ACRRGs show elevated expression in hcc indicating potential as prognostic markers for HCC

We extracted data on genes involved in the ATP-dependent chromatin remodeling pathway from the KEGG database. By comparing their expression in HCC tissues to adjacent non-tumorous liver samples, and evaluating their prognostic significance through the TCGA database, we observed that the majority of ACRRGs exhibited elevated mRNA levels in HCC samples (Fig. 1A and Figure S2). Univariate Cox regression analysis indicated that a significant subset of these genes could serve as predictors for HCC patient survival (Fig. 1B and Figure S3).

Fig. 1

ATP-dependent chromatin remodeling-related genes (ACRRGs) exhibit elevated expression levels in tumor tissues compared to normal tissues, serving as potential prognostic markers for HCC. A Expression levels of ACRRGs in cancerous and adjacent non-tumor tissues in the TCGA database. B The impact of high versus low expression of ACRRGs on overall survival in HCC

Construction and validation of a prognostic model for hepatocellular carcinoma based on ACRRDEGs

By intersecting the DEGs between tumorous and non-tumorous liver samples with the ACRRGs(Fig. 2A), we identified 35 overlapping genes (ACRRDEGs) for subsequent analysis. The contribution of the 35 ACRRDEGs to overall survival (OS) in HCC patients was evaluated using the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm to mitigate multicollinearity. This approach identified MORF4L1, HDAC1, VPS72, and RUVBL2 as pivotal prognostic indicators (Fig. 2B, C, Table S4).A

Fig. 2

The risk model based on ACRRDEGs effectively forecasts the overall survival of HCC patients. A Intersection of differentially expressed genes (DEGs) from tumor and adjacent normal tissues with ACRRGs. B and C The process of selecting key genes using the LASSO method. D and G HCC patients are scored using the model, with scores determining assignment to either a high-risk or low-risk group. Survival proportions of patients in both the training set (D) and the validation set (G) are displayed, alongside the expression levels of key genes. E and H Survival curves for the high-risk and low-risk groups in both the training (E) and validation (H) sets. F and I The predictive performance of the risk model on the 1, 3, and 5-year overall survival of HCC patients in high-risk and low-risk groups across both the training set (F) and the validation set (I)

risk-scoring model was developed based on the expression levels of these genes, employing the following equation for risk score calculation: Risk Score = (0.26 × Expression Level of MORF4L1) + (0.25 × Expression Level of RUVBL2) + (0.23 × Expression Level of HDAC1) + (0.15 × Expression Level of VPS72). The median risk score was used to categorize patients into high- and low-risk groups, with the former showing a significantly greater mortality(Fig. 2D). Kaplan–Meier analysis confirmed longer OS in the low-risk group (p = 0.0002, Fig. 2E). The model demonstrated substantial predictive accuracy for OS, as.

evidenced by area under the curve (AUC) values of the ROC curves (Fig. 2F). Validation using the GSE14520 dataset from the GEO database corroborated the model's efficacy (Fig. 2G–I).

Development of a prognostic nomogram for hepatocellular carcinoma using risk score and clinical factors

Subsequent Cox regression analysis within the TCGA cohort established the risk score, alongside age and T stage, as independent prognostic factors for OS in HCC patients (Fig. 3A). A prognostic nomogram integrating these factors exhibited superior predictive.

Fig. 3

The nomogram, established by integrating risk scores with clinical indicators, demonstrates an improved predictive capability for the prognosis of HCC patients. A Cox univariate and multivariate analyses were conducted on clinical factors such as age, gender, tumor TNM staging, and risk score. B A nomogram that utilizes age, tumor T stage, and risk score was constructed to predict the 1-, 3-, and 5-year survival rates of HCC patients. (C) Calibration curves of the nomogram for predicting the overall survival rates at 1, 3, and 5 years for HCC patients compared to actual survival rates. D The concordance index (CI) over time of the risk prediction model and clinical univariate models such as T staging, age, and a model combining these factors. E and F Receiver operating characteristic (ROC) curves for the T stage model, age model, risk score model, and a comprehensive model combining all three, in predicting the overall survival at 1 and 3 years for HCC patients

performance compared to each individual factor (Fig. 3B). Calibration curves indicated high congruence between the nomogram's predictions and actual survival outcomes, underscoring the model’s predictive reliability (Fig. 3C). Time-dependent C-index and ROC analyses further demonstrated that the nomogram outperforms single clinical predictors in forecasting HCC prognosis (Figs. 3D–F).

