The genomics data of GSE146719/GPL20795 was downloaded from the Gene Expression Omnibus (GEO) database and analyzed using the DESeq2 package (version 1.39.8; http://www.ncbi.nlm.nih.gov/geo/). Normalized RNA-seq data (HTSeq-FPKM) and survival data of the Pan-Cancer Atlas (PANCAN) and the liver hepatocellular carcinoma (LIHC) project of The Cancer Genome Atlas (TCGA) were accessed from the GDC website (https://portal.gdc.cancer.gov/) on March 1, 2023, and the data set is referred to as GDC-TCGA-LIHC in this article. Normalized gene expression (normalized_read_count) and donor information of the liver cancer project (named: LIRI-JP) of the International Cancer Genome Consortium (ICGC) were downloaded from the ICGC data portal (https://dcc.icgc.org/), and the data set is referred to in this article as ICGC-LIRI. Immunohistochemical images of AKR1B10 in tumor samples were analyzed and accessed from The Human Protein Atlas database website (https://www.proteinatlas.org/). The gene effect and expression of HCC cell lines were analyzed and downloaded from the Broad Institute DepMap website (https://depmap.org/).

Normalized quantification of gene expression in GDC-TCGA-LIHC was converted into transcripts per million (TPM) and then logarithmically transformed [log₂(TPM+1)]. In ICGC-LIRI, the expression values were directly log₂ (normalized_read_count) transformed since they were all >0 (17). No normal samples were included in the prognosis analysis in GDC-TCGA-LIHC and ICGC-LIRI. Missing values were considered in the GDC-TCGA dataset if the clinical information of patients was unavailable or unknown.

Human HCC cell lines Huh7 and Hep3B were obtained from the Cell Bank of the Chinese Academy of Sciences and cultured in DMEM (Thermo Fisher Scientific, Inc.), supplemented with 10% FBS (Thermo Fisher Scientific, Inc.), and 1% penicillin/streptomycin (HyClone, Cytiva; cat. no. SV30010) at 37°C with 5% CO₂. HCC cells were authenticated via short tandem repeat analysis. Mycoplasma-free cells were used in all experiments.

Cells were seeded into six-well plates (3–5×10⁵ cells/well). Sh/oe-AKR1B10 in HCC cells was performed using the lentivirus (DNA:16 µg/100 ml; Shanghai GenePharma Co., Ltd.), when the cell confluency reached 60–70%. The cells were treated with 100 µl/ml (1×10⁹ TU/ml) lentivirus for 8 h. Stably transduced cell lines were selected with 4 µg/ml puromycin (YEASEN, China). Full-length AKR1B10 sequences were ligated into the lentivirus vectors pCMV-3×HA. The shRNA sequences (5′-3′) were as follows: shAKR1B10, CGCTCCTACTGACTCCTATTT, and Scramble, CCTAAGGTTAAGTCGCCCTCG. The reagents and antibodies used are given in Table I.

HCC cell proliferation was assessed using CCK-8 assays (Beyotime Institute of Biotechnology). Briefly, 3–5×10³ stable cells transfected with sh/oeAKR1B10 or corresponding scramble oligos were plated into 96-well plates and incubated for 4 days.

The cells were seeded in 12-well plates (1×10⁴ cells/well) for 2 weeks to determine colony formation. The methanol-fixed colonies were then stained with crystal violet solution at room temperature.

Transfected Huh7 cells were seeded into six-well plates (1×10⁵ cells/well). With a 10 µl pipette tip, wounds were made when the cell confluency reached 80–90%, and the cells were cultured in serum-free DMEM at 37°C. The wounds were observed under an inverted light microscope (×100). ImageJ v1.8.0 (National Institutes of Health) was used to evaluate the cell migration rate as follows: Wound closure surface area/wound total surface area ×100%.

Using RIPA buffer and PMSF (Beyotime Institute of Biotechnology) with phosphatase inhibitor (Beijing Solarbio Science & Technology Co., Ltd.), total cell proteins were extracted and analyzed by western blotting. Briefly, proteins (40 µg) were loaded onto 10% SDS-PAGE for electrophoresis and transferred to polyvinylidene difluoride membranes (MilliporeSigma). Membranes were blocked with 5% BSA at room temperature for 1 h and then incubated with primary antibodies overnight at 4°C and then secondary antibodies at 37°C for 1 h, according to the manufacturer's recommendations. Finally, the protein levels were confirmed using enhanced chemiluminescence (Tanon 4800; Tanon Science and Technology Co., Ltd.) and normalized with an anti-GAPDH antibody. The images were analyzed using ImageJ (version 1.8.0.345, National Institutes of Health).

Typically, 1×10⁵ transfected Huh7/Hep3B cells in serum-free media were seeded into the upper chambers with (invasion) or without (migration) Matrigel coating (diluted in DMEM; Corning, Inc.), and the bottom chambers were filled with 600 µl supplemented DMEM medium. After 36 h, the cells that had migrated and invaded through to the bottom of the inserts were fixed with methanol and stained with crystal violet for 30 min at room temperature, respectively. In five random fields of view, the numbers of cells that had migrated/invaded were viewed under an inverted light microscope (magnification, ×200) and imaged and quantified.

For cell cycle analysis, cells were fixed with 70% ethanol (4°C, >30 min), washed with PBS (5 min, 3 times), and then stained with RNase/PI staining solution (room temperature, 30 min). Flow cytometry (Beckman Coulter, Inc.) was used to measure the DNA content (>10,000 cells per sample), and ModFIT LT v3.1 software (Verity Software House, Inc.) was used for analysis.

