CGR11 promotes hepatocellular carcinoma progression by regulating autophagy through the PI3K/AKT pathway.

1
Introduction
Primary liver cancer remains a major global health burden, ranking as the sixth most commonly diagnosed malignancy and the fourth leading cause of cancer-related mortality worldwide (Sung et al., 2021; Xia et al., 2022). Hepatocellular carcinoma (HCC), which accounts for 75%–85% of primary liver cancers, represents a highly aggressive malignancy that contributes disproportionately to cancer-related deaths, particularly in China (Sung et al., 2021; Wang et al., 2024). Despite significant advances in therapeutic modalities including surgical resection, liver transplantation, molecular targeted therapies, and immune checkpoint inhibitors, the prognosis for advanced HCC remains poor, with 5-year survival rates persistently below 20% (Villanueva, 2019; Llovet et al., 2021). The limited efficacy of current treatments is largely attributed to profound molecular heterogeneity, adaptive drug resistance, and the complex tumor microenvironment, highlighting an urgent need to identify novel molecular targets and clarify the signaling mechanisms that drive HCC progression and therapy resistance (Sonkin et al., 2024). HCC pathogenesis is governed by intricate molecular networks involving oncogenic signaling, metabolic reprogramming, and dysregulated autophagy (Xu et al., 2025). Among these, the phosphatidylinositol 3-kinase (PI3K)/AKT/mTOR pathway plays a central role in regulating cell proliferation, survival, and metabolism (Tian et al., 2023). Hyperactivation of this pathway suppresses autophagy initiation through inhibition of the ULK1/2 complex and PTEN inactivation, leading to enhanced tumor growth and resistance to apoptosis (Wang et al., 2018; Quan et al., 2025). Moreover, crosstalk between autophagy and the PI3K/AKT axis contributes to malignant phenotypes such as epithelial–mesenchymal transition (EMT), immune evasion, and remodeling of the tumor microenvironment (Zhang et al., 2019; Peng et al., 2020; Huang et al., 2022). Despite these insights, the upstream regulatory factors that modulate this pathway in HCC remain poorly defined.
Recent studies have highlighted secreted proteins as critical modulators of tumor biology and promising therapeutic targets (Quail and Joyce, 2013; Zhang et al., 2013). Acting as mediators of intercellular communication, secreted proteins orchestrate signaling within the tumor microenvironment, influence metastatic behavior, and often serve as accessible biomarkers for cancer diagnosis and prognosis (Quail and Joyce, 2013). In this context, the Cell Growth Regulatory Gene 11 (CGR11) molecule has emerged as a potentially relevant factor. CGR11 encodes a secreted protein containing EF-hand calcium-binding domains and is broadly expressed in several tissues, including the liver, kidney, and small intestine (Fagerberg et al., 2014). Previous studies have shown that CGR11 regulates cell proliferation and differentiation, and its aberrant overexpression has been documented in multiple solid tumors (Díaz de la Guardia-Bolívar et al., 2022; Wei et al., 2024; Gao et al., 2025).
Functionally, CGR11 binds calcium ions and activates oncogenic pathways such as MAPK and Wnt/β-catenin, thereby promoting tumor cell proliferation and survival (Devnath et al., 2009; Deng et al., 2015; Wei et al., 2024). These findings suggest that CGR11 may act as a tumor-promoting factor, yet its biological significance and function in HCC remain largely unclear.
Through comprehensive bioinformatic analyses of public datasets (GEO, TCGA, and CPTAC) and validation in clinical HCC specimens, we identified a marked upregulation of CGR11 in HCC tissues. Increased CGR11 expression correlated with advanced clinicopathological features and poor patient prognosis. Mechanistically, our data indicate that CGR11 promotes HCC progression by inhibiting autophagy via activation of the PI3K/AKT signaling pathway. Collectively, these findings identify CGR11 as a novel regulator of the PI3K/AKT-autophagy axis and suggest that targeting CGR11 may provide a promising therapeutic strategy and prognostic biomarker for HCC.

