Clinical Pathological Significance and Biological Function of APIP in Hepatocellular Carcinoma

Clinical Pathological Significance and Biological Function of APIP in Hepatocellular Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical Pathological Significance and Biological Function of APIP in Hepatocellular Carcinoma cong yu, Jiang-Hua Huang, Rui Zhang, Kang-Lai Wei, Zhen-Bo Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6843055/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Cancer Cell International → Version 1 posted 12 You are reading this latest preprint version Abstract Background & aims With a high incidence and fatality rate, hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors in the world. In addition to examining its clinical pathogenic importance in HCC, our goal is to study the expression of Apaf-1-interacting protein( APIP) in HCC and investigate its effects on the biological functions of HCC cells and the underlying mechanisms. Methods To determine the expression level and clinical significance of APIP in HCC, we employed internal tissue samples for Real-time quantitative polymerase chain reaction,(RT-qPCR) and immunohistochemistry(IHC) procedures. In order to verify the findings, we obtained external datasets. We investigated the upstream regulatory transcription factors of APIP and talked about the molecular mechanism of the biological role of APIP in HCC through experiments on cell proliferation, cell cycle, and cell apoptosis as well as scratch and transwell assays to assess the effects of APIP on the proliferation, apoptosis, migration, and invasion capabilities of HCC cells. Results We discovered that HCC tissues exhibited higher levels of APIP expression than nearby non-tumor tissues, and that APIP could differentiate HCC from healthy liver tissues. Additionally, the age, gender, and poor prognosis of individuals with HCC were linked to APIP expression. In the meantime, down regulating APIP may encourage HCC cells to undergo apoptosis and prevent their growth, migration, and invasion. Furthermore, the transcription factor Serum response factor(SRF) and APIP may have a regulatory connection. conclusion By influencing the proliferation, apoptosis, migration, and invasion of HCC cells, APIP plays a significant role in the formation and progression of HCC and may provide a novel therapeutic target. Hepatocellular carcinoma APIP Diagnostic marker Prognosis biological functions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background One of the most prevalent solid cancers and a major global cause of cancer-related mortality is hepatocellular carcinoma (HCC). In 2020, the World Health Organization reported that HCC ranks third in mortality and sixth in incidence worldwide, with approximately 900,000 new cases and 830,000 deaths year [1] .The development of treatment technology has improved the prognosis of patients with HCC [2] . However, overall survival rates for HCC patients remain low [3, 4] . Notably, studies have shown that multiple molecules play a key role in the development and progression of HCC [5] . Therefore, the search for reliable molecular biomarkers to identify key genes involved in the pathogenesis of liver cancer will help to develop new therapies. Apaf-1-interacting protein( APIP )was previously isolated as an inhibitor of mitochondrial cell death interacting with Apaf-1 [6] , is also a MtnB enzyme in the methionine rescue pathway [7] and is associated with various types of cell death processes. For example, the direct binding of APIP and APAF-1 can effectively inhibit mitochondria-mediated apoptosis; Additionally, through its role in the methionine rescue pathway, APIP can inhibit Caspase-1-mediated pyroptosis induced by Salmonella infection [8] Moreover, an increasing number of studies have found that APIP is associated with a variety of tumors. First, APIP levels are dysregulated in prostate cancer [9] , tongue and throat squamous cell carcinoma [10] , and non-small cell lung cancer [11] . Second, APIP can interact with ERBB3 to stimulate gastric cell proliferation and tumorigenesis [8] . Given the significant role of APIP in tumors, its expression in HCC and related biological roles warrant further investigation, as they are not yet clear. In this study, we investigated the expression of APIP in HCC cell lines and clinical samples and further analyzed its clinical significance in HCC patients. Additionally, we demonstrated that APIP plays an oncogenic role in HCC development and that its expression can influence the proliferation, apoptosis, migration, and invasion of HCC cells in vitro. These findings may provide a novel therapeutic target for treating HCC patients. Method Patient sample collection A total of 83 HCC tissue specimens and 83 non-tumor liver tissue specimens were collected from the the First Affiliated Hospital of GuangXi Medical University. 91 cancer tissues and 82 corresponding adjacent tissues used for research were obtained from the Department of Pathology of the First Affiliated Hospital of Guangxi Medical University. The removed tissues were stored in liquid nitrogen for further use. Meanwhile, the clinical data of patients were collected. Inclusion criteria for patients: Patients with primary hepatocellular carcinoma, who had not received radiotherapy, chemotherapy, or endocrine therapy as adjuvant treatments before surgery. Informed consent was obtained from patients, and the study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. Real-time quantitative polymerase chain reaction Real-time quantitative polymerase chain reaction (RT-qPCR) experiments were conducted using the 7500 Quantitative PCR instrument (Thermo Fisher) to detect the expression level of APIP mRNA in HCC. Total RNA was extracted from HCC cell lines SK-HEP-1 (SK), Huh7, normal liver cell line (LO2), and 83 pairs of HCC tissues and non-cancerous tissues using the Mini BEST Universal RNA extraction KIT (TaKaRa), and the purity and concentration of total RNA were detected using NanoDrop One (Thermo Scientific). At the same time, reverse transcription synthesis of cDNA was performed using the Prime-Script RT Master Mix (TaKaRa). The APIP primer sequences are as follows: APIP -F: 5'-ACTGGGACTGGAGGAGGAATTAGC-3'; APIP -R: 5'-CGATGGCGAAGGTCCACTTATGTC-3'. Data were obtained using the 7500 Software v2.3 and analyzed using the 2-ΔΔCT method, with ACTIN as the endogenous control. Each sample was prepared in triplicate for each experiment. Immunohistochemical experiment Immunohistochemical(IHC) method: Paraffin sections were oven-dried at 75°C for dewaxing, subjected to high-pressure antigen retrieval with citrate buffer, blocked with peroxidase reagent, and then blocked with goat serum. One anti-body (APIP antibody at a concentration of 1:200) was dropped separately, and incubated at 37°C for 1.5 hours. After PBS rinsing, a second anti-body was dropped and incubated at 37°C for 20 minutes. After PBS rinsing, DAB staining was performed, followed by hematoxylin counterstaining, dehydration, transparency, and mounting with neutral gum. The APIP positive control was normal intestinal tissue that was proven positive by pre-experiment, and the negative control was PBS instead of the anti-body. Record the expression status of APIP protein in high-power field of view. Staining intensity: no color (0 points), pale yellow (1 point), brownish yellow (2 points), dark brown (3 points). The percentage of stained cells is classified as: 0 staining points ( 75%). The final staining score is the product of the staining intensity score and the number of staining points, with a maximum of 12 points. A total score of 0 - 7 indicates low expression, and 8 - 12 indicates high expression。 High-throughput data collection and analysis Public high-throughput RNA microarray and RNA sequencing data from HCC and non-HCC liver tissues were collected from public databases such as Genotype-Tissue Expression (GTEx, https://gtexportal.org), the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih). The following search strategy was used in the GEO database to preliminarily screen mRNA expression chips related to HCC: (hepatocellular OR HCC OR hepatic OR liver) AND (tumour OR tumor OR carcinoma OR cancer OR neoplasm* OR malignancy *). After the preliminary search using the above search strategy, chips meeting the requirements were further screened according to the following inclusion and exclusion criteria. The inclusion criteria for high-throughput data are: (1) the species of the research object is human; (2) the data set contains HCC tissues and non-HCC liver tissues; (3) the data set contains mRNA expression data. The exclusion criteria are as follows: (1) the species of the research object is animal or plant; (2) the samples in the data set are not HCC tissues and non-HCC liver tissues, such as HCC-related cell lines; (3) there are no non-HCC liver tissue samples as control groups in the data set; (4) the data set does not contain mRNA expression data. The data from the same platform were merged using R packages (sva, limma, dplyr) and Perl language to remove batch effects between platforms. Before processing all the data, the raw expression data were standardized and log2 processed. By integrating and analyzing the downloaded data, the differences in the expression of APIP between HCC and normal tissues were explored. Clinical application potential of APIP in HCC To determine the clinical value of APIP in HCC, standardized mean difference (SMD), receiver operating characteristic (ROC) curve, summary receiver operating characteristic (SROC) curve, and combined sensitivity and specificity were used to evaluate the clinical potential of APIP . Stata 12.0 version was used for the above analyses. Kaplan-Meier curves (GEPIA2, http://gepia2.cancer-pku.cn/#analysis) and nomograms (R v3.6.1, "survival" and "rms") were used to explore the prognostic potential of APIP in HCC. Meanwhile, the clinical pathological characteristics of HCC patients were collected, and the correlation between APIP expression level and various clinical pathological parameters was analyzed using the X 2 test. If P < 0.05, it was considered that APIP was related to the prognosis and clinical characteristics of HCC patients. Cell Culture Human HCC cells SK and Huh7 were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The cells were cultured in DMEM medium containing 10% fetal bovine serum and 1% penicillin-streptomycin mixture at 37°C in a humidified environment with 5% CO 2 . Lentivirus Transfection Lentivirus vector (LV- APIP -RNAi (100111-1)) was purchased from Genechem Co., Ltd. (Shanghai, China). HCC cells were seeded in 6-well plates and pre-cultured for 24 hours. According to the experimental protocol, when the cell confluence reached approximately 30%, lentivirus with RNAi targeting APIP was transfected into SK and Huh7 cells as the experimental group ( APIP i group); and a negative virus was transfected as the control group (NC group). After 24 hours of transfection, puromycin was added to improve the transfection efficiency. The transfection efficiency was determined by fluorescence microscopy and RT-qPCR experiments. The stronger the fluorescence intensity, the higher the transfection efficiency. Cell proliferation experiment The proliferation activity of cells in the APIP i group and the NC group was analyzed by Cell Counting Kit-8 (CCK8, meilunbio, Dalian, China) proliferation assay. Huh7 cells in the APIP i group and the NC group were seeded at a density of 3×10 3 cells per well in 96-well microplates with 100 μL of culture medium; SK cells were seeded at a density of 2×10 3 cells per well in 100 μL of culture medium and then placed in a cell incubator. Then, at 0 h, 24 h, 48 h and 72 h, 10 μL of CCK8 reagent was added to each well and incubated for 2 h. The wells without cells were used as blank controls. Finally, measured using a Multiskan FC microplate spectrophotometer at 450 nm, and the proliferation of cells was expressed by the absorbance value. Cell cycle experiment The cell cycle distributions of SK and Huh7 in the APIP i and NC groups were analyzed using flow cytometry. Cells for cell cycle analysis were detected using the cell cycle Detection Kit (meilunbio,Dalian, China). Cells in the logarithmic growth phase were dissociated with trypsin, resuspended in PBS, and then fixed with pre-cooled ethanol (75%) overnight in a 4°C refrigerator. On the day of the test, centrifuge the cells at 1000 rpm for 5 minutes, discard the ethanol, add PBS at room temperature, and let it stand for 15 minutes to hydrate the cells. Then centrifuge, discard the supernatant, add 300μL of DNA staining solution and incubate at 37 °C for 30 minutes. Subsequently, the lowest sample loading speed was selected and detected by flow cytometry. Cell apoptosis experiment The apoptosis rates of SK and Huh7 cells in the APIP i and NC groups were analyzed by flow cytometry. The cells used for apoptosis analysis were detected using the Annexin v-APC/7-AAD Apoptosis Kit (MULTI SCIENCES) in accordance with the instructions. Digest the cells with trypsin, add pre-cooled PBS, centrifuge and wash, and collect 1-10 ×10 5 cells (including the cells in the culture supernatant). Resuspend the cells with 500μl of 1 × Binding Buffer, and then add 5μl of Annexin V-APC and 10μl of 7-AAD. After gentle vortex mixing, incubate at room temperature in the dark for 5 minutes. Detection was carried out by flow cytometry. Cell migration experiment The Transwell chamber is a type of transparent cup-shaped device. After being placed in a 24-well culture plate, it is divided into two chambers: the Transwell chamber is called the upper chamber and is used to hold the upper layer of culture medium and cells. The area inside the culture plate is called the lower chamber, which is used to hold the culture medium at the lower layer. The upper and lower layers of culture medium are separated by a polycarbonate film. The migration ability of cells was determined by comparing the number of cells in different groups that passed through the small pores of the polycarbonate membrane within the same period of time. SK and Huh7 cell suspensions were added to the upper chamber at densities of 3×10 4 / well and 4×10 4 / well respectively, and 500μl of serum-free DMEM was added to the lower chamber. After culturing the cells for 24 hours, they were stained with 0.1% crystal violet. After staining, they were imaged under a microscope. Three fields of view were randomly selected for counting, and the images were quantified using Image J software. Cell invasion experiment The invasive ability of cells was determined by comparing the number of cells in different groups that passed through the small pores of the polycarbonate membrane within the same period of time. Dilute Matrigel with serum-free DMEM. The ratio of Matrigel to DMEM is 1:8. Add 