GraphPad Prism version 8.0 (GraphPad Software, Inc.) was used for statistical analysis. ImageJ was used for densitometry analysis. Comparisons between groups were assessed using a Student's t-test or an ANOVA followed by a post-hoc Tukey's test for multi-group comparison. P<0.05 was considered to indicate a statistically significant difference.

To evaluate the biological implications of the differential expression of genes in HCC vs. normal liver tissues, expression analysis in the GSE146719 dataset was performed. Moreover, 1,109 genes with q-value (p.adj) <0.05 were selected for subsequent processing (Fig. 1A). Deep analysis of these genes was performed using a volcano plot and mean differential plot maps (Fig. 1B). The top 10 genes from the above-analyzed data were selected for repeated analysis. Subsequently, the AKR1B10 was screened out. AKR1B10 status in HCC by exploring its expression in this disease. Higher AKR1B10 expression in pan-cancerous tissues suggested that this oncogene may be upregulated during the development of HCC. As shown in Fig. 1E and F, AKR1B10 expression in HCC tissues was significantly higher than in normal liver tissues. Combined with the public IHC datasets from The Human Protein Atlas database, the expression and localization of AKR1B10 were detected. AKR1B10 was primarily expressed in the cytoplasm (Fig. 1G), consistent with its function as an enzyme catalyzing redox reactions. HCC patients were divided into two groups based on the median value of AKR1B10 expression in GDC-TCGA-PANCAN and GDC-TCGA-LIHC datasets, and patients labeled as low-AKR1B10 had significantly longer survival than the remaining high-AKR1B10 (Fig. 1H and I; P<0.0001 and P=0.0021, respectively). Similarly, patients classified with the low-AKR1B10 group had a significantly better prognosis in the ICGC-LIRI dataset (Fig. 1J; P=0.0071). Thus, high expression of AKR1B10 in HCC patients was associated with poorer outcomes, and AKR1B10 was shown to be a useful biomarker for the disease (13,16).

Due to the differential expression of AKR1B10 and its prognostic value in HCC, its potential functions in this disease were examined. Using the DepMap dataset, a gene effect CRISPR (DepMap Public 22Q4 + Score, Chronos) analysis of this gene was performed. The results showed a significant gene effect of AKR1B10 in most HCC cell lines (Fig. 2A), especially in Huh 7 cell line (Fig. 2B). Next, the expression of AKR1B10 in HCC cell lines was analyzed using the DepMap dataset. The results showed that AKR1B10 was expressed at differentially high levels in all HCC cell lines, including Huh7 (Fig. 2C and D). The above results indicated that AKR1B10 was highly expressed in HCC tissues and showed a significant gene effect in HCC and Huh7 cells, and thus these cells were selected as an ideal HCC cell line for further experimental validation.

After transfection of sh-AKR1B10 into Huh7 cells, AKR1B10 expression was significantly decreased (Fig. 3A). AKR1B10 knockdown significantly reduced Huh7 cell viability and proliferation in CCK-8 and colony formation assays (Fig. 3B and C). Further studies illustrated that AKR1B10 promoted the migration and invasion of Huh7 cells in wound healing and Transwell assays (Fig. 3D-F). In addition, the above effects of AKR1B10 overexpression on Hep3B, a relatively low-expressing AKR1B10 HCC cell line, were assessed. The results showed that cellular activity was significantly promoted in Hep3B cells (Fig. 3G-I). The above results suggested that AKR1B10 played a prominent role in the proliferation, migration, and invasion of HCC.

Since AKR1B10 was upregulated in HCC tissues and exhibited a notable influence on HCC cell behavior; therefore, its potential mechanisms in promoting this disease were assessed. Next, the association between AKR1B10 expression and the cell proliferation-related gene CCND1 (18,19) and EMT-related hub genes in GDC-TCGA tumor samples. The EMT-related hub genes were selected as E-cadherin, N-cadherin, Vimentin, and TWIST1. The results showed that all of the above genes were significantly associated with the expression of AKR1B10 (P<0.0001; Fig. 4A-E). Next, another dataset, DepMap, was selected for a similar correlation analysis. The expression of E-cadherin (P=0.0217), N-cadherin (P=0.0245), and Vimentin (P=0.0073) genes in DepMap showed varying degrees of significant correlations with AKR1B10, apart from TWIST1 (P=0.7343; P-value cut-off=0.05; Fig. 4F-J).

To further validate the above functions of AKR1B10, we measured the effects of AKR1B10 on the gene expression of CCND1 and EMT-related genes in HCC cells. AKR1B10 knockdown suppressed CCND1 expression and abrogated the cell cycle distribution in HCC cells (Fig. 5A and B). In addition, the expression of E-cadherin was increased, and other EMT biomarkers, including N-cadherin, Vimentin, and TWIST-1, were downregulated after AKR1B10 knockdown (Fig. 5C). Based on these results, it is possible that AKR1B10 plays a significant role in the proliferation and EMT of HCC, affecting its proliferation, migration, and invasion.

As reported recently, AKR1B10 promotes tumor cell proliferation and migration through PI3K/AKT signaling (11). Furthermore, the degree of phosphorylation of proteins involved in the PI3K/AKT pathway was assessed, and it was found that phosphorylation was significantly reduced by AKR1B10 knockdown (Fig. 5D). Meanwhile, although cells transfected with AKR1B10 were subsequently treated with GDC-0941 (100 nM; PI3K/AKT pathway antagonist; MedChemExpress) for 24 h, western blotting showed that the protein content of AKR1B10 failed to be significantly altered (Fig. 5E). Based on these experiments, it was suggested that the PI3K/AKT signaling was involved in the pathological progression of HCC.