Research ArticleExperimental Studies
Open Access

C1orf50 Accelerates Epithelial-Mesenchymal Transition and the Cell Cycle of Hepatocellular Carcinoma

ATSUSHI TANAKA, YUSUKE OTANI, MASAKI MAEKAWA, ANNA ROGACHEVSKAYA, TIRSO PEÑA, VANESSA D. CHIN, SHINICHI TOYOOKA, MICHAEL H. ROEHRL and ATSUSHI FUJIMURA
Cancer Genomics & Proteomics November 2025, 22 (6) 836-849; DOI: https://doi.org/10.21873/cgp.20541

Abstract

Background/Aim: Hepatocellular carcinoma (HCC) is a heterogeneous liver cancer with limited treatment options and a poor prognosis in advanced stages. To identify novel biomarkers and therapeutic targets, we investigated the role of chromosome 1 open reading frame 50 (C1orf50), a gene with a previously uncharacterized function in HCC.

Materials and Methods: We performed a comprehensive transcriptome data analysis of the human hepatocellular carcinoma project from The Cancer Genome Atlas (TCGA) and subsequently validated the oncogenic roles of C1orf50 using HCC cell lines.

Results: Using transcriptomic and clinical data from TCGA, we stratified 355 primary HCC samples based on C1orf50 expression levels. Patients with high C1orf50 expression exhibited significantly shorter overall survival, suggesting its association with aggressive tumor behavior. Differential expression and enrichment analyses revealed that C1orf50-high tumors were enriched in oncogenic pathways, including epithelial-mesenchymal transition (EMT), cell cycle activation, and stemness-related properties. Transcriptional regulatory network analysis detected 456 significantly dysregulated regulons, including ZEB1/2 and E2F2, key drivers of EMT and cell cycle, in the C1orf50-high group. In addition, we observed increased YAP1/TAZ signaling, further linking C1orf50 to stemness and therapeutic resistance. Functional data from CRISPR-based dependency screening suggested that several transcription factors up-regulated in the C1orf50-high state, such as ZBTB11 and CTCE, are essential for the survival of HCC cells. These findings indicate potential therapeutic vulnerabilities and support the rationale for targeting C1orf50-associated pathways.

Conclusion: C1orf50 is a novel biomarker of poor prognosis in HCC and a key regulator of oncogenic features such as EMT, cell cycle progression, and stemness. This study highlights the therapeutic potential of targeting C1orf50-related networks in aggressive subtypes of liver cancer.

Keywords:

Introduction

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and remains a leading cause of cancer-related mortality worldwide. Despite advances in molecular profiling and targeted therapies (1-4), the prognosis of patients with advanced HCC remains poor, primarily due to the disease’s inherent heterogeneity and resistance to conventional treatment (2). Understanding the molecular drivers underlying HCC progression is essential to identify novel biomarkers and therapeutic vulnerabilities. Chromosome 1 open reading frame 50 (C1orf50), a previously uncharacterized gene, has been suggested to play potential roles in tumorigenesis and progression in breast cancer and melanoma (5, 6), yet its biological functions and regulatory mechanisms in HCC are largely unknown. In this study, we aimed to elucidate the clinical and molecular significance of C1orf50 in HCC by integrating transcriptomic, regulatory, and functional pathway analyses using The Cancer Genome Atlas (TCGA) dataset and HCC cell lines. We demonstrate that high expression of C1orf50 is associated with poor patient survival and enrichment of oncogenic processes, including epithelial-mesenchymal transition (EMT), cell cycle activation, and cancer stemness pathways. Furthermore, transcriptional regulatory network analyses identified key transcription factors, such as ZEB1/2 and E2F2, as master regulators in C1orf50-high HCC, suggesting a potential role for C1orf50 as a driver of tumor aggressiveness. These findings shed light on the oncogenic potential of C1orf50 and offer insights into novel therapeutic strategies targeting this molecular axis for HCC.

