Introduction

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality globally, accounting for 90% of primary liver cancer cases1,2,3. With approximately 830,000 deaths annually from primary liver cancer, incidence and mortality rates are projected to rise, potentially exceeding one million new cases per year in the near future4. Most HCC cases are diagnosed at advanced stages, rendering curative treatments like surgery unfeasible. As a result, despite the promising potential of immunotherapy for advanced HCC, patient prognosis remains poor5,6. The high heterogeneity and metastatic frequency of HCC, particularly among resectable patients, contribute to recurrent metastasis in 70% of cases post-surgery7. This underscores the need to elucidate molecular mechanisms underlying HCC progression and metastasis and to identify biomarkers for prognosis and therapy. Such insights will improve diagnostic approaches, therapeutic strategies, and personalized precision medicine.

The tumor microenvironment (TME) in HCC is a complex ecosystem composed of various cell types, including tumor cells, immune cells, and fibroblasts, which interact to drive tumor invasion, growth, and metastasis8,9. The heterogeneity of the TME and its cellular components not only plays a critical role in tumor detection, treatment, and prognosis but also represents a source of potential biomarkers10,11. Notably, dysfunction in the innate and adaptive immune systems can lead to the formation of an immunosuppressive TME, facilitating immune evasion by tumor cells12,13. Macrophages, key components of the TME, have garnered significant attention due to their diversity and phenotypic plasticity14. Tumor-associated macrophages (TAMs) are closely associated with tumor metastasis, invasion, angiogenesis, and immunosuppression15,16. TAMs are typically classified into pro-inflammatory, anti-tumor M1-type and anti-inflammatory, pro-tumorigenic M2-type macrophages17,18. Identifying TAM-specific biomarkers is essential for targeted immunotherapy, though research in this area remains relatively limited19.

In this study, we performed cell classification and annotation on single-cell RNA sequencing (scRNA-seq) data to identify distinct cell subtypes within HCC, with a focus on macrophage signature genes. Combined with the TCGA cohort of HCC, we developed a risk scoring model to predict patient prognosis and immunotherapy outcomes. Using various analytical techniques, we identified and characterized the immune and clinical features of patients classified as high- or low-risk based on the risk score. Additionally, we explored the prognostic role of risk score-related genes in HCC and, most importantly, validated in vitro that suppression of the key gene FCER1G enhances HCC cell proliferation and migration.

Materials and methods

Data acquisition

Transcriptomic and clinical data for liver cancer were obtained from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/, accessed August 13, 2024), comprising 374 liver cancer and 50 normal samples. Clinical variables included survival time, survival status, age, gender, and TNM stage. Additionally, the cRNA-seq dataset GSE112271, containing tumor samples from seven regions of two patients, was retrieved from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/, accessed August 13, 2024).

Single-cell sequencing analysis

Single-cell RNA sequencing (scRNA-seq) was conducted for high-resolution transcriptomic profiling at the single-cell level. Data were rigorously filtered to remove low-quality samples, followed by further analysis using the Seurat package20. Dimensionality reduction techniques such as PCA and t-SNE were employed for clustering on default parameters, and cell types were annotated using the SingleR package for precise identification of key genes21.

Pseudotime and cell-cell interaction network analysis

Pseudotime analysis of tumor cell subpopulations was performed using Monocle3 (v0.2.3)22, and results were visualized in two-dimensional UMAP23. The CellChat package was employed to assess ligand-receptor interactions in scRNA-seq data on default parameters24, identifying potential signaling pathways based on ligand-receptor expression patterns across cell subtypes.

Model construction and validation

Key prognostic genes were identified using univariate Cox regression, and a prognostic model was built with Lasso regression25. Risk scores were calculated for each patient, enabling comprehensive evaluation of their prognostic relevance in liver cancer. Kaplan-Meier survival analysis was conducted for high- and low-risk groups, and differential gene expression was illustrated with heatmaps. Predictive performance was assessed using ROC curves, and a nomogram was developed to estimate 1-, 3-, and 5-year patient prognoses. Calibration curves were employed to validate the accuracy of the clinical model.

Functional enrichment analysis and protein-protein interaction network construction

Functional enrichment of ubiquitination-related genes (URG) was performed using DAVID (v.6.8, https://david.ncifcrf.gov/, accessed August 13, 2024) with p < 0.05 and FDR < 25%, where the KEGG database comes from Kanehisa laboratory26. The STRING database (v.11.0, https://string-db.org, accessed August 13, 2024) was used to construct the PPI network.

