Role of Glucocerebrosidase in Metabolic Reprogramming and Prognosis of Hepatocellular Carcinoma: Insights from a Comprehensive Gene Expression Analysis

doi:10.21203/rs.3.rs-5016814/v1

Introduction: Hepatocellular carcinoma (HCC) poses a substantial global public health concern, with its intricate pathogenesis remaining incompletely elucidated. Metabolic reprogramming is pivotal in liver cancer progression. This study investigates the role of the lysosomal enzyme Glucocerebrosidase (GBA) in HCC initiation.

Methods: We analyzed GBA-related gene expressions in 1003 primary liver cancer samples from the GEO database and 433 liver cancer samples from the TCGA database to examine GBA expression patterns and their association with liver cancer prognosis. Additionally, we manipulated GBA and glucosylceramide synthase (UGCG) expressions in the MHCC-97H cell line to investigate their effects on lysosomal and non-lysosomal metabolic genes.

Results: GBA expression was significantly elevated in liver cancer samples and closely associated with poor prognosis. Overexpression of GBA led to upregulation of related lysosomal metabolic genes (NEU1, CTSD, CTSA, GALNS, GLB1) and non-lysosomal metabolic genes (ACOT8, FDPS, PMVK, PIGC, B4GALT3). Non-lysosomal genes were involved in N-acetyl metabolism, fatty acid β-oxidation, and cholesterol synthesis. Co-upregulation of UGCG and GBA resulted in a dose-dependent increase in ACOT family gene expressions (ACOT8, ACOT4, ACOT9, ACOT11). Survival analysis indicated high expression of these genes was related to lower short-term survival rates in liver cancer patients.

Conclusion: Our findings suggest GBA plays a role in the metabolic reprogramming of HCC, influencing disease progression and prognosis by modulating genes involved in N-acetyl metabolism and lysosomal complexes. Downregulating GBA expression may present a potential therapeutic strategy for managing HCC.

GBA

Lysosome

Metabolism

HCC

Metabolic reprogramming

Liver cancer presents a substantial global health burden, as evidenced by the 841,080 new cases reported worldwide in 2018[1]. Projections suggest that the incidence of liver cancer will surpass one million by 2025[2]. The one-year survival rate for liver cancer is below 50%, with a five-year survival rate of approximately 10%[3]. Hepatocellular carcinoma (HCC), the predominant form of liver cancer, comprises roughly 90% of all cases[4]. HCC arises from hepatocytes and is driven by persistent genomic instability resulting from dysregulation of multiple metabolic pathways[4]. This dysregulation leads to the dedifferentiation of hepatocytes and the emergence of HCC precursor cells expressing liver progenitor cell markers. These precursor cells ultimately differentiate into fully transformed HCC cells[4]. Perturbations in metabolic pathways, often associated with metabolic disorders, are pivotal in the initiation and progression of HCC[2]. Metabolic dysregulation has been identified as a catalyst for the activation of various cancer signaling pathways, thereby facilitating the development of HCC[2]. Approximately 25% of HCC cases are associated with actionable gene mutations that induce alterations in metabolic profiles, contributing to the pathogenesis of HCC[3, 5]. Nevertheless, the translation of these mutational sites into effective therapeutic targets is constrained by a limited understanding of the mechanisms through which these genetic loci drive the initiation of HCC[3]. Prior research conducted by our research group has demonstrated that elevated levels of GBA can enhance the progression of HCC through the upregulation of cholesterol synthesis, although the precise underlying mechanisms have yet to be fully elucidated[6].

GBA, also referred to as acid β-glucosylceramidase or GCase, is an acidic hydrolase localized within the lysosomes of somatic cells[7]. Its primary function within the lysosome is the hydrolysis of glucosylceramide (GlcCer) into glucose (Glc) and ceramide (Cer)[7]. Insufficient levels or impaired activity of GBA result in the accumulation of GlcCer within the lysosomes, leading to lysosomal swelling and the development of type 1 Gaucher's disease[7]. In mammalian cells, the synthesis of GlcCer from Glc and Cer occurs in the Golgi apparatus through the enzymatic action of glucosylceramide synthase (UGCG)[8]. Subsequent modifications of GlcCer lead to the generation of diverse glycolipid species[8]. Type 1 Gaucher's disease, resulting from deficient activity of the enzyme GBA, is commonly managed through intravenous administration of recombinant GBA enzyme, known as enzyme replacement therapy[9]. Clinical randomized controlled trials and cohort studies on enzyme replacement therapy have demonstrated an increase in serum cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol levels following treatment[10]. Additionally, cohort studies involving enzyme replacement therapy have indicated a heightened risk of HCC development[11].

