Introduction

Liver hepatocellular carcinoma (LIHC) is the main histological subtype of primary liver cancer. LIHC is highly aggressive and therapeutic options are limited. Thus, the prognosis for patients with LIHC is very poor [1, 2]. The current drugs for treating LIHC include sorafenib, lenvatinib, and regorafenib [3], which are multitarget tyrosine kinase inhibitors, and atezolizumab, pembrolizumab, nivolumab, and ipilimumab [4], which are immunotherapeutic agents. Although these drugs have achieved significant success in the treatment of LIHC, treatment benefits are limited to a small subset of patients [5, 6]. LIHC is an extremely heterogeneous tumor, which limits the efficacy of cancer therapies [7]. Therefore, new effective diagnostic, prognostic, and therapeutic biomarkers based on single-cell analyses are urgently needed to develop personalized therapeutic strategies against LIHC.

Adenosine monophosphate-activated kinase (AMPK), a serine/threonine protein kinase, consists of AMPKα (catalytic core; α1 or α2), AMPKβ and AMPKγ (regulatory units; β1 or β2, and γ1, γ2, or γ3) [8]. AMPK activates or inhibits metabolic-related pathways in response to changes in intracellular AMP/ATP ratios [

Methods

Data acquisition

RNA-seq data for 31 normal tissues and LIHC samples were obtained from the Genotype-Tissue Expression (GTEx, https://gtexportal.org/home/) and the Cancer Genome Atlas (TCGA, https://www.cancer. gov/tcga) databases, respectively. The normal tissue types included adipose, adrenal gland, bladder, blood, blood vessel, bone marrow, brain, breast, cervix uteri, colon, esophagus, fallopian tube, heart, kidney, liver, lung, muscle, nerve, ovary, pancreas, pituitary, prostate, salivary gland, skin, small intestine, spleen, stomach, testis, thyroid, uterus, and vagina. Single-cell RNA-seq (scRNA-seq) data for LIHC were collected from the China National Genebank Database (CNGBdb, https://db.cngb.org/search/project/CNP0000650); accession code: CSE0000008).

Correlation analysis

Based on prior reports in the literature, 106 AMPK substrates were extracted [11]. The expression correlation between AMPK subunits and AMPK substrates in 31 normal tissues was determined by Pearson correlation analysis (Correlation coefficients > 0.2).

Differential expression analysis of AMPK subunits

To explore differences in the expression of AMPK subunits between tumor samples and their matched normal tissue controls in pan-cancer, we used the gene set cancer analysis (GSCA) online tool (http://bioinfo.life.hust.edu.cn/GSCA/). A false discovery rate-adjusted p-value less than 0.05 indicated a significant difference.

Survival and immune infiltration analyses

Based on the median PRKAA2 level, patients were divided into high- and low-expression subgroups. The R packages “survival” and “survminer” were employed for Kaplan–Meier survival analyses. The abundance of each infiltrating immune cell type was analyzed using a single-sample gene set enrichment analysis. Wilcoxon signed-rank test was performed and p less than 0.05 implied a significant difference.

Single-cell RNA-seq data processing

The scRNA-seq matrices were collected for more than 300 transcripts/cell and more than 3 cells/gene condition. The NormalizeData function from the R package “Seurat” was applied to normalize scRNA-seq data. The clustering analysis was conducted based on the integrated joint embedding generated by the Harmony algorithm. The top 15 principal components and the top 2000 variable genes were selected for subsequent analysis. Cell clusters were detected using the FindClusters function in Seurat (resolution = 0.6), and cell clustering results were visualized by uniform manifold approximation and projection or t-distributed stochastic neighbor embedding (t-SNE) analysis.

Cell–cell communication analysis

We performed cell communication analysis using CellphoneDB [20]. Average expression levels were calculated based on the annotated ligand-receptor pairs attained from the STRING database. The ligand-receptor pairs with p < 0.05 values were identified, and the interactions between two cell types using these identified pairs were analyzed. Cytokines play a critical role in cell communication; thus, cytokine signaling, based on the transcriptomic profiles, was determined using CytoSig.