2
Methods
2.1
Bioinformatics analysis of CGR11 expression
The CGR11 mRNA expression, clinical, and survival data for Liver Hepatocellular Carcinoma (LIHC) were obtained from The Cancer Genome Atlas (TCGA) via the UCSC Xena browser (http://xena.ucsc.edu/) (Goldman et al., 2020). Normal liver tissue RNA-seq data were obtained from the Genotype-Tissue Expression (GTEx) database. Additional validation was performed using GEO datasets (GSE36376, GSE57957, GSE76427, GSE25097, GSE36411, GSE45436, and GSE54236). Protein expression profiles from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) were accessed through UALCAN (https://ualcan.path.uab.edu/) (Chandrashekar et al., 2022). GEPIA2 (http://gepia2.cancer-pku.cn/) was used to compare CGR11 expression across tumor and normal tissues, and to analyze overall (OS) and recurrence-free survival (RFS). Prognostic significance was validated using the KM Plotter database (http://www.kmplot.com).

2.2
Analysis of tumor microenvironmental characteristics
The expression of CGR11 in different cell types (including individual malignant cells) within LIHC was analyzed via Human Liver Browser and Single-cell Atlas in Liver Cancer (scAtlasLC) (Ma et al., 2021). CGR11 expression and immune infiltration across cancers were analyzed through the TIMER2.0 platform (Li et al., 2020), which also provided correlations between immune cell abundance and CGR11 levels.

2.3
Cell-cell communications analysis in scRNA-seq data
Cell-cell communication networks were inferred using the CellChat R package (Jin et al., 2021), based on ligand-receptor interactions in the CellChatDB database. Interaction probabilities were summarized to construct intercellular communication networks and assess signaling between CGR11-high malignant clusters and immune-related populations.

2.4
DNA methylation analysis
CGR11 methylation (CpG β-values) and gene expression correlations were evaluated via MethSurv (Modhukur et al., 2018) and the SMART platform (http://www.bioinfo-zs.com/smartapp/). CpG sites were classified according to genomic context, and their prognostic significance was analyzed using Kaplan-Meier plots (log-rank test, P < 0.05).

2.5
Gene enrichment analysis
Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA) were performed using the cluster Profiler package. Adjusted P < 0.05 indicated statistical significance.

2.6
Immune infiltration analysis
The GSVA package (ssGSEA method) was used to quantify immune cell infiltration related to CGR11 expression (Hänzelmann et al., 2013). Stromal, immune, and ESTIMATE scores were calculated using the ESTIMATE R package, and Spearman correlations between CGR11 expression and immune infiltration were computed using the psych package.

2.7
RNA extraction and quantitative real-time PCR (RT-qPCR)
Total RNA was extracted using TRIzol™ (Invitrogen) and reverse-transcribed with the Beyotime cDNA synthesis kit. GAPDH served as the internal control. qPCR was performed using BeyoFast™ SYBR Green on a QuantStudio 3 system (Applied Biosystems). Relative expression was calculated using the 2−ΔΔCT method.
Primers: GAPDH-F:5′-GAACGGGAAGCTCACTGG-3′,
GAPDH-R:5′-GCCTGCTTCACCACCTTCT-3’;
CGR11-F:5′-ACGATGACAGTGTTAATCCTGC-3′,
CGR11-R:5′-CCTAGTCCCTTTAGGTAGCTCTG-3’.

2.8
Cell culture and reagents
HCC cell lines PLC/PRF/5, Hep3B,and SNU449, as well as Human normal liver cells THLE2 were obtained from the American Type Culture Collection (ATCC, Rockville, MD). HCCLM3 and MHCC-97H were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai). Cells were cultured in high-glucose DMEM with 10% fetal bovine serum (FBS, BioInd) and 1% penicillin-streptomycin (Biosharp, BL505A) at 37 °C with 5% CO2. MK2206 (#HY-10358) and SC79 (#HY-18749) were purchased from MedChemExpress and dissolved in DMSO at 5 μM and 4 μM, respectively.