Materials and Methods

TCGA data. The transcriptome data (STAR count data and TPM data) and clinical information of the TCGA-LIHC (liver cancer cohort) cohort were retrieved from the Cancer Genome Atlas Pan-Cancer analysis project database (7) by the TCGAbiolinks package (version 2.30.4) in R (8). Among 374 tumor samples, we selected only 355 samples whose metadata are “Hepatocellular carcinoma, NOS” and “primary tumor” to reduce sample-wise heterogeneity. Based on the median of mRNA TPM values, we divided the 355 tumor samples into 2 groups, named as the C1orf50-low (n=177) and the C1orf50-high (n=178). Two samples from the C1orf50-low group were excluded from survival analysis due to their lack of data.

Differential expression analysis and gene set enrichment analysis. Bioinformatics data analysis was performed as described previously (9-11). In brief, using transcriptomics count data obtained from the TCGA dataset, we performed differential expression analysis using the edgeR package (version 4.0.16) (12) and calculated fold-change values with q-values between specified groups. For the C1orf50 comparison, we divided the tumor samples into two groups: C1orf50-low and C1orf50-high, according to their median TPM value. To identify enriched pathways and biological processes, we used clusterProfiler (version 3.16.1) with a ranked gene list ordered by fold-change values (13). Genes with FDR <0.01 were included in the ranked gene list. Using gene sets from the Molecular Signatures Database (MSigDB version 7.4), we conducted enrichment tests and calculated normalized enrichment score (NES) for each term. For the functional characterization of each sample, we performed gene set variation analysis (GSVA) using the MSigDB database via the R GSVA package (version 1.50.5) and calculated enrichment scores for each gene set in each sample (14).

Transcriptional regulatory network analysis. To infer transcriptional regulatory activity of hepatocellular carcinoma along with C1orf50, we assessed it by RTN R package (version 2.26.0) as previously described (15-17), which tests the association between a given transcription factor (TF) and all potential targets and enrichment status of network using either RNA-Seq or microarray transcriptome data. In brief, using normalized HCC transcriptome data (n=355) and Lambert’s human TF list (18) (1612 TFs), we first constructed a potential transcriptional regulatory network (TRN) and then removed non-significant associations by permutation analysis (permutation n=1,000, p-value cutoff <1E-7) and bootstrapping, followed by the ARACNe algorithm (19) to keep direct TF-targets interactions (removing redundant indirect interactions). The refined TRN dataset includes the regulator gene name, its target gene name, and adjusted p-values as a confidence level. To conduct enrichment analysis over a list of regulons in the refined TRN between the C1orf50-low and the C1orf50-high groups, we first applied the Master Regulator Analysis (MRA) (20) over a list of regulons (with corrections for multiple hypothesis testing). The MRA assessed the overlap between each regulon and the significantly dysregulated genes between the C1orf50-low and the C1orf50-high, and provided adjusted p-value, resulting in 764 confident regulons (adjusted p-value <0.05) as targets for downstream analyses. We then calculated these regulons’ enrichment score between the C1orf50-low and the C1orf50-high groups using expression fold-change values (FDR <1E-4) and the Gene Set Enrichment Analysis (GSEA)-2T (two-tailed GSEA analysis) function from the RTN R package. To characterize the regulatory program similarities and differences between samples in our cohort, we computed each regulon activity score. We then tested if the regulon activity scores were significantly different between C1orf50-low and C1orf50-high groups.