This study examines the expression of GBA in different types of cancer, with a focus on its effect on survival rates in liver cancer, utilizing 433 unpaired samples from the TCGA database. The investigation places emphasis on the biological processes and signaling pathways influenced by GBA to elucidate its role in liver cancer development. The results aim to offer valuable insights for the advancement of preventive and therapeutic approaches for liver cancer, particularly in the exploration of potential therapeutic targets linked to GBA and its associated metabolic pathways.

2.1 Reagents and Materials

PCR Concentration Detection: Nanodrop Spectrophotometer (SIMPLNANO). MHCC-97H Cell Lines: Obtained from Servicebio, Wuhan Servicebio Technology Co., Ltd. (https://www.servicebio.cn/). Phosphate Buffered Saline (PBS): Purchased from Servicebio. PrimeScript™ RT Master Mix (RR036A) and TB Green Premix Ex Taq™ II (RR820A): Acquired from Takara (https://www.takarabio.com). Fetal Bovine Serum (Prime) (FSP500): Purchased from Shanghai Jitaiyi Kesai Biotechnology Co., Ltd. (http://www.excellbio.com). High Glucose DMEM (C3113-0500): Purchased from VicaCell (https://www.vivacell.de/). The transfection reagent was purchased from Zeta™ Life (available at: http://www.zeta-life.com/).

2.2 Experimental Plasmids and siRNA

SiRNA Sequence, Sense: CCAAUGGAGCGGUGAAUGGTT,Antisense: CCAUUCACCGCUCCAUUGGTT, Purchased from Shanghai Genepharma Co., Ltd. (http://www.genepharma.com/). GBA overexpression vector: Utilizes the pcDNA3.1(+) vector, incorporating the GBA gene (length 1617 bp) and resistance to ampicillin, Purchased from GENERULOR (http://www.genepharma.com/). UGCG overexpression vector Purchased from Sangon Biotech (https://www.sangon.com/).

2.3 Search Method

GEO Dataset: Search for “Hepatocellular Carcinoma”, “homo sapiens”, “Expression profiling by array”, and “tissue”. TCGA Database: Search parameters include “Primary Site is ‘liver and intrahepatic bile ducts’ AND Program Name is ‘TCGA’ AND Project Id is ‘TCGA-LIHC’ AND Data Category is ‘transcriptome profiling’.”

2.4 Inclusion and Exclusion Criteria

Inclusion Criteria:① Samples are primary liver cancer.② Patients had not received drug treatments or other interventions that could influence the development of liver cancer prior to sampling. Exclusion Criteria: ① Chip probe information includes annotations for GBA. ② The dataset contains both tumor and non-tumor tissue samples.

2.5 Cell Culture and Transfection

Cell Culture: As previously described[12]. Transfection: All MHCC-97H cells in the logarithmic growth phase were digested with trypsin at 37°C for 30 seconds, followed by removal of trypsin and resting at room temperature for 1 minute. Fresh culture medium was used to prepare the MHCC-97H cell suspension at a density of 1.5 x 10^5 cells/ml. 2 ml of the cell suspension was seeded into each well of a 6-well plate and cultured for 12 hours before transfection with plasmids or siRNA. Plasmid concentration was 4 mg per well, and siRNA was 20 µmol per well. Cells were harvested 48 hours post-transfection for RNA extraction.

2.6 RNA Extraction and Reverse Transcription

RNA Extraction: Performed using the OMEGA kit according to the manufacturer's instructions. RNA Concentration and Quality Measurement: Using the NanoDrop spectrophotometer instrument. Subsequently, reverse transcription was carried out in a reaction volume of 20 µl containing 1000 ng of total RNA and 4 µl of MasterMix at 37°C for 15 minutes, followed by 85°C for 5 seconds.

2.7 qPCR and data states

Real-time quantitative PCR (qPCR) analyses were performed using TB Green® Premix Ex Taq™ II (Tli RNase H Plus). The cycling conditions employed were as follows: an initial denaturation step at 95°C for 2 minutes, followed by 40 cycles, each consisting of denaturation at 95°C for 15 seconds and annealing/extension at 60°C for 30 seconds.The gene primers used are as shown in Table 1.Calculation of Gene Expression Levels:Calculation of ΔCt for each sample: ΔCt = Ct (target gene) - Ct (reference gene). Calculation of ΔΔCt for each sample: ΔΔCt = ΔCt(sample) - ΔCt (control group). Calculation of fold change in target gene expression: Fold Change = 2^(-ΔΔCt).

