilution and then inoculate them into culture dishes containing culture medium (time: 2 weeks). The process is stopped when visual clones begin to appear in the dish. Discard the supernatant and fix the samples with 4% paraformaldehyde. Use crystal violet solution for staining (staining time: 30 minutes). Finally, calculate the clone generation rate and perform statistical tests on the results.

[0042] 12 Cell apoptosis assay

[0043] Apoptotic cells were identified with the help of Annexin V-FITC / Propidium iodide (PI) apoptosis detection kit (VazymeBiotech Co., Ltd). The study was performed according to the instructions provided by the manufacturer. Trypsin was used to isolate stable cell lines. EDTA was not used in this process. The samples were centrifuged at 1000 rpm for 5 min at 4°C, pre-cooled with PBS, and washed twice. Subsequently, the samples were repeatedly centrifuged to form pellets. The cell pellet was collected before being suspended with 100 μL of mixing buffer. Annexin V-labeled FITC (5 μL) and PI (5 μL) were introduced into the cell suspension and the reaction was allowed to proceed (15 minutes; ambient temperature; dark). Finally, the binding buffer (400 μL) was introduced into the cell suspension and the solution was gently mixed. Flow cytometry technology was used to determine the level of cell apoptosis (time: 1 hour). Cell apoptosis data were examined with the help of FlowJo software.

[0044] 13 Transwell assay

[0045] The migration and invasion abilities of cells were investigated using Transwell chambers (Millipore, USA). The cell lines were implanted in the upper chamber. The samples were cultured using 200 μL of DMEM without FBS. The lower chamber was filled with 500 μL of complete medium containing 10% fetal bovine serum. The cells were induced to enter the bottom lumen through the Transwell membrane. The cells were cultured for one day, and then we discarded the culture medium. The Transwell Chambers were rinsed twice using PBS. We then fixed the cells using methanol within 20 min. Crystal violet (0.1%) was used to stain the invasive and migrating cells (room temperature; dark; 30 min). The stained cells were rinsed three times with PBS and observed with the help of an optical microscope.

[0046] 14 Wound healing assay

[0047] HepG2 and Hep3B and cell lines (density: 5×105 cells / well) were implanted in 6-well plates. Once the cells reached confluence conditions, they were rinsed twice with PBS. A uniform linear scratch was made in the middle of the well using a sterilized pipette tip (volume: 200 μL). The distance between the wound edges was recorded at 0 h after two cycles of PBS washing. The results were used as a baseline. Images of the same location were obtained 48 h later. The purpose of determining the change in distance was to investigate the extent of cell migration and repair.

[0048] 15 RNA extraction and qRT-PCR detection

[0049] Easypurure RNA kit (Regent bio) was used to isolate total RNA. The experiments were performed following the guidelines specified by the manufacturer. In brief, in order to mix the tissues or cells, a lysis buffer was used. After effective lysis, the lysate was collected and spun for 10 seconds. Subsequently, an equal amount of absolute ethanol was added to the mixture. The lysate was transferred to the RNA column. Using a centrifuge tube, the centrifugation process was carried out at a rate of 4000g and a temperature of 4°C. A clean buffer (500 μL) was used and the centrifugation time was 1 min. In addition, the samples were centrifuged at 12000g for 1 min. RNAse-free tubes were used in the second half of the experiment. The RNA column was transferred to a new RNAse-free tube, and the tube was subsequently air-dried for 2 minutes. The central column of RNA was treated with elution buffer (20 μL) and the waiting time was 2 min (centrifugation conditions: 12000g; 1 min). The extracted RNA was stored at a temperature of -80°C for further experiments. The reaction mixture (20 μL / well) consisted of total RNA (1 μg), 5×HiScriptⅡqRT SuperMix (4 μL; VazymeBiotech Co., LTD) and ddH2O.

[0050] We used the ChamQTM Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd) system to conduct qRT-PCR research. The primer sequences used are as follows:

[0051] CFDP1: forward sequence 5'-CCACAGGCTAATGTTCCTTCAGC-3'

[0052] Reverse sequence 5'-GTCCAGTTGGACTTCTCAAGGG-3'

[0053] β-actin: forward sequence 5'-TGACGTGGACATCCGCAAAG-3'

[0054] Reverse sequence 5'-CTGGAAGGTGGACAGCGAGG-3'

[0055] 16 Immunohistochemistry (IHC) staining

[0056] The samples were fixed with 4% formaldehyde solution and then embedded in paraffin. The paraffin system was cut into 4-μm slices and the samples were incubated with CFDP1 and ki-67 (Abcam) primary antibodies at 4 °C (incubation time: 12 h). After this, the sections were incubated with the help of HRP-conjugated secondary antibodies at ambient temperature for 1 h. The samples were examined by 3,3'-diaminobenzidine and hematoxylin staining. Two independent pathologists evaluated the staining positivity and intensity in a blinded study. For the experiments, ten areas were randomly selected from each slide.