Immunostaining. Immunostaining of the hepatocellular carcinoma tissue microarray was performed as previously described (6). The slide of human hepatocellular carcinoma tissue microarray (catalog number: OD-CT-DgLiv01-012), which contains 53 specimens of non-HCC and 51 specimens of HCC, was purchased from TissueArray.Com (Derwood, MD, USA). After deparaffinization, the slide was subjected to antigen retrieval using HistoVT One (catalog number: 06380-05, Nacalai Tesque, Kyoto, Japan) and blocking with 1% bovine serum albumin (BSA) (catalog number: 01860-07, Nacalai Tesque) in phosphate buffered saline with 0.05% Triton X-100 (PBST, pH=7.4) at room temperature (approximately 25°C) for 1 h. Then, the slide was incubated with the primary antibody in 1% BSA-PBST at 4°C for 16 h. After three washes with PBST for 5 min, the slide was incubated with secondary antibody in 1% BSA-PBST at room temperature for 1 h, followed by three washes with PBST for 5 min and mounting with DAPI-Fluoromount-G (catalog number: 0100-20, Southern Biotech, Birmingham, AL, USA), which contains 4′,6-diamidino-2-phenylindole (DAPI) to stain DNA. The slide was observed using a confocal microscope, LSM780 (Carl Zeiss, Oberkochen, Germany), and the mean fluorescence intensity was analyzed using ZEN software (Carl Zeiss). The following antibodies were used in this study, listed as [Antigen/Source/Identifier/Dilution]: [C1orf50/Proteintech/20957-1-AP/1:100]; [NANOG/R&D/AF1997/1:100]; [Donkey anti-rabbit IgG Alexa Fluor Plus 594/Thermo Fisher Scientific (Waltham, MA, USA)/A32754/1:500]; [Donkey anti-goat IgG Alexa Fluor Plus 647/Thermo Fisher Scientific/A32849/1:500]. Potential ethical issues for using the tissue microarray were evaluated by the Ethics Committee of Okayama University.

Cell culture and RNAi experiments. Human hepatocellular carcinoma cell lines, HLE and huH1, were obtained from the Japanese Collection of Research Bioresources (JCRB, Ibaraki, Osaka, Japan). Cells were cultured in low glucose Dulbecco’s modified Eagle’s medium (DMEM, catalog number: 041-29775, Fujifilm-Wako, Osaka, Japan) supplemented with 10% fetal bovine serum (FBS, catalog number: 553-06905, Corning Inc., Corning, NY, USA) and 1% penicillin/streptomycin/L-glutamine (catalog number: 161-23201, Fujifilm-Wako). RNAi experiments were performed using either short hairpin RNA (shRNA) or small interfering RNA (siRNA). The shRNA-expressing lentivirus particles were prepared as previously described (21). Briefly, 293FT cells were transfected with pLKO.1-puro plasmid, psPAX, and pMD2.G (obtained from Addgene, catalog numbers: 8453, 12260, and 12259, respectively) using TransIT-LT1 Reagent (catalog number: MIR2306, Takara Bio, Kusatsu, Shiga, Japan). After 72 h of transfection, the virus-containing medium was harvested and filtered through a polysulfone membrane (catalog number: S-2504, Kurabo, Osaka, Japan). One milliliter of the virus-containing medium was added to the cell culture medium on a 60 mm dish. The following sequences of shRNA were used in this study: Control [CCTAAGGTTAAGTCGCCCTCG]; Human C1orf50 #1 [CTGCACCATGTAGCTTGTAAT]; Human C1orf50 #2 [GTCAGTCAGTTTCAGAGTATT]. For siRNA transfection, Lipofectamine RNAiMAX (catalog number: 13778150, Thermo Fisher Scientific) and Opti-MEM (catalog number: 31985070, Thermo Fisher Scientific) were used according to the manufacturer’s recommendations. The following siRNAs were used in this study, listed as [Target gene/Source/Identifier]: [negative control/Thermo Fisher Scientific/4390844]; [human C1orf50/Thermo Fisher Scientific/s35534]; [human C1orf50/Thermo Fisher Scientific/s35535].

Sphere-formation assay and MTS assay. The sphere-formation assays were performed as previously reported (22). After two days of shRNA infection, the cells were trypsinized with TrypLE Express Enzyme (catalog number: 12604021, Thermo Fisher Scientific) and resuspended in Neurobasal medium (catalog number: 21103049, Thermo Fisher Scientific) supplemented with 1 × B-27 supplement (catalog number: 17504001, Thermo Fisher Scientific), 1 × N-2 supplement (catalog number: 17502001, Thermo Fisher Scientific), 20 ng/ml human epidermal growth factor (catalog number: 053-07871, Fujifilm-Wako), 20 ng/ml human basic fibroblast growth factor (catalog number: 068-05384, Fujifilm-Wako), 10 μg/ml heparin (catalog number: H3149-100KU, Sigma-Aldrich, St. Louis, MO, USA) and 1% penicillin/streptomycin/L-glutamine (catalog number: 161-23201, Fujifilm-Wako). One thousand cells per well were seeded on ultra-low attachment 24-well plates (catalog number: 3473, Corning) in 1.5 ml of Neurobasal medium and cultured at 37°C containing 5% CO2 for 7 days.