60μl of diluted Matrigel to the upper chamber. Then, the SK and HuH7 cell suspensions were added to the upper chamber covered with matrix gel at densities of 3.5×10 4 / well and 4.5×10 4 / well respectively. In the lower chamber, 500μl of DMEM containing 5% fetal bovine serum was added, and the cells were cultured at 37°C for 24 hours. Stained with 0.1% crystal violet. After staining, imaging was performed under a microscope. Three fields of view were randomly selected for counting, and the images were quantified using Image J software. Cell scratch experiment HCC cells were inoculated in 6-well plates and cultured overnight. The number of inoculated cells in the Huh7 cell line was 6×10 5 per well, and the number of inoculated cells in the SK cell line was 3×10 5 per well. The number of inoculated cells in the APIP i group and the NC group was the same. The cell layer was damaged with a sterile 200μL pipette tip and washed twice with PBS, and then replaced with serum-free medium for culture. The images of the cells were captured under a microscope at different time points. The capture time points of SK cells were 0h, 12h, and 24h. The shooting time nodes of Huh7 cells are 0h, 24h and 48h. Finally, the cell healing rate at each time point was evaluated using Image J software and the formula [(0h scratch width - Scratch width after culture)/0h scratch width ×100%]. The potential molecular mechanism of APIP in HCC More and more studies have found that transcription factors can regulate the occurrence and progression of various tumors [12-16] . This is no exception in HCC [17, 18] . However, whether APIP is regulated by transcription factors has not been reported yet. Therefore, We use the database PROMO usage (https://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3), hTFtarget (http://bioinfo.lif e.hust.edu.cn/hTFtarget#! /), The Signaling Pathways Project (SPP, http://www.signalingpathways.org/index.jsf), The forecast of APIP upstream transcription factors, We used the Cistrome DB website (http://cistrome.org/db/#/) and the UCSC Genome Browser (https://genome.ucsc.edu/) to determine whether transcription factor binding peaks occurred in the APIP promoter region. In order to mine the potential transcription factors related to APIP and further explore the potential molecular regulatory mechanism of APIP in HCC. All data were analyzed under the software of IBM SPSS v19.0, STATA v12.0 and R v3.6.1. Independent sample t-tests and χ 2 tests were used to compare the expression data of APIP in liver cancer tissues and non-tumor liver tissues. When combining SMD and hazard ratio (HR), I 2 <50%, and the fixed-effect model was used to handle significant heterogeneity. When I 2 ≥50%, a random effects model was used to handle significant heterogeneity. ROC curve was plotted to determine the sensitivity and specificity. ROC curve was used to describe the area under the curve (AUC), aggregated sensitivity and aggregated specificity. The likelihood ratio is used to determine the discriminatory ability of APIP for HCC and non-HCC tissues. Kaplan-Meier curve analysis was conducted to evaluate the prognostic value of APIP . P < 0.05 indicates significance. Result The expression of APIP in HCC and its relationship with clinicopathological characteristics The RT-qPCR results showed that, compared with the normal hepatocyte cell line, the expression of APIP mRNA was upregulated in the HCC cell line (Figure 1a); Compared with 83 cases of normal liver tissues, APIP mRNA was significantly upregulated in HCC tissues (Fig 1b), and the difference was statistically significant. The results of RT-qPCR ROC indicated that APIP had a weak ability to distinguish HCC from normal liver tissues (AUC = 0.661, Fig 1b). Meanwhile, the expression of APIP protein in paraffin specimens of 91 cancer tissues and 82 corresponding adjacent tissues was detected using IHC. The results showed that in HCC tissues, the APIP protein was located in the cell nucleus and was brownish-yellow granules. The following figure shows the negative and positive expression of APIP protein in adjacent tissues (Fig 1c) and cancer tissues (Fig 1d), respectively. Among 91 cases of HCC tissues, 78 cases had high expression of APIP protein (85.7%), and 13 cases had low expression of APIP protein (14.3%). Among the 82 cases of adjacent liver tissues, only 21 cases had high expression of APIP protein (25.6%), and 61 cases had low expression of APIP protein (74.4%). The results showed that compared with the adjacent liver tissues, APIP protein was significantly highly expressed in HCC tissues ( P < 0.001), as shown in Table 1. Furthermore, it was found that the differential expression of APIP protein was statistically significant in the gender and age of HCC patients ( P =0.029, P =0.013), suggesting that the level of APIP protein was related to the gender and age of HCC patients, as shown in Table 1. It is worth mentioning that the APIP protein has a good ability to distinguish HCC from normal liver tissue(AUC=0.863, Fig 1e). Similar to the above results, the APIP protein is upregulated in HCC and has a certain ability to diagnose and distinguish HCC, suggesting that APIP has the potential to become a diagnostic biomarker for HCC. Table 1 The expression of APIP protein in HCC and non-HCC and its relationship with clinicopathological characteristics Group n APIP χ 2 P High expression(n) Low expression(n) Tissue HCC 91 78 13 63.657 <0.001 Non-HCC 82 21 61 Gender male 65 59 6 4.747 0.029 female 26 19 7 Age <60 62 57 5 6.149 0.013 ≥60 29 21 8 Cirrhosis yes 39 33 6 0.034 0.855 no 43 37 6 Hyperspleen yes 28 26 2 1.045 0.307 no 55 45 10 AFP (ng/mL) ≥400 34 27 7 1.761 0.185 500 53 46 7 0.206 0.650 ≤500 36 30 6 Tumor size (cm) ≥5 50 44 6 0.473 0.491 <5 41 34 7 Tumor nodule single 68 58 10 0.000 1.000 several 21 18 3 Microvascular invasion yes 42 38 4 0.523 0.470 no 47 39 8 Invasion of major vessels yes 17 16 1 / 0.448 no 67 57 10 Extrahepatic metastasis yes 18 15 3 0.000 1.000 no 66 57 9 Microsatellite nodules yes 17 16 1 / 0.446 no 66 56 10 BCLC stage 0-A 58 51 7 0.000 1.000 B-C 28 25 3 Ki-67 (%) <30 46 40 6 0.000 0.987 ≥30 38 34 4 P53 + 69 60 9 / 0.682 - 15 14 1 VEGF + 55 50 5 1.073 0.300 - 30 25 5 Expression and Prognostic Analysis of APIP in HCC Based on Public Databases RNA-seq expression profiles of HCC and normal liver tissues were downloaded from the TCGA and GTEx public databases, which included expression data of 374 HCC cases and 160 non-cancer controls. Additionally, 70 mRNA chips related to HCC from 33 platforms were collected from GEO, including 3,134 liver cancer samples and 2,155 non-cancer samples. The above datasets were combined for meta-analysis, and the SMD forest plot was drawn using STATA software to further verify the expression level of APIP mRNA in HCC. The results showed that APIP mRNA was highly expressed in HCC (SMD = 0.87; 95% CI : 0.64 - 1.11; I 2 = 91.2%; P = 0.000) (Fig 2a). Meanwhile, the Begg's funnel plot (Fig 2b) showed that most of the datasets were bilaterally symmetrical, and the p-value of the Begg test based on this SMD model was 0.07, indicating no publication bias. SROC curve was 0.82 (95% CI : 0.78-0.85) (Fig 2c), suggesting that APIP has a good ability to distinguish HCC from non-cancerous tissues.Furthermore, the correlation between APIP and the prognosis of HCC patients was analyzed through the GEPIA database. The Kaplan-Meier survival analysis results indicated that HCC patients with high APIP expression had a shorter overall survival (OS) (Fig 2d, Logrank P = 0.00046) and disease-free survival (DFS) (Fig 2e, Logrank P = 0.00046); To further explore the prognostic prediction ability of APIP in HCC, we also drew a nomogram (Fig 2f), and found that the higher the expression level of APIP , the higher the corresponding single score. Combining the single scores of other clinical parameters could obtain a higher total score, and the corresponding prognosis of HCC patients would be worse. This also indicates that APIP can affect the prognosis of HCC patients. The calibration plot was used to verify the performance of the prediction model, and it was found that the three-year and five-year OS survival periods of HCC patients had a high degree of fit, and there was no significant deviation between the fitting line and the reference line (Fig 2f), proving that the prediction model had high predictive power. The above results indicate that APIP is highly expressed in HCC, which may lead to poor prognosis in HCC patients, suggesting its important role in the development of HCC cells. Identification of APIP Silencing Cell Lines Construction To investigate the biological function of APIP in HCC cells, we successfully established APIP silencing cell lines by transfecting lentivirus into Huh7 and SK cells. The RT-qPCR verification experiment results showed that the expression level of APIP mRNA in APIP -silenced SK cells and Huh7 cells was lower compared with the NC group (Fig 3b-c). Meanwhile, under fluorescence microscopy, it was observed that the HCC cells transfected with lentivirus displayed green fluorescence (Fig 3a), which proved that APIP -silencing lentivirus-transformed cell lines were successfully established in SK and Huh7 cells. Silencing APIP inhibits the proliferation and promotes apoptosis of HCC cells Through the CCK-8 proliferation assay, we found that compared with the NC group, the APIP i group significantly inhibited the growth of Huh7 and SK cells (Fig 4a-b), suggesting that APIP may promote the proliferation of HCC cells in vitro. By flow cytometry, we found that in Huh7 cells, the proportion of G1 phase cells in the NC group was lower than that in the APIP i group, while the proportion of S phase and G2M phase cells was higher than that in the APIP i group, indicating that silencing APIP inhibited the G1-S phase transition in Huh7 cells and arrested them in the G1 phase of the cell cycle (Fig 4c-e); in SK cells, the proportion of S phase cells in the APIP i group was higher than that in the NC group, indicating that it inhibited the S-G2M phase transition in SK cells and arrested DNA synthesis in the S phase of the cell cycle (Fig 4f-h). Although the arrested phases were different in these two HCC cell lines, it was speculated that this might be due to the asynchronous proliferation rates of the two cell lines. However, the final results all demonstrated that silencing APIP could arrest DNA synthesis in Huh7 and SK cells, indicating that silencing APIP could inhibit the proliferation of Huh7 and SK cells. Additionally, the flow cytometry results for cell apoptosis showed that compared with the NC group, the apoptosis rate of SK cells (Fig 4i-k) and Huh7 cells (Fig 6l-n) in the APIP i group significantly increased, with statistically significant differences. These results suggest that APIP promotes the proliferation and inhibits the apoptosis of HCC cells. Silencing APIP inhibits the invasion and migration of HCC cells Furthermore, the results of the transwell migration and invasion experiments indicated that compared with the cells in the NC group, the number of SK and Huh7 penetrating cells in the APIP i group was significantly reduced (Fig 5a,b). This experiment also further explored the effect of APIP on the migration ability of HCC cells through scratch experiments. The results indicated that, compared with the NC group, silencing APIP could inhibit the migration of SK and Huh7 cells (Fig 6a,b). Consistent with the results of the transwell migration assay, APIP can promote the migration of SK and Huh7 cells. Therefore, APIP may enhance the migration and invasion abilities of SK and Huh7 cells. Regulate the potential upstream transcription factors of APIP Through to the PROMO usage (https://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3), hTFtarget (http://bioinfo.life.hust .edu.cn/hTFtarget#! /), SPP (SPP, http://www.signalingpathways.org/index.jsf) database predictive results of the three databases in intersection, found APIP may be a transcription factor YY1, the TBP, SRF, MAZ regulation of the downstream targets (Fig 7a). Then using the JASPAR database (https://jaspar.genereg.net/) predict the four transcription factors and the combination of APIP score and combine with APIP promoter region of the sequence. The binding scores of APIP to the above four transcription factors are respectively: 10.563、8.797、12.764、10.025 (Supplementary Material 1), that is, the binding score of the transcription factor SRF to the target gene APIP is the highest, that is, the possibility that SRF is the upstream transcription factor of the regulated APIP is the greatest. The binding sequence of SRF and the APIP promoter is GGGCCAAATAAGGGAA (Fig 7b). In order to further explore the possibility of the combination of the transcription factor SRF and APIP , The Cistrome DB (http://cistrome.org/db/#/) and UCSC Genome Browser(https://genome.ucsc.edu/) were used to observe whether peaks of binding to the transcription factor SRF appeared in the promoter region of APIP . The result is shown in Figure 7c. It can be seen that there is indeed a binding peak of SRF in the APIP promoter region. In addition, the TIMER2(http://timer.cistrome.org/) database was used to predict the correlation between SRF transcription factors and gene APIP , and it was found that there was a positive correlation between APIP and SRF (Fig 7d, r=0.444, P <0.001). Therefore, in HCC, SRF may be a potentially regulated transcription factor upstream of APIP , participating in the regulation of the biological role of APIP in HCC cells. Discussion HCC is one of the deadliest malignant cancers of the digestive system globally, characterized by high incidence and mortality rates [1] . The prognosis for HCC patients is generally poor, with nearly 600,000 deaths occurring worldwide annually [19, 20] . Due to the lack of specific symptoms in the early stages of HCC, most patients present with middle or advanced stage disease when seeking medical attention [21] , leading to suboptimal therapeutic outcomes. Therefore, early diagnosis and accurate assessment of disease progression are crucial for improving the prognosis of HCC patients.In recent years, the advancement of molecular bioinformatics has enabled the identification of an increasing number of gene expression profiles that can be utilized for the diagnosis and prognosis evaluation of malignant tumors. These molecular markers offer valuable insights for disease prevention and treatment [22, 23] . Consequently, the search for new diagnostic, prognostic, or therapeutic biomarkers for HCC remains a scientific priority. As a member of the anti-apoptotic molecules, APIP is involved in the negative regulation of apoptosis [24, 25] . Additionally, its expression level has a potential association with tumors. For example, APIP has been found to be amplified and upregulated in squamous cell