Pseudotime analysis

To detect the association between PRKAA2 expression and T-cell evolution during tumor progression in single cells, a Pseudotime analysis was performed using R packages “monocle2” [21]. The monocle subject was built by applying the function “newCellDataSet”. Trajectory analysis was performed based on the differentially expressed genes determined by the R package “Seurat”. The “reduceDimension” function was used for dimensionality reduction, and cells were placed on Pseudotime trajectories using the “orderCells” functions.

Metabolic pathway analysis

Single-cell metabolic activity was evaluated using the R package “scMetabolism” based on a previously reported method [22]. Differences in the metabolic pathway scores between subgroups with high or low PRKAA2 expression were analyzed using Wilcoxon signed-rank test, and p less than 0.05 implied a significant difference.

Functional enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted using Metascape. Genes enriched more than 3-fold and p less than 0.05 were considered significantly different.

Cell culture and gene expression assays

The human LIHC cell line, HepG2, was acquired from the American Type Cell Culture Collection. Cells were grown in Dulbecco Modified Eagle Medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) containing 10% fetal bovine serum (FBS, BI) at 37 °C and 5% CO2. PRKAA2 knockout cells were generated by transfecting cells with the lentiviral-based short hairpin RNA (shRNA) vector pGPU6/GFP/Neo (Genechem, Shanghai, China). The shRNA sequences were as follows: shRNA-1: GTGGCTTATCATCTTATCATT and shRNA-2: GTCATCCTCATATTATCAAAC. PRKAA2 mRNA levels were measured using quantitative real-time PCR (qPCR). Total RNA was extracted using an RNA extraction reagent (Takara, 9108, Japan), and 2X Super SYBR Green qPCR Master Mix (ES Science, Guangzhou, China) was used to detect PRKAA2 levels. The qPCR primer sequences for PRKAA2 and GAPDH were as follows: PRKAA2 forward, 5′-CGGGTGAAGATCGGACACTA-3′;PRKAA2 reverse, 5′-TCCAACAACATCTAAACTGCGA′; GAPDH forward, 5′-GACCTGACCTGCCGTCTA-3′;and GAPDH reverse, 5′-AGGAGTGGGTGTCGCTGT-3′.

Cell proliferation, clonogenic, migration, and invasion assays

Cells (8000/well) were seeded into 96-well plates (Servicebio, WuHan, China), and proliferation was evaluated using Cell Counting Kit-8 (Biosharp Biotechnology, Bei**g, China) reagent. The optical density was read at 450 nm. For clone formation assays, cells were cultured for 14 days, fixed with 4% paraformaldehyde for 15 min, and stained with 0.1% crystal violet for 15 min (Meilunbio, Dalian, China). For scratch assays a linear wound was created in a monolayer of serum-starved cells using a 10-μL pipette tip, and cell coverage across this line was determined. For Transwell migration assays, cells were seeded into the upper chamber (8 μm; BIOFIL, Guangzhou, China) and incubated with serum-free medium. The lower chamber contained medium with 10% FBS. After 24 hours, cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet.

Statistical analysis

Continuous variables were compared using Student’s T-test (parametric) analyzed with GraphPad Prism 8 software or Wilcoxon rank sum test (nonparametric) analyzed with R software package. In vitro experiments were repeated three times, and all results are presented as means ± standard deviation. A p-value less than 0.05 indicates a significant difference.

Results

Expression patterns and physiological functions of AMPK in human tissues

The expression levels of AMPK subunits, including AMPKα (α1 and α2), AMPKβ (β1 and β2), and AMPKγ (γ1, γ2, and γ3), in different human tissues were assessed using GTEx bulk RNA-seq data. PRKAA1, PRKAB1, and PRKAG1 were the main subunits expressed in almost all tissue types. PRKAA2, PRKAB2, and PRKAG3 were the predominant subunits expressed in muscle tissues and PRKAG2 was the major subunit expressed in heart tissues (Fig. 1). Thus, the expression of AMPK subunits was tissue-specific.