Table 1

the list of primer
Gene symbol	forward sequence (5'to 3')	Reverse sequence (5'to 3')
SDHC	CTGTTGCTGAGACACGTTGGT	ACAGAGGACGGTTTGAACCTA
CLN3	CGCCCACGACATCCTTAGC	AGCAGCCGTAGAGACAGAGTT
CTSD	TGCTCAAGAACTACATGGACGC	CGAAGACGACTGTGAAGCACT
B4GALT3	CGAGATCAGGGACCGACATTT	GATCGTTCTGGACAGTAGGGC
CTSA	GTCGCCCAGAGCAATTTTGAG	TCTCCCCGGTCAGGAAAAGTT
GALNS	TGCCAGAAACGCCTACACACC	CACTTGCCGACAATCTTGCTGAC
GLB1	CCGTGCTGGAACTGGAGTGG	CAGGTTTGGAGGGATGATCGTAGG
ACTB	CATGTACGTTGCTATCCAGGC	CTCCTTAATGTCACGCACGAT
NEU1	GGAGGCTGTAGGGTTTGGG	CACCAGACCGAAGTCGTTCT
FDPS	TGTGACCGGCAAAATTGGC	GCCCGTTGCAGACACTGAA
PMVK	CCTTTCGGAAGGACATGATCC	TCTCCGTGTGTCACTCACCA
PIGC	TCAACCTGTGACTAACACCAAGG	GCCGGTCCACATAGTTATCAG
ACOT8	CTGTTTGGTGGTCAGATCGTG	GAAGCTCGACCCTGTTCGTG
CLN3	CGCCCACGACATCCTTAGC	AGCAGCCGTAGAGACAGAGTT
SDHC	GCTGCGTTCTTGCTGAGACA	ATCTCCTCCTTAGCTGTGGTT
BPGM	TGCTTGGAATAAGGAGAACCGT	CCACAGTTCCGAGCTTCCTC
UGCG	GAATGGCCGTCTTCGGGTT	AGGTGTAATCGGGTGTAGATGAT
ACOT4	AGCGGGACGTACAGATTCCTT	GAGGCTGGCTCGATATTCCA
ACOT7	TCTCCCATGTGCATCGGTG	TTTTCGGACATCACGTTGACC
ACOT9	AGTTGCGGGAGATAGTAGGAG	AGGCAGTCCATCCTGTGATTT
ACOT11	ATCCAGAATGTCGGAAATCACC	CGCACGTAAGGCTGACTTC
ACOT13	GGGAGGTGATAAAGGCCATGA	TGCCTATTGCATTGGTATGCTC

2.8 Statistical Analysis Methods

All data analyses and processing were conducted using R language (R version 4.2.1). For the GEO gene expression matrix, when a single probe in a single sample corresponds to multiple gene annotations, the expression level for that probe is considered as the expression level for all corresponding genes. Gene expression correlation and differential expression analyses were performed using the "limma" package; GSEA was conducted using the "clusterProfiler" package, with gene ranking based on descending correlation with GBA; gene ID information was converted and annotated using the "org.Hs.eg.db (3.18.0)" package. Gene interaction analysis was performed using the "GeneMANIA (3.5.2)" plugin in Cytoscape 3.7.2; GO analysis was conducted using "BiNGO (3.0.5)," and GO interaction analysis was performed using clueGO (2.5.10); all gene, protein, and GO interaction networks were visualized using Cytoscape (3.7.2). All statistical analyses considered a P-value < 0.05 as statistically significant.

2.9 Ethics Statement

This study utilized publicly available data from the GEO and TCGA databases, as well as established human cell lines. According to the guidelines of our Institutional Review Board (IRB), research involving publicly available de-identified data and commercially available cell lines is exempt from requiring ethical approval. Therefore, this study did not require approval from an IRB or Ethics Committee. Additionally, as this study did not involve direct human participants or patient material, no informed consent was required.

2.10 Data Availability Statement

The data supporting the findings of this study are publicly available. The gene expression data analyzed in this study were obtained from the GEO (Gene Expression Omnibus) database under the following accession numbers: GSE5975, GSE17548, GSE45434, GSE45435, GSE45436, GSE46444, GSE62232, GSE89377, GSE101685, GSE116174, GSE164760, GSE14811, GSE45050, GSE46408, GSE57957, GSE59259, GSE64041, GSE74656, GSE76427, GSE84005, and GSE99807. Data from The Cancer Genome Atlas (TCGA) liver cancer project (project ID: TCGA-LIHC) were also utilized. The in vitro experimental data generated during this study are available from the corresponding author upon reasonable request.

3.1 Retrieval Results

A total of 433 non-paired samples were retrieved from the TCGA database, while 188 datasets were obtained from GEO. After reviewing dataset descriptions against inclusion criteria, 165 datasets were excluded, with an additional 2 datasets excluded based on exclusion criteria. The exclusion of dataset GSE25097 was due to the absence of GBA annotation in chip information, and dataset GSE33814 was excluded as an HCC sample gene expression matrix could not be located. Ultimately, 21 datasets were deemed suitable for analysis, encompassing 1003 liver cancer samples for correlation analysis.