MTS assay was performed as previously reported (23). Prior to the MTS assay, the cells were transfected with siRNA as described above. 5000 cells were seeded into one well of a 96-well plate (catalog number: 130188, Thermo Fisher Scientific) in 100 μl of cell culture medium. On the next day (Day 1) and the three days after seeding (Day 3), after incubation with 20 μl of CellTiter 96 AQueous One solution (catalog number: G3581, Promega, Madison, WI, USA) for 30 min, the absorbance at 490 nm was measured using 96-well plate reader (DS Pharma Biomedical, Osaka, Japan).

Western blot. Western blotting was performed as previously described (24). Briefly, after two days of shRNA infection, the cells were reseeded in one well of a 6-well plate. On the next day, the cells were harvested and lysed in a cell lysis buffer (20 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, and 0.5% Triton X-100) supplemented with a cOmplete protease inhibitor cocktail (catalog number: 4693116001, Sigma-Aldrich) and PhoSTOP phosphatase inhibitor cocktail (catalog number: 4906837001, Sigma-Aldrich). After sonication and centrifugation, the supernatant was boiled in a sodium dodecyl sulfate (SDS) sample buffer (240 mM Tris-HCl, pH 6.8, 8% SDS, 40% glycerol, 0.1% bromophenol blue, and 20% 2-mercaptoethanol). The samples were subjected to SDS-polyacrylamide gel electrophoresis (SDS-PAGE). After being transferred to a polyvinylidene difluoride membrane, the membranes were subjected to immunoreaction. Signals were developed using a Clarity Max Western ECL Substrate (catalog number: 1705062, Bio-Rad, Hercules, CA, USA) and detected using a ChemiDoc Touch imaging system (Bio-Rad). The following antibodies were used in this study, listed as [Antigen/Source/Identifier/Dilution]: [ZEB1/Cell Signaling Technology/3396/1:2,000]; [NCAD/Cell Signaling Technology/13116/1:2,000]; [YAP-TAZ/Santa Cruz Biotechnology/sc-101199/1:2,000]; [CYR61/Cell Signaling Technology/39382/1:2,000]; [c-MYC/Cell Signaling Technology/5605/1:2,000]; [C1orf50/Proteintech/20957-1-AP/1:2,000]; [VINCULIN/Proteintech/66305-1-Ig/1:20,000]; [Anti-Mouse IgG, HRP-linked/Sigma-Aldrich/A9044/1:20,000]; [Anti-Rabbit IgG, HRP-linked/CST/7074/1:2,000].

Statistical analysis. All analyses were conducted using R (version 4.3.2, University of Auckland, Auckland, North Island, New Zealand) (25). Numerical values were analyzed by the Wilcoxon rank-sum test. Survival analyses were performed using the Kaplan-Meier method and compared by the log-rank test. If we used other statistical test methods, we noted it in the description.

Results

High C1orf50 expression of HCC is associated with short overall survival time and oncogenic signatures. First, we inquired whether the expression level of the C1orf50 in HCC is associated with patient outcomes. We evaluated overall survival time between the C1orf50-low and the C1orf50-high groups and observed that the C1orf50-high group exhibited shorter survival compared to the C1orf50-low group (Figure 1A), suggesting that C1orf50 is potentially connected to diverse oncogenic processes. We then performed differential expression analysis of transcriptome data between the C1orf50-low and the C1orf50-high and identified 751 genes that were significantly dysregulated (q-value<0.05) with a log2 fold change >1 or <−1 (441 genes up-regulated and 310 genes down-regulated in the C1orf50-high group) (Figure 1B). Enrichment analysis of GOBP, KEGG, and Hallmark cancer terms revealed that various oncogenic features were enriched in the C1orf50-high group (Figure 1C-E). As GOBP terms, biological processes related to cell adhesion, extracellular matrix, or morphogenesis were enriched in the C1orf50-high group. As KEGG and Hallmark terms, extracellular matrix-related molecules or epithelial-mesenchymal transition (EMT) were enriched in the C1orf50-high group. Cell cycle-related terms and the Hedgehog signaling pathway were also enriched in it. These results suggest that C1orf50 may have some functional roles in specific oncogenic signatures, such as EMT and cell cycle. Since EMT and cell cycle processes are associated with cancer stemness property (26, 27), we elucidated stemness-related features and found that “stem cell differentiation” and “stem cell population maintenance” of GOBP terms were enriched in the C1orf50-high group (Figure 1F), suggesting an association of C1orf50 with stemness property.