Immunoassays

The Cibersort algorithm quantified the infiltration of 22 immune cell types in each liver cancer patient, and the relationship between immune cells and risk scores was analyzed27,28. Specifically, the gene expression profile was uploaded to the Cibersort web tool for calculation to obtain the immune score of each sample (https://cibersortx.stanford.edu/,, accessed August 13, 2024). MSI and TMB were used to evaluate immunotherapy efficacy in high- and low-risk groups29. A tumor stemness index was introduced to assess tumor cell differentiation, and the TIDE algorithm evaluated the relationship between risk scores and immunotherapy response. Spearman correlation analysis was used to examine the link between risk scores and immune checkpoints30.

Protein structure prediction and gene mutation analysis

Protein structure prediction was conducted by uploading gene symbols to Genecard (https://www.genecards.org/, accessed August 13, 2024), and mutations were analyzed by identifying SNPs, ONPs, insertions, and deletions. Mutation analysis between high- and low-risk groups helped elucidate liver cancer’s molecular mechanisms.

Cell culture and transfection

The hepatocellular carcinoma cell line HepG2 was kindly donated by Professor Tang from the School of Basic Medical Sciences, Chengdu Medical College31. HepG2 cells were cultured in DMEM supplemented with 10% FBS (Gibco) and 1% PS (Invitrogen) at 37 °C with 5% CO₂. Cells were transfected with siRNA targeting FCER1G using Lipofectamine 2000 (BGI, China) with a final concentration of 20 nM.: si-FCER1G-F: UUCUCAUAGCUGGUUAUAGCU; si-FCER1G-R: CUAUAACCAGCUAUGAGAAAU.

Western blot analysis

Western blot analysis was performed 48 h after transfection with siRNA. HepG2 cells were lysed in 1% SDS lysis buffer, and protein concentration was measured with a BCA Protein Assay Kit (Thermo Scientific). Immunoblots were probed with primary antibodies ((Snail 2 (proteintech, 12129-1-AP), Snail 1 (abcam, ab216347), Twist 1 (proteintech, 25465-1-AP), E-cad (abcam, ab231303), N- cad(abcam, ab76011), β-Actin(abcam, ab8226)), and bands were visualized using ECL reagent (Smart-Lifesciences).

Real-time quantitative PCR (RT-qPCR)

RT-qPCR was performed following the method described by Shao et al.32. RT-qPCR was performed using TRIzol-extracted RNA and cDNA synthesized with the iScript cDNA Synthesis Kit (Bio-Rad). PCR amplification was done with Fast SYBR Green Master Mix (Bio-Rad), with 18 S as the reference gene. Gene expression levels were calculated using the 2 − ΔΔCt method. The 18 S gene served as the reference gene for normalization. The relative expression levels of target genes were calculated using the 2 − ΔΔCt method (ΔCt = Ct of 18 S − Ct of the target gene). Primer sequences used were as follows: FCER1G-F: TTCCAGCAGTGGTCTTGCTC; FCER1G-R: TGCCTTTCGCACTTGGATCT; 18 S F: GTAACCCGTTGAACCCCATT; 18 S R: CCATCCAATCGGTAGTAGCG.

Colony formation assay

Long-term cell survival was assessed using the colony formation assay. Briefly, 1000 cells were seeded in 6-well plates and allowed to grow for two weeks. Cells were fixed with 4% paraformaldehyde for 15 min and stained with 0.5% (w/v) crystal violet (Sigma-Aldrich) for visualization. Colonies were scanned using an Odyssey Scanner (LI-COR, Lincoln, NE, USA) and quantified using Image J software.

Cell viability assay

Cell viability was measured 48 h post-transfection using the Cell Counting Kit-8 (CCK-8) assay (MedChem Express, Monmouth Junction, NJ, USA). Si-NC or si-FCER1G cells were seeded into 96-well plates at a density of 2 × 10^4 cells per well and incubated for 12 h. Subsequently, 100 µL of DMEM and 10 µL of CCK-8 solution were added to each well and incubated for 1 h. Absorbance at 450 nm was measured using a microplate reader (Thermo Scientific, Waltham, MA, USA). All experiments were performed in triplicate.

Wound healing assays

Cell migration was assessed using the wound healing assay. Si-NC or si-FCER1G cells were seeded into 6-well plates at a density of 4 × 10^5 cells per well and incubated for 12 h. Upon reaching 70–80% confluence, a wound was created using a 200-µL pipette tip. The cells were cultured in DMEM medium supplemented with 1% FBS. Wound closure was monitored at 0, 12, and 24 h using an inverted microscope.