[0057] 17 5-Ethynyl-2'-deoxyuridine (EdU) assay

[0058] The proliferation capacity of the cell lines was determined using an EdU test kit (RiboBio, Guangzhou, China). The experiments were performed following the guidelines specified by the manufacturer. 96-well plates were used to seed the cell lines (density: 2×104 cells / well). Complete medium was used to culture the samples (time: 24 h). Cells were incubated with EdU (50 μmol / L; RiboBio) (incubation time: 2 h; incubation temperature: 37°C). The cell lines were fixed with 4% formaldehyde solution (time: 30 min). TritonX-100 (0.5%) was used to permeabilize the cell lines (time: 10 min). The cell lines were rinsed three times with PBS. Subsequently, EdU-positive cells were visualized using 1×ApolloR reaction mixture (400 μL) and Hoechest33342 (400 μL). Finally, images of the cells were recorded under a microscope.

[0059] 18 TUNEL assay

[0060] TUNEL staining was used to detect DNA fragmentation signatures of apoptosis in paraffin-embedded formalin-fixed tumor sections. The Klenow-Fragel DNA fragmentation detection kit (Roche, Basel, Switzerland) was used and the protocol outlined by the manufacturer was followed. Ten areas were randomly selected from each slide for examination.

[0061] 19 Animal Experimentation

[0062] All animal-based experiments were approved by the Institutional Animal Care and Use Committee of Nanjing Medical University. Female BALB / c nude mice aged 4 weeks were purchased from the Experimental Animal Center of Nanjing Medical University for this study. PBS (100 μL) was used to resuspend the transfected cell lines (1×106 cells). The suspension was injected subcutaneously into nude mice (n=6 per group) to monitor the subcutaneous growth of tumors. To detect metastasis, a total of 1×106 luciferase-expressing HCC cells were injected into the tail vein of nude mice (n=12 per group). The IVIS100 imaging system (Caliper Life Sciences, Waltham, MA, USA) was used to observe distant metastasis after 5 weeks.

[0063] 20 Statistical analysis

[0064] The SurvMiner package in R was used to stratify patients into low-risk and high-risk groups. This method was based on the maximum selective rank statistic. The optimal threshold expression value corresponding to each hub gene was considered. We analyzed relapse-free survival (RFS) and overall survival (OS) using the Kaplan-Meier method, and compared the survival curves by using the log-rank test. GraphPad Prism (GraphPad Software, San Diego, CA, version 5.0) and R (version 4.0.3) were used for statistical analyses. P < 0.05 was established as the criterion for statistical significance.

[0065] Example 1 Construction of co-expression module

[0066] From the dataset GSE148355( Figure 1 Expression matrices corresponding to 52 samples were obtained in A and B. We selected the top 5000 mutated genes for co-expression analysis. Cases for which complete clinical data were not available were excluded from this study. The GSE148355 samples were clustered using the average linkage method to determine the quality of the microarray. Samples with outlier differentially expressed genes were also filtered out using this method ( Figure 2 A, B). The power of β = 6 was set as the soft threshold (scale-free R2 = 0.89). A total of 27 modules were constructed by the average linkage hierarchical clustering method. Brown modules were used for further analysis. Finally, we observed that the Brown module was significantly associated with OS time ( Figure 2 CE).

[0067] Example 2 Identification of key genes

[0068] 327 genes were considered to be key genes in the brown module and had high functional significance. LASSO regression analysis was used to further narrow the range of genes used to construct the prognostic model. Subsequently, the most differentially expressed genes associated with survival were screened in the brown module, namely CFDP1 ( Figure 2 FH).

[0069] Example 3 Up-regulated expression of CFDP1 in HCC: poor prognosis.