Fig. 1
figure 1

The expression levels of AMPK subunits in 31 tissue types. AMPK subunits were grouped based on the subunit types. The normal tissue types included adipose, adrenal gland, bladder, blood, blood vessel, bone marrow, brain, breast, cervix uteri, colon, esophagus, fallopian tube, heart, kidney, liver, lung, muscle, nerve, ovary, pancreas, pituitary, prostate, salivary gland, skin, small intestine, spleen, stomach, testis, thyroid, uterus, and vagina, and the data came from the Genotype-Tissue Expression database

Full size image

AMPK exerts biological functions by activating distinct substrates. Thus, gene expression correlations between different subunits of AMPK and AMPK substrates were assessed. As shown in Fig. 2A, different AMPK subunits significantly correlated with distinct substrates. PRKAA1 expression correlated with BRAF, C18orf25, EEF2K, and EP300 expression. PRKAA2 expression correlated with CRTC2, TNNI3, KCNA5, and TFEB expression. These results indicate that AMPK subunits may be characterized by substrate specificity. Differences in the enriched pathways for different AMPK subunits were determined using KEGG analysis. Diverse AMPK isoforms participated in different regulatory programs (Fig. 2B). These results indicate that the substrate specificity of AMPK isoforms may lead to functional differences between AMPK isoforms.

Fig. 2
figure 2

Substrate specificity of AMPK. A Association between the expression of AMPK subunits and AMPK substrates (Pearson correlation test; correlation coefficients > 0.2). B Kyoto Encyclopedia of Genes and Genomes analysis of signaling pathway enrichment in different AMPK subunit groups. The circle color represents the number of enriched pathways, and the circle size indicates the p-value

Full size image

Relationship between PRKAA2 expression, LIHC prognosis, and immune cell infiltration

The expression levels of AMPK subunits in tumor tissue and matched adjacent normal tissue were compared using the GSCA database. Details of datasets were shown in Table S1. PRKAA2 was differentially expressed in most tumor types (Fig. 3A). Of note, PRKAA2 was significantly upregulated in LIHC, and this upregulation was in the TCGA-LIHC cohort (Fig. 3B). Subsequent Kaplan-Meier survival analyses indicated that high PRKAA2 expression was linked to unfavorable prognosis in patients with LIHC (Fig. 3C). The TME status in response to different PRKAA2 expression patterns was determined by calculating the infiltration abundances of 23 immune cell types. Patients with low PRKAA2 expression displayed a significantly higher degree of immune cell infiltration, indicative of an immune hot phenotype, compared with patients with high PRKAA2 expression, which exhibited an immune cold phenotype lacking immune cell infiltration (Fig. 3D).

Fig. 3
figure 3

PRKAA2 expression correlates with prognosis and immune cell infiltration. A AMPK subunit expression levels in tumor tissues and matched adjacent normal tissues in 14 tumor types were evaluated using the gene set cancer analysis (GSCA) database. An adjusted p-value < 0.05 indicates a significant difference. B The expression of PRKAA2 in tumor tissues and normal controls in the TCGA-LIHC cohort (Wilcoxon rank test; tumor samples: n = 368, normal tissues: n = 50). C Differences in survival probability between patients with high PRKAA2 expression and patients with low PRKAA2 expression in the TCGA-LIHC cohort (log-rank test). D The difference in tumor-infiltrating immune cell scores between patients with high PRKAA2 expression and patients with low PRKAA2 expression in the TCGA-LIHC cohort (n = 368; Wilcoxon test; *P < 0.05; **P < 0.01; ***P < 0.001)