3.2 Analysis of GBA Expression Across Various Cancers

Examination of liver cancer specimens obtained from the TCGA database indicated that GBA exhibits elevated expression levels in ten distinct cancer types, namely BLCA, BRCA, CHOL, ESCA, HNSC, KIRC, LIHC, LUAD, READ, STAD, and UCEC, while demonstrating reduced expression in three other cancer types: COAD, KICH, and THCA (Fig. 1).

3.3 Analysis of the Impact of GBA on Survival Rates in HCC

In order to evaluate the influence of GBA on the survival outcomes of patients with HCC, we conducted an analysis of the impact of GBA expression on time-to-survival rates across various demographic subgroups, including gender, age (≥ 60 years and < 60 years), and body mass index (BMI) categories (> 25 kg/m² and < 25 kg/m²). Given the larger sample sizes in stages I and II, patients were aggregated into two groups: stages I and II combined, and stages III and IV combined.The study found that individuals with higher GBA expression levels exhibited a greater survival rate, as indicated by a hazard ratio (HR) of 1.53 (95% CI: 1.08, 2.16) (Fig. 2A). Furthermore, in patients aged 60 and above, those with low GBA expression levels demonstrated a significantly higher overall survival probability with an HR of 2.61 (95% CI: 1.60, 4.26), p < 0.001, while no statistically significant differences were observed in patients under the age of 60 (Figs. 2B and 2C).There were no statistically significant differences in overall survival rates across various BMI stratifications (Figs. 2D and 2E). However, among patients with a BMI > 25 kg/m², individuals with high GBA expression had a hazard ratio of 1.72 (95% CI: 0.99, 3.00), p = 0.05 compared to those with low GBA expression. Similarly, no significant disparities were observed in overall survival rates based on different levels of GBA expression among different genders (Figs. 2F and 2G). Nevertheless, in male patients, those with high GBA expression had a hazard ratio of 1.53 (95% CI: 0.98, 2.40), p = 0.063 compared to those with low GBA expression.

3.4 Gene Sets Related to GBA in HCC Cases in the GEO Database

A total of 188 datasets were initially obtained from the Gene Expression Omnibus (GEO) database. Following a thorough review of dataset descriptions against predetermined inclusion criteria, 165 datasets were deemed ineligible for further analysis. Additionally, 2 datasets were excluded based on specific exclusion criteria; dataset GSE25097 was excluded due to the absence of GBA annotation in chip information, while dataset GSE33814 was excluded due to the unavailability of an HCC sample gene expression matrix. Ultimately, 21 datasets met the criteria for inclusion in the analysis, encompassing a total of 1003 liver cancer samples for correlation analysis, as detailed in Table 2.

Table 2

Basic Information of GSE Datasets Included in the Study
GSE number	Etiology Description	Paired Samples	Number of HCC Samples	Number of Control Samples	Control Tissue Status	Peritumoral Tissue Status	Viral Infection Status	Number of GBA-related Genes
GSE5975	HBV	N	238	0	NR	Cirrhosis	HBV	8846
GSE17548	NR	N	17	20	Cirrhosis	Cirrhosis	HBV	582
GSE45434	NR	N	16	0	NR	NR	NR	1846
GSE45435	NR	N	31	0	NR	NR	NR	3230
GSE45436	NR	N	93	41	NR	NR	NR	6474
GSE46444	NR	N	88	48	Cirrhosis	NR	NR	2603
GSE62232	various etiologies	N	81	10	NR	NR	NR	4712
GSE89377^[47]	NR	N	40	67	normal	NR	NR	6186
GSE101685	NR	N	8	24	normal	NR	NR	12827
GSE116174	NR	N	64	0	NR	NR	NR	3060
GSE164760	NR	N	53	29	adjacent HCC	NASH	NR	2790
GSE14811	HBV	Y	56	56	NR	NR	NR	1103
GSE45050	NR	Y	6	6	a	NR	NR	572
GSE46408	NR	Y	6	6	NR	NR	NR	2610
GSE57957	NR	Y	39	39	NR	NR	NR	2796
GSE59259	alcoholic	Y	8	8	NR	NR	NR	2231
GSE64041	NR	Y	60	60	NR	NR	NR	9671
GSE74656	NR	Y	5	5	NR	NR	NR	2306
GSE76427	NR	Y	52	52	NR	NR	NR	2987
GSE84005	NR	Y	38	38	NR	NR	NR	2885
GSE99807	NR	Y	4	4	steatosis	NR	NR	1387
a: 2 Cirrhosis and 2 fatty change and 3 normal;NR: Not Report; N: Yes; N: No; HBV: hepatitis B virus.