[0070] Data corresponding to RNA sequencing were downloaded from TCGA (369 HCC and 160 non-tumor tissues) to explore the expression profile of CFDP1 in HCC. The results showed that the expression level of CFDP1 in HCC tissues was significantly increased compared with that recorded in normal tissues ( Figure 3 A). To determine whether CFDP1 is significantly overexpressed in HCC samples, we used qRT-PCR to detect the relative mRNA levels of CFDP1 in 60 pairs of HCC and corresponding adjacent non-tumor samples. The results showed that the relative level of CFDP1 mRNA in HCC tissues was significantly increased ( Figure 3 B). In addition, 10 randomly selected HCC tissue pairs were analyzed by Western blotting, and the results showed upregulation of CFDP1 ( Figure 3 C). The relative levels of CFDP1 mRNA and protein were increased in Huh7, HepG2, Hep3B, LM3, SMMC7721, and Focus HCC cell lines. The levels in these cases were higher than those recorded in the normal liver cell line LO2 ( Figure 3 D, E). IHC results also showed that the expression level of CFDP1 in HCC samples was significantly increased compared with the CFDP1 expression level recorded in adjacent normal tissues ( Figure 3 F). Then, the relationship between CFDP1 and survival and tumor stage was analyzed using the online bioinformatics tool GEPIA database. In addition, we observed that CFDP1 expression was positively correlated with HCC stage. Patients with HCC characterized by high CFDP1 expression experienced high recurrence rate and poor overall survival ( Figure 3 GI). We divided 60 patients into high CFDP1 and low CFDP1 groups to investigate the specific relationship between CFDP1 expression and clinicopathological characteristics of HCC patients. Analysis of the findings showed that large tumor size, increased microvascular invasion, and high TNM and Edmondson stages could be attributed to high levels of CFDP1 (Table 1). Overall, these results suggest that CFDP1 is overexpressed in HCC under both in vitro and in vivo conditions. These findings suggest that CFDP1 may play a potential role in the occurrence and progression of HCC. Therefore, it may serve as a prognostic biomarker for HCC.

[0071] Table 1. Correlation between CFDP1 expression and clinicopathological characteristics in HCC patients (n=60)

[0072]

[0073]

[0074]

[0075] *P<0.05, **P<0.01, ***P<0.001

[0076] Example 4 CFDP1 promotes the malignant progression of HCC.

[0077] The results obtained using western blotting and QRT-PCR techniques were analyzed. Based on the results, Hep3B cells were selected for CFDP1 gene knockdown experiments. This is because Hep3B cells expressed high levels of CFDP1 among the six HCC cell lines examined. At the same time, HepG2 cells, which are characterized by low CFDP1 expression levels, were selected for CFDP1 overexpression experiments. The highest knockout efficiency was taken into account, and the results obtained using qRT-PCR and western blotting techniques were analyzed. Based on these findings, sh1 was selected for further experiments. Upregulated expression of CFDP1 was observed in HepG2 cells transfected with Lv-CFDP1. Therefore, it confirms the overexpression efficiency of CFDP1 ( Figure 4 A, B).

[0078] First, we evaluated the correlation between CFDP1 and proliferating cell nuclear antigen (PCNA). We found that there was a positive correlation between CFDP1 expression and PCNA ( Figure 4 C). The results obtained by CCK-8 assay showed that downregulation of CFDP1 expression could reduce the proliferation ability of Hep3B cells ( Figure 4 D). In addition, the clonal survival rate of Hep3B cells was significantly reduced after CFDP1 knockdown ( Figure 4 E). The results are in good agreement with those obtained previously. Considering the specificity and sensitivity of CFDP1, we used EdU incorporation to evaluate its effect on the actual cell proliferation process from multiple perspectives. The results showed that inhibition of CFDP1 can lead to inhibition of Hep3B cell proliferation ( Figure 4 G). After that, we performed wound healing and Transwell assays to investigate the effects of CFDP1 knockdown on the migration and invasion processes of HCC cells. Figure 2As shown in Figure 1, after CFDP1 knockdown, the migration degree of Hep3B cells was reduced. The results of the Transwell experiment showed that CFDP1 knockdown led to the inhibition of Hep3B cell migration and invasion ( Figure 2 K). To thoroughly understand the function of CFDP1, we performed EdU assay, wound healing assay, and Transwell assay using HepG2 cells that overexpress CFDP1. As expected, overexpression of CFDP1 significantly enhanced the proliferation, migration, and invasion abilities of HepG2 cells ( Figure 2 H, J, L).

[0079] Example 5 Overexpression of CFDP1 inhibits cell apoptosis and promotes EMT.