2.9
Western blot analysis
Isolate total proteins from frozen HCC tissues and cells by utilizing RIPA buffer containing protease inhibitors. Then the extracted total proteins were quantified by BCA assay, separated by SDS-PAGE (NCM, Suzhou, China), and transferred to PVDF membranes. After blocking, membranes were incubated with relevant antibodies (Supplementary Table S1) and visualized using ECL chemiluminescence kit (NCM, Suzhou, China).

2.10
Immunohistochemistry(IHC)
IHC was performed on paraffin-embedded sections using a two-step detection kit (ZSGB-BIO, Beijing, China). Briefly, after deparaffinization, hydration, antigen retrieval, and blocking, sections were sequentially incubated with primary antibody and reaction enhancers before visualization. The IHC score of target proteins was performed based on both the staining intensity and the proportion of positively stained cells (Su et al., 2014). Briefly, staining intensity was graded as follows: 0, negative; 1, weak; 2, moderate; and 3, strong. The percentage of positive cells was scored as: 0 for <5%, 1 for 5%–25%, 2 for 26%–50%, 3 for 51%–75%, and 4 for >75%. The final immunostaining score, obtained by combining these two parameters, ranged from 0 to 12.

2.11
Vector construction and transfection
Overexpression and knockdown lentiviruses as well as control lentiviruses were purchased from Shanghai He Yuan Biological Co. Full-length CGR11 cDNA was cloned into lentiviral vectors for overexpression. shRNA sequences targeting CGR11 were used for knockdown. Lentivirus infection and puromycin selection (2 μg/mL, Biosharp, BL528A) generated stable lines. CGR11 expression was validated by RT-qPCR and Western blot. Transfections were performed using Lipofectamine 2000 (Invitrogen).

2.12
Cell proliferation experiment
Cell proliferation viability was assessed by CCK-8 and colony formation assays. Approximately 1–5 × 103 cells were seeded into each well of a 96-well plate. Following cell attachment, 10 μL of CCK-8 reagent was added to each well and incubated in a cell culture incubator for 1 h. Absorbance measurements were performed at 450 nm using a microplate reader.
The colony formation assay was performed by seeding 4 × 102 cells per well in 6-well plates. Cells were cultured for 2 weeks in a humidified incubator (37 °C, 5% CO2) to allow colony formation. Subsequently, cell colonies were fixed and stained with 1% crystal violet solution (Beyotime Biotechnology, Shanghai, China) for quantitative analysis. Colonies containing ≥50 cells were counted under bright-field microscopy to evaluate clonogenic survival capacity.

2.13
EdU assay
EdU assay (RiboBio, Guangzhou, China) was used to evaluate DNA synthesis. HCC cells were seeded into 24-well plates at a density of 1 × 105 cells per well and incubated with 300 μL of culture medium containing 50 μM EdU for 2 h. Cells were fixed and stained according to the manufacturer’s protocol. Fluorescence imaging was performed using an inverted fluorescence microscope (Nikon Eclipse Ti2). The percentage of EdU-positive cells was quantified by ImageJ software (NIH). All experiments included triplicate biological replicates.

2.14
Wound healing assay and transwell invasion assay
HCC cells were inoculated in six-well plates and cultured in DMEM medium. A 10 μL sterile pipette tip was used to create a wound on the monolayer of cells, and the cells were cultured with the replacement medium. Photographs were taken at 0 h, 24 h and 48 h to record the width of the wound. In the Transwell invasion assay, approximately 4 × 105 cells in 200 μL of serum-free DMEM medium (BD Biosciences, Franklin Lakes, NJ) were inoculated in the upper chamber of the Transwell inserts, and the lower chamber was added with DMEM medium containing 10% FBS, and placed into the cell culture incubator. After 24 h of incubation, cells invading the basement membrane were fixed with 4% para-formaldehyde for 10 min and stained with 0.1% crystal violet (Beyotime Biotechnology), observed under a microscope and photographed.