3.5 GSEA Analysis to Explore Pathways Involving GBA-Related Genes

In order to investigate the influence of GBA on HCC progression, we conducted a correlation analysis between GBA and other genes within the gene expression profiles of HCC samples sourced from multiple datasets. Genes exhibiting significant correlations with GBA were organized into gene sets (referred to as GBA sets or GBAs) for subsequent Gene Set Enrichment Analysis (GSEA). Our analysis demonstrated enrichment of GBAs across ten datasets, as detailed in Table 3. Specifically, the GSEA results indicated enrichment of ten gene sets associated with hsa04142 (Lysosome), nine gene sets linked to hsa01100 (Metabolic pathways), and three gene sets related to hsa00600 (Sphingolipid metabolism).

Table 3

the results of GSE analysis of GBA-related genes in the dataset
GSE number	Pathway contain GBA	Set Size	Enrichment Score	NES	P value	rank
GSE5975	hsa04142	82	0.39	1.96	0.00	2705
GSE17548	none
GSE45434	hsa01100	147	0.25	2.73	0.00	900
GSE45434	hsa04142	24	0.45	2.43	0.00	367
GSE45435	hsa01100	201	0.25	3.04	0.00	775
GSE45435	hsa04142	37	0.55	3.55	0.00	702
GSE45436	hsa01100	465	0.19	1.74	0.00	2043
GSE45436	hsa04142	67	0.43	2.79	0.00	1254
GSE46444	none
GSE62232	hsa04142	37	0.35	1.92	0.01	632
GSE62232	hsa00600	12	0.54	1.98	0.00	87
GSE89377	hsa01100	399	0.17	1.72	0.00	1572
GSE101685	hsa04142	79	0.46	4.19	0.00	3802
	hsa00600	26	0.32	1.85	0.02	483
	hsa00511	10	0.53	1.96	0.01	833
GSE116174	hsa01100	216	0.28	3.18	0.00	1291
GSE116174	hsa04142	35	0.49	3.05	0.00	779
GSE164760	hsa01100	281	0.29	2.47	0.00	887
GSE57957	hsa04142	170	0.23	2.23	0.00	515
GSE57957	hsa01100	22	0.47	2.25	0.00	459
GSE59259	none
GSE64041	hsa01100	498	0.30	4.12	0.00	3311
	hsa04142	47	0.48	3.33	0.00	3100
	hsa00600	18	0.39	1.84	0.02	2541
GSE74656	hsa01100	211	0.32	5.04	0.00	871
GSE76427	none
GSE84005	hsa04142	39	0.36	2.18	0.00	1451
GSE99807	none
GSE14811	none
GSE45050	none
GSE46408	none
hsa04142:Lysosome; hsa01100: Metabolic pathways; hsa00600: Sphingolipid metabolism.

3.6 Impact of GBA on Metabolism

In order to investigate the impact of GBA on metabolism in HCC samples, a co-expression analysis was performed on genes within the hsa01100 (Metabolic pathways) from GBAs across nine gene sets. The occurrence of each gene across the nine metabolic pathways was recorded, resulting in a total of 715 genes from the union of gene sets within these pathways. Within these pathways, 46 genes were found to appear at least five times, as detailed in Tables 4 and 5.

Table 4

Genes Appearing at Least Five Times in Various Metabolic Pathways
symbol	frequency	average correlation	symbol	frequency	average correlation
GBA	10	1.00	FDPS	6	0.41
PMVK	10	0.61	GALE	6	0.43
DPM3	9	0.53	GGPS1	6	0.44
FLAD1	9	0.57	GNS	6	0.50
HEXB	8	0.47	GPX1	6	0.49
NDUFS2	8	0.61	LPIN1	6	0.47
NEU1	8	0.52	PIGM	6	0.59
SDHC	8	0.58	PYCR2	6	0.40
ACOT8	7	0.52	QPRT	6	0.50
ACSM1	7	0.44	SEPHS2	6	0.50
ATP6V1E1	7	0.43	AKR1C3	5	0.46
B4GALT3	7	0.51	ALG1	5	0.44
COX8A	7	0.54	ALG9	5	0.50
GMPPA	7	0.36	ATP6V0A1	5	0.42
PI4KB	7	0.50	BPGM	5	0.59
PIGC	7	0.56	CANT1	5	0.38
PIP4K2C	7	0.48	CERS2	5	0.56
PPOX	7	0.55	CYP2R1	5	0.44
THTPA	7	0.47	GLB1	5	0.40
AMDHD2	6	0.44	GPAA1	5	0.47
ATP6AP1	6	0.45	IMPA2	5	0.38
ATP6V0B	6	0.48	NDUFA1	5	0.48
ATP6V0C	6	0.44	NME7	5	0.42
ATP6V0E2	6	0.49	NUDT5	5	0.52
ATP6V1D	6	0.49	PDE6D	5	0.46
BPNT1	6	0.42	PIGU	5	0.42
DOLK	6	0.43	TK1	5	0.49