Full size image

High PRKAA2 expression correlated with LIHC progression in malignant cells

The scRNA-seq dataset for LIHC was obtained from the CNGBdb (CSE0000008). The dataset included 12 primary tumors and 6 relapsed tumors (Fig. 4A). Ten cell subgroups were identified using the analysis of t-SNE clustering of single-cell samples. Based on the gene expression of cell-type specific markers, cell subgroups were annotated as known cell lineages, including immune cells (B cells, myeloid cells, NK cells, pDC, plasma cells, and T cells), malignant cells, hematopoietic stem cells, endothelial cells, and epithelial cells (Fig. 4B). PRKAA2 was predominantly expressed in malignant cells. Malignant cells were extracted and clustered. As shown in Fig. 4C, a total of 10 subgroups were obtained, and cells with high PRKAA2 expression clustered mainly in subgroups 1, 6, and 7, indicating heterogeneity in PRKAA2 expression. Malignant cells in the high PRKAA2 expression group exhibited a more malignant phenotype than cells in the low PRKAA2 expression group (Fig. 4D). Gene set variation analysis (GSVA) revealed that signaling pathways associated with tumor progression, including PI3K/Akt/mTOR signaling, Notch signaling, angiogenesis signaling, and epithelial–mesenchymal transition (EMT), were significantly enriched in malignant cells with high PRKAA2 expression (Fig. 4E). PRKAA2 expression was higher in malignant cells from relapsed tumors compared with PRKAA2 expression in malignant cells from primary tumor samples. Genes involved in TGF-β signaling and EMT were also higher in malignant cells from relapsed tumors compared with malignant cells from primary tumor samples (Fig. 4F). Conversely, DNA repair and p53 signaling pathways were more pronounced in primary tumor samples. Finally, cyclins were upregulated in malignant cells with high PRKAA2 expression, suggesting that these cells are in the activated cell cycle state (Fig. 4G). Altogether, these findings reveal that PRKAA2 may contribute to LIHC development.

Fig. 4
figure 4

PRKAA2 expression in malignant cells is associated with liver hepatocellular carcinoma (LIHC) development. A Clinical and molecular features of 18 LIHC patients from the Chinese National Genebank Database (CSE0000008) and the proportion of cell types in each patient. B T-distributed stochastic neighbor embedding plot of 14,236 cells colored based on cell types or PRKAA2 expression. C Uniform manifold approximation and projection plot of 2730 malignant cells colored based on cell subgroups or PRKAA2 expression. D Heatmap of copy number profile genes in malignant cells with high PRKAA2 expression and low PRKAA2 expression. E The enriched pathways in malignant cells with high PRKAA2 expression and low PRKAA2 expression using gene set variation analysis (Bayesian t-test). F Differences in PRKAA2 expression and pathway scores between primary tumors and relapsed tumors (Wilcoxon rank test). G Differences in the expression of cyclin genes between malignant cells with high PRKAA2 expression and low PRKAA2 expression (Wilcoxon rank test; *P < 0.05, **P < 0.01, ***P < 0.001)

Full size image

PRKAA2 contributes to immune escape in LIHC

To evaluate the relationship between PRKAA2 and the LIHC immune microenvironment, patients (n = 18) were divided into high and low PRKAA2 expression groups. The proportion of immune cells was significantly lower in tumors with high PRKAA2 expression compared with the proportion of immune cells in the low PRKAA2 expression group (43.8% vs. 75.84%; Fig. 5A). Single-sample GSVA to detect differentially expressed genes between the two subgroups revealed that the IFN-γ response pathways were enriched in the malignant cells of tumors with low PRKAA2 expression (Fig. 5B). This finding was confirmed using GO enrichment analysis (Fig. 5C). Notably, GO analysis also revealed that the MHC protein complex assembly signaling pathway was significantly upregulated in the low PRKAA2 expression subgroup. The interferon-gamma (IFN-γ) signaling pathway plays a key role in regulating MHC-I expression. Thus, the levels of MHC-I genes were assessed in the two subgroups. Malignant cells with high PRKAA2 expression exhibited low levels of MHC-I gene expression (Fig. 5D), indicative of weak immunogenicity. In agreement with this finding, the high-expression group also had a significantly low immunogenic cell death pathway score. The functional consequence of high PRKAA2 expression was increased immune escape (Fig. 5E).