Statistical analysis

Differences between groups were analyzed using the Wilcoxon test, and correlations were assessed using Spearman’s rank correlation test. Survival analysis was performed using Kaplan-Meier curves and the log-rank test. Cox regression analysis was conducted with the “survival” package in R to calculate hazard ratios (HR) and 95% confidence intervals (CI). All statistical tests were two-sided, with statistical significance set at P < 0.05. Analyses were conducted using R software (version 4.2.2).

Results

Single-cell sequencing analysis

We obtained single-cell sequencing data from the GEO database (GSE112271), comprising seven liver cancer samples for further analysis. Initially, we examined each sample’s nFeature and nCount and calculated the percentage of mitochondrial gene expression relative to total gene expression (Fig. 1A-D). Based on these metrics, we applied stringent filtering criteria (nFeature_RNA > 50 & percent_MT < 5%) to select high-quality cells, ensuring reliable downstream analyses. Next, we identified the top 2,000 highly variable genes exhibiting significant expression differences across cells, which were critical for cell type identification (Fig. 1E). Principal component analysis (PCA) and t-SNE dimensionality reduction were then performed. Clustering analysis successfully segregated the samples into 17 distinct cell clusters (Fig. 1F, G), with the top 10 heterogeneous genes specifically expressed in these clusters (Fig. 1H). Further annotation revealed six major cell types: adipocytes, endothelial cells, epithelial cells, erythrocytes, fibroblasts, and macrophages (Fig. 1I). A volcano plot visualized the key differential genes between the cell clusters (Fig. 1J). Cluster 3, mainly composed of macrophages, was characterized by differential expression of genes such as PILRA, TREM2, IGFBP7, and CRIP2.

Extraction of macrophage-specific genes and cell-cell communication analysis

Recognizing the critical role of macrophages in liver cancer development, we identified 482 genes closely associated with macrophages (Fig. 2A). Pathway enrichment analysis revealed significant involvement of these genes in tuberculosis, phagosome, and hematopoietic cell lineage pathways (Fig. 2B), predominantly localized to the Golgi membrane and participating in cytokine-mediated signaling pathways (Fig. 2C). A protein-protein interaction network constructed using STRING ranked key genes through Cytohubba’s EPC, Degree, and MNC algorithms on default parameters, with CD4 identified as the central node, followed by FCER1G (Fig. 2D-G). Cell-cell communication networks inferred via CellChat highlighted the importance of the MHC-II signaling pathway mediated by CD4 in macrophage communication (Fig. 2H, I, J, K). Additionally, adhesion molecules such as CD99 and ITGB2, previously identified in pathway enrichment, played essential roles in macrophage interaction with other cells (Fig. 2L, M).

Construction of a prognostic model based on macrophage-specific genes using the LASSO-COX algorithm

To identify macrophage-specific genes with prognostic relevance, we conducted univariate survival analysis, narrowing down 482 genes to 76 (P < 0.01) (Fig. 3A). Using LASSO regression, we selected 10 key genes—P2RY6, DLGAP3, TNFRSF11A, FCER1G, LAMB1, NCF2, CSF3R, PLA2G7, STC2, and HMOX1—to construct a prognostic risk score (λ = 0.7145) (Fig. 3B, C).

$$\begin{aligned} {\text{RiskScore}}{\mkern 1mu} = & {\mkern 1mu} 0.0{\text{294}} \times {\text{P2RY6}}{\mkern 1mu} + {\mkern 1mu} 0.{\text{1179}} \times {\text{DLGAP3}}{\mkern 1mu} + {\mkern 1mu} 0.0{\text{3}}0{\text{7}} \times {\text{TNFRSF11A}}{\mkern 1mu} \\ & + {\mkern 1mu} 0.000{\text{5}} \times {\text{FCER1G}}{\mkern 1mu} + {\mkern 1mu} 0.000{\text{5}} \times {\text{LAMB1}}{\mkern 1mu} \\ & + {\mkern 1mu} 0.00{\text{28}} \times {\text{NCF2}} + {\mkern 1mu} 0.00{\text{21}} \times {\text{CSF3R}}{\mkern 1mu} + {\mkern 1mu} 0.00{\text{28}} \times {\text{PLA2G7}}{\mkern 1mu} \\ &+ {\mkern 1mu} 0.00{\text{53}} \times {\text{STC2}}{\mkern 1mu} + {\mkern 1mu} 0.000{\text{2}} \times {\text{HMOX1}}. \\ \end{aligned}$$