Table 5

Genes Appearing at Least Five Times in Lysosome Pathways
symbol	frequency	average correlation	symbol	frequency	average correlation
GBA	11	1.00	MCOLN1	6	0.48
HEXB	10	0.51	AP1B1	5	0.42
CLN3	9	0.57	ATP6V0A1	5	0.45
GNS	9	0.53	ATP6V0C	5	0.46
NEU1	9	0.52	ATP6V0D1	5	0.52
ATP6AP1	8	0.44	CTNS	5	0.40
CTSA	8	0.57	CTSF	5	0.53
TPP1	8	0.53	DNASE2	5	0.50
ATP6V0B	7	0.47	GLB1	5	0.53
CTSD	7	0.51	GNPTG	5	0.41
SLC17A5	7	0.48	IGF2R	5	0.43

3.7 High-Frequency Gene BP Interaction Analysis

The biological processes mediated by GBA-related genes are predominantly involved in metabolic pathways within HCC samples, as illustrated in Fig. 3. These processes encompass cellular metabolic activities, nitrogen compound metabolism, lipid metabolism, carbohydrate metabolism, small molecule metabolism, phosphorus metabolism, peptide metabolism, and sulfur metabolism. The interactions occurring within these processes have a significant impact on fundamental metabolic pathways, including monosaccharide and N-acetylglucosamine metabolic processes, ceramide metabolic processes, glycosphingolipid metabolic processes, carboxylic acid metabolic processes, and glutathione metabolic processes. These interactions ultimately influence the GPI anchor biosynthetic process and ATP biosynthetic and metabolic processes, potentially leading to alterations in metabolic homeostasis within organisms.

3.8 High-Frequency Gene GO Analysis

In order to delve deeper into the biological mechanisms associated with GBA-related genes, a ClueGO analysis was performed on these frequently occurring genes. The findings indicate that these genes are primarily involved in nine level-three GO terms, including primary active transmembrane transporter activity, lysosome organization, glycerophospholipid biosynthetic process, organophosphate biosynthetic process, membrane lipid metabolic process, organic hydroxy compound biosynthetic process, mannosylation, ribonucleoside bisphosphate metabolic process, and alpha-amino acid biosynthetic process (Figs. 4A). Significantly, GBA interacts with CTSD, PIP4K2C, CLN3, TPP1, HEXB, and PI4KB in vacuole organization and collaborates with CLN3 and TPP1 in the lysosomal protein catabolic process (Figs. 4B). Furthermore, GBA is involved in autophagosome organization in conjunction with PIP4K2C and CTSD, and participates in the respiratory electron transport chain with COX8A, NDUFA1, NDUFS2, and SDHC (Figs. 4C). In the context of membrane lipid metabolism, GBA is predominantly involved in various catabolic and biosynthetic processes related to glycosphingolipids, carbohydrates, ceramides, sphingolipids, lipoproteins, and proteins. Key genes implicated in these processes include NEU1, HEXB, and GLB1. Additionally, GBA plays a role in alcohol and polyol biosynthesis (Figs. 4D).

CC analysis reveals that GBA is predominantly localized within the Golgi apparatus, extracellular exosome, organelle subcompartment, endoplasmic reticulum, and membrane-bounded organelle (Figs. 4E). At the molecular function level, GBA primarily demonstrates glycosyltransferase activity and catalytic activity (Figs. 4F). Interaction analysis was further conducted on the 67 high-frequency genes, as depicted in Fig. 5, indicating that GBA exhibits co-expression relationships with 24 high-frequency genes, including shared protein domains with HEXB and GLB1.

3.9 Regulatory Effects of Induced High and Low Expression of GBA on Downstream Genes in MHCC-97H Cells

In the MHCC-97H hepatocellular carcinoma cell line, the overexpression of GBA expression levels demonstrated a positive correlation with the upregulation of lysosomal metabolic genes NEU1, CTSD, CTSA, GALNS, and CLN3, as well as non-lysosomal genes ACOT8, FDPS, PMVK, PIGC, and GALNS. Conversely, the downregulation of GBA expression was associated with a corresponding decrease in the expression levels of NEU1 and CLN3 (see Fig. 6).

3.10 Relationship between GBA-Regulated Gene Expression Levels and Survival Time in Liver Cancer Patients

Analysis of the expression levels of NEU1, CTSD, CTSA, GALNS, CLN3, ACOT8, FDPS, PMVK, and PIGC in liver cancer patients using the TCGA database indicated that upregulation of these genes is strongly correlated with a poor prognosis in this patient population (see Figs. 7A-K).