carcinoma cell lines of the tongue and larynx [10] . Conversely, in non-small cell lung cancer cells, both APIP mRNA and protein levels are downregulated [11] . In this study, we first examined the expression level of APIP in HCC using RT-qPCR and IHC experiments. We then conducted a comprehensive analysis of APIP expression in HCC using datasets from multiple databases. The results consistently showed that APIP expression is upregulated in HCC. Moreover, high APIP expression is associated with a poor prognosis in HCC patients. These findings suggest that APIP expression is dysregulated in HCC and may serve as a prognostic biomarker, playing a significant role in HCC tumorigenesis and progression. Furthermore, this study revealed that APIP has strong specificity and can effectively distinguish between HCC and non-HCC samples, thus validating its role as a diagnostic marker for HCC.Unlike the routine detection of HER2, EGFR, BRAF, and KRAS genes in breast cancer, lung cancer, colorectal cancer, and other tumors, HCC, due to its heterogeneity, lacks clear genetic phenotypes related to prognosis and treatment [26] . Therefore, according to the 8th edition of the American Joint Committee on Cancer, genetic testing is not recommended for routine clinical use in diagnosing HCC [27] . Although an increase in AFP levels [28] can indicate the occurrence of HCC to some extent, AFP is a non-specific tumor marker and must be combined with imaging to effectively diagnose HCC. Thus, AFP has limitations in the early diagnosis of HCC. Therefore, the discovery of APIP is not only beneficial for routine genetic testing in HCC but also for the early diagnosis of HCC. Given the abnormal expression and prognostic diagnostic value of APIP in HCC, further investigation into the clinical significance of APIP in HCC revealed that the protein level of APIP is associated with the age and gender of HCC patients. In this study, 91 HCC patients were included in the IHC analysis. Among the 65 male HCC patients, 59 exhibited high APIP expression, accounting for 90.8%. Among the 26 female HCC patients, 19 had high APIP expression, representing 73.1%. These findings suggest that the prevalence of HCC is higher in males than in females in this study. Consistent with this observation, studies have shown that gender differences in HCC prevalence are evident in almost all countries, with men being more commonly affected than women. The incidence of HCC in men is typically 2 to 3 times higher than that in women [29] . The higher incidence in men may be attributed to a higher prevalence of HBV, HCV, alcohol consumption, and smoking, or it may be due to differences in steroid hormones, immune responses, and epigenetics between men and women [30] . In conjunction with the results of this study, it appears that there may also be gender differences in the upregulation of APIP protein in HCC, with elevated APIP protein levels potentially contributing to the higher incidence of HCC in men. The incidence of HCC also varies significantly with age. Generally, the likelihood of developing HCC is extremely low in individuals under the age of 40 [31] . However, studies have shown a significant increase in the incidence of gastrointestinal tumors in young adults over the past few decades, with HCC being one of them [32, 33] . In a study by Abbas Ali Tasneem et al [34] , among 163 tumor patients aged 40 or younger, hepatobiliary tumors were the second most common, with HCC being the most prevalent among them. This indicates a shift towards younger age groups in the HCC patient population.In the IHC experiments of this study, it was also observed that among the 62 HCC patients under the age of 65, 57 cases had high expression of the APIP protein, accounting for 87.7%. Among the 29 HCC patients aged 65 or older, 21 cases had high expression of the APIP protein, accounting for 72.4%. This indicates that the number of cases with high APIP protein expression was higher in HCC patients under the age of 65 than in those aged 65 or older. Therefore, it is speculated that APIP may be a potential oncogenic factor in HCC, and its upregulation may be associated with the development of HCC. To explore the potential biological functions of APIP in the progression of HCC, we employed the CCK8 proliferation assay to investigate whether APIP could regulate the proliferation of Huh7 and SK cells. The results demonstrated that silencing APIP effectively inhibited the growth of HCC cells. To further elucidate the specific pathways through which APIP inhibits HCC cell growth, we utilized flow cytometry for cell cycle analysis. The findings revealed that silencing APIP increased the proportion of Huh7 cells in the G0/G1 phase and induced stagnation of DNA synthesis in the G0/G1 phase, as well as stagnation of DNA synthesis in the S phase of the cell cycle in SK cells. Thus, the experimental results suggest that the proliferative-promoting effect of APIP in HCC cells may be associated with transitions in the G1/G0 and S phases of the cell cycle, as well as cell cycle regulation. The apoptosis experiment results showed that the apoptosis rate in the APIP -silenced group was significantly higher than that in the control group. This indicates that silencing APIP not only inhibits the proliferation of HCC cells but also promotes their apoptosis. Additionally, the transwell migration and invasion assays, as well as the wound healing assay, indicated that silencing APIP significantly reduced the migration and invasion capabilities of Huh7 and SK cells. In other words, APIP itself can promote the migration and invasion of HCC cells. These results further suggest that APIP may positively regulate the occurrence and development of HCC. It is widely recognized that cancer can be viewed as the culmination of a series of genetic alterations, during which normal cells transform into malignant ones. One of the fundamental changes driving this malignant transformation is the evasion of cell death [35] . As early as the 1970s, Kerr et al. linked apoptosis to the elimination of potential malignant cells, proliferation, and tumor progression [36] . Apoptosis, or programmed cell death, is a complex process [37] that controls cell proliferation and maintains the necessary balance within the body [38] .Recent studies in cancer biology have underscored the significant influence of apoptosis and its regulatory genes on the carcinogenesis process. Disruption of the apoptosis signaling pathway can directly lead to cancer development and progression [39] . Moreover, an increasing body of experimental evidence indicates that the programmed cell death pathway is a key factor in the occurrence of HCC. In most malignant tumors, apoptotic deficiency is a pivotal step in cellular malignant transformation, as apoptosis helps maintain genomic integrity [35] . Nehal M Elsherbiny et al [40] alconfirmed that disruption of the apoptotic signaling pathway in parenchymal hepatocytes can lead to the development of HCC. On the other hand, stimulating cell apoptosis has been explored as a method for cancer treatment, with successful verification in a mouse model of liver cancer using tumor-targeted TRAIL fusion proteins [41] . The results of this study indicate that APIP plays a role in inhibiting apoptosis in HCC, thereby disrupting the balance between proliferation and apoptosis in HCC cells. To some extent, this maintains the continuous proliferation of HCC cells, contributing to the occurrence and development of HCC. Furthermore, invasion and metastasis are two of the most critical indicators of cancer and the primary causes of death among tumor patients [42] . Patients with tumor invasion and metastasis typically have poorer treatment outcomes and shorter survival periods. In this study, APIP was found to promote the metastasis and invasion of HCC cells, and its high expression is associated with a poor prognosis in HCC patients. It is speculated that APIP may affect the prognosis of HCC patients by promoting the malignant progression of HCC cells, leading to a poor prognosis and shortened survival period for these patients. Here, we also speculate that the transcription factor SRF may target APIP . Serum response factor (SRF) is a transcription factor composed of 508 amino acids and contains three main regions: the serum response element DNA-binding domain, one transactivation domain, and multiple phosphorylation sites [43] . Studies have shown that SRF is not only a tumor-promoting factor for HCC but also exerts its effects by upregulating the expression of protein-coding genes, participating in HCC progression, and promoting tumor development [44, 45] . Specifically, SRF can enhance the migration and invasion of HCC cells by upregulating two matrix metalloproteinases, MMP-2 and MMP-9 [43] . In this study, we found that APIP can also promote the migration and invasion of HCC cells. Given the involvement of both SRF and APIP in these processes, it is plausible that the role of APIP in HCC may be mediated through the regulation of SRF. However, further experiments are needed to confirm the existence of a targeted regulatory relationship between SRF and APIP in HCC. This study has several limitations. First, the patients included in this experiment were not followed up, and thus, the prognostic predictive ability of APIP for HCC patients could not be further explored. Second, there is a lack of experiments related to the effects of APIP on the in vivo biological behavior of HCC cells. Third, in-depth studies on the regulatory relationship between APIP and the transcription factor SRF have not yet been conducted. Further studies are needed to validate these findings. Conclusion Overall, our study provides a comprehensive analysis of APIP expression in HCC and its correlation with the clinical characteristics of HCC patients, elucidating the potential clinical significance of APIP in this context. Experimental evidence further supports the notion that APIP acts as a tumor-promoting factor in HCC progression and is associated with the occurrence and development of the disease. Therefore, APIP may serve as a novel molecular target for the detection and treatment of HCC. However, further experiments are required to investigate and confirm the specific regulatory mechanisms underlying APIP ’s malignant biological effects in HCC. Declarations Ethics approval and consent to participate The current research was ratified by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. Funding The present study was funded by Fund of National Natural Science Foundation of China(NSFC82260581);Research Project of the Health Commission of the Autonomous Region(Z-B20241484). Author Contribution CY: Conducted the experiments, processed the experimental data, and wrote the manuscript.J-HH: Processed the experimental data.RZ: Created the figures and tables.K-LW: Provided guidance and revised the manuscript.Z-BF: Provided guidance and revised the manuscript.All authors contributed to the writing of the manuscript. References Sung H, Ferlay J.Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries . CA Cancer J Clin.2021.doi:10.3322/caac.21660. Yarchoan M, Agarwal P.Recent Developments and Therapeutic Strategies against Hepatocellular Carcinoma . Cancer Res.2019.doi:10.1158/0008-5472.Can-19-0803. Forner A, Llovet J M.Hepatocellular carcinoma . Lancet.2012.doi:10.1016/s0140-6736(11)61347-0. Siegel R, Naishadham D.Cancer statistics, 2012 . CA Cancer J Clin.2012.doi:10.3322/caac.20138. Faivre S, Bouattour M.Novel molecular therapies in hepatocellular carcinoma . Liver Int.2011.doi:10.1111/j.1478-3231.2010.02395.x. Cho D H, Lee H J.Suppression of hypoxic cell death by APIP-induced sustained activation of AKT and ERK1/2 . Oncogene.2007.doi:10.1038/sj.onc.1210080. Kang W, Hong S H.Structural and biochemical basis for the inhibition of cell death by APIP, a methionine salvage enzyme . Proc Natl Acad Sci U S A.2014.doi:10.1073/pnas.1308768111. Hong S H, Lee W J.APIP, an ERBB3-binding partner, stimulates erbB2-3 heterodimer formation to promote tumorigenesis . Oncotarget.2016.doi:10.18632/oncotarget.7802. Wang N, Li R.Apaf-1 interacting protein, a new target of microRNA-146a-3p, promotes prostate cancer cell development via the ERK1/2 pathway . Cell Biol Int.2022.doi:10.1002/cbin.11796. Järvinen A K, Autio R.High-resolution copy number and gene expression microarray analyses of head and neck squamous cell carcinoma cell lines of tongue and larynx . Genes Chromosomes Cancer.2008.doi:10.1002/gcc.20551. Moravcikova E, Krepela E.Down-regulated expression of apoptosis-associated genes APIP and UACA in non-small cell lung carcinoma . Int J Oncol.2012.doi:10.3892/ijo.2012.1397. Zhou T, Chen S.miR-145-5p affects the differentiation of gastric cancer by targeting KLF5 directly . J Cell Physiol.2019.doi:10.1002/jcp.27525. Yang N, Hui L.Overexpression of SOX2 promotes migration, invasion, and epithelial-mesenchymal transition through the Wnt/β-catenin pathway in laryngeal cancer Hep-2 cells . Tumour Biol.2014.doi:10.1007/s13277-014-2045-3. Miao Z F, Liu X Y.Tbx3 overexpression in human gastric cancer is correlated with advanced tumor stage and nodal status and promotes cancer cell growth and invasion . Virchows Arch.2016.doi:10.1007/s00428-016-2007-9. Mudduluru G, Vajkoczy P.Myeloid zinc finger 1 induces migration, invasion, and in vivo metastasis through Axl gene expression in solid cancer . Mol Cancer Res.2010.doi:10.1158/1541-7786.Mcr-09-0326. Guo L, Li F.SIX1 amplification modulates stemness and tumorigenesis in breast cancer . J Transl Med.2023.doi:10.1186/s12967-023-04679-2. Song J, Xie C.Transcription factor AP-4 promotes tumorigenic capability and activates the Wnt/β-catenin pathway in hepatocellular carcinoma . Theranostics.2018.doi:10.7150/thno.25194. Zhu L, Dai L.Transcription factorIRX5 promotes hepatocellular carcinoma proliferation and inhibits apoptosis by regulating the p53 signalling pathway . Cell Biochem Funct.2020.doi:10.1002/cbf.3517. McGlynn K A, Petrick J L.Global epidemiology of hepatocellular carcinoma: an emphasis on demographic and regional variability . Clin Liver Dis.2015.doi:10.1016/j.cld.2015.01.001. Yang J D and Roberts L R.Hepatocellular carcinoma: A global view . Nat Rev Gastroenterol Hepatol.2010.doi:10.1038/nrgastro.2010.100. Ogunwobi O O, Harricharran T.Mechanisms of hepatocellular carcinoma progression . World J Gastroenterol.2019.doi:10.3748/wjg.v25.i19.2279. He R Q, Li J D.LPCAT1 overexpression promotes the progression of hepatocellular carcinoma . Cancer Cell Int.2021.doi:10.1186/s12935-021-02130-4. Hu J, Li R.Identification of key genes for esophageal squamous cell carcinoma via integrated bioinformatics analysis and experimental confirmation . J Thorac Dis.2020.doi:10.21037/jtd.2020.01.33. Cho D H, Hong Y M.Induced inhibition of ischemic/hypoxic injury by APIP, a novel Apaf-1-interacting protein . J Biol Chem.2004.doi:10.1074/jbc.M405747200. Ko D C, Gamazon E R.Functional genetic screen of human diversity reveals that a methionine salvage enzyme regulates inflammatory cell death . Proc Natl Acad Sci U S A.2012.doi:10.1073/pnas.1206701109. Kamarajah S K, Frankel T L.Critical evaluation of the American Joint Commission on Cancer (AJCC) 8th edition staging system for patients with Hepatocellular Carcinoma (HCC): A Surveillance, Epidemiology, End Results (SEER) analysis . J Surg Oncol.2018.doi:10.1002/jso.24908. Liu F, Liang J.ZCCHC17 Served as a Predictive Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma . Front Oncol.2021.doi:10.3389/fonc.2021.799566. Singal A G, Lampertico P.Epidemiology and surveillance for hepatocellular carcinoma: New trends . J Hepatol.2020.doi:10.1016/j.jhep.2019.08.025. Petrick J L, Florio A A.International trends in hepatocellular carcinoma incidence, 1978-2012 . Int J Cancer.2020.doi:10.1002/ijc.32723. Dorak M T and Karpuzoglu E.Gender differences in cancer susceptibility: an inadequately addressed issue . Front Genet.2012.doi:10.3389/fgene.2012.00268. El-Serag H B and Rudolph K L.Hepatocellular carcinoma: epidemiology and molecular carcinogenesis . Gastroenterology.2007.doi:10.1053/j.gastro.2007.04.061. Sung H, Siegel R L.Emerging cancer trends among young adults in the USA: analysis of a population-based cancer registry . Lancet Public Health.2019.doi:10.1016/s2468-2667(18)30267-6. Shaw J J and Shah S A.Rising incidence and demographics of hepatocellular carcinoma in the USA: what does it mean?Expert Rev Gastroenterol Hepatol.2011.doi:10.1586/egh.11.20. Tasneem A A and Luck N H.Digestive tract neoplasms in young individuals: Demographics, staging and risk factors . Cancer Rep (Hoboken).2021.doi:10.1002/cnr2.1319. Hanahan D and Weinberg R A.The hallmarks of cancer . Cell.2000.doi:10.1016/s0092-8674(00)81683-9. Kerr J F, Wyllie A H.Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics . Br J Cancer.1972.doi:10.1038/bjc.1972.33. Reed J C.Mechanisms of apoptosis . Am J Pathol.2000.doi:10.1016/s0002-9440(10)64779-7. Hadian K and Stockwell B R.The therapeutic potential of targeting regulated non-apoptotic cell death . Nat Rev Drug Discov.2023.doi:10.1038/s41573-023-00749-8. Lowe S W and Lin A W.Apoptosis in cancer . Carcinogenesis.2000.doi:10.1093/carcin/21.3.485. Elsherbiny N M, Eisa N H.Chemo-preventive effect of crocin against experimentally-induced hepatocarcinogenesis via regulation of apoptotic and Nrf2 signaling pathways . Environ Toxicol Pharmacol.2020.doi:10.1016/j.etap.2020.103494. Wahl K, Siegemund M.Increased apoptosis induction in hepatocellular carcinoma by a novel tumor-targeted TRAIL fusion protein combined with bortezomib . Hepatology.2013.doi:10.1002/hep.26082. Hanahan D and Weinberg R A.Hallmarks of cancer: the next generation . Cell.2011.doi:10.1016/j.cell.2011.02.013. Kim K R, Bae J S.The role of serum response factor in hepatocellular carcinoma: an association with matrix metalloproteinase . Oncol Rep.2011.doi:10.3892/or.2011.1421. Kwon C Y, Kim K R.The role of serum response factor in hepatocellular carcinoma: implications for disease progression . Int J Oncol.2010.doi:10.3892/ijo_00000734. Pellegrino R, Thavamani A.Serum Response Factor (SRF) Drives the Transcriptional Upregulation of the MDM4 Oncogene in HCC . Cancers (Basel).2021.doi:10.3390/cancers13020199. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterials.zip Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Cancer Cell International → Version 1 posted Editorial decision: Revision requested 04 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviews received at journal 25 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers invited by journal 16 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 15 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6843055","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471705834,"identity":"c4675b84-cf1a-4cd0-98bc-41916497374e","order_by":0,"name":"cong yu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"cong","middleName":"","lastName":"yu","suffix":""},{"id":471705835,"identity":"b9859b28-6d24-45e5-bea9-95def95e4fa4","order_by":1,"name":"Jiang-Hua Huang","email":"","orcid":"","institution":"The Fourth Afiliated Hospital of GuangxiMedical University, Liuzhou Worker's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiang-Hua","middleName":"","lastName":"Huang","suffix":""},{"id":471705836,"identity":"337b7b41-06f0-4eff-b08a-12c8ebb0510c","order_by":2,"name":"Rui Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of GuangXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""},{"id":471705837,"identity":"dffd9a96-79a0-4dba-ac4b-a399db423bf9","order_by":3,"name":"Kang-Lai Wei","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kang-Lai","middleName":"","lastName":"Wei","suffix":""},{"id":471705838,"identity":"6fbab757-f4f1-4f41-b8d2-ad929cb49964","order_by":4,"name":"Zhen-Bo Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBAC+wNAgrGBgcEASH3mbTssx8befgCvFgMGhBZmad5zh435eM4kEK2FjXHmv8OJ8yQcDPBrYe89/PLnDps8c/YGNoaP2w6nt0kwJDD8qNiG2y8859IsJM+kFVv2HGBjSNx2OLdNuvEAY8+Z27htkcgxMzBsO5y44UYCmwVYi8yBBGbGNjxa5N+YGSS2/QdrkfjYdjidTSLBAL8WCR7jBwfbDoC1SM5sO5xAWAtPjhljY1ty4s6eA8BAbkszbAMG8kG8fmE/Y/zxZ5td4nb2BlBU2sjLt7cffPCjArcWIGCTgND8H+BCB/CpBwLmDwQUjIJRMApGwUgHALaxXnrLjNBeAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of GuangXi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhen-Bo","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2025-06-07 13:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6843055/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6843055/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12935-025-04018-z","type":"published","date":"2025-10-22T16:16:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84894064,"identity":"c316f97a-358c-4e55-ab92-1e4a8966d850","added_by":"auto","created_at":"2025-06-18 13:36:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":501134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression level and clinical significance of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAPIP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in HCC.\u003c/strong\u003e (a) Expression of \u003cem\u003eAPIP\u003c/em\u003e mRNA in cell lines. (b) Expression of \u003cem\u003eAPIP\u003c/em\u003e mRNA in HCC tissues and corresponding adjacent liver tissues and ROC curve. (c) The nuclear staining of the adjacent normal liver tissues was negative (×40, ×400). (d) The nuclear staining of the HCC tissues was positive (×40, ×400). (e) APIP protein has a good ability to distinguish HCC from adjacent normal liver tissues. (*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05,***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001,)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6843055/v1/3d7ecce8eca8811523388bf4.png"},{"id":84893747,"identity":"cf920c84-553c-4bc8-a94e-4cd1c50c56bc","added_by":"auto","created_at":"2025-06-18 13:28:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147431,"visible":true,"origin":"","legend":"\u003cp\u003eThe ability of \u003cem\u003eAPIP\u003c/em\u003e to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. (a) High expression of \u003cem\u003eAPIP\u003c/em\u003ein public database datasets. (b) Begg's funnel plot. (c) SROC curve of \u003cem\u003eAPIP\u003c/em\u003ebased on the TCGA cohort. (d) OS-Kaplan-Meier survival curve. (e) DFS-Kaplan-Meier survival curve. (f) Nomogram and calibration plot.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6843055/v1/32232b1f9ccf4b3c94be268f.png"},{"id":84893748,"identity":"0f37acdd-cd61-473f-abdf-b364bf67b83f","added_by":"auto","created_at":"2025-06-18 13:28:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":435838,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of \u003cem\u003eAPIP\u003c/em\u003ei cell lines. (a) Fluorescence intensity of cells in the NC group and \u003cem\u003eAPIP\u003c/em\u003ei group. (b-c) RT-qPCR demonstrated successful construction of \u003cem\u003eAPIP\u003c/em\u003e knockdown models in SK and Huh7 cells (*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05,****\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6843055/v1/04a66514d65bc3fa1affc7c7.png"},{"id":84893752,"identity":"99da87f1-3a16-43d9-aed6-a8a6c68395b8","added_by":"auto","created_at":"2025-06-18 13:28:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":458163,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of \u003cem\u003eAPIP\u003c/em\u003ei on the proliferation and apoptosis of HCC cells. (a-b) \u003cem\u003eAPIP\u003c/em\u003ei inhibits the proliferation of Huh7 and SK cells. (c-e) \u003cem\u003eAPIP\u003c/em\u003ei can block the cell cycle transition of Huh7 cells. (f-h) \u003cem\u003eAPIP\u003c/em\u003ei can block the cell cycle transition of SK cells. (i-k) \u003cem\u003eAPIP\u003c/em\u003ei promotes apoptosis of SK cells. (l-n) \u003cem\u003eAPIP\u003c/em\u003ei promotes apoptosis of Huh7 cells. (*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, ****\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6843055/v1/86733ce381cb8e8ba8701735.png"},{"id":84894067,"identity":"ad8f9ff9-4fa9-475b-af0e-082dd2e9da77","added_by":"auto","created_at":"2025-06-18 13:36:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":455713,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of\u003cem\u003e APIP\u003c/em\u003ei on the migration and invasion abilities of HCC cells. (a) \u003cem\u003eAPIP\u003c/em\u003e promotes the migration of Huh7 and SK cells. (b) \u003cem\u003eAPIP\u003c/em\u003epromotes the invasion of Huh7 and SK cells (* * * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6843055/v1/082031bfb81fa6affafd351a.png"},{"id":84893755,"identity":"c75f0ab1-c361-44aa-8b7d-60f827959520","added_by":"auto","created_at":"2025-06-18 13:28:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":537103,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of silencing \u003cem\u003eAPIP\u003c/em\u003e on the migration ability of HCC cells. (a) \u003cem\u003eAPIP \u003c/em\u003einhibit Huh7 cells migration.(b) \u003cem\u003eAPIP\u003c/em\u003e inhibit SK cell migration.(* * * * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.000).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6843055/v1/42886e9f3dce2d4e573a9f6a.png"},{"id":84894069,"identity":"7a6934ce-beb2-4e37-be42-8f5fc90c34d8","added_by":"auto","created_at":"2025-06-18 13:36:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":279123,"visible":true,"origin":"","legend":"\u003cp\u003eUpstream transcription factors regulating \u003cem\u003eAPIP\u003c/em\u003e. (a) Common transcription factors predicted by three databases. (b) Binding sites of transcription factor SRF to \u003cem\u003eAPIP\u003c/em\u003e. (c) Binding peaks of transcription factor SRF to the promoter region of \u003cem\u003eAPIP\u003c/em\u003e.(d) Correlation graph of SRF and \u003cem\u003eAPIP\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6843055/v1/6e305d7153fe6708020e2abf.png"},{"id":94490582,"identity":"84ca5b4b-3a53-488f-94de-12faa3f9f60b","added_by":"auto","created_at":"2025-10-27 17:12:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3744703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6843055/v1/6406855c-7271-41ba-9276-df8165e6db48.pdf"},{"id":84893746,"identity":"53187229-7efd-4984-920f-c2e8b4f8ea60","added_by":"auto","created_at":"2025-06-18 13:28:57","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":87344,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.zip","url":"https://assets-eu.researchsquare.com/files/rs-6843055/v1/73ae102fade48a808fad4df7.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Pathological Significance and Biological Function of APIP in Hepatocellular Carcinoma","fulltext":[{"header":"Background","content":"\u003cp\u003eOne of the most prevalent solid cancers and a major global cause of cancer-related mortality is hepatocellular carcinoma (HCC). In 2020, the World Health Organization reported that HCC ranks third in mortality and sixth in incidence worldwide, with approximately 900,000 new cases and 830,000 deaths year\u003csup\u003e[1]\u003c/sup\u003e.The development of treatment technology has improved the prognosis of patients with HCC\u003csup\u003e[2]\u003c/sup\u003e. However, overall survival rates for HCC patients remain low\u003csup\u003e[3, 4]\u003c/sup\u003e. Notably, studies have shown that multiple molecules play a key role in the development and progression of HCC\u003csup\u003e[5]\u003c/sup\u003e. Therefore, the search for reliable molecular biomarkers to identify key genes involved in the pathogenesis of liver cancer will help to develop new therapies.\u003c/p\u003e \u003cp\u003eApaf-1-interacting protein(\u003cem\u003eAPIP\u003c/em\u003e)was previously isolated as an inhibitor of mitochondrial cell death interacting with Apaf-1\u003csup\u003e[6]\u003c/sup\u003e, is also a MtnB enzyme in the methionine rescue pathway\u003csup\u003e[7]\u003c/sup\u003eand is associated with various types of cell death processes. For example, the direct binding of \u003cem\u003eAPIP\u003c/em\u003e and APAF-1 can effectively inhibit mitochondria-mediated apoptosis; Additionally, through its role in the methionine rescue pathway, \u003cem\u003eAPIP\u003c/em\u003e can inhibit Caspase-1-mediated pyroptosis induced by Salmonella infection\u003csup\u003e[8]\u003c/sup\u003e Moreover, an increasing number of studies have found that \u003cem\u003eAPIP\u003c/em\u003e is associated with a variety of tumors. First, \u003cem\u003eAPIP\u003c/em\u003e levels are dysregulated in prostate cancer\u003csup\u003e[9]\u003c/sup\u003e, tongue and throat squamous cell carcinoma\u003csup\u003e[10]\u003c/sup\u003e, and non-small cell lung cancer\u003csup\u003e[11]\u003c/sup\u003e. Second, \u003cem\u003eAPIP\u003c/em\u003e can interact with ERBB3 to stimulate gastric cell proliferation and tumorigenesis\u003csup\u003e[8]\u003c/sup\u003e. Given the significant role of \u003cem\u003eAPIP\u003c/em\u003e in tumors, its expression in HCC and related biological roles warrant further investigation, as they are not yet clear.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the expression of \u003cem\u003eAPIP\u003c/em\u003e in HCC cell lines and clinical samples and further analyzed its clinical significance in HCC patients. Additionally, we demonstrated that \u003cem\u003eAPIP\u003c/em\u003e plays an oncogenic role in HCC development and that its expression can influence the proliferation, apoptosis, migration, and invasion of HCC cells in vitro. These findings may provide a novel therapeutic target for treating HCC patients.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003ePatient sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 83 HCC tissue specimens and 83 non-tumor liver tissue specimens were collected from the the First Affiliated Hospital of GuangXi Medical University. 