Heatmap analysis revealed generally consistent expression patterns for the selected genes, except for RAMP3 (Fig. 3D). Comparative survival analysis indicated significantly higher mortality in the high-risk group compared to the low-risk group (Fig. 3E). The model’s prognostic performance was assessed using ROC curves, with AUC values of 0.78, 0.72, and 0.71 for 1-, 3-, and 5-year survival, respectively (Fig. 3F). A nomogram combining clinical parameters with the risk score was developed to predict 1-, 3-, and 5-year outcomes (Fig. 3G). KM analysis indicated worse outcomes for patients with higher nomogram scores (Fig. 3H), and further ROC analysis showed improved AUC values of 0.79, 0.78, and 0.78 for 1, 3, and 5 years, respectively (Fig. 3I). Calibration curves confirmed the model’s robust predictive performance, and also suggested that the 1-year survival rate would be overestimated, while the 3- and 5-year survival rates would be underestimated in the survival prediction of low-risk populations (Fig. 3J).

Immune landscape of the risk score model

We explored the association between the risk score and immune cell types by analyzing 22 immune cell populations using the CIBERSORT algorithm. The results revealed correlations between the risk score and macrophages, monocytes, and resting mast cells (Fig. 4A). Significant differences were also observed in T cells, macrophages, monocytes, and resting mast cells between high- and low-risk groups (Fig. 4B). High-risk patients exhibited significantly higher tumor stemness scores compared to low-risk patients (Fig. 4C), while MSI and TMB scores showed no differences (Fig. 4D, E). Immunotherapy response analyses using ESTIMATE and TIDE algorithms revealed a lower stromal score in the high-risk group (Fig. 4F) and reduced “Merck 18” score and expected immunotherapy response (Fig. 4G, H). Additionally, immune checkpoint markers such as IL1A, IL12A, CD80, TNFRSF18, SELP, IL1B, HAVCR2, and CD276 were significantly associated with the risk score (Fig. 4I).

Prognostic and single-cell analysis of risk score genes

To further elucidate the role of the 10 risk score genes in hepatocellular carcinoma (HCC), Kaplan-Meier (KM) survival analysis was conducted to assess their association with patient outcomes. The results demonstrated that patients in the TCGA cohort could be effectively stratified based on the expression levels of these genes, with significant differences in prognosis (Fig. 5A). Additionally, single-cell t-SNE expression heatmaps revealed distinct expression patterns for all 10 genes, with FCER1G showing the highest expression level and concentration (Fig. 5B). To gain deeper insights into their roles in cellular development, we conducted pseudotime analysis across six annotated cell types (Fig. 5C). Focusing specifically on the macrophage developmental trajectory, we identified four distinct developmental stages within the macrophage population (Fig. 5D, E). The top 10 differentially expressed genes along this trajectory were highlighted, including ALB, APOA2, HP, RBP4, TTR, APCS, GSTA2, HULC, TMSB4X, and TYROBP (Fig. 5F). Among the risk score genes, PLA2G7, HMOX1, and FCER1G were found to closely align with macrophage developmental pathways, suggesting their critical involvement in macrophage differentiation and function (Fig. 5G).

Bioinformatics analysis of FCER1G’s potential role in HCC

FCER1G (Fc Epsilon Receptor Ig) is a tetrameric protein-coding gene (Fig. 6A) whose protein and RNA levels were significantly downregulated in HCC tumor samples (Fig. 6B, C). Abnormal phosphorylation modifications at sites such as S61, Y65, T66, T75, and Y76 were observed in HCC tumor samples (Fig. 6D), despite no differences in immunohistochemistry results (Fig. 6E). STRING analysis revealed FCER1G’s interactions with MAPK signaling pathway proteins, JAK family proteins, and leukocyte differentiation antigens (Fig. 6F). Gene mutation landscape analysis showed that the low expression group of FCER1G had a higher mutation frequency of genes such as TP53, SDK1, and NBEA, while the high expression group had a higher mutation frequency of genes such as AXIN1, OR2J3, and MYH11 (Fig. 6G). In addition, the transcription level of FCER1G was also associated with various pathways, including apoptosis, ferroptosis, TP53 pathway, PI3K/AKT pathway, tumor inflammatory response, etc. ( Fig. 6H ).