2.15
Autophagy analyses
Autophagy levels were assessed using multiple complementary approaches, including Western blotting, IHC and the mRFP-GFP-LC3 dual-fluorescence reporter assay. For Western blot analysis, cell samples were collected after the indicated treatments, and total cellular proteins were extracted. The expression levels of LC3 II and p62 were detected by immunoblotting to evaluate autophagic activity. To visualize autophagosome formation and monitor autophagic flux dynamically, cells were infected with a dual-fluorescent mRFP-GFP-LC3 lentivirus (Hanbio Biotechnology, Shanghai, China). After transfection, fluorescent puncta were observed under a confocal microscope (LEICA). In this system, yellow puncta (GFP + RFP+) represent autophagosomes, whereas red-only puncta (RFP+) indicate autolysosomes; thus, the ratio of red to yellow puncta reflects autophagic flux.

2.16
Flow cytometry
The proportion of apoptotic cells was detected by flow cytometry (Novocyte flow cytometer, ACEA Biosciences) using the apoptosis detection kit (Apoptosis Detection Kit, Yeasen). The data were analyzed using FlowJo (version 7.6.5).

2.17
Transmission electron microscopy (TEM)
After washed with pre-cooled PBS, HCC cells were fixed with electron microscopy fixative (Servicebio, Wuhan, China). Samples were dehydrated, embedded, sectioned and stained for ultrastructural observation under TEM.

2.18
Animal studies
BALB/c nude mice (4–5 weeks old, male), purchased from Henan Huanyu Biotechnology Co., were used for subcutaneous and orthotopic HCC xenograft models. The length (L) and width (W) of the subcutaneous tumors were measured and recorded. Tumor volume was calculated as V = (L × W2)/2. Orthotopic implantation was performed under anesthesia with pentobarbital sodium (40 mg/kg, i.p.), and tumor growth was monitored by in vivo fluorescence imaging system. Six weeks later, the mice were euthanized with isoflurane (5%, inhalation), and the livers and lungs were removed for pathological examination. The final volume and weight of the orthotopic tumors were measured. All animal studies were carried out in accordance with relevant laws and regulations and approved by the Institutional Animal Ethics Committee of Hunan Provincial People’s Hospital/Hunan Normal University First Affiliated Hospital (Ethics Review Number: [2025]-115).

2.19
RNA sequencing and data analysis
Total RNA was extracted from MHCC-97H-Vector and MHCC-97H-shCGR11 cells using Trizol reagent (Invitrogen, Carlsbad, CA). The quantity and integrity of RNA were detected by using the K5500 ultrafine spectrophotometer (Kaiao, Beijing, China) and Agilent 2200 tapstation (Agilent Technologies, Santa Clara, CA). RNA-seq data were analyzed by GSEA using Hallmark gene sets.

2.20
Statistical analyses
SPSS 22.0 (SPSS Inc.,Chicago, IL) and GraphPad Prism (Version 10) were used for statistical analyses. Data were shown the mean ± SD of at least three independent experiments. Student’s t tests, and Spearman and Pearson correlation were used to analyze the correlation. Univariate and multivariate analyses were conducted by the Cox proportional hazards model to identify independent risk factors. P < 0.05 is statistically significant for the difference.

3
Results
3.1
CGR11 is upregulated in multiple human tumors and HCC tissues
Recent studies have shown that CGR11 is associated with the malignant characteristics of several tumors (Wei et al., 2024; Gao et al., 2025). To explore CGR11 expression across cancers, we analyzed mRNA and protein levels using TIMER 2.0 and Xiantao Academic databases. CGR11 showed high expression in bladder, breast, colon, kidney, esophageal, liver, lung, prostate, rectal, and gastric cancers, while showing low expression in glioblastoma, head and neck, thyroid, and uterine cancers (Figure 1A). In TCGA, GTEx, GEO, and CPTAC datasets, CGR11 expression was markedly increased in HCC compared to normal liver tissues (Figure 1B). Consistent results were obtained from multiple GEO datasets (GSE36376, GSE76427, GSE57957, GSE25097, GSE36411, GSE45436, and GSE54236). CPTAC data confirmed that CGR11 protein levels were significantly higher in HCC tissues.
IHC analysis of TMA including 100 paired clinical specimens demonstrated increased CGR11 staining in tumor tissues relative to adjacent non-tumor liver (Figure 1C). Similarly, Western blotting and qRT-PCR of 14 paired tissues verified higher CGR11 expression in HCC (Figure 1D; Supplementary Figure S1). The expression of CGR11 was also found to be increased in liver cancer cell lines (Hep3B, HCC-LM3, MHCC-97H, SNU449) compared to primary hepatocytes (THLE-2), while no significant difference was observed in PLC/PRF/5 (Figure 1E). The results of qRT-PCR analysis were consistent with those of Western blot, further validating the expression changes of CGR11 in liver cancer cell lines (Figure 1E). Collectively, these results reveal that CGR11 is significantly overexpressed in HCC.