Fig. 5
figure 5

PRKAA2 is associated with the immune escape of tumor cells. A The proportion of epithelial cells, immune cells, malignant cells, and stromal cells in tumors with high PRKAA2 expression and low PRKAA2 expression. B Differences in interferon-alpha (IFN-α) and interferon-gamma (IFN-γ) pathway scores between malignant cells from high and low PRKAA2 expression patient subgroups. Gene set variation analysis was performed. C Analysis of Gene Ontology enrichment of differentially expressed genes between high and low PRKAA2 expressing malignant cells. D The levels of human leukocyte antigen molecules in high and low PRKAA2 expressing malignant cells. E Differences in the enriched immunogenic cell death pathways scores and immune escape between two different scoring subgroups (Wilcoxon rank test)

Full size image

PRKAA2 associated with dynamic changes in T cells during LIHC progression

To explore the relationship between PRKAA2 and the evolution of T cells, we first identified 6179 T cells. All T cells were clustered into eight subgroups and annotated as CD4+ T cells (cytotoxic CD4+ T cells, exhausted CD4+ T cells, native CD4+ T cells, CD4+ T helper (Th) cells, and CD4+ T regulatory (Treg) cells) and CD8+ T cells (cytotoxic CD8+ T cells, exhausted CD8+ T cells, and native CD8+ T cells) (Fig. 6A). T cells with high PRKAA2 expression were primarily enriched in exhausted T cells and CD4+ Treg cells. These results were confirmed by analyzing the composition and proportion of T-cell types in high and low PRKAA2 expression subgroups (Fig. 6B). Compared with the other CD8+ T-cell types, exhausted CD8+ T cells expressed higher levels of PRKAA2 (Fig. 6C-D). In addition, the expression levels of multiple immune checkpoint proteins, including CTLA4, HAVCR2, PDCD1, TIGIT, CD27, and LAG3, were higher in the exhausted CD8+ T-cell subgroup with high PRKAA2 expression (Fig. 6E-G), implying that the functional exhaustion of CD8+ T cells may be a potential mechanism for PRKAA2-mediated cancer cell immune evasion. Immune cells in the tumor microenvironment evolve with the progression of the tumor, and this dynamic process can be described by the monocle algorithm. Pseudotime and trajectory analyses showed that CD8+ T cells tended to be exhausted during tumor progression, which was associated with high expression of PRKAA2 (Fig. 6H-J). Thus, PRKAA2 may promote tumor progression by contributing to CD8+ T-cell exhaustion.

Fig. 6
figure 6

PRKAA2 expression is associated with dynamic change in CD8+ T cells during tumor progression. (A) T-distributed stochastic neighbor embedding plot of 6179 T cells colored based on cell types or PRKAA2 expression. B The proportions of eight types of T cells with high or low PRKAA2 expression levels. C Uniform manifold approximation and projection plot of 1366 CD8+ T cells colored based on cell types or PRKAA2 expression levels. D The proportions of CD8+ cyto, CD8+ exhausted, and CD8+ native T cells with high or low PRKAA2 expression. (E-F) The levels of immune checkpoint proteins, including CTLA4, HAVCR2, PDCD1, TIGIT, CD27, and LAG3 among the CD8+ cyto, CD8+ exhausted, and CD8+ native T cells E and in T cells expressing high or low levels of PRKAA2 F. G The proportion of CD8+ exhausted T cells expressing high or low levels of PRKAA2 (Wilcoxon rank test). H-J Differentiation trajectory of CD8+ T cells, colored for cell types H, PRKAA2 expression I, and pseudotime J