[0080] The cell apoptosis rate plays an important role in the occurrence and development of HCC. Flow cytometry was used to study the cell apoptosis rate. Our results showed that downregulation of CFDP1 promoted the apoptosis of Hep3B cells, while overexpression of CFDP1 reduced the degree of apoptosis of HepG2 cells ( Figure 5 A, C). Western blotting results showed that the expression level of the apoptosis-related protein Bax in Hep3B cells was increased after CFDP1 knockdown. It was also observed that the expression levels of the anti-apoptotic proteins BCL-XL and Bcl-2 were decreased under these conditions. However, the opposite effect was observed in HepG2 cells characterized by overexpression of CFDP1 ( Figure 5 B, D).

[0081] We evaluated the expression of “EMT master genes” and analyzed the results to examine whether CFDP1 enhances the ability of cells to migrate and invade through EMT. The results obtained from immunofluorescence experiments showed low-level expression of Vimentin (mesenchymal marker) and high-level expression of E-cadherin (epithelial marker) in Hep3B-sh1 cells. These results are consistent with those obtained from Western blot ( Figure 5 E, F). Low expression of E-cadherin and high expression of vimentin were observed in CFDP1 cells overexpressing HepG2 cells ( Figure 5 G,H). The above findings indicate that CFDP1 overexpression inhibits apoptosis and promotes EMT in HCC cell lines.

[0082] Example 6 CFDP1 contributes to xenograft tumor growth and liver cancer metastasis in vivo.

[0083] To thoroughly explore the effect of CFDP1 on tumor growth, we generated a subcutaneous xenograft tumor mouse model of HCC. In mice injected with Hep3B-sh1 cells, tumor growth was slow. In this case, the average tumor weight and volume were low ( Figure 6 A, C, and E). In contrast, rapid tumor growth was observed in mice injected with HepG2 cells and treated with overexpressed CFDP1. In this case, the average tumor size and weight were higher than those recorded in the previous case ( Figure 6 B, D, and F). These findings suggest that CFDP1 may increase tumor growth in vivo. The results obtained by performing TUNEL assays and Ki-67 staining confirmed that CFDP1 enhanced tumorigenicity. The results showed that hep3B-sh1 xenografts exhibited lower proliferation activity and stronger apoptosis compared with Hep3B-shNC. In contrast, upregulation of CFDP1 led to increased proliferation activity and decreased apoptosis in HepG2 xenografts ( Figure 6 G). We injected luciferase-expressing and stably transfected hepatoma cells into BALB / C nude mice via the tail vein to further investigate whether CFDP1 affects the in vivo metastasis of hepatoma. The results were compared with those obtained by studying the corresponding control groups. The results showed that downregulation of CFDP1 in Hep3B cells played a role in the development of a few lung metastases. An increased degree of metastasis was observed in the group overexpressing HepG2-CFDP1 ( Figure 6 H). In each group, we counted the number of mice that developed lung metastases ( Figure 6 I).

[0084] Example 7 CFDP1 interacts with NEDD4 to promote the growth and migration of HCC cells: PTEN / PI3K / AKT pathway.