ATP-dependent chromatin remodeling-related genes drive tumor stemness and modulate the immune microenvironment in HCC

To elucidate the potential mechanisms by which ATP-dependent chromatin remodeling affects HCC prognosis, we initially analyzed datasets from high- and low-risk groups using the DESeq2 R package, aiming to identify differentially expressed genes (Fig. 4A). These genes were subsequently analyzed through GSEA. The results highlighted an upregulation of key pathways such as the 'smoothened signaling pathway,' 'stem cell differentiation,' 'stem cell proliferation,' and 'Wnt signaling pathway' in the high-risk group (Fig. 4B), implying an association between ACRRGs and tumorigenic stemness. To quantify this association, we employed the mRNA Expression-Based Stemness Index (mRNAsi), a novel metric assessing oncogenic dedifferentiation levels [10]. Notably, the high-risk group exhibited a markedly elevated stemness index (Fig. 4C). Further, Pearson correlation analysis underscored a significant positive relationship between ACRRGs’s expression and liver cancer stem cell markers (Fig. 4D), strengthening the evidence that ACRRGs contribute to HCC stemness. To ascertain the influence of ATP-dependent chromatin remodeling on HCC’s immune milieu, we analyzed immune cell compositions and scores in both risk groups using CIBERSORT and the ESTIMATE algorithm. This analysis revealed reduced 'CD8 T cells,'

Fig. 4

ACRRGs are associated with the stemness and immune escape of HCC. A A volcano plot illustrating the differential genes (DEGs) between the high-risk and low-risk groups from TCGA cohort. B Results from the Gene Set Enrichment Analysis (GSEA) conducted on DEGs. C The distribution of the stemness index (mRNAsi) between the high and low-risk groups. D Correlation analysis between ACRRGs and tumor stemness markers. E The content of 22 immune cells in the high and low-risk groups was predicted using the CIBERSORT algorithm. F The relative expression levels of immune checkpoints in the high and low-risk groups. G The response of HCC patients in the high and low-risk groups to immunotherapy was predicted using TIDE

'Activated NK cells,' and 'M1 Macrophages' presence in the high-risk group, coupled with diminished 'immuneScore' and 'StromalScore' (Fig. 4E and Figure S4). Investigation into immune checkpoint expression showed elevated levels of PD-1, PD-L1, CTLA4, LAG3, TIM3, and IDO1 in the high-risk group (Fig. 4F), suggesting ACRRGs' involvement in immune escape. Utilizing the TIDE algorithm, we predicted a lower likelihood of responsiveness to immunotherapy in the high-risk group, indicated by elevated 'TIDE,' 'Dysfunction,' and 'Exclusion' scores, albeit with a lower MSI score (Fig. 4G). These findings point towards a complex interplay between ACRRGs and the immune microenvironment, potentially influencing HCC's immunotherapeutic response.

Single-cell analysis reveals differentiation trajectories and the pivotal role of MORF4L1 in hepatocellular carcinoma stem cells

Through the Seurat workflow, we processed the single-cell dataset GSE149614, implementing steps such as quality control, data standardization,and normalization, removing the influence of cell cycle factors, dimensionality reduction, clustering, and cell identification. We identified seven major cell types (Figs. 5A, B): Hepatocytes (ALB, SERPINA1), T/NK cells (CD3E, CD3D, NKG7), Myeloid cells (CD68, CD14, CD163), Fibroblasts (ACAT2, COL1A1, COL1A2), Endothelial cells (VWF, PECAM1), CSCs (EPCAM, CD24), and B cells (IGHG1, JCHAIN, CD79A). Following this, we classified Hepatocytes into malignant cells and hepatocytes based on whether their origin was from tumor or normal liver tissue. CSCs are primarily distributed in primary HCC lesions, portal vein tumor thrombi, and metastatic lymph nodes. Additionally, the proportion of CSCs is higher in advanced-stage HCC compared to early-stage HCC(Supplementary File 2). This observation highlights the potential relationship between CSC abundance and HCC progression.Copy number variations (CNVs) are implicated in influencing both the progression and the maintenance of stemness characteristics in a variety of tumors [11,12,13]. Through CNV.