Figure 1.
Figure 1.
Figure 1.

Clinical outcome and biological process characteristics of C1orf50. (A) Kaplan-Meier plot of overall survival time. The C1orf50-high group harbors significantly shorter survival time. (B) Volcano plot of differential expression analysis between C1orf50-low and high groups. Red and blue dots represent significantly dysregulated genes (q-value <0.05) with a log2>1 or <−1. The horizontal dashed line represents q-value=0.05. (C-E) Bar plots of gene set enrichment analysis results with Hallmark, KEGG, and GOBP gene sets. A positive score of NES means enrichment in the C1orf50-high group. The top 15 up-regulated and down-regulated gene sets of GOBP and KEGG are shown. For Hallmark gene sets, all significant results are shown. (F) GSE plot of stemness-related GOBP terms between C1orf50 low and high groups. Target genes of stemness-related features are significantly enriched in the C1orf50-high group. (G) Representative images of immunostaining analysis on a HCC tissue microarray. Compared to a non-HCC liver specimen, HCC specimens exhibit stronger signals of the C1orf50 (red) and NANOG (gray). Scale bars, 100 μm. (H) Mean fluorescence intensity (MFI) of C1orf50 in non-HCC and HCC tissues. The t-test is used for a statistical significance test. (I) A scatterplot graph describing that the MFI of C1orf50 is correlated with that of NANOG in HCC tissues. C1orf50: Chromosome 1 open reading frame 50; KEGG: Kyoto Encyclopedia of Genes and Genomes; GOBP: Gene Ontology Biological Process; NES: normalized enrichment score; GSE: Gene Set Enrichment; HCC: hepatocellular carcinoma.

To confirm this in human HCC pathological specimens, we stained tissue microarray, which consists of 53 non-HCC specimens (2 normal liver; 1 inflammation; 2 degenerative liver; 37 chronic hepatitis; 7 cirrhosis; 4 hepatic steatosis) and 51 HCC specimens (6 stage I; 32 stage II; 7 stage III; 6 stage IV) and found that the expression levels of C1orf50 are elevated in HCC samples (Figure 1G and H). Importantly, the mean fluorescence intensity (MFI) of C1orf50 and that of NANOG, a stem cell marker in HCC, are significantly correlated (Figure 1I). These results demonstrate that C1orf50 is associated with cancer stem cell (CSC)-related properties in HCC.

Regulatory network analysis reveals characteristic transcription factor activity profile. To infer the regulatory network differences between the C1orf50-low and the C1orf50-high, we performed regulon analysis using the RTN R package (15-17). Among 1,612 Lambert’s human TFs (18), we found 1,290 regulons (a group of genes that are regulated as a unit by each TF) in the HCC transcriptome data after removing non-significant and redundant regulons. We then asked which kinds of regulons are enriched in the C1orf50-high group and found that 456 were significantly dysregulated (adjusted p-value<0.05) (369 up-regulated and 87 down-regulated in the C1orf50-high). As expected, ZEB1/2 (EMT inducer) and E2F2 regulons (cell cycle driver) are significantly enriched in the C1orf50-high group (Figure 2A), which is compatible with pathway enrichment analyses (Figure 1C). To characterize the regulon activity profile across this cohort, along with C1orf50 expression levels, we computed the regulon activity score for each sample and visualized the results as a heatmap (Figure 2B). Although the heatmap shows a heterogeneous activity profile, we can see various regulons, including EMT-related TF, chromatin remodeling factors, or methylation-related TF (KMT2A), are significantly up-regulated in the C1orf50-high group, suggesting the regulatory network complexity of the C1orf50-high group. In addition, based on the CRISPR perturbation score from the Cancer Dependency Map (DepMap) Project (28) of 21 hepatocellular cell lines, the inhibition of CTCF and ZBTB11 at the gene level impaired cancer cell survival, suggesting these genes may have important roles in C1orf50-high cancers (Figure 2B, right).