91 cancer tissues and 82 corresponding adjacent tissues used for research were obtained from the Department of Pathology of the First Affiliated Hospital of Guangxi Medical University. The removed tissues were stored in liquid nitrogen for further use. Meanwhile, the clinical data of patients were collected. Inclusion criteria for patients: Patients with primary hepatocellular carcinoma, who had not received radiotherapy, chemotherapy, or endocrine therapy as adjuvant treatments before surgery. Informed consent was obtained from patients, and the study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReal-time quantitative polymerase chain reaction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReal-time quantitative polymerase chain reaction (RT-qPCR) experiments were conducted using the 7500 Quantitative PCR instrument (Thermo Fisher) to detect the expression level of \u003cem\u003eAPIP\u003c/em\u003e mRNA in HCC. Total RNA was extracted from HCC cell lines SK-HEP-1 (SK), Huh7, normal liver cell line (LO2), and 83 pairs of HCC tissues and non-cancerous tissues using the Mini BEST Universal RNA extraction KIT (TaKaRa), and the purity and concentration of total RNA were detected using NanoDrop One (Thermo Scientific). At the same time, reverse transcription synthesis of cDNA was performed using the Prime-Script RT Master Mix (TaKaRa). The \u003cem\u003eAPIP\u003c/em\u003e primer sequences are as follows: \u003cem\u003eAPIP\u003c/em\u003e-F: 5\u0026apos;-ACTGGGACTGGAGGAGGAATTAGC-3\u0026apos;; \u003cem\u003eAPIP\u003c/em\u003e-R: 5\u0026apos;-CGATGGCGAAGGTCCACTTATGTC-3\u0026apos;. Data were obtained using the 7500 Software v2.3 and analyzed using the 2-\u0026Delta;\u0026Delta;CT method, with ACTIN as the endogenous control. Each sample was prepared in triplicate for each experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemical experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmunohistochemical(IHC) method: Paraffin sections were oven-dried at 75\u0026deg;C for dewaxing, subjected to high-pressure antigen retrieval with citrate buffer, blocked with peroxidase reagent, and then blocked with goat serum. One anti-body (APIP antibody at a concentration of 1:200) was dropped separately, and incubated at 37\u0026deg;C for 1.5 hours. After PBS rinsing, a second anti-body was dropped and incubated at 37\u0026deg;C for 20 minutes. After PBS rinsing, DAB staining was performed, followed by hematoxylin counterstaining, dehydration, transparency, and mounting with neutral gum. The APIP positive control was normal intestinal tissue that was proven positive by pre-experiment, and the negative control was PBS instead of the anti-body.\u003c/p\u003e\n\u003cp\u003eRecord the expression status of APIP protein in high-power field of view. Staining intensity: no color (0 points), pale yellow (1 point), brownish yellow (2 points), dark brown (3 points). The percentage of stained cells is classified as: 0 staining points (\u0026lt; 5%), 1 staining point (5% - 24%), 2 staining points (25% - 49%), 3 staining points (50% - 75%), 4 staining points (\u0026gt; 75%). The final staining score is the product of the staining intensity score and the number of staining points, with a maximum of 12 points. A total score of 0 - 7 indicates low expression, and 8 - 12 indicates high expression。\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-throughput data collection and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublic high-throughput RNA microarray and RNA sequencing data from HCC and non-HCC liver tissues were collected from public databases such as Genotype-Tissue Expression (GTEx, https://gtexportal.org), the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih). The following search strategy was used in the GEO database to preliminarily screen mRNA expression chips related to HCC: (hepatocellular OR HCC OR hepatic OR liver) AND (tumour OR tumor OR carcinoma OR cancer OR neoplasm* OR malignancy *). After the preliminary search using the above search strategy, chips meeting the requirements were further screened according to the following inclusion and exclusion criteria. The inclusion criteria for high-throughput data are: (1) the species of the research object is human; (2) the data set contains HCC tissues and non-HCC liver tissues; (3) the data set contains mRNA expression data. The exclusion criteria are as follows: (1) the species of the research object is animal or plant; (2) the samples in the data set are not HCC tissues and non-HCC liver tissues, such as HCC-related cell lines; (3) there are no non-HCC liver tissue samples as control groups in the data set; (4) the data set does not contain mRNA expression data. The data from the same platform were merged using R packages (sva, limma, dplyr) and Perl language to remove batch effects between platforms. Before processing all the data, the raw expression data were standardized and log2 processed. By integrating and analyzing the downloaded data, the differences in the expression of \u003cem\u003eAPIP\u003c/em\u003e between HCC and normal tissues were explored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical application potential of \u003cem\u003eAPIP\u003c/em\u003e in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the clinical value of \u003cem\u003eAPIP\u003c/em\u003e in HCC, standardized mean difference (SMD), receiver operating characteristic (ROC) curve, summary receiver operating characteristic (SROC) curve, and combined sensitivity and specificity were used to evaluate the clinical potential of \u003cem\u003eAPIP\u003c/em\u003e. Stata 12.0 version was used for the above analyses. Kaplan-Meier curves (GEPIA2, http://gepia2.cancer-pku.cn/#analysis) and nomograms (R v3.6.1, \u0026quot;survival\u0026quot; and \u0026quot;rms\u0026quot;) were used to explore the prognostic potential of \u003cem\u003eAPIP\u003c/em\u003e in HCC. Meanwhile, the clinical pathological characteristics of HCC patients were collected, and the correlation between \u003cem\u003eAPIP\u003c/em\u003e expression level and various clinical pathological parameters was analyzed using the X\u003csup\u003e2\u003c/sup\u003e test. If \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, it was considered that\u003cem\u003e\u0026nbsp;APIP\u003c/em\u003e was related to the prognosis and clinical characteristics of HCC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman HCC cells SK and Huh7 were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The cells were cultured in DMEM medium containing 10% fetal bovine serum and 1% penicillin-streptomycin mixture at 37\u0026deg;C in a humidified environment with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLentivirus Transfection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLentivirus vector (LV-\u003cem\u003eAPIP\u003c/em\u003e-RNAi (100111-1)) was purchased from Genechem Co., Ltd. (Shanghai, China). HCC cells were seeded in 6-well plates and pre-cultured for 24 hours. According to the experimental protocol, when the cell confluence reached approximately 30%, lentivirus with RNAi targeting \u003cem\u003eAPIP\u003c/em\u003e was transfected into SK and Huh7 cells as the experimental group (\u003cem\u003eAPIP\u003c/em\u003ei group); and a negative virus was transfected as the control group (NC group). After 24 hours of transfection, puromycin was added to improve the transfection efficiency. The transfection efficiency was determined by fluorescence microscopy and RT-qPCR experiments. The stronger the fluorescence intensity, the higher the transfection efficiency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell proliferation experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proliferation activity of cells in the \u003cem\u003eAPIP\u003c/em\u003ei group and the NC group was analyzed by Cell Counting Kit-8 (CCK8, meilunbio, Dalian, China) proliferation assay. Huh7 cells in the \u003cem\u003eAPIP\u003c/em\u003ei group and the NC group were seeded at a density of 3\u0026times;10\u003csup\u003e3\u003c/sup\u003e cells per well in 96-well microplates with 100 \u0026mu;L of culture medium; SK cells were seeded at a density of 2\u0026times;10\u003csup\u003e3\u0026nbsp;\u003c/sup\u003ecells per well in 100 \u0026mu;L of culture medium and then placed in a cell incubator. Then, at 0 h, 24 h, 48 h and 72 h, 10 \u0026mu;L of CCK8 reagent was added to each well and incubated for 2 h. The wells without cells were used as blank controls. Finally, measured using a Multiskan FC microplate spectrophotometer at 450 nm, and the proliferation of cells was expressed by the absorbance value.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell cycle experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cell cycle distributions of SK and Huh7 in the \u003cem\u003eAPIP\u003c/em\u003ei and NC groups were analyzed using flow cytometry. Cells for cell cycle analysis were detected using the cell cycle Detection Kit (meilunbio,Dalian, China). Cells in the logarithmic growth phase were dissociated with trypsin, resuspended in PBS, and then fixed with pre-cooled ethanol (75%) overnight in a 4\u0026deg;C refrigerator. On the day of the test, centrifuge the cells at 1000 rpm for 5 minutes, discard the ethanol, add PBS at room temperature, and let it stand for 15 minutes to hydrate the cells. Then centrifuge, discard the supernatant, add 300\u0026mu;L of DNA staining solution and incubate at 37 \u0026deg;C for 30 minutes. Subsequently, the lowest sample loading speed was selected and detected by flow cytometry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell apoptosis experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe apoptosis rates of SK and Huh7 cells in the \u003cem\u003eAPIP\u003c/em\u003ei and NC groups were analyzed by flow cytometry. The cells used for apoptosis analysis were detected using the Annexin v-APC/7-AAD Apoptosis Kit (MULTI SCIENCES) in accordance with the instructions. Digest the cells with trypsin, add pre-cooled PBS, centrifuge and wash, and collect 1-10 \u0026times;10\u003csup\u003e5\u003c/sup\u003e cells (including the cells in the culture supernatant). Resuspend the cells with 500\u0026mu;l of 1 \u0026times; Binding Buffer, and then add 5\u0026mu;l of Annexin V-APC and 10\u0026mu;l of 7-AAD. After gentle vortex mixing, incubate at room temperature in the dark for 5 minutes. Detection was carried out by flow cytometry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell migration experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Transwell chamber is a type of transparent cup-shaped device. After being placed in a 24-well culture plate, it is divided into two chambers: the Transwell chamber is called the upper chamber and is used to hold the upper layer of culture medium and cells. The area inside the culture plate is called the lower chamber, which is used to hold the culture medium at the lower layer. The upper and lower layers of culture medium are separated by a polycarbonate film. The migration ability of cells was determined by comparing the number of cells in different groups that passed through the small pores of the polycarbonate membrane within the same period of time.\u003c/p\u003e\n\u003cp\u003eSK and Huh7 cell suspensions were added to the upper chamber at densities of 3\u0026times;10\u003csup\u003e4\u003c/sup\u003e/ well and 4\u0026times;10\u003csup\u003e4\u003c/sup\u003e/ well respectively, and 500\u0026mu;l of serum-free DMEM was added to the lower chamber. After culturing the cells for 24 hours, they were stained with 0.1% crystal violet. After staining, they were imaged under a microscope. Three fields of view were randomly selected for counting, and the images were quantified using Image J software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell invasion experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe invasive ability of cells was determined by comparing the number of cells in different groups that passed through the small pores of the polycarbonate membrane within the same period of time. Dilute Matrigel with serum-free DMEM. The ratio of Matrigel to DMEM is 1:8. Add 60\u0026mu;l of diluted Matrigel to the upper chamber. Then, the SK and HuH7 cell suspensions were added to the upper chamber covered with matrix gel at densities of 3.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e/ well and 4.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e/ well respectively. In the lower chamber, 500\u0026mu;l of DMEM containing 5% fetal bovine serum was added, and the cells were cultured at 37\u0026deg;C for 24 hours. Stained with 0.1% crystal violet. After staining, imaging was performed under a microscope. Three fields of view were randomly selected for counting, and the images were quantified using Image J software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell scratch experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCC cells were inoculated in 6-well plates and cultured overnight. The number of inoculated cells in the Huh7 cell line was 6\u0026times;10\u003csup\u003e5\u003c/sup\u003e per well, and the number of inoculated cells in the SK cell line was 3\u0026times;10\u003csup\u003e5\u003c/sup\u003e per well. The number of inoculated cells in the \u003cem\u003eAPIP\u003c/em\u003ei group and the NC group was the same. The cell layer was damaged with a sterile 200\u0026mu;L pipette tip and washed twice with PBS, and then replaced with serum-free medium for culture. The images of the cells were captured under a microscope at different time points. The capture time points of SK cells were 0h, 12h, and 24h. The shooting time nodes of Huh7 cells are 0h, 24h and 48h. Finally, the cell healing rate at each time point was evaluated using Image J software and the formula [(0h scratch width - Scratch width after culture)/0h scratch width \u0026times;100%].