3.2
Association between CGR11 expression and the tumor microenvironment (TME)
To characterize the cellular distribution of CGR11, we analyzed the scAtlasLC single-cell dataset. CGR11 was predominantly expressed in malignant hepatocytes, with low expression in immune and stromal cells, including T cells, B cells, CAFs, TAMs, and TECs (Figure 2A). Clustering analysis identified 15 malignant subclusters, with cluster 8 showing the highest CGR11 expression (Figure 2B). GSEA and KEGG analyses revealed enrichment of pathways related to PI3K/AKT, MAPK signaling, autophagy, oxidative phosphorylation, and protein export in the CGR11-high cluster (Figures 2C,D). Cell-cell interaction analysis demonstrated strong communication between CGR11-high tumor cells and immune components (TAMs, TECs, T cells) via ligand-receptor pairs such as CD6, CD46, MIF, VEGF, and TRAIL (Figure 2E). DNA methylation analysis (SMART and MethSurv databases) indicated reduced CGR11 methylation in HCC tissues, with low CpG methylation levels correlating with poorer survival prognosis (Supplementary Figure S3). These findings suggest that CGR11 upregulation in HCC may be partly due to hypomethylation and is associated with immune-related signaling within the TME.

3.3
High CGR11 expression predicts poor prognosis in HCC
Kaplan-Meier analysis using the TCGA-LIHC cohort revealed that high CGR11 expression correlated with shorter overall survival (OS), relapse-free survival (RFS), and disease-specific survival (DSS), though not with progression-free survival (PFS) (Figure 3A). Stratification by clinical stage and tumor grade showed that patients with low tumor grade or early-stage disease had longer survival (Figure 3B). The clinical characteristics of HCC patients are listed in Supplementary Table S2. Immune infiltration analysis indicated that high CGR11 expression combined with increased CD4+ T cells, macrophages, neutrophils, and MDSCs predicted worse prognosis (Figure 3C). No significant association was found for CD8+ T or B cells. CIBERSORTx analysis and GEO meta-analysis were also explored CGR11-immune interaction (Supplementary Figure S4). Univariate Cox analysis identified T stage, M stage, overall clinical stage, and CGR11 expression as risk factors for OS. Multivariate analysis confirmed CGR11 as an independent prognostic biomarker for poor survival (Figure 3D).
These results collectively demonstrated that high CGR11 expression was correlated with poor prognosis of HCC patients and could serve as a valuable independent prognostic factor.

3.4
CGR11 promotes proliferation, migration, invasion, and inhibits apoptosis in vitro
Based on endogenous expression, MHCC-97H cells were used for CGR11 knockdown, and PLC/PRF/5 cells for overexpression (validation shown in Supplementary Figures S1,S2). Colony formation assays, CCK-8 proliferation assays and EdU incorporation experiments demonstrated that knockdown of CGR11 significantly inhibited cell proliferation and colony formation, whereas its overexpression promoted cell growth (Figures 4A–D). Wound-healing and Transwell assays showed that CGR11 knockdown significantly reduced cell migration and invasion, whereas CGR11 overexpression had the opposite effect (Figures 4E,F). Flow cytometry revealed that CGR11 knockdown increased apoptosis, while CGR11 overexpression suppressed it (Figure 4G). Taken together, these results suggest that CGR11 enhances the malignant behavior of HCC cells in vitro.