[0085] We applied enrichment analysis and GeneCards online tools to further identify possible signaling pathways mediated by CFDP1. The results of enrichment analysis showed that the genes in the Brown module were mainly enriched in protein folding and endocytosis ( Figure 7 A) As we all know, endocytosis is closely related to the progression, development and metastasis of HCC. Therefore, our next work mainly focuses on endocytosis. Figure 7 As shown in B, proteins that may be associated with CFDP1 were obtained using the GeneCards online analysis tool. It has been reported that one of the main functions of NEDD4 is to regulate a large number of membrane proteins through its ubiquitination activity. It has the ability to mediate endocytosis and promote HCC cell growth and migration through the PTEN / PI3K / AKT signaling pathway. In addition, GeneCards analysis also confirmed the relationship between NEDD4 and PTEN ( Figure 7C). Next, we investigated the relationship between CFDP1, NEDD4, and the PTEN / PI3K / AKT signaling pathway. We detected several core proteins, including CFDP1, NEDD4, and the PTEN / PI3K / AKT pathway. In Hep3B cells with CFDP1 downregulation, the protein expression of NEDD4 and P-AKT was decreased, while that of PTEN was increased. In contrast, the opposite trend in protein levels was observed in HepG2 cells with CFDP1 overexpression ( Figure 8 A, B). The data obtained indicated that CFDP1 may promote the malignant development of HCC through the NEDD4 / PTEN / PI3K / AKT pathway. Subsequently, Hep3B-sh1 cells were transfected with LV-NEDD4 (or LV-NEDD4) vector to confirm whether the effect of CFDP1 depends on the NEDD4 pathway. The results obtained using Western blotting showed that when cells were transfected with LV-NEDD4, the expression of PTEN was attenuated and the expression of P-AKT was increased in Hep3B cells downregulated by CFDP1 ( Figure 8 C, D). CCK-8 results showed that NEDD4 overexpression increased the proliferation capacity of Hep3B cells with downregulated CFDP1 ( Figure 8 E). Consistent with this, overexpression of NEDD4 significantly increased the clonal survival rate of Hep3B cells in which CFDP1 was downregulated ( Figure 8 F). Figure 8 As shown in G, the results obtained from the EdU experiment showed that overexpressed NEDD4 promoted the proliferation of Hep3B cells with downregulated CFDP1. The results obtained from the Transwell experiment showed that overexpression of NEDD4 enhanced the migration and invasion of Hep3B cells with downregulated CFDP1 ( Figure 8 H). Meanwhile, the results obtained by flow cytometry showed that NEDD4 overexpression reduced the apoptosis rate of Hep3B cells with downregulated CFDP1 ( Figure 8 I). In summary, the above data suggest that CFDP1 promotes the malignancy of HCC cells through the NEDD4 / PTEN / PI3K / AKT pathway. Sequence Listing <110> Jinan Microecology Biomedicine Provincial Laboratory <120> Application of short hairpin RNA for knocking down CFDP in the preparation of drugs for treating hepatocellular carcinoma <130> 20220611 <160> 3 <170> SIPOSequenceListing 1.0 <210> 1 <211> twenty one <212> RNA <213> Artificial sequence (.) <400> 1 ggguggagag uauagugaag a 21 <210> 2 <211> twenty one <212> RNA <213> Artificial sequence (.) <400> 2 ggaggaugcc aauucagaau c 21 <210> 3 <211> twenty one <212> RNA <213> Artificial sequence (.) <400> 3 ggccagcuuc cucaaugaug u 21

Claims

1. Application of short hairpin RNA for knocking down CFDP1 in the preparation of drugs for the treatment of hepatocellular carcinoma.

2. Use of the short hairpin RNA for knocking down CFDP1 as claimed in claim 1 in the preparation of a drug for treating hepatocellular carcinoma, wherein the sequence of the short hairpin RNA is as shown in the sequence listing SEQ ID NO:

1.

3. Use of the short hairpin RNA for knocking down CFDP1 as claimed in claim 1 in the preparation of a drug for treating hepatocellular carcinoma, wherein the sequence of the short hairpin RNA is as shown in the sequence listing SEQ ID NO:

2.

4. Use of the short hairpin RNA for knocking down CFDP1 as claimed in claim 1 in the preparation of a drug for treating hepatocellular carcinoma, wherein the sequence of the short hairpin RNA is as shown in SEQ ID NO: 3 in the sequence listing.

5. Use of a short hairpin RNA for knocking down CFDP1 in the preparation of a preparation for inhibiting proliferation, migration and EMT of hepatocellular carcinoma cells, wherein the sequence of the short hairpin RNA is as shown in the sequence listing SEQ ID NO:

1.

6. Use of a short hairpin RNA for knocking down CFDP1 in the preparation of a preparation for inhibiting proliferation, migration and EMT of hepatocellular carcinoma cells, wherein the sequence of the short hairpin RNA is as shown in the sequence listing SEQ ID NO:

2.

7. Use of a short hairpin RNA for knocking down CFDP1 in the preparation of a preparation for inhibiting proliferation, migration and EMT of hepatocellular carcinoma cells, wherein the sequence of the short hairpin RNA is as shown in the sequence listing SEQ ID NO:

3.

8. Use of a short hairpin RNA for knocking down CFDP1 in the preparation of a preparation for inducing apoptosis of hepatocellular carcinoma cells, wherein the sequence of the short hairpin RNA is as shown in the sequence listing SEQ ID NO:

1.

9. Use of a short hairpin RNA for knocking down CFDP1 in the preparation of a preparation for inducing apoptosis of hepatocellular carcinoma cells, wherein the sequence of the short hairpin RNA is as shown in the sequence listing SEQ ID NO:

2.

10. Use of a short hairpin RNA for knocking down CFDP1 in the preparation of a preparation for inducing apoptosis of hepatocellular carcinoma cells, wherein the sequence of the short hairpin RNA is as shown in the sequence listing SEQ ID NO: 3.

Images