3.11 Impact of GBA on Downstream Genes Potentially Related to the Concentration of Its Substrate - Glucosylceramidase

In the studies described, it was found that GBA expression levels regulate the expression of ACOT8. Considering that the ACOT family genes are a group of isoenzymes involved in the hydrolysis of acetyl groups from medium and long-chain fatty acids, the impact of GBA on ACOT was explored by inducing high expression of UGCG (thereby increasing the concentration of glucosylceramide) and simultaneous high expression of GBA. The results showed that ACOT4, ACOT9, and ACOT11 were upregulated along with the upregulation of UGCG and GBA, (Fig. 8) .

In the aforementioned studies, it was determined that GBA expression levels play a role in regulating the expression of ACOT8. Given that the ACOT gene family consists of isoenzymes involved in the hydrolysis of acetyl groups from medium and long-chain fatty acids, the influence of GBA on ACOT was investigated by inducing high levels of UGCG (resulting in increased glucosylceramide concentration) and simultaneous high levels of GBA. The findings indicated that the upregulation of UGCG and GBA led to the upregulation of ACOT4, ACOT9, and ACOT11, as depicted in Fig. 8.

GBA is synthesized in the endoplasmic reticulum and undergoes modification in the Golgi apparatus before being transported to the lysosome through the mannose-6-phosphate pathway[13]. Within the lysosome, GBA primarily acts to catalyze the hydrolysis of GlcCer into Glc and Cer[14]. Previous studies have predominantly focused on GBA in the context of Gaucher's disease, a condition characterized by mutations in the GBA gene that result in reduced enzymatic activity, leading to the accumulation of GlcCer and the development of Type I and Type II Gaucher's disease[15]. The primary function of GBA is to catalyze the hydrolysis of GlcCer into Glc and Cer, which are subsequently further hydrolyzed by acidic ceramidase to yield fatty acids and sphingosine[13]. Cer is synthesized through a series of enzymatic reactions involving palmitoyl-CoA, serine, and acetyl-CoA fatty acids, and it forms complexes with Glc or lactose to produce GlcCer or lactosylceramide[16]. However, lactosylceramide can be converted into GlcCer, GlcCer plays a crucial role in neuronal growth and differentiation, hormone secretion in the endocrine system, and various physiological processes in multiple organs and tissues, rendering it a bioactive compound within cellular and bodily environments[17, 18]. Consequently, GBA functions as a highly active enzyme responsible for the regulation of metabolic equilibrium[19, 20]. In individuals afflicted with Gaucher's disease, characterized by reduced GBA expression, a marked hypocholesterolemia is observed, which demonstrates substantial improvement following treatment with imiglucerase[10]. Recent research has demonstrated a heightened prevalence of multiple cancers in cohorts with Type I Gaucher's disease[21, 22], as well as symptoms of hyperglycemia unaccompanied by insulin resistance[13]. Additionally, a notable occurrence of Parkinson's syndrome, associated with lipid metabolism irregularities, has been observed in individuals with Gaucher's disease. Our prior investigations suggest that elevated levels of GBA expression may contribute to the development of HCC, concomitant with significant disruptions in lipid metabolism[23]. These phenomena illustrate the involvement of GBA in intricate lipid metabolic regulation, with both low and high expressions of GBA capable of disturbing metabolic equilibrium and inducing metabolic reprogramming. Recent research indicates a strong correlation between metabolic reprogramming and the onset and advancement of liver cancer. Nevertheless, the precise mechanisms through which GBA influences metabolism remain incompletely understood.

In the present investigation, it was noted that the upregulation of GBA resulted in elevated levels of PMVK and FDPS, both of which are intermediary enzymes in the beta-oxidation pathway of fatty acids leading to acetyl-CoA production and subsequent cholesterol synthesis. Prior studies suggest that GBA's substrate, GlcCer, undergoes metabolic breakdown into acetyl-CoA, Glc, and fatty acids in vivo[18, 24, 25].

Therefore, the upregulation of GBA leads to elevated levels of free fatty acids, Glc, and acetyl-CoA, impacting the beta-oxidation pathway and de novo fatty acid synthesis, ultimately resulting in increased cholesterol synthesis[26]. This discovery is consistent with prior research indicating that individuals with Gaucher's disease undergoing imiglucerase treatment experience heightened fasting blood Glc and hyperlipidemia without developing insulin resistance[27, 28].

Conversely, the glucose moiety within GlcCer has the potential to undergo glycosyl transfer catalyzed by GBA, resulting in the transfer of the glucose residue to a lactosyl residue[17]. This enzymatic process may contribute to an accumulation of lactosylceramide, thereby upregulating the expression of B4GALT3.

In this investigation, it was found that concurrent upregulation of GlcCer and GBA expression leads to increased levels of ACOT4, ACOT9, ACOT11, and ACOT8 enzymes, which play a role in the metabolic breakdown of lysosomal products into simpler molecules within the cytoplasm. However ACOT family of genes consists of genes that have a specific affinity for N-acetyl medium-chain fatty acids[29].