Fig. 5

Cancer stem cells have been identified, with MORF4L1 potentially being a key factor in promoting HCC stemness. A Identification of cell clusters using single-cell data from GSE149614. B Molecular markers corresponding to cell clustering. C Copy number variations (CNVs) in cancer stem cells (CSCs), malignant cells, hepatocytes, and reference cell groups (endothelial cells and B cells). D CNV scores among hepatocytes, malignant cells, and CSCs. E, F, and G Pseudotime analysis results for hepatocytes, malignant cells, and CSCs. H The most significantly changed genes (Cytogenes) during the progression from hepatocytes to CSCs. I Intersection of Cytogenes and ACRRGs yields key stemness-regulating genes. J Correlation analysis of key genes with the progression of stemness in HCC

analysis (Figs. 5C, D), we observed that CSCs exhibited the highest CNV mutations, followed by malignant cells, with the least mutations found in hepatocytes. This suggests that CNV mutations could impact the progression of HCC stemness. Through the CytoTRACE analysis, we compared the differentiation potential of hepatocytes, malignant cells, and cancer stem cells (CSCs). We found that CSCs exhibited the highest differentiation potential, followed by malignant cells, and hepatocytes showed the lowest(Fig. 5E–G), which further validates the credibility of our identification of cancer stem cells. In the Cytotrace analysis, the score of each cytogene measures its correlation with cell differentiation and stemness. Among these cytogenes, we identified some genes previously characterized as ACRRGs in this article that are strongly associated with HCC stemness, with MORF4L1 being the most notable. (5H-J).

MORF4L1 is upregulated in HCC and enhances the stem-like characteristics of HCC cells in vitro

Through Real-Time Quantitative Reverse Transcription PCR (qRT-PCR), Western Blot, and immunohistochemistry analyses on tumor and adjacent normal liver tissues, we observed significantly higher MORF4L1 expression levels in HCC tissues at both mRNA and protein levels (Figs. 6A–C). This upregulation was confirmed across eight HCC cell lines compared to a normal liver cell line, with MHCC-97L and Huh7 cells displaying the highest and lowest MORF4L1 levels, respectively (Fig. 6D). Lentiviral transfection experiments to modulate MORF4L1 expression in these cell lines demonstrated that MORF4L1 downregulation inhibited, while its upregulation promoted, cellular proliferation, migration, and invasion, as evidenced by plate cloning, CCK8, EdU, and Transwell assays (Fig. 6E–I).

Fig. 6

MORF4L1 is expressed at higher levels in HCC and promotes stem-like characteristics of HCC cell lines in vitro. A PCR was employed to assess the relative expression of MORF4L1 mRNA in 65 pairs of HCC and adjacent non-tumor liver tissues. B and C Western blot and immunohistochemistry were used to measure the protein expression levels of MORF4L1 in HCC and adjacent non-tumor liver tissues. D The relative mRNA and protein expression levels of MORF4L1 were determined in eight HCC cell lines and one normal liver cell line(HHL5). E Western blot and qRT-PCR were used to verify the efficiency of MORF4L1 knockdown and overexpression. F–H Plate cloning, CCK8, and EdU assays were utilized to assess cell proliferation capabilities. I Transwell assay was used to evaluate cell migration and invasion abilities. J Representative images of spheroids formed by HCC cells with MORF4L1 knockdown, MORF4L1 overexpression, and their control cells. Scale bar: 50 μm. K mRNA expression levels of cancer stem cells (CSCs) markers changed in the indicated cells. L Results from limiting dilution assays in the indicated cells. M Cell viability assays were conducted on the indicated cells with varying concentrations of lenvatinib. Untreated cells served as the baseline with 100% viability