Full size image

CD4+ Treg cells, which are an important component of CD4+ T-cell types, are mainly responsible for maintaining immunological tolerance and homeostasis. High PRKAA2 expression in CD4+ Treg cells significantly enhanced CD27, TIGIT, TNFRSF9, ICOS, TNFRSF4, CTLA4, TNFRSF18, and CD28 expression levels (Fig. 7A-D). This suggests that CD4+ Treg cells mediate the promotion of tumor immune escape by PRKAA2. Differentiation trajectory analysis of CD4+ T cells revealed that PRKAA2 may be involved in the formation of CD4+ Treg cells (Fig. 7E-G). Increased metabolism of Treg cells is a key factor in maintaining their immunosuppressive effects. Thus, the enrichment of metabolic-related signaling pathways in Treg cells and other T-cell types was examined. The metabolic pathways enriched in Treg cells included beta-alanine metabolism, citrate cycle (TCA cycle), glycolysis/gluconeogenesis, and oxidative phosphorylation (Fig. 7H). These signaling pathways were also enriched in T cells with high PRKAA2 expression (Fig. 7I). Taken together, our findings suggest that PRKAA2 may contribute to CD8+ T-cell exhaustion and the formation of CD4+ Treg cells to facilitate immune escape.

Fig. 7
figure 7

PRKAA2 is associated with dynamic change of CD4+ T cells during tumor progression. A Uniform manifold approximation and projection plot of 4813 CD4+ T cells colored based on cell types or PRKAA2 expression. B The proportion of five types of CD4+ T cells expressing high or low levels of PRKAA2. C The levels of immune checkpoint proteins, including CD27, TIGIT, TNFRSF9, ICOS, TNFRSF4, CTLA4, TNFRSF18, and CD28 in the five types of CD4+ T cells. D The proportion of CD4+ Treg cells expressing high or low levels of PRKAA2 (Wilcoxon rank test). (E-G) Differentiation trajectory of CD4+ T cells colored for cell types E, PRKAA2 expression F, and pseudotime G. (H-I) The significantly enriched metabolic pathways in Treg cells H and T cells with high PRKAA2 expression I were identified by gene set enrichment analysis

Full size image

Resha** intercellular interactions by PRKAA2 in the TME

The interaction between malignant cells and T cells within the TME is crucial for tumor progression. Therefore, a cell-cell communication network was built to explore the role of PRKAA2 in cell communication. The expression levels of chemokines from malignant cells, including CXCL12, CXCL10, and CXCL1, were significantly changed by PRKAA2 (Fig. 8A). In addition, high PRKAA2 expression suppressed costimulatory molecules, including TNFSF4, TNFSF10, ICAM3, and APP, generated by malignant cells (Fig. 8B). However, coinhibitory signals were enhanced in malignant cells with high PRKAA2 expression (Fig. 8C). Of note, synergistic interaction between T cells and malignant cells with high PRKAA2 expression activated Treg cells and promoted T-cell exhaustion (Fig. 8D). Conversely, induction of multiple ligand-receptors pairs, including CXCL10/DPP4, CXCL1/CXCR2, TNFSF4/TNFRSF4, and FAM3C/CLEC2D, reinforced the accumulation of T cells in response to low PRKAA2 expression (Fig. 8E).

Fig. 8
figure 8

Cell-cell communication analysis. (A-C) Malignant cell-T-cell interactions through chemokines (A), costimulatory molecules (B), and coinhibitory molecules (C). Circle size and color indicate the P values and expression levels of ligand-receptor molecules, respectively. (D-E) Ligand-receptor connections between malignant cells and T-cell subtypes with high (D) or low (E) PRKAA2 expression. (F) The activities of cytokine pathways in PRKAA2 high-expression malignant cells or liver hepatocellular carcinoma (LIHC) patients positively linked to T-cell markers (r > 0.2 and P < 0.05). (G) Signaling activities of cytokines in malignant cells and T cells with high and low PRKAA2 expression

Full size image

Cytokines regulate cell-cell communications within the immune system. Thus, the relationship between PRKAA2 and cytokine signaling at the single-cell level was investigated. Seventeen cytokine-related pathways were activated in malignant cells with high PKRAA2 expression; 10 of these pathways were significantly and positively associated with marker genes from T cells (Fig. 8F). Differences in the activation of cytokine signaling pathways, such as IL6 and IL3, between high and low PRKAA2 expression subgroups contributed to the distinct communication behaviors in T cells and malignant cells (Fig. 8G). Consequently, the functional role of PRKAA2 in resha** cell-cell interactions and cytokine signaling activity in the TME suggests that PRKAA2 is a main regulator of T-cell exhaustion in LIHC.