Figure 2.
Figure 2.

Regulatory network landscape. (A) Two-tailed GSEA shows significant positive enrichment of ZEB1, ZEB2, and E2F2 regulons in the C1orf50-high group. (B) Regulatory activity profile of HCC transcriptome (n=355) with C1orf50 status. Computed regulon activity scores are shown as a heatmap. Regulon selection criteria for visualization are i) regulons after MRA process with adjusted p<1E-8, and ii) regulon activity score shows statistically significance between C1orf50-low and -high groups with adjusted p<1E-4. The boxplot on the right side of the heatmap shows the CRISPR gene effect score of 21 hepatocellular cell lines. The CRISPR gene effect scores are downloaded from the DepMap portal. GSEA: Gene Set Enrichment Analysis; MRA: Master Regulator Analysis; CRISPR: clustered regularly interspaced short palindromic repeats.

C1orf50 drives EMT and evokes cancer stemness features via the YAP1/TAZ axis. Since pathway enrichment analyses revealed EMT-related signatures and the ZEB1-targeted network was enriched in the C1orf50-high group, we elucidated the EMT feature in detail. We asked if EMT-related transcription factors are expressed differently between the C1orf50-low and the C1orf50-high groups. ZEB1/2 showed significant overexpression in the C1orf50-high group (Figure 3A). SNAI3 also had significantly higher expression in the C1orf50-high group compared to the C1orf50-low group, but with a relatively small difference. We then checked TGF-β pathway activation since the EMT signature and TGF-β pathway are interlinked (29), and the TGF-β pathway may drive EMT in this tumor. We found significant TGF-β pathway activation in the C1orf50-high group (Figure 3B) and a positive correlation between them (Figure 3C). The TGF-β pathway consists of the canonical SMAD pathway and non-SMAD pathway (i.e., ERK, JNK, P38, NF-kB, mTOR, RHOA, PAK2) (29). We then asked which parts of them are involved in this setting and found that all components, except P38, were significantly enriched in the C1orf50-high group (Figure 3D), suggesting that various TGF-β pathway components may collaborate to control this EMT feature of HCC. Since EMT is connected with cancer-stemness, we checked YAP/TAZ pathway gene expression and found significant up-regulation of YAP1, WWTR1 (encoding TAZ), TEAD1-3, and their target genes AXL, CCN1, CCN2 in the C1orf50-high group (Figure 3E). Using human hepatocellular carcinoma cell lines HLE and huH1 cells, we validated these results. We transduced the cells with lentiviruses carrying either a non-targeting control shRNA (shControl) or shRNA targeting C1orf50 (shC1orf50), and cultured them for 72 h. After 72 h, we harvested the cells and analyzed them by Western blotting, and confirmed that C1orf50 knockdown decreased the expression levels of EMT factors ZEB1 and NCAD, as well as YAP1/TAZ and their targets CYR61 and c-MYC (Figure 3F). We further demonstrated that C1orf50 is required to sustain the self-renewal capacity, which is an indispensable trait of EMT-induced and CSC-like cells (Figure 3G). These results showed that C1orf50 is essential for EMT and CSC-related traits in HCC cells.

Figure 3.
Figure 3.