Inhibition of FCER1G expression enhances proliferation and migration of HepG2 cells

To investigate FCER1G’s role in HCC, we knocked down its expression using si-FCER1G, confirmed by real-time PCR (Fig. 7A). Cell viability assays (CCK-8) and colony formation experiments demonstrated that FCER1G suppression enhanced HepG2 cell proliferation (Fig. 7B, C). Additional, parallel experiments in Huh7and PLC/PRF5 also confirmed this finding (Fig. 7D-G). EMT marker analysis revealed that si-FCER1G increased Snail 1, TWIST 1, and N-cadherin expression, while reducing E-cadherin levels, promoting cell migration (Fig. 7H). This was confirmed by wound healing assays, which showed accelerated wound closure in si-FCER1G-treated HepG2 cells (Fig. 7I).

Discussion

In this study, we conducted an extensive analysis of single-cell sequencing data from liver cancer samples, focusing on the identification and functional characterization of macrophage-associated genes. We selected high-quality cells, clustering them into 17 distinct populations and annotating six major cell types. Among these, the macrophage cluster stood out, revealing 482 differentially expressed genes. Pathway enrichment and protein-protein interaction network analyses highlighted key genes such as CD4 and FCER1G. Furthermore, we developed a prognostic model based on macrophage-specific genes using the LASSO-COX algorithm, demonstrating significant correlations between risk scores, immune landscapes, and patient outcomes.

While previous studies, such as Chen et al.‘s work on ubiquitination-related genes and T-cell exhaustion using RNA sequencing data33, focused on different immune subsets, our study broadens this knowledge by identifying macrophage-related prognostic genes in liver cancer. These findings provide new insights into the tumor microenvironment’s complexity, with genes like CD4 and FCER1G emerging as central hubs in cell communication networks. These genes likely mediate complex macrophage-immune cell interactions through MHC-II signaling, influencing immune responses within the tumor microenvironment34,35. Our macrophage-specific gene-based prognostic model demonstrates moderate predictive accuracy (AUC values ​​at 1, 3, and 5 years are 0.79.0.78, 0.78, respectively) for hepatocellular carcinoma (HCC) patient outcomes, aligning with findings from similar studies33,36,37,38. There were deviations in the 1-, 3-, and 5-year survival predictions, which not only points out the shortcomings of this study, but also suggests that future research should focus on optimizing and improving the prediction model. Notably, immune landscape analysis revealed correlations between risk scores and specific immune cell populations, particularly macrophages and T cells, suggesting that macrophage-related gene expression may reflect tumor immune status and influence prognosis and therapeutic responses.

FCER1G (Fc Epsilon Receptor Ig), a protein-coding gene linked to autoreactive T-cell immunity39,40, is implicated in diseases such as atopic dermatitis and Alzheimer’s disease41,42. In liver cancer, our study highlights FCER1G as a critical factor, with its downregulation correlating with tumor progression. The association of FCER1G with key pathways, including apoptosis and the P53 signaling pathway, suggests a potential tumor-suppressive role43,44. Experimental validation in HepG2 cells demonstrated that silencing FCER1G promotes cell proliferation and migration, reinforcing its role in inhibiting tumor growth.

Despite these important findings, some limitations must be acknowledged. The single-cell data analyzed were derived from a specific cohort, Validation in larger, diverse, and multi-center datasets is necessary to confirm. Additionally, while our prognostic model is promising, its clinical utility should be further assessed through prospective studies. Moreover, additional mechanistic research is needed to elucidate the precise roles of FCER1G and other key genes in liver cancer biology.

Conclusion

Our study provides a comprehensive analysis of macrophage-related genes in liver cancer, revealing their prognostic significance and potential as therapeutic targets. By integrating single-cell sequencing data with functional experiments, we have laid the groundwork for future investigations into the complex interactions within the tumor microenvironment and their implications for liver cancer treatment.

Fig. 1
figure 1

Single-cell sequencing analysis identifies macrophage-related genes. (A–D) Violin plots depicting the distribution of nFeature_RNA, nCount_RNA, percent_MT, and percent_HC across 10 HCC samples. (E) Volcano plot illustrating the top 1,500 highly variable genes. (F) Heatmap showing the expression of key differential genes across different clusters. (G) PCA and t-SNE dimensionality reduction separating cells into 17 distinct clusters. (H) Bubble plot highlighting the main differential genes in various cell types. (I) Cell annotation using the SingleR package, categorizing cells into 8 major groups. (J) Volcano plot displaying key differential genes across different cell types.