Sphere formation assays indicated that MORF4L1 silencing reduced, whereas its overexpression increased, the number of spheres formed, suggesting MORF4L1's involvement in maintaining HCC stemness (Fig. 6J). This was further supported by the significant changes in cancer stem cell (CSC) marker expression (CD133, CD24, EPCAM) corresponding to MORF4L1 expression levels (Fig. 6K). Limiting dilution assays revealed that MORF4L1 manipulation significantly altered sphere formation efficiency, aligning with its role in stem cell self-renewal (Fig. 6L). Moreover,CSCs have been reported to be involved in resistance to lenvatinib [14, 15], and response assays to lenvatinib demonstrated that MORF4L1 modulation significantly impacted drug sensitivity in HCC cell lines (Fig. 6M).

MORF4L1 promotes tumorigenesis, metastasis, and recurrence in HCC In Vivo

In a subcutaneous tumor model using nude mice, MORF4L1 overexpression accelerated tumor growth and tumorigenesis, whereas its silencing had an inhibitory effect (Fig. 7A–C). Immunohistochemistry revealed altered expression levels of KI67 and stemness markers (CD133, EPCAM, CD24) corresponding to MORF4L1 levels(Fig. 7D). A lung metastasis model further confirmed MORF4L1's role in enhancing HCC metastasis (Fig. 7E–G). Lenvatinib treatment studies demonstrated MORF4L1's influence on drug resistance and tumor recurrence, with knockdown cells showing sustained tumor growth inhibition (Fig. 7H–I).

Fig. 7

MORF4L1 promotes tumorigenesis, metastasis and recurrence in HCC in vivo. A Images show subcutaneous xenograft tumors in nude mice formed by injecting the indicated cells. B Volume growth curves of subcutaneous xenograft tumors. C Weights of the subcutaneous xenograft tumors. D Histological (H&E staining) and immunohistochemical staining for KI67, CD133, EPCAM and CD24 were performed on subcutaneous xenograft tumors. E and F Representative images of lung metastases from tail vein injection of indicated cells, along with H&E staining photographs of these metastases. G Counts of metastatic foci in the lungs. H and I Subcutaneous injection of MORF4L1-knockdown or MORF4L1-overexpressing cells into nude mice. Upon tumors reaching an average volume of 0.35 cm3 (day 15), mice received daily oral administration of lenvatinib (20 mg/kg) for a week. On the left are representative images of subcutaneous tumors, with corresponding tumor growth curves on the right

MORF4L1 enhances hepatocellular carcinoma stemness by activating the hedgehog signaling pathway

Differential gene expression analysis of TCGA HCC samples stratified by MORF4L1 expression highlighted enriched 'stem cell differentiation' and 'Hedgehog signaling pathway' genes (Fig. 8A–C). The Hedgehog pathway is known to be associated with the proliferation and differentiation of CSCs [16, 17]. Western Blot analysis confirmed that MORF4L1 levels directly affected the expression of key Hedgehog pathway proteins (SHH, SMO, GLI1) (Fig. 8D). Interventional experiments using the SHH pathway activator (SAG) on MORF4L1-knockdown cells restored spheroid formation capacity and in vivo tumorigenicity, establishing MORF4L1’s pivotal role in HCC stemness through the Hedgehog pathway activation (Figs. 8E–J).

Fig. 8

MORF4L1 activates the Hedgehog pathway to promote stemness in HCC. A Volcano plot depicting differential gene expression (DEGs) between groups with high and low MORF4L1 expression in TCGA-LIHC. B and C Pathways identified from GSEA analysis of DEGs. D Western blot analysis for critical Hedgehog pathway proteins in HCC cells with MORF4L1 knockdown or overexpression. E Representative images of spheroids by indicated cells. Scale bar: 50 μm. F RT-PCR analysis for relative mRNA expression levels of CSC markers in indicated cells. G Limiting dilution assay reveals sphere formation frequency in indicated cells. H and I Assessments of plate colony formation and transwell migration/invasion assays in indicated cells. J Growth curves of subcutaneous xenograft tumors in nude mice injected with indicated HCC cells. Data are presented as means ± SD. (*p < 0.05; **p < 0.01; *p < 0.001)