EMT signature and cancer stemness. (A) Boxplot of EMT-related transcription factors between C1orf50-low and high groups. n.s., Not statistically significant; *p<0.05; **p<0.01; ***p<0.001. (B) Boxplot of GSVA TGF-β signaling scores. The C1orf50-high group shows a higher score compared to the C1orf50-low group. (C) The GSVA Hallmark EMT score shows a significant positive correlation with Hallmark TGF-β signaling score. (D) Boxplot of canonical SMAD pathway molecules and non-canonical pathway molecules. Most of the molecules show significantly higher expression in the C1orf50-high group. n.s., Not statistically significant; *p<0.05; **p<0.01; ***p<0.001. (E) Boxplot of YAP1/TAZ-axis molecules between C1orf50-low and -high groups. All molecules except TEAD4 are significantly up-regulated in the C1orf50-high group. n.s., Not statistically significant; *p<0.05; **p<0.01; ***p<0.001. (F) Representative images of immunoblotting analyses in shRNA-induced HCC cells. C1orf50 knockdown reduces the expression levels of EMT factors ZEB1 and NCAD (N-cadherin), YAP/TAZ, and their target gene CYR61, and stem cell factor c-MYC. VINCULIN serves as a loading control. (G) Sphere-formation assays in shRNA-induced HCC cell lines HLE and huH1. C1orf50 is required to sustain the self-renewal capacity of HCC cells. Both shC1orf50#1 and shC1orf50#2 show a significant decrease in sphere number (n=4, error bars indicate relative value±SD) (t-test). All biological experiments were repeated three times. EMT: Epithelial-mesenchymal transition; GSVA: gene set variation analysis.

C1orf50 regulated cell cycle progression in HCC cells. We then elucidated cell cycle features because pathway enrichment analyses found cell cycle enrichment and E2F target gene enrichment in the C1orf50-high group. Expression of main cell cycle regulators shows a clear positive correlation with C1orf50 expression (Figure 4A). We then performed GSEA to evaluate whether high C1orf50 expression is associated with the activation of cell cycle–related pathways. Both KEGG and Reactome cell cycle gene pathway gene sets showed significant positive enrichment in the C1orf50-high group (Figure 4B). Specifically, the KEGG_CELL_CYCLE pathway exhibited a normalized enrichment score (NES) of 1.90 (adjusted p=0.035), and the REACTOME_CELL_CYCLE pathway showed an NES of 1.38 (adjusted p=0.048). These results support the notion that elevated C1orf50 expression is linked to the transcriptional activation of core cell cycle programs. To validate this, we transfected the HCC cell line huH1 with siRNA against C1orf50. One day before seeding on a 96-well plate, we transfected huH1 cells with a negative control siRNA or siRNAs against C1orf50. One day after seeding (Day 1) and the three days after seeding (Day 3), we performed the MTS assay and confirmed that C1orf50 knockdown significantly attenuated the growth of huH1 cells (Figure 4C). These data unveil that C1orf50 is involved in the cell cycle regulation in HCC cells.

Figure 4.
Figure 4.

C1orf50 accelerates the cell cycle. (A) Heatmap of cell cycle-related genes with C1orf50 expression level. Representative molecules of each cell cycle step are shown. Columns represent samples and are ordered by C1orf50 expression levels. Bar charts on the right-hand side show Spearman’s correlation coefficients between each gene and C1orf50 expression. All correlations shown here are statistically significant. (B) GSE plot of cell cycle terms from KEGG and Reactome gene sets. Both cell cycle terms show positive enrichment in the C1orf50-high group. (C) Bar plot describing the growth of huH1 cells transfected with siRNA. C1orf50 knockdown significantly attenuated cell growth (n=6, error bars indicate relative value±SD). Statistical analysis was performed using a t-test. The experiment was repeated three times.

Discussion

In this study, we identified C1orf50 as a novel biomarker of poor prognosis in hepatocellular carcinoma and characterized its association with key oncogenic features: EMT, cell cycle dysregulation, and stemness-related pathways. Our survival analysis demonstrated that patients with high C1orf50 expression had significantly shorter overall survival, suggesting a potential role for C1orf50 in promoting tumor progression.

Differential expression and pathway enrichment analyses revealed that C1orf50-high tumors exhibited significant up-regulation of EMT and cell cycle signaling, which are commonly associated with aggressive tumor behavior and metastatic potential. Notably, transcriptional network analysis confirmed up-regulation of ZEB1/2 and E2F2 regulons, which drive EMT and cell cycle, respectively, further reinforcing the mechanistic connection between C1orf50 and these oncogenic programs.