PRKAA2-mediated metabolic reprogramming in malignant cells

The role of PRKAA2 in the metabolic reprogramming of malignant cells was investigated. Compared to other cell types, metabolic pathways, including glycolysis/gluconeogenesis and TCA cycle, were upregulated in malignant cells (Fig. 9A). Overall metabolic pathway abundance was significantly higher in malignant cells with high PRKAA2 expression compared with malignant cells with low PRKAA2 expression (Fig. 9B). Differences in each metabolic pathway activity between high- and low-expression subgroups are shown in Fig. 9C. Differentially expressed genes in the two subgroups were extracted (Fig. 9D) and a KEGG analysis was conducted. The analysis shows that genes were mainly enriched in metabolism-related signaling pathways such as oxidative phosphorylation, glycolysis/gluconeogenesis, and TCA; oxidative phosphorylation was the most enriched pathway (Fig. 9E). Oxidative phosphorylation was closely related to glycolysis and hypoxia, and glycolysis was substantially linked to the response to hypoxia (Fig. 9F). We also observed that PRKAA2 was significantly associated with hypoxia. Thus, associations between oxidative phosphorylation and glycolysis and between oxidative phosphorylation and hypoxia indicate that coupling between aerobic respiration and hypoxia-associated pathways may be a feature of malignant cells in the TME in response to high PRKAA2 expression.

Fig. 9
figure 9

Metabolic landscape for liver hepatocellular carcinoma with different PRKAA2 expression patterns. A The activities of metabolic signaling pathways in each cell type, including malignant cells, plasma cells, pDC, NK cells, B cells, T cells, epithelial cells, myeloid cells, endothelial cells, and hematopoietic stem cells (Wilcoxon rank test; P < 0.05). B Overall metabolic pathway abundance in plasma cells, pDC, NK cells, B cells, T cells, epithelial cells, myeloid cells, and endothelial cells, and in malignant cells with high and low PRKAA2 expression. C Differences in the metabolic scores in malignant cells with high or low PRKAA2 expression levels (Wilcoxon rank test; ***P < 0.001). D Differentially expressed genes in malignant cells expressing high or low levels of PRKAA2 (Wilcoxon rank test; ***P < 0.001). E Gene Ontology analysis of differentially expressed genes. F Comparison of glycolysis, oxidative phosphorylation, and response to hypoxia in malignant cells (Spearman’s rank correlation)

Full size image

The role of PRKAA2 in the development of LIHC

To explore the functional role of PRKAA2 in LIHC progression, we knocked PRKAA2 down in LIHC HepG2 cells using shRNAs (Fig. 10A). Proliferation was significantly inhibited in PRKAA2-deficient HepG2 cells compared to control cells (sh-Control) (Fig. 10B). Clonogenic ability was also significantly decreased in response to PRKAA2 silencing (Fig. 10C). PRKAA2 depletion impaired the healing ability of HepG2 cells compared to sh-Control cells (Fig. 10D). Migratory and invasive behaviors of HepG2 cells were remarkably limited by PRKAA2 depletion in Transwell assays (Fig. 10E). Thus, PRKAA2 may be critical for LIHC progression and metastasis.

Fig. 10
figure 10

Functional role of PRKAA2 in liver hepatocellular carcinoma. A PRKAA2 expression was depleted with shRNAs in HepG2 cells. B-E PRKAA2 knockdown significantly suppressed the proliferation (B), colony formation (C), scratch healing (D), and migration and invasion (E) of HepG2 cells. (Student’s t-test; *P < 0.05; **P < 0.01; ***P < 0.001)

Full size image

Discussion

Individualized cancer therapy is paramount to improving the clinical outcomes of patients [23]. The current study reveals that AMPK plays pivotal roles in the progression and metastasis of multiple tumor types and is a potential therapeutic target for LIHC. AMPKα2, encoded by PRKAA2, functions as the catalytic core of AMPK; however, the role of AMPKα2 in the LIHC TME is unclear.