This indicates that an elevation in GlcCer activity results in a corresponding increase in the levels of acetyl-CoA, altering the distribution of acyl groups in the intracellular milieu. This perturbation can induce a reorganization of metabolic pathways and potentially trigger post-translational modifications, such as acetylation, on proteins, consequently impacting cellular metabolism and adaptive responses[30].

In the present investigation, it was observed that a number of lysosomal genes, including NEU1, CTSD, CTSA, GALNS, and CLN3, exhibit upregulation in conjunction with heightened expression of GBA, while NEU1 and CLN3 demonstrate decreased expression subsequent to GBA downregulation. Despite their diverse functionalities, these genes display shared attributes. Mutations in any of these lysosomal genes have the potential to result in lysosomal disorders typified by the clinical manifestation of hepatosplenomegaly due to the accumulation of glycolipids[31–33]. Another common feature among these proteins is their participation in the hydrolysis of biomacromolecules. For example, NEU1 is primarily responsible for the breakdown of sialic acid in glycoproteins and glycolipids[34], while CTSD is involved in the degradation of proteins within the cellular environment[35]. Additionally, CTSA plays a vital role in the lysosome by assisting in the maintenance of enzyme function, such as GLB1 and NEU1[36]. The CLN3 protein serves various functions within the cell, including the regulation of substance transport, maintenance of lysosomal pH balance, and involvement in autophagy and other essential cellular processes[37].The enzyme GALNS is primarily responsible for the hydrolysis of glycosaminoglycans, specifically keratan sulfate and chondroitin 6-sulfate[38]. Recent studies have identified a protein complex consisting of NEU1, CTSA, and GALNS within the lysosome, with the activity of its components being closely linked to the formation of this complex[36]. The findings of this research suggest that GBA, NEU1, CTSD, CTSA, and GALNS may also be integral components of this lysosomal complex. These genes are crucial in the regulation of fundamental metabolic pathways within the cell.

The findings of this research indicate that alterations in GBA expression have a substantial impact on metabolic reprogramming in liver cancer, specifically through the modulation of PMVK and FDPS expression, thereby influencing fatty acid beta-oxidation and cholesterol synthesis pathways. Additionally, GBA, in conjunction with lysosomal genes NEU1, CTSD, CTSA, and GALNS, contributes to the assembly and operation of a lysosomal complex. This complex is essential for overseeing the breakdown of macromolecules within the cell and upholding cellular metabolic equilibrium.

This study utilizes a comprehensive analysis of 1003 liver cancer samples sourced from various datasets to establish a robust data foundation, thereby augmenting the statistical power and generalizability of the findings. Furthermore, through a detailed examination of genes associated with GBA, the research has successfully pinpointed a central cluster of genes under GBA regulation, significantly enhancing the precision and efficacy of the investigation.While the potential regulatory roles of GBA in multiple metabolic pathways, such as those related to fatty acids, carbohydrates, and proteins, have been elucidated, the absence of direct observation and quantification of metabolic phenotypes at the cellular level may hinder a comprehensive understanding of the underlying mechanisms of these regulatory interactions and their practical implications. To address this constraint, forthcoming studies could employ cellular-level experiments to confirm the impact of GBA through biochemical analysis utilizing designated metabolic markers.Furthermore, the utilization of systems biology tools to further analyze the intricacies of the GBA regulatory network may offer enhanced understanding of the mechanisms involved in disease management.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

Xin Li performed all data analyses, drew the figures, and was responsible for writing and editing the manuscript. Xin also conducted plasmid transfections and RNA extractions.

Jie Ma collaborated with Xin Li in measuring qPCR and revising the initial drafts of the manuscript. Jie and Kun Wang participated in ordering and designing plasmids and primers for qPCR.

Kun Wang assisted in the procurement and amplification of plasmids and primers necessary for qPCR, contributing to the experimental setup.

Jiarui Li and Yu Xu were responsible for data verification and participated in the initial manuscript revision, ensuring the accuracy of the results reported.

Yanhui Yang and Yi Yang are the corresponding authors who oversaw the entire project, ensuring the integrity and reliability of the work.

Xin Li and Jie Ma are designated as co-first authors due to their significant contributions to the study's concept, experimental design, data analysis, and manuscript preparation.

Funding

National Natural Science Foundation of China (82160171), Natural Science Foundation of Ningxia Province (2023AAC02038) and Key research and development program of Ningxia Province (2023BEG02020)

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No competing interests reported.

Role of Glucocerebrosidase in Metabolic Reprogramming and Prognosis of Hepatocellular Carcinoma: Insights from a Comprehensive Gene Expression Analysis

Status:

Version 1

Abstract

Figures

1 Introduction

2 Materials and Methods

3 Results

4 Discussion

5 Conclusion

6 Strengths and Limitations

Declarations

References

Additional Declarations

Status:

Version 1