3.5
CGR11 promotes HCC tumor growth in vivo
To further investigate the role of CGR11 in vivo, we established subcutaneous xenograft and orthotopic implantation models in nude mice. We divided the experimental cohorts into four groups: MHCC-97H-Control, MHCC-97H-shCGR11, PLC/PRF/5-Control, and PLC/PRF/5-CGR11-Overexpression. CGR11 knockdown significantly reduced tumor size and weight, while CGR11 overexpression markedly enhanced tumor growth (Figures 5A,B). The orthotopic tumor model further confirmed that CGR11 knockdown suppressed intrahepatic tumor growth by ∼72%, whereas CGR11 overexpression accelerated progression by 2.3-fold (Figure 5C). Subsequent IHC staining of PCNA expression further supported that CGR11 enhanced proliferative activity in vivo (Figure 5D). Collectively, these findings confirm that CGR11 promotes HCC tumor growth.

3.6
CGR11 influences the PI3K/AKT pathway in HCC cells
To further elucidate the underlying mechanisms, RNA-seq analysis of MHCC-97H-shCGR11 cells identified 282 differentially expressed genes through Volcano plot analysis (Figure 6A). A hierarchical clustering heatmap demonstrated clear differences in transcriptional profiles between MHCC-97H shCGR11 cells and the control group (Figure 6B). KEGG and GSEA results highlighted significant involvement of PI3K/AKT, MAPK, and autophagy pathways (Figures 6C,D). GO Enrichment analysis revealed that CGR11 regulates pathways related to extracellular stimulus response, cell-matrix interaction, and transmembrane transport, predominantly localized at the membrane (Figure 6E). Western blot validation confirmed that CGR11 knockdown decreased the levels of phosphorylated PI3K(p-PI3K) and AKT (p-AKT), while CGR11 overexpression increased the levels of p-PI3K and p-AKT (Figure 6F). IHC of xenografts showed a positive correlation among CGR11, p-PI3K, p-AKT, LC3 and p62 expression (Figure 6G), confirming that CGR11 activates the PI3K/AKT pathway. These findings align with our prior single-cell transcriptomic profiling results (Figure 2G).

3.7
CGR11 influences autophagy in HCC cells
Given CGR11’s functional association with autophagy-related pathways, we hypothesized its regulatory role in modulating autophagic processes in HCC. Public database analyses showed a negative correlation between CGR11 expression and autophagy-related markers (LC3, p62/SQSTM1) (Figures 7A–C). IHC analysis of LC3 and p62 in xenografts from mice with CGR11 overexpression and downregulation models revealed a negative correlation between LC3II expression and CGR11 levels, whereas p62 expression showed a positive correlation with CGR11 (Figure 6G). Using the mRFP-GFP-LC3 reporter assay, we observed that CGR11 overexpression decreased autophagic flux in HCC cells, whereas knockdown enhanced autophagic flux (Figure 7D). As p62 is delivered to autolysosomes via membrane binding for LC3II degradation, cytoplasmic p62 accumulation indicates impaired autophagic degradation. Western blot analysis further confirmed that CGR11 knockdown increased LC3-II levels and decreased p62 levels, while overexpression showed the opposite effects (Figure 7E). TEM revealed abundant autophagosomes in CGR11-knockdown cells, but markedly fewer in CGR11-overexpressing cells (Supplementary Figure S5A). Taken together, these data indicate that CGR11 inhibits autophagy, likely through PI3K/AKT pathway activation.

3.8
CGR11 inhibits autophagy by regulating the PI3K/AKT pathway
Given that PI3K/AKT suppresses autophagy through ULK1/2 inhibition and PTEN interaction, we investigated whether CGR11 acts through this axis (Wang et al., 2018; Quan et al., 2025). Treatment with the AKT activator (SC79) restored p-AKT levels and cell proliferation in CGR11-knockdown cells, whereas the AKT inhibitor (MK2206) reversed the tumor-promoting effects of CGR11 overexpression (Figures 8A–C). Similarly, wound healing and invasion assays showed that modulation of AKT reversed CGR11-mediated effects on cell migration and invasion (Figures 8D,E). LC3 dual-fluorescence reporter assays demonstrated that SC79 suppressed autophagy in CGR11-knockdown cells, while MK2206 restored autophagy in CGR11-overexpressing cells (Figure 8F). TEM results also showed that treatment with SC79 reduced the numbers of autophagosomes, whereas treatment with MK2206 increased their numbers (Supplementary Figure S5B). Collectively, these findings indicate that CGR11 promotes HCC proliferation and invasion by activating PI3K/AKT signaling to inhibit autophagy.