Our results show that AMPK subunits exhibited tissue-specific expression patterns and could be substrate-specific. PRKAA2 was highly expressed in LIHC and was associated with poor prognosis. In vitro experiments show that PRKAA2 knockdown inhibited the proliferation, migration, invasion, and metastasis of LIHC cells. Furthermore, PRKAA2 expression was significantly associated with the tumor immune microenvironment. Patients with high PRKAA2 expression lacked immune cell infiltration, indicative of an immune cold phenotype. Single-cell transcriptome atlas analysis showed that PRKAA2 contributes to tumor progression. This conclusion is supported by the following evidence: 1) TGF-β signaling and EMT are enhanced in malignant cells; 2) metabolic reprogramming is induced in malignant cells; 3) CD8+ T-cell exhaustion and the formation of CD4+ Treg cells is promoted in T cells; and 4) Treg cell activation and T-cell depletion is driven by changes in the interactions between malignant cells and T cells in the TME. Taken together, our study demonstrates that the development of PRKAA2-based treatment strategies for LIHC holds great promise.

The tissue specificity of AMPK isoform distribution is the basis for a variety of AMPK biological functions [24]. AMPK can exert antitumor or tumor-promoting effects depending on the cellular context [25, 26]. AMPK activation leads to cell cycle arrest and inhibition of tumor growth, which contribute to the prevention of multiple cancer types, including lung, colorectal, and breast cancers [27,28,29]. In contrast, under conditions of oncogenic stress or hypoxia and nutrient deficiency in the TME, cancer cells exhibit an increased dependence on AMPK function to promote cancer cell survival [30, 31]. Our data show that functional differences between AMPK subunits could be mediated by tissue-specific expression patterns and the high substrate specificity of AMPK subunits. High expression of PRKAA2, which drives metabolic reprogramming and immune escape of tumor cells, contributes to LIHC development.

Our results demonstrate that the TME is closely linked to tumor heterogeneity and regulates antitumor immune responses. Among the immune cell types in the TME, T cells play a dominant role in immune regulation and antitumor activity [32]. Treg cells are an important component of immune homeostasis. Treg cells maintain immune self-tolerance and inhibit anticancer immunity [33], while CD8+ T cells are cytotoxic T lymphocytes that kill tumor cells [34]. CD8+ T cells may become exhausted during tumor progression. Our findings suggest that PRKAA2 promotes immune escape of tumor cells via CD8+ T-cell depletion and Treg cell generation, eventually leading to tumor progression. Furthermore, malignant cells that express high levels of PRKAA2 evade immune suppression via IFN-γ/JAK/STAT-mediated loss of MHC-I molecules [35, 36]. Altogether, PRKAA2-mediated tumor immune escape may be due to the activation of immune escape mechanisms in malignant cells and the formation of an immunosuppressive tumor microenvironment.

Metabolic reprogramming is a crucial pathway for the proliferation and metastasis of tumor cells [37]. Thus, understanding the features of malignant cell and non-malignant cell metabolism is important in develo** a foundation for LIHC patient therapy. Our results show metabolic heterogeneity in LIHC, and malignant cells have significantly higher metabolic activity than non-malignant cells. Metabolic pathways, including oxidative phosphorylation, the TCA cycle, and glycolytic signaling, were enhanced in malignant cells that express high levels of PRKAA2, indicating that high PRKAA2 expression enhances energy metabolism in malignant cells. Oxidative phosphorylation was remarkably upregulated in malignant cells, which is consistent with previous single-cell studies [

Conclusions

Our study revealed that PRKAA2 affects the metabolic reprogramming of malignant cells by coupling aerobic respiration with hypoxia-associated pathways. In addition, PRKAA2 facilitates immune escape of tumor cells by promoting CD8+ T-cell exhaustion and the formation of CD4+ Treg cells by resha** the interaction between malignant cells and T cells and driving dynamic changes in T cells in the TME. Patients with high PRKAA2 expression may benefit from inhibitors of AMPK/AMPKα2 signaling. This study provides a theoretical basis for the development of PRKAA2-based personalized therapy strategies.