We also observed enrichment of stemness-related biological processes, including “stem cell differentiation” and “stem cell population maintenance”, in the C1orf50-high group. This aligns with the up-regulation of YAP1/TAZ signaling, which has been previously linked to cancer stemness and therapeutic resistance in HCC (30, 31). These findings suggest that C1orf50 may contribute to a stem-like, therapy-resistant cellular phenotype in liver cancer.

Importantly, our study also highlighted actionable vulnerabilities in the C1orf50-high HCC. Data from CRISPR dependency screening (DepMap) indicated that certain transcription factors up-regulated in the C1orf50-high state (e.g., ZBTB11 and CTCF) are critical for tumor cell survival, suggesting potential combinatorial targets. Additionally, our findings indicate that C1orf50 promotes cell cycle progression in HCC. The enrichment of cell cycle and E2F target pathways in the C1orf50-high group, along with its positive correlation with key cell cycle regulators, suggests a role in driving proliferation. Functional experiments showed that C1orf50 knockdown significantly reduced HCC cell growth, supporting its involvement in tumor cell proliferation. These results, together with its association with poor prognosis, highlight C1orf50 as a potential oncogenic driver and therapeutic target in HCC.

In conclusion, C1orf50 emerges as a key molecular player in aggressive HCC, orchestrating EMT, cell cycle progression, and stemness through a complex transcriptional regulatory network. These findings position C1orf50 as a candidate biomarker for prognostic stratification and a potential target for therapeutic intervention in HCC.

Conclusion

This study revealed that C1orf50 is a novel biomarker of poor prognosis in HCC and a key regulator of oncogenic features such as EMT, cell cycle progression, and stemness.

Acknowledgements

The Authors are grateful to the Central Research Laboratory of Okayama University Medical School for their support with confocal microscopy. We thank all members of our laboratory for their valuable contributions to this study. The results published here are in whole or part based upon data generated by the TCGA Research Network.

Footnotes

  • Conflicts of Interest

    MR is a member of the Scientific Advisory Board of Universal DX. This affiliation didn’t influence this study. The other Authors have no conflicts of interest.

  • Authors’ Contributions

    Atsushi Tanaka: Data curation; visualization; methodology; writing – original draft; writing – review and editing. Yusuke Otani: Data curation; visualization; methodology; writing – original draft; writing – review and editing. Masaki Maekawa: Data curation; visualization; methodology; writing – original draft; writing – review and editing. Anna Rogachevskaya - Data curation; editing and reviewing original draft; Tirso Peña: Data curation; writing – review and editing. Vanessa D. Chin: Data curation; writing – review and editing. Shinichi Toyooka: Project administration; writing – review and editing. Michael H. Roehrl: Project administration; writing – review and editing. Atsushi Fujimura: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; supervision; validation; writing – original draft; writing – review and editing.

  • Funding

    This work was supported by a Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Sciences, and Technology of Japan (grant numbers: JP23K06676 to A.F.), the Japan Agency for Medical Research and Development (grant numbers: JP19cm0106143 and JP22cm0106179 to A.F.), and the Naito Foundation (A.F.).

  • Artificial Intelligence (AI) Disclosure

    No artificial intelligence (AI) tools, including large language models or machine learning software, were used in the preparation, analysis, or presentation of this manuscript.

  • Received July 5, 2025.
  • Revision received July 24, 2025.
  • Accepted August 8, 2025.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

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C1orf50 Accelerates Epithelial-Mesenchymal Transition and the Cell Cycle of Hepatocellular Carcinoma
ATSUSHI TANAKA, YUSUKE OTANI, MASAKI MAEKAWA, ANNA ROGACHEVSKAYA, TIRSO PEÑA, VANESSA D. CHIN, SHINICHI TOYOOKA, MICHAEL H. ROEHRL, ATSUSHI FUJIMURA
Cancer Genomics & Proteomics Nov 2025, 22 (6) 836-849; DOI: 10.21873/cgp.20541
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