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe potential molecular mechanism of \u003cem\u003eAPIP\u003c/em\u003e in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMore and more studies have found that transcription factors can regulate the occurrence and progression of various tumors\u003csup\u003e[12-16]\u003c/sup\u003e. This is no exception in HCC\u003csup\u003e[17, 18]\u003c/sup\u003e. However, whether \u003cem\u003eAPIP\u003c/em\u003e is regulated by transcription factors has not been reported yet. Therefore, We use the database PROMO usage (https://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3), hTFtarget (http://bioinfo.lif e.hust.edu.cn/hTFtarget#! /), The Signaling Pathways Project (SPP, http://www.signalingpathways.org/index.jsf), The forecast of \u003cem\u003eAPIP\u003c/em\u003e upstream transcription factors, We used the Cistrome DB website (http://cistrome.org/db/#/) and the UCSC Genome Browser (https://genome.ucsc.edu/) to determine whether transcription factor binding peaks occurred in the \u003cem\u003eAPIP\u003c/em\u003e promoter region. In order to mine the potential transcription factors related to \u003cem\u003eAPIP\u003c/em\u003e and further explore the potential molecular regulatory mechanism of \u003cem\u003eAPIP\u003c/em\u003e in HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAll data were analyzed under the software of IBM SPSS v19.0, STATA v12.0 and R v3.6.1. Independent sample t-tests and \u0026chi;\u003csup\u003e2\u003c/sup\u003e tests were used to compare the expression data of \u003cem\u003eAPIP\u003c/em\u003e in liver cancer tissues and non-tumor liver tissues. When combining SMD and hazard ratio (HR), I\u003csup\u003e2\u003c/sup\u003e\u0026lt;50%, and the fixed-effect model was used to handle significant heterogeneity. When I\u003csup\u003e2\u003c/sup\u003e\u0026ge;50%, a random effects model was used to handle significant heterogeneity. ROC curve was plotted to determine the sensitivity and specificity. ROC curve was used to describe the area under the curve (AUC), aggregated sensitivity and aggregated specificity. The likelihood ratio is used to determine the discriminatory ability of \u003cem\u003eAPIP\u003c/em\u003e for HCC and non-HCC tissues. Kaplan-Meier curve analysis was conducted to evaluate the prognostic value of \u003cem\u003eAPIP\u003c/em\u003e. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 indicates significance.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eThe expression of \u003cem\u003eAPIP\u003c/em\u003e in HCC and its relationship with clinicopathological characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RT-qPCR results showed that, compared with the normal hepatocyte cell line, the expression of \u003cem\u003eAPIP\u003c/em\u003e mRNA was upregulated in the HCC cell line (Figure 1a); Compared with 83 cases of normal liver tissues, \u003cem\u003eAPIP\u003c/em\u003e mRNA was significantly upregulated in HCC tissues (Fig 1b), and the difference was statistically significant. The results of RT-qPCR ROC indicated that \u003cem\u003eAPIP\u003c/em\u003e had a weak ability to distinguish HCC from normal liver tissues (AUC = 0.661, Fig 1b).\u003c/p\u003e\n\u003cp\u003eMeanwhile, the expression of APIP protein in paraffin specimens of 91 cancer tissues and 82 corresponding adjacent tissues was detected using IHC. The results showed that in HCC tissues, the APIP protein was located in the cell nucleus and was brownish-yellow granules. The following figure shows the negative and positive expression of APIP protein in adjacent tissues (Fig 1c) and cancer tissues (Fig 1d), respectively. Among 91 cases of HCC tissues, 78 cases had high expression of APIP protein (85.7%), and 13 cases had low expression of APIP protein (14.3%). Among the 82 cases of adjacent liver tissues, only 21 cases had high expression of APIP protein (25.6%), and 61 cases had low expression of APIP protein (74.4%). The results showed that compared with the adjacent liver tissues, APIP protein was significantly highly expressed in HCC tissues (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), as shown in Table 1. Furthermore, it was found that the differential expression of APIP protein was statistically significant in the gender and age of HCC patients (\u003cem\u003eP\u003c/em\u003e=0.029, \u003cem\u003eP\u003c/em\u003e=0.013), suggesting that the level of APIP protein was related to the gender and age of HCC patients, as shown in Table 1. It is worth mentioning that the APIP protein has a good ability to distinguish HCC from normal liver tissue(AUC=0.863, Fig 1e). Similar to the above results, the APIP protein is upregulated in HCC and has a certain ability to diagnose and distinguish HCC, suggesting that\u003cem\u003e\u0026nbsp;APIP\u003c/em\u003e has the potential to become a diagnostic biomarker for HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eThe expression of APIP protein in HCC and non-HCC and its relationship with clinicopathological characteristics\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003eAPIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003eHigh expression(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003eLow expression(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eTissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eHCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e63.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eNon-HCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e4.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e6.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eCirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eHyperspleen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e1.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAFP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026ge;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e1.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026lt;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eHBV-DNA (IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026gt; 500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026le;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eTumor size (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eTumor nodule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eseveral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMicrovascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eInvasion of major vessels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; /\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eExtrahepatic metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMicrosatellite nodules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; /\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eBCLC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e0-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003eB-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eKi-67 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; /\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eVEGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9589%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.16438%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8151%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6438%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eExpression and Prognostic Analysis of \u003cem\u003eAPIP\u003c/em\u003e in HCC Based on Public Databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-seq expression profiles of HCC and normal liver tissues were downloaded from the TCGA and GTEx public databases, which included expression data of 374 HCC cases and 160 non-cancer controls. Additionally, 70 mRNA chips related to HCC from 33 platforms were collected from GEO, including 3,134 liver cancer samples and 2,155 non-cancer samples. The above datasets were combined for meta-analysis, and the SMD forest plot was drawn using STATA software to further verify the expression level of \u003cem\u003eAPIP\u003c/em\u003e mRNA in HCC. The results showed that \u003cem\u003eAPIP\u003c/em\u003e mRNA was highly expressed in HCC (SMD = 0.87; 95% \u003cem\u003eCI\u003c/em\u003e: 0.64 - 1.11; I\u003csup\u003e2\u003c/sup\u003e = 91.2%; \u003cem\u003eP\u003c/em\u003e = 0.000) (Fig 2a). Meanwhile, the Begg\u0026apos;s funnel plot (Fig 2b) showed that most of the datasets were bilaterally symmetrical, and the p-value of the Begg test based on this SMD model was 0.07, indicating no publication bias. SROC curve was 0.82 (95% \u003cem\u003eCI\u003c/em\u003e: 0.78-0.85) (Fig 2c), suggesting that \u003cem\u003eAPIP\u003c/em\u003e has a good ability to distinguish HCC from non-cancerous tissues.Furthermore, the correlation between \u003cem\u003eAPIP\u003c/em\u003e and the prognosis of HCC patients was analyzed through the GEPIA database. The Kaplan-Meier survival analysis results indicated that HCC patients with high \u003cem\u003eAPIP\u003c/em\u003e expression had a shorter overall survival (OS) (Fig 2d, Logrank \u003cem\u003eP\u003c/em\u003e = 0.00046) and disease-free survival (DFS) (Fig 2e, Logrank \u003cem\u003eP\u003c/em\u003e = 0.00046); To further explore the prognostic prediction ability of \u003cem\u003eAPIP\u003c/em\u003e in HCC, we also drew a nomogram (Fig 2f), and found that the higher the expression level of \u003cem\u003eAPIP\u003c/em\u003e, the higher the corresponding single score. Combining the single scores of other clinical parameters could obtain a higher total score, and the corresponding prognosis of HCC patients would be worse. This also indicates that \u003cem\u003eAPIP\u003c/em\u003e can affect the prognosis of HCC patients. The calibration plot was used to verify the performance of the prediction model, and it was found that the three-year and five-year OS survival periods of HCC patients had a high degree of fit, and there was no significant deviation between the fitting line and the reference line (Fig 2f), proving that the prediction model had high predictive power. The above results indicate that \u003cem\u003eAPIP\u003c/em\u003e is highly expressed in HCC, which may lead to poor prognosis in HCC patients, suggesting its important role in the development of HCC cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of \u003cem\u003eAPIP\u003c/em\u003e Silencing Cell Lines Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the biological function of \u003cem\u003eAPIP\u003c/em\u003e in HCC cells, we successfully established \u003cem\u003eAPIP\u003c/em\u003e silencing cell lines by transfecting lentivirus into Huh7 and SK cells. The RT-qPCR verification experiment results showed that the expression level of\u003cem\u003e\u0026nbsp;APIP\u003c/em\u003e mRNA in \u003cem\u003eAPIP\u003c/em\u003e-silenced SK cells and Huh7 cells was lower compared with the NC group (Fig 3b-c). Meanwhile, under fluorescence microscopy, it was observed that the HCC cells transfected with lentivirus displayed green fluorescence (Fig 3a), which proved that \u003cem\u003eAPIP\u003c/em\u003e-silencing lentivirus-transformed cell lines were successfully established in SK and Huh7 cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSilencing \u003cem\u003eAPIP\u003c/em\u003e inhibits the proliferation and promotes apoptosis of HCC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough the CCK-8 proliferation assay, we found that compared with the NC group, the \u003cem\u003eAPIP\u003c/em\u003ei group significantly inhibited the growth of Huh7 and SK cells (Fig 4a-b), suggesting that \u003cem\u003eAPIP\u003c/em\u003e may promote the proliferation of HCC cells in vitro. By flow cytometry, we found that in Huh7 cells, the proportion of G1 phase cells in the NC group was lower than that in the \u003cem\u003eAPIP\u003c/em\u003ei group, while the proportion of S phase and G2M phase cells was higher than that in the \u003cem\u003eAPIP\u003c/em\u003ei group, indicating that silencing \u003cem\u003eAPIP\u003c/em\u003e inhibited the G1-S phase transition in Huh7 cells and arrested them in the G1 phase of the cell cycle (Fig 4c-e); in SK cells, the proportion of S phase cells in the \u003cem\u003eAPIP\u003c/em\u003ei group was higher than that in the NC group, indicating that it inhibited the S-G2M phase transition in SK cells and arrested DNA synthesis in the S phase of the cell cycle (Fig 4f-h). Although the arrested phases were different in these two HCC cell lines, it was speculated that this might be due to the asynchronous proliferation rates of the two cell lines. However, the final results all demonstrated that silencing \u003cem\u003eAPIP\u003c/em\u003e could arrest DNA synthesis in Huh7 and SK cells, indicating that silencing \u003cem\u003eAPIP\u003c/em\u003e could inhibit the proliferation of Huh7 and SK cells. Additionally, the flow cytometry results for cell apoptosis showed that compared with the NC group, the apoptosis rate of SK cells (Fig 4i-k) and Huh7 cells (Fig 6l-n) in the \u003cem\u003eAPIP\u003c/em\u003ei group significantly increased, with statistically significant differences. These results suggest that \u003cem\u003eAPIP\u003c/em\u003e promotes the proliferation and inhibits the apoptosis of HCC cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSilencing \u003cem\u003eAPIP\u003c/em\u003e inhibits the invasion and migration of HCC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, the results of the transwell migration and invasion experiments indicated that compared with the cells in the NC group, the number of SK and Huh7 penetrating cells in the \u003cem\u003eAPIP\u003c/em\u003ei group was significantly reduced (Fig 5a,b). This experiment also further explored the effect of \u003cem\u003eAPIP\u003c/em\u003e on the migration ability of HCC cells through scratch experiments. The results indicated that, compared with the NC group, silencing \u003cem\u003eAPIP\u003c/em\u003e could inhibit the migration of SK and Huh7 cells (Fig 6a,b). Consistent with the results of the transwell migration assay, \u003cem\u003eAPIP\u003c/em\u003e can promote the migration of SK and Huh7 cells. Therefore, \u003cem\u003eAPIP\u003c/em\u003e may enhance the migration and invasion abilities of SK and Huh7 cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegulate the potential upstream transcription factors of \u003cem\u003eAPIP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough to the PROMO usage (https://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3), hTFtarget (http://bioinfo.life.hust .edu.cn/hTFtarget#! /), SPP (SPP, http://www.signalingpathways.org/index.jsf) database predictive results of the three databases in intersection, found \u003cem\u003eAPIP\u003c/em\u003e may be a transcription factor YY1, the TBP, SRF, MAZ regulation of the downstream targets (Fig 7a). Then using the JASPAR database (https://jaspar.genereg.net/) predict the four transcription factors and the combination of \u003cem\u003eAPIP\u003c/em\u003e score and combine with \u003cem\u003eAPIP\u003c/em\u003e promoter region of the sequence. The binding scores of \u003cem\u003eAPIP\u003c/em\u003e to the above four transcription factors are respectively: 10.563、8.797、12.764、10.025 (Supplementary Material 1), that is, the binding score of the transcription factor SRF to the target gene \u003cem\u003eAPIP\u003c/em\u003e is the highest, that is, the possibility that SRF is the upstream transcription factor of the regulated \u003cem\u003eAPIP\u003c/em\u003e is the greatest. The binding sequence of SRF and the \u003cem\u003eAPIP\u003c/em\u003e promoter is GGGCCAAATAAGGGAA (Fig 7b). In order to further explore the possibility of the combination of the transcription factor SRF and \u003cem\u003eAPIP\u003c/em\u003e, The Cistrome DB (http://cistrome.org/db/#/) and UCSC Genome Browser(https://genome.ucsc.edu/) were used to observe whether peaks of binding to the transcription factor SRF appeared in the promoter region of \u003cem\u003eAPIP\u003c/em\u003e. The result is shown in Figure 7c. It can be seen that there is indeed a binding peak of SRF in the \u003cem\u003eAPIP\u003c/em\u003e promoter region. In addition, the TIMER2(http://timer.cistrome.org/) database was used to predict the correlation between SRF transcription factors and gene \u003cem\u003eAPIP\u003c/em\u003e, and it was found that there was a positive correlation between \u003cem\u003eAPIP\u003c/em\u003e and SRF (Fig 7d, r=0.444, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Therefore, in HCC, SRF may be a potentially regulated transcription factor upstream of \u003cem\u003eAPIP\u003c/em\u003e, participating in the regulation of the biological role of \u003cem\u003eAPIP\u003c/em\u003e in HCC cells.