4
Discussion
Current research on the molecular mechanisms underlying the malignant progression of HCC remains limited, and the intricate interplay between oncogenic drivers and epigenetic regulatory networks in mediating tumor plasticity yet to be fully elucidated (Wu et al., 2020). CGR11, a classical pathway-regulated secretory protein, has demonstrated prognostic significance in prostate cancer and osteosarcoma (Díaz de la Guardia-Bolívar et al., 2022; Wei et al., 2024). However, the precise molecular mechanisms by which CGR11 contributes to HCC progression remain obscure. A recent study by Gao et al. (Gao et al., 2025) identified CGR11 as a regulatory factor promoting HCC cell proliferation and migration via the EIF3H-mediated Wnt/β-catenin signaling axis. Nevertheless, that study lacked comprehensive in vivo validation, as well as sufficient in vitro and genomic analyses such as RNA sequencing to, substantiate the proposed EIF3H-Wnt/β-catenin pathway.
In contrast, our study integrated bioinformatics analyses, molecular assays, RNA sequencing, and functional validations in both in vitro and in vivo models including subcutaneous and orthotopic xenografts, to define how CGR11 facilitates HCC malignant progression through activation of the PI3K/AKT pathway and suppression of autophagy. Through integrated multi-omics analysis of TCGA, GEO, and CPTAC datasets, we demonstrated that CGR11 is significantly upregulated in HCC tissues, correlates positively with aggressive clinicopathological features, and serves as an independent prognostic factor for both OS and RFS. Furthermore, CGR11 overexpression was significantly associated with aberrant DNA methylation and immune cell infiltration within the tumor microenvironment. In conjunction with single-cell transcriptomic data showing predominant CGR11 expression within malignant cell clusters, these findings suggest that CGR11 may not only function as a critical regulator promoting HCC progression but also as a participant in intercellular communication and immune modulation within the TME. Recent study has emphasized extracellular vesicles (EVs) as vital mediators of metabolic and signaling crosstalk between tumor and stromal compartments (Encarnação CC et al., 2024). It is plausible that CGR11 may influence or interact with EV-associated metabolic and immune communication networks. Future investigations employing EV profiling, co-culture systems, or spatial-omics approaches could further elucidate how CGR11-driven PI3K/AKT activation integrates with TME remodeling and metabolic adaptation.
Mechanistically, our functional studies revealed that CGR11 sustains malignant phenotypes in HCC cells by inhibiting autophagy levels through activation of the PI3K/AKT pathway. Consistent with this, single-cell transcriptomic profiling confirmed predominant CGR11 overexpression specifically within malignant hepatocyte clusters. Functionally, CGR11 overexpression enhanced HCC cell proliferation, invasion, and migration, whereas its knockdown markedly attenuated these phenotypes. Comprehensive in vitro and in vivo assays confirmed CGR11’s oncogenic role in maintaining HCC malignancy and promoting tumor growth.
Integrated RNA-seq and single-cell analyses further identified the PI3K/AKT signaling axis as a major pathway downstream of CGR11. The PI3K/AKT pathway is a well-established regulator of proliferation, survival, and metabolism, and its aberrant activation in HCC has been consistently linked to tumor progression, metastasis, and drug resistance (Li et al., 2022; Wan et al., 2024). This process, initiated by PI3K-mediated activation of AKT and subsequent phosphorylation of targets such as mTOR, promotes cell growth. Another study, including one identifying RNA editing as a driver of hepatocarcinogenesis via the COPA-PI3K/AKT axis (Song et al., 2021), further underscores the centrality of this pathway in HCC biology.
Autophagy is a lysosome-dependent degradation mechanism that maintains cellular homeostasis by eliminating damaged organelles and macromolecules, playing a dual role in HCC. While it can suppress malignant transformation at early stages by removing abnormal proteins and organelles, it often supports tumor survival and therapeutic resistance in advanced disease by providing metabolic flexibility (Debnath et al., 2023). Therefore, it is crucial to dissect the dynamic regulatory networks that govern autophagy in order to develop targeted HCC therapy.