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Fig. 2
figure 2

Extraction of macrophage-specific genes and cell-cell communication. (A) Volcano plot of 482 differentially expressed genes in macrophage clusters. (B) KEGG pathway enrichment analysis of the 482 genes. (C) GO term enrichment analysis of the 482 genes. (D) Protein-protein interaction network for the 482 genes. (E–G) Central nodes identified in the protein interaction network using the EPC, Degree, and MNC topological network methods, respectively. (H–I) Cell-cell communication networks among 6 cell types, with connections colored by communication quantity (H) or strength (I). (J) Specific ligand-receptor interactions within macrophage clusters. (K–M) MHC-II signaling pathway (K), CD99 signaling pathway (L), and ITGB2 signaling pathway (M) in macrophage interactions with other cells.

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Fig. 3
figure 3

Development of a risk score model using the LASSO algorithm and integration with clinical parameters. (A) Univariate Cox analysis identifying 76 prognostic genes. (B, C) Gene selection using the LASSO-Cox algorithm for constructing the optimal model. (D) Expression patterns and survival time relationships of 10 risk score genes. (E) Kaplan-Meier analysis demonstrating poorer prognosis in the high-risk group compared to the low-risk group. (F) ROC curve analysis showing AUC values for the risk score model at 1, 3, and 5 years. (G) Nomogram integrating risk scores and clinical factors to predict 1-, 3-, and 5-year survival rates. (H) Kaplan-Meier analysis indicating worse prognosis for patients with higher nomogram scores. (I) ROC curve showing AUC values for the nomogram scores at 1, 3, and 5 years. (J) Calibration curve demonstrating the model’s accuracy in predicting patient survival rates.

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Fig. 4
figure 4

Analysis of risk scores and immune landscape. (A) Cibersort algorithm calculated immune cell scores for 22 immune cell types in TCGA cohort samples, with Spearman correlation analysis against risk scores. (B) Differences in immune cell scores between high-risk and low-risk groups as defined by risk scores. (C) Higher tumor stemness scores observed in the high-risk group. (D, E) No significant differences in MSI and TMB scores between high- and low-risk groups. (F) Estimate algorithm calculating three immune scores in TCGA cohort samples, with differential analysis between risk groups. (G, H) TIDE algorithm assessing “Merck 18” scores (G) and expected immune response to treatment (H) in high- and low-risk groups. (I) Correlation between risk scores and 60 immune checkpoints. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001.

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Fig. 5
figure 5

Prognostic significance and single-cell analysis of risk score genes. (A) Kaplan-Meier survival analysis of 10 risk score genes. (B) Expression distribution of 10 risk score genes in single-cell t-SNE plots. (C) 3D pseudotime trajectories of six cell types. (D, E) 2D cell developmental trajectories of macrophages, highlighting four main cell clusters. (F) Top 10 characteristic developmental genes in macrophage developmental trajectories. (G) Expression changes of 10 risk score genes during macrophage development.

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Fig. 6
figure 6

Bioinformatics analysis of FCER1G. (A) Protein structure of FCER1G from the PDB database. (B) Downregulation of FCER1G protein levels in liver cancer tissues from the CPTAC database. (C) Downregulation of FCER1G transcript levels in liver cancer tissues from the TCGA-LIHC cohort. (D) Abnormal phosphorylation of FCER1G protein in liver cancer tissues from CPTAC phosphoproteomics data. (E) Immunohistochemical detection of FCER1G expression in normal and HCC tissues. (F) Protein interaction network of FCER1G from the STRING database. (G) Mutation analysis of TCGA-LIHC cohort grouped by FCER1G transcript levels. (H) ssGSEA algorithm calculating enrichment scores for each pathway, with Spearman correlation between FCER1G transcript levels and pathway scores.

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Fig. 7
figure 7

Effects of FCER1G silencing on HepG2 cell proliferation and migration. (A) Quantitative PCR verification of si-FCER1G knockdown efficiency in HepG2 cells. (B, C) CCK-8 (B) and colony formation assays (C) showing the impact of si-FCER1G on HepG2 cell proliferation. (D, E) CCK-8 (D) and colony formation assays (E) showing the impact of si-FCER1G on HepG2 cell proliferation. (F, G) CCK-8 (F) and colony formation assays (G) showing the impact of si-FCER1G on HepG2 cell proliferation. (H) Western blotting of HepG2 cell lines transfected with si-NC or si-FCER1G using EMT-related antibodies. (I) Wound healing assay using yellow pipette tips on HepG2 cells in 6-well plates, with images and relative quantification of wound width at 0, 24, and 48 h.

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