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHCC is one of the deadliest malignant cancers of the digestive system globally, characterized by high incidence and mortality rates\u003csup\u003e[1]\u003c/sup\u003e. The prognosis for HCC patients is generally poor, with nearly 600,000 deaths occurring worldwide annually\u003csup\u003e[19, 20]\u003c/sup\u003e. Due to the lack of specific symptoms in the early stages of HCC, most patients present with middle or advanced stage disease when seeking medical attention\u003csup\u003e[21]\u003c/sup\u003e, leading to suboptimal therapeutic outcomes. Therefore, early diagnosis and accurate assessment of disease progression are crucial for improving the prognosis of HCC patients.In recent years, the advancement of molecular bioinformatics has enabled the identification of an increasing number of gene expression profiles that can be utilized for the diagnosis and prognosis evaluation of malignant tumors. These molecular markers offer valuable insights for disease prevention and treatment\u003csup\u003e[22, 23]\u003c/sup\u003e. Consequently, the search for new diagnostic, prognostic, or therapeutic biomarkers for HCC remains a scientific priority.\u003c/p\u003e \u003cp\u003eAs a member of the anti-apoptotic molecules, \u003cem\u003eAPIP\u003c/em\u003e is involved in the negative regulation of apoptosis\u003csup\u003e[24, 25]\u003c/sup\u003e. Additionally, its expression level has a potential association with tumors. For example, \u003cem\u003eAPIP\u003c/em\u003e has been found to be amplified and upregulated in squamous cell carcinoma cell lines of the tongue and larynx\u003csup\u003e[10]\u003c/sup\u003e. Conversely, in non-small cell lung cancer cells, both \u003cem\u003eAPIP\u003c/em\u003e mRNA and protein levels are downregulated\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we first examined the expression level of \u003cem\u003eAPIP\u003c/em\u003e in HCC using RT-qPCR and IHC experiments. We then conducted a comprehensive analysis of \u003cem\u003eAPIP\u003c/em\u003e expression in HCC using datasets from multiple databases. The results consistently showed that \u003cem\u003eAPIP\u003c/em\u003e expression is upregulated in HCC. Moreover, high \u003cem\u003eAPIP\u003c/em\u003e expression is associated with a poor prognosis in HCC patients. These findings suggest that \u003cem\u003eAPIP\u003c/em\u003e expression is dysregulated in HCC and may serve as a prognostic biomarker, playing a significant role in HCC tumorigenesis and progression. Furthermore, this study revealed that \u003cem\u003eAPIP\u003c/em\u003e has strong specificity and can effectively distinguish between HCC and non-HCC samples, thus validating its role as a diagnostic marker for HCC.Unlike the routine detection of HER2, EGFR, BRAF, and KRAS genes in breast cancer, lung cancer, colorectal cancer, and other tumors, HCC, due to its heterogeneity, lacks clear genetic phenotypes related to prognosis and treatment\u003csup\u003e[26]\u003c/sup\u003e. Therefore, according to the 8th edition of the American Joint Committee on Cancer, genetic testing is not recommended for routine clinical use in diagnosing HCC\u003csup\u003e[27]\u003c/sup\u003e. Although an increase in AFP levels\u003csup\u003e[28]\u003c/sup\u003ecan indicate the occurrence of HCC to some extent, AFP is a non-specific tumor marker and must be combined with imaging to effectively diagnose HCC. Thus, AFP has limitations in the early diagnosis of HCC. Therefore, the discovery of \u003cem\u003eAPIP\u003c/em\u003e is not only beneficial for routine genetic testing in HCC but also for the early diagnosis of HCC.\u003c/p\u003e \u003cp\u003eGiven the abnormal expression and prognostic diagnostic value of \u003cem\u003eAPIP\u003c/em\u003e in HCC, further investigation into the clinical significance of \u003cem\u003eAPIP\u003c/em\u003e in HCC revealed that the protein level of \u003cem\u003eAPIP\u003c/em\u003e is associated with the age and gender of HCC patients. In this study, 91 HCC patients were included in the IHC analysis. Among the 65 male HCC patients, 59 exhibited high \u003cem\u003eAPIP\u003c/em\u003e expression, accounting for 90.8%. Among the 26 female HCC patients, 19 had high \u003cem\u003eAPIP\u003c/em\u003e expression, representing 73.1%. These findings suggest that the prevalence of HCC is higher in males than in females in this study. Consistent with this observation, studies have shown that gender differences in HCC prevalence are evident in almost all countries, with men being more commonly affected than women. The incidence of HCC in men is typically 2 to 3 times higher than that in women\u003csup\u003e[29]\u003c/sup\u003e. The higher incidence in men may be attributed to a higher prevalence of HBV, HCV, alcohol consumption, and smoking, or it may be due to differences in steroid hormones, immune responses, and epigenetics between men and women\u003csup\u003e[30]\u003c/sup\u003e. In conjunction with the results of this study, it appears that there may also be gender differences in the upregulation of APIP protein in HCC, with elevated APIP protein levels potentially contributing to the higher incidence of HCC in men.\u003c/p\u003e \u003cp\u003eThe incidence of HCC also varies significantly with age. Generally, the likelihood of developing HCC is extremely low in individuals under the age of 40\u003csup\u003e[31]\u003c/sup\u003e. However, studies have shown a significant increase in the incidence of gastrointestinal tumors in young adults over the past few decades, with HCC being one of them\u003csup\u003e[32, 33]\u003c/sup\u003e. In a study by Abbas Ali Tasneem et al\u003csup\u003e[34]\u003c/sup\u003e, among 163 tumor patients aged 40 or younger, hepatobiliary tumors were the second most common, with HCC being the most prevalent among them. This indicates a shift towards younger age groups in the HCC patient population.In the IHC experiments of this study, it was also observed that among the 62 HCC patients under the age of 65, 57 cases had high expression of the APIP protein, accounting for 87.7%. Among the 29 HCC patients aged 65 or older, 21 cases had high expression of the APIP protein, accounting for 72.4%. This indicates that the number of cases with high APIP protein expression was higher in HCC patients under the age of 65 than in those aged 65 or older. Therefore, it is speculated that \u003cem\u003eAPIP\u003c/em\u003e may be a potential oncogenic factor in HCC, and its upregulation may be associated with the development of HCC.\u003c/p\u003e \u003cp\u003eTo explore the potential biological functions of \u003cem\u003eAPIP\u003c/em\u003e in the progression of HCC, we employed the CCK8 proliferation assay to investigate whether \u003cem\u003eAPIP\u003c/em\u003e could regulate the proliferation of Huh7 and SK cells. The results demonstrated that silencing \u003cem\u003eAPIP\u003c/em\u003e effectively inhibited the growth of HCC cells. To further elucidate the specific pathways through which \u003cem\u003eAPIP\u003c/em\u003e inhibits HCC cell growth, we utilized flow cytometry for cell cycle analysis. The findings revealed that silencing \u003cem\u003eAPIP\u003c/em\u003e increased the proportion of Huh7 cells in the G0/G1 phase and induced stagnation of DNA synthesis in the G0/G1 phase, as well as stagnation of DNA synthesis in the S phase of the cell cycle in SK cells. Thus, the experimental results suggest that the proliferative-promoting effect of \u003cem\u003eAPIP\u003c/em\u003e in HCC cells may be associated with transitions in the G1/G0 and S phases of the cell cycle, as well as cell cycle regulation.\u003c/p\u003e \u003cp\u003eThe apoptosis experiment results showed that the apoptosis rate in the \u003cem\u003eAPIP\u003c/em\u003e-silenced group was significantly higher than that in the control group. This indicates that silencing \u003cem\u003eAPIP\u003c/em\u003e not only inhibits the proliferation of HCC cells but also promotes their apoptosis. Additionally, the transwell migration and invasion assays, as well as the wound healing assay, indicated that silencing \u003cem\u003eAPIP\u003c/em\u003e significantly reduced the migration and invasion capabilities of Huh7 and SK cells. In other words, \u003cem\u003eAPIP\u003c/em\u003e itself can promote the migration and invasion of HCC cells. These results further suggest that \u003cem\u003eAPIP\u003c/em\u003e may positively regulate the occurrence and development of HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eIt is widely recognized that cancer can be viewed as the culmination of a series of genetic alterations, during which normal cells transform into malignant ones. One of the fundamental changes driving this malignant transformation is the evasion of cell death\u003csup\u003e[35]\u003c/sup\u003e. As early as the 1970s, Kerr et al. linked apoptosis to the elimination of potential malignant cells, proliferation, and tumor progression\u003csup\u003e[36]\u003c/sup\u003e. Apoptosis, or programmed cell death, is a complex process\u003csup\u003e[37]\u003c/sup\u003ethat controls cell proliferation and maintains the necessary balance within the body\u003csup\u003e[38]\u003c/sup\u003e.Recent studies in cancer biology have underscored the significant influence of apoptosis and its regulatory genes on the carcinogenesis process. Disruption of the apoptosis signaling pathway can directly lead to cancer development and progression\u003csup\u003e[39]\u003c/sup\u003e. Moreover, an increasing body of experimental evidence indicates that the programmed cell death pathway is a key factor in the occurrence of HCC. In most malignant tumors, apoptotic deficiency is a pivotal step in cellular malignant transformation, as apoptosis helps maintain genomic integrity\u003csup\u003e[35]\u003c/sup\u003e. Nehal M Elsherbiny et al\u003csup\u003e[40]\u003c/sup\u003e alconfirmed that disruption of the apoptotic signaling pathway in parenchymal hepatocytes can lead to the development of HCC. On the other hand, stimulating cell apoptosis has been explored as a method for cancer treatment, with successful verification in a mouse model of liver cancer using tumor-targeted TRAIL fusion proteins\u003csup\u003e[41]\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe results of this study indicate that \u003cem\u003eAPIP\u003c/em\u003e plays a role in inhibiting apoptosis in HCC, thereby disrupting the balance between proliferation and apoptosis in HCC cells. To some extent, this maintains the continuous proliferation of HCC cells, contributing to the occurrence and development of HCC. Furthermore, invasion and metastasis are two of the most critical indicators of cancer and the primary causes of death among tumor patients\u003csup\u003e[42]\u003c/sup\u003e. Patients with tumor invasion and metastasis typically have poorer treatment outcomes and shorter survival periods. In this study, \u003cem\u003eAPIP\u003c/em\u003e was found to promote the metastasis and invasion of HCC cells, and its high expression is associated with a poor prognosis in HCC patients. It is speculated that \u003cem\u003eAPIP\u003c/em\u003e may affect the prognosis of HCC patients by promoting the malignant progression of HCC cells, leading to a poor prognosis and shortened survival period for these patients.\u003c/p\u003e \u003cp\u003eHere, we also speculate that the transcription factor SRF may target \u003cem\u003eAPIP\u003c/em\u003e. Serum response factor (SRF) is a transcription factor composed of 508 amino acids and contains three main regions: the serum response element DNA-binding domain, one transactivation domain, and multiple phosphorylation sites\u003csup\u003e[43]\u003c/sup\u003e. Studies have shown that SRF is not only a tumor-promoting factor for HCC but also exerts its effects by upregulating the expression of protein-coding genes, participating in HCC progression, and promoting tumor development\u003csup\u003e[44, 45]\u003c/sup\u003e. Specifically, SRF can enhance the migration and invasion of HCC cells by upregulating two matrix metalloproteinases, MMP-2 and MMP-9\u003csup\u003e[43]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we found that \u003cem\u003eAPIP\u003c/em\u003e can also promote the migration and invasion of HCC cells. Given the involvement of both SRF and \u003cem\u003eAPIP\u003c/em\u003e in these processes, it is plausible that the role of \u003cem\u003eAPIP\u003c/em\u003e in HCC may be mediated through the regulation of SRF. However, further experiments are needed to confirm the existence of a targeted regulatory relationship between SRF and \u003cem\u003eAPIP\u003c/em\u003e in HCC.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the patients included in this experiment were not followed up, and thus, the prognostic predictive ability of \u003cem\u003eAPIP\u003c/em\u003e for HCC patients could not be further explored. Second, there is a lack of experiments related to the effects of \u003cem\u003eAPIP\u003c/em\u003e on the in vivo biological behavior of HCC cells. Third, in-depth studies on the regulatory relationship between \u003cem\u003eAPIP\u003c/em\u003e and the transcription factor SRF have not yet been conducted. Further studies are needed to validate these findings.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eOverall, our study provides a comprehensive analysis of \u003cem\u003eAPIP\u003c/em\u003e expression in HCC and its correlation with the clinical characteristics of HCC patients, elucidating the potential clinical significance of \u003cem\u003eAPIP\u003c/em\u003e in this context. Experimental evidence further supports the notion that \u003cem\u003eAPIP\u003c/em\u003e acts as a tumor-promoting factor in HCC progression and is associated with the occurrence and development of the disease. Therefore, \u003cem\u003eAPIP\u003c/em\u003e may serve as a novel molecular target for the detection and treatment of HCC. However, further experiments are required to investigate and confirm the specific regulatory mechanisms underlying \u003cem\u003eAPIP\u003c/em\u003e’s malignant biological effects in HCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe current research was ratified by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe present study was funded by Fund of National Natural Science Foundation of China(NSFC82260581);Research Project of the Health Commission of the Autonomous Region(Z-B20241484).