Furthermore, insights from traditional medicine and ion channel research provide additional translational relevance. A paradigm shift is underway in cancer therapy, with novel strategies that concurrently target oncogenic signaling pathways and modulate the TME, ultimately reshaping the treatment landscape (Joshi et al., 2024). Liu et al. have demonstrated that certain toxic components of traditional Chinese medicine exert potent anti-cancer effects through modulation of ion channels and autophagy pathways (Liu, 2021; 2022; 2023). These findings underscore the intricate convergence between autophagy regulation, ion flux, and therapeutic response. Moreover, the intricate crosstalk between PI3K/AKT signaling and autophagy represents a pivotal axis in cancer progression. PI3K/AKT activation has been shown to promote tumor-enhancing autophagy (Li et al., 2024) and to facilitate oncogenesis by repressing or functionally impairing key autophagy mediators such as Beclin-1, LC3, and p62 (Cocco et al., 2022; Wang et al., 2023). For instance, SDC1 overexpression in HCC suppresses autophagy via PI3K/AKT/mTOR activation, thereby enhancing tumor survival and metastatic potential (Yu et al., 2020). Similarly, FDX1 downregulation promotes tumor progression through coordinated activation of mitophagy and PI3K/AKT signaling linked to excessive ROS production in HCC(Sun et al., 2024). Meanwhile, NLRP6 was reported by the Sun Yat-sen University group to potentiate PI3K/AKT signaling through autophagic degradation of p85α, an effect reversed by pharmacologic inhibition (Zhi et al., 2023).
In our study, Western blotting, IHC analyses, and mRFP-GFP-LC3 flux assays revealed that CGR11-mediated PI3K/AKT activation correlates with altered expression of LC3 II and p62, indicative of autophagy inhibition. Impaired autophagy reduced the cellular capacity to remove damaged organelles and misfolded proteins, thereby enhancing malignant potential. Pharmacological experiments further confirmed that inhibition of AKT with MK2206 partially reversed CGR11-induced motility and invasion, whereas activation of AKT using SC79 exacerbated these effects. These results validate PI3K/AKT signaling as a critical downstream effector of CGR11 and are consistent with previous findings showing that SOCS5 promotes HCC metastasis through PI3K/AKT/mTOR-mediated inhibition of autophagy (Zhang et al., 2019). Collectively, our data establish CGR11 as a pivotal regulator of HCC progression by suppressing autophagy via activation of the PI3K/AKT pathway, thereby enhancing cellular proliferation and invasiveness.
Finally, in vivo validation using BALB/c nude mouse models confirmed that CGR11 overexpression significantly accelerated tumor growth, as evidenced by increased xenograft volume and mass, while CGR11 knockdown markedly reduced both parameters. Orthotopic liver models yielded consistent results, reinforcing the conclusion that CGR11 promotes HCC progression via PI3K/AKT pathway activation coupled with autophagy inhibition. Looking forward, in the post-pandemic era of accelerated biomedical integration, multi-omics and personalized therapeutic frameworks are becoming central to precision oncology (Chen, 2024). Positioning CGR11 within such integrative, multi-dimensional paradigms of HCC biology may facilitate its translation into novel diagnostic and therapeutic strategies.
Despite these advances, several limitations must be acknowledged. The mechanisms driving CGR11 overexpression, whether transcriptional, epigenetic, or EV-mediated, remain unclear. The link between CGR11-induced PI3K/AKT activation and tumor microenvironment remodeling also requires further study. Clarifying these processes will aid in understanding how CGR11 regulates metabolic reprogramming and therapy response, facilitating the development of targeted HCC treatments.

5
Conclusion
In summary, our findings identify CGR11 acts as a critical regulator promoting HCC progression through PI3K/AKT-mediated suppression of autophagy, highlighting its potential as both a prognostic biomarker and a therapeutic target.