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eCY: Conducted the experiments, processed the experimental data, and wrote the manuscript.J-HH: Processed the experimental data.RZ: Created the figures and tables.K-LW: Provided guidance and revised the manuscript.Z-BF: Provided guidance and revised the manuscript.All authors contributed to the writing of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J.Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003cem\u003e.\u003c/em\u003eCA Cancer J Clin.2021.doi:10.3322/caac.21660.\u003c/li\u003e\n\u003cli\u003eYarchoan M, Agarwal P.Recent Developments and Therapeutic Strategies against Hepatocellular Carcinoma\u003cem\u003e.\u003c/em\u003eCancer Res.2019.doi:10.1158/0008-5472.Can-19-0803.\u003c/li\u003e\n\u003cli\u003eForner A, Llovet J M.Hepatocellular carcinoma\u003cem\u003e.\u003c/em\u003eLancet.2012.doi:10.1016/s0140-6736(11)61347-0.\u003c/li\u003e\n\u003cli\u003eSiegel R, Naishadham D.Cancer statistics, 2012\u003cem\u003e.\u003c/em\u003eCA Cancer J Clin.2012.doi:10.3322/caac.20138.\u003c/li\u003e\n\u003cli\u003eFaivre S, Bouattour M.Novel molecular therapies in hepatocellular carcinoma\u003cem\u003e.\u003c/em\u003eLiver Int.2011.doi:10.1111/j.1478-3231.2010.02395.x.\u003c/li\u003e\n\u003cli\u003eCho D H, Lee H J.Suppression of hypoxic cell death by APIP-induced sustained activation of AKT and ERK1/2\u003cem\u003e.\u003c/em\u003eOncogene.2007.doi:10.1038/sj.onc.1210080.\u003c/li\u003e\n\u003cli\u003eKang W, Hong S H.Structural and biochemical basis for the inhibition of cell death by APIP, a methionine salvage enzyme\u003cem\u003e.\u003c/em\u003eProc Natl Acad Sci U S A.2014.doi:10.1073/pnas.1308768111.\u003c/li\u003e\n\u003cli\u003eHong S H, Lee W J.APIP, an ERBB3-binding partner, stimulates erbB2-3 heterodimer formation to promote tumorigenesis\u003cem\u003e.\u003c/em\u003eOncotarget.2016.doi:10.18632/oncotarget.7802.\u003c/li\u003e\n\u003cli\u003eWang N, Li R.Apaf-1 interacting protein, a new target of microRNA-146a-3p, promotes prostate cancer cell development via the ERK1/2 pathway\u003cem\u003e.\u003c/em\u003eCell Biol Int.2022.doi:10.1002/cbin.11796.\u003c/li\u003e\n\u003cli\u003eJ\u0026auml;rvinen A K, Autio R.High-resolution copy number and gene expression microarray analyses of head and neck squamous cell carcinoma cell lines of tongue and larynx\u003cem\u003e.\u003c/em\u003eGenes Chromosomes Cancer.2008.doi:10.1002/gcc.20551.\u003c/li\u003e\n\u003cli\u003eMoravcikova E, Krepela E.Down-regulated expression of apoptosis-associated genes APIP and UACA in non-small cell lung carcinoma\u003cem\u003e.\u003c/em\u003eInt J Oncol.2012.doi:10.3892/ijo.2012.1397.\u003c/li\u003e\n\u003cli\u003eZhou T, Chen S.miR-145-5p affects the differentiation of gastric cancer by targeting KLF5 directly\u003cem\u003e.\u003c/em\u003eJ Cell Physiol.2019.doi:10.1002/jcp.27525.\u003c/li\u003e\n\u003cli\u003eYang N, Hui L.Overexpression of SOX2 promotes migration, invasion, and epithelial-mesenchymal transition through the Wnt/\u0026beta;-catenin pathway in laryngeal cancer Hep-2 cells\u003cem\u003e.\u003c/em\u003eTumour Biol.2014.doi:10.1007/s13277-014-2045-3.\u003c/li\u003e\n\u003cli\u003eMiao Z F, Liu X Y.Tbx3 overexpression in human gastric cancer is correlated with advanced tumor stage and nodal status and promotes cancer cell growth and invasion\u003cem\u003e.\u003c/em\u003eVirchows Arch.2016.doi:10.1007/s00428-016-2007-9.\u003c/li\u003e\n\u003cli\u003eMudduluru G, Vajkoczy P.Myeloid zinc finger 1 induces migration, invasion, and in vivo metastasis through Axl gene expression in solid cancer\u003cem\u003e.\u003c/em\u003eMol Cancer Res.2010.doi:10.1158/1541-7786.Mcr-09-0326.\u003c/li\u003e\n\u003cli\u003eGuo L, Li F.SIX1 amplification modulates stemness and tumorigenesis in breast cancer\u003cem\u003e.\u003c/em\u003eJ Transl Med.2023.doi:10.1186/s12967-023-04679-2.\u003c/li\u003e\n\u003cli\u003eSong J, Xie C.Transcription factor AP-4 promotes tumorigenic capability and activates the Wnt/\u0026beta;-catenin pathway in hepatocellular carcinoma\u003cem\u003e.\u003c/em\u003eTheranostics.2018.doi:10.7150/thno.25194.\u003c/li\u003e\n\u003cli\u003eZhu L, Dai L.Transcription factorIRX5 promotes hepatocellular carcinoma proliferation and inhibits apoptosis by regulating the p53 signalling pathway\u003cem\u003e.\u003c/em\u003eCell Biochem Funct.2020.doi:10.1002/cbf.3517.\u003c/li\u003e\n\u003cli\u003eMcGlynn K A, Petrick J L.Global epidemiology of hepatocellular carcinoma: an emphasis on demographic and regional variability\u003cem\u003e.\u003c/em\u003eClin Liver Dis.2015.doi:10.1016/j.cld.2015.01.001.\u003c/li\u003e\n\u003cli\u003eYang J D and Roberts L R.Hepatocellular carcinoma: A global view\u003cem\u003e.\u003c/em\u003eNat Rev Gastroenterol Hepatol.2010.doi:10.1038/nrgastro.2010.100.\u003c/li\u003e\n\u003cli\u003eOgunwobi O O, Harricharran T.Mechanisms of hepatocellular carcinoma progression\u003cem\u003e.\u003c/em\u003eWorld J Gastroenterol.2019.doi:10.3748/wjg.v25.i19.2279.\u003c/li\u003e\n\u003cli\u003eHe R Q, Li J D.LPCAT1 overexpression promotes the progression of hepatocellular carcinoma\u003cem\u003e.\u003c/em\u003eCancer Cell Int.2021.doi:10.1186/s12935-021-02130-4.\u003c/li\u003e\n\u003cli\u003eHu J, Li R.Identification of key genes for esophageal squamous cell carcinoma via integrated bioinformatics analysis and experimental confirmation\u003cem\u003e.\u003c/em\u003eJ Thorac Dis.2020.doi:10.21037/jtd.2020.01.33.\u003c/li\u003e\n\u003cli\u003eCho D H, Hong Y M.Induced inhibition of ischemic/hypoxic injury by APIP, a novel Apaf-1-interacting protein\u003cem\u003e.\u003c/em\u003eJ Biol Chem.2004.doi:10.1074/jbc.M405747200.\u003c/li\u003e\n\u003cli\u003eKo D C, Gamazon E R.Functional genetic screen of human diversity reveals that a methionine salvage enzyme regulates inflammatory cell death\u003cem\u003e.\u003c/em\u003eProc Natl Acad Sci U S A.2012.doi:10.1073/pnas.1206701109.\u003c/li\u003e\n\u003cli\u003eKamarajah S K, Frankel T L.Critical evaluation of the American Joint Commission on Cancer (AJCC) 8th edition staging system for patients with Hepatocellular Carcinoma (HCC): A Surveillance, Epidemiology, End Results (SEER) analysis\u003cem\u003e.\u003c/em\u003eJ Surg Oncol.2018.doi:10.1002/jso.24908.\u003c/li\u003e\n\u003cli\u003eLiu F, Liang J.ZCCHC17 Served as a Predictive Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma\u003cem\u003e.\u003c/em\u003eFront Oncol.2021.doi:10.3389/fonc.2021.799566.\u003c/li\u003e\n\u003cli\u003eSingal A G, Lampertico P.Epidemiology and surveillance for hepatocellular carcinoma: New trends\u003cem\u003e.\u003c/em\u003eJ Hepatol.2020.doi:10.1016/j.jhep.2019.08.025.\u003c/li\u003e\n\u003cli\u003ePetrick J L, Florio A A.International trends in hepatocellular carcinoma incidence, 1978-2012\u003cem\u003e.\u003c/em\u003eInt J Cancer.2020.doi:10.1002/ijc.32723.\u003c/li\u003e\n\u003cli\u003eDorak M T and Karpuzoglu E.Gender differences in cancer susceptibility: an inadequately addressed issue\u003cem\u003e.\u003c/em\u003eFront Genet.2012.doi:10.3389/fgene.2012.00268.\u003c/li\u003e\n\u003cli\u003eEl-Serag H B and Rudolph K L.Hepatocellular carcinoma: epidemiology and molecular carcinogenesis\u003cem\u003e.\u003c/em\u003eGastroenterology.2007.doi:10.1053/j.gastro.2007.04.061.\u003c/li\u003e\n\u003cli\u003eSung H, Siegel R L.Emerging cancer trends among young adults in the USA: analysis of a population-based cancer registry\u003cem\u003e.\u003c/em\u003eLancet Public Health.2019.doi:10.1016/s2468-2667(18)30267-6.\u003c/li\u003e\n\u003cli\u003eShaw J J and Shah S A.Rising incidence and demographics of hepatocellular carcinoma in the USA: what does it mean?Expert Rev Gastroenterol Hepatol.2011.doi:10.1586/egh.11.20.\u003c/li\u003e\n\u003cli\u003eTasneem A A and Luck N H.Digestive tract neoplasms in young individuals: Demographics, staging and risk factors\u003cem\u003e.\u003c/em\u003eCancer Rep (Hoboken).2021.doi:10.1002/cnr2.1319.\u003c/li\u003e\n\u003cli\u003eHanahan D and Weinberg R A.The hallmarks of cancer\u003cem\u003e.\u003c/em\u003eCell.2000.doi:10.1016/s0092-8674(00)81683-9.\u003c/li\u003e\n\u003cli\u003eKerr J F, Wyllie A H.Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics\u003cem\u003e.\u003c/em\u003eBr J Cancer.1972.doi:10.1038/bjc.1972.33.\u003c/li\u003e\n\u003cli\u003eReed J C.Mechanisms of apoptosis\u003cem\u003e.\u003c/em\u003eAm J Pathol.2000.doi:10.1016/s0002-9440(10)64779-7.\u003c/li\u003e\n\u003cli\u003eHadian K and Stockwell B R.The therapeutic potential of targeting regulated non-apoptotic cell death\u003cem\u003e.\u003c/em\u003eNat Rev Drug Discov.2023.doi:10.1038/s41573-023-00749-8.\u003c/li\u003e\n\u003cli\u003eLowe S W and Lin A W.Apoptosis in cancer\u003cem\u003e.\u003c/em\u003eCarcinogenesis.2000.doi:10.1093/carcin/21.3.485.\u003c/li\u003e\n\u003cli\u003eElsherbiny N M, Eisa N H.Chemo-preventive effect of crocin against experimentally-induced hepatocarcinogenesis via regulation of apoptotic and Nrf2 signaling pathways\u003cem\u003e.\u003c/em\u003eEnviron Toxicol Pharmacol.2020.doi:10.1016/j.etap.2020.103494.\u003c/li\u003e\n\u003cli\u003eWahl K, Siegemund M.Increased apoptosis induction in hepatocellular carcinoma by a novel tumor-targeted TRAIL fusion protein combined with bortezomib\u003cem\u003e.\u003c/em\u003eHepatology.2013.doi:10.1002/hep.26082.\u003c/li\u003e\n\u003cli\u003eHanahan D and Weinberg R A.Hallmarks of cancer: the next generation\u003cem\u003e.\u003c/em\u003eCell.2011.doi:10.1016/j.cell.2011.02.013.\u003c/li\u003e\n\u003cli\u003eKim K R, Bae J S.The role of serum response factor in hepatocellular carcinoma: an association with matrix metalloproteinase\u003cem\u003e.\u003c/em\u003eOncol Rep.2011.doi:10.3892/or.2011.1421.\u003c/li\u003e\n\u003cli\u003eKwon C Y, Kim K R.The role of serum response factor in hepatocellular carcinoma: implications for disease progression\u003cem\u003e.\u003c/em\u003eInt J Oncol.2010.doi:10.3892/ijo_00000734.\u003c/li\u003e\n\u003cli\u003ePellegrino R, Thavamani A.Serum Response Factor (SRF) Drives the Transcriptional Upregulation of the MDM4 Oncogene in HCC\u003cem\u003e.\u003c/em\u003eCancers (Basel).2021.doi:10.3390/cancers13020199.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, APIP, Diagnostic marker, Prognosis, biological functions","lastPublishedDoi":"10.21203/rs.3.rs-6843055/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6843055/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u0026amp; aims\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith a high incidence and fatality rate, hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors in the world. In addition to examining its clinical pathogenic importance in HCC, our goal is to study the expression of Apaf-1-interacting protein(\u003cem\u003eAPIP)\u003c/em\u003e in HCC and investigate its effects on the biological functions of HCC cells and the underlying mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the expression level and clinical significance of \u003cem\u003eAPIP\u003c/em\u003e in HCC, we employed internal tissue samples for \u0026nbsp;Real-time quantitative polymerase chain reaction,(RT-qPCR) and immunohistochemistry(IHC) procedures. In order to verify the findings, we obtained external datasets. We investigated the upstream regulatory transcription factors of \u003cem\u003eAPIP\u003c/em\u003e and talked about the molecular mechanism of the biological role of \u003cem\u003eAPIP\u003c/em\u003e in HCC through experiments on cell proliferation, cell cycle, and cell apoptosis as well as scratch and transwell assays to assess the effects of \u003cem\u003eAPIP\u003c/em\u003e on the proliferation, apoptosis, migration, and invasion capabilities of HCC cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe discovered that HCC tissues exhibited higher levels of \u003cem\u003eAPIP\u003c/em\u003e expression than nearby non-tumor tissues, and that \u003cem\u003eAPIP\u003c/em\u003e could differentiate HCC from healthy liver tissues. Additionally, the age, gender, and poor prognosis of individuals with HCC were linked to \u003cem\u003eAPIP \u003c/em\u003eexpression. In the meantime, down regulating \u003cem\u003eAPIP\u003c/em\u003e may encourage HCC cells to undergo apoptosis and prevent their growth, migration, and invasion. Furthermore, the transcription factor Serum response factor(SRF) and \u003cem\u003eAPIP\u003c/em\u003e may have a regulatory connection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003econclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy influencing the proliferation, apoptosis, migration, and invasion of HCC cells, \u003cem\u003eAPIP \u003c/em\u003eplays a significant role in the formation and progression of HCC and may provide a novel therapeutic target.\u003c/p\u003e","manuscriptTitle":"Clinical Pathological Significance and Biological Function of APIP in Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 13:28:52","doi":"10.21203/rs.3.rs-6843055/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-05T01:30:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T11:08:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T05:34:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T08:37:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157442633307224469317740616322463256001","date":"2025-08-20T00:02:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2306634022645211626877745030789932138","date":"2025-08-19T01:41:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269232743125618127839215708437485303497","date":"2025-08-18T20:20:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T07:24:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309568162703606995231498875737253076343","date":"2025-08-18T02:40:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T05:27:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-16T05:09:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2025-06-16T02:50:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e16a9ee-7916-4592-869d-95114842515c","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:24:57+00:00","versionOfRecord":{"articleIdentity":"rs-6843055","link":"https://doi.org/10.1186/s12935-025-04018-z","journal":{"identity":"cancer-cell-international","isVorOnly":false,"title":"Cancer Cell International"},"publishedOn":"2025-10-22 16:16:13","publishedOnDateReadable":"October 22nd, 2025"},"versionCreatedAt":"2025-06-18 13:28:52","video":"","vorDoi":"10.1186/s12935-025-04018-z","vorDoiUrl":"https://doi.org/10.1186/s12935-025-04018-z","workflowStages":[]},"version":"v1","identity":"rs-6843055","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6843055","identity":"rs-6843055","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}