2.1 Analysis of ATG12 expression across clinical variables in TCGA-LIHC

To investigate the clinical significance of ATG12 in HCC progression, the expression patterns across different clinical stages were analyzed using TCGA-LIHC data. RNA-seq data processed through the STAR pipeline and corresponding clinical information were downloaded from TCGA database (https://portal.gdc.cancer.gov). The analysis focused on pathological stage, pathological T stage, pathological N stage, and pathological M stage. After excluding normal samples and cases without clinical information, the expression data were transformed using log2(value + 1). Statistical analysis was performed using R version 4.2.1 with ggplot2 (3.3.6), stats (4.2.1), and car (3.1–0) packages. For pathological stage analysis, Kruskal–Wallis test followed by Dunn's test for multiple comparisons was employed. The differences in ATG12 expression among pathological T stages were assessed using Kruskal–Wallis test. Independent t-tests were used to evaluate the differences in ATG12 expression between groups for both pathological N stage and M stage analyses. Only groups with more than three samples and non-zero variance were included in the statistical analysis to ensure reliability.

2.2 Co-expression analysis

To identify genes co-expressed with ATG12, we performed correlation analysis using the cBioPortal platform (https://www.cbioportal.org/). We analyzed the Liver Hepatocellular Carcinoma dataset from TCGA PanCancer Atlas, which includes 353 samples with mutation and CNA data. The mRNA expression z-scores relative to normal samples (log RNA Seq V2 RSEM) were used for correlation analysis. Spearman's correlation coefficients were calculated between ATG12 and all other genes. Genes were ranked based on their correlation coefficient values, and the top 20 positively and negatively correlated genes were selected for further analysis.

2.3 Construction and analysis of protein–protein interaction network

To further explore the regulatory network of ATG12, we performed protein–protein interaction (PPI) network analysis using the NetworkAnalyst platform (https://www.networkanalyst.ca/). NetworkAnalyst is a comprehensive web-based platform for network-based visual analytics that integrates genomic data from multiple public databases for constructing and analyzing biomolecular interaction networks. Based on the STRING Interactome database with medium (400) to high (1000) confidence scores, we set the confidence score cutoff at 900 and included only experimentally validated interactions. A generic PPI network was constructed using ATG12 as the seed gene, and network visualization was performed with the seed gene highlighted in pink and interacting proteins marked in blue.

2.4 Network analysis and functional enrichment based on GeneMANIA

To explore the functional interaction network and associated pathways of ATG12, we performed network analysis using GeneMANIA (https://genemania.org/), a web-based interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. ATG12 was used as the query gene to construct the interaction network. The resulting network shows genes (nodes) and their relationships (edges), where different edge colors represent different types of interactions. Functional enrichment analysis was automatically performed by GeneMANIA to identify significantly enriched biological pathways and Gene Ontology (GO) terms. The statistical significance was evaluated using false discovery rate (FDR), and terms with FDR < 0.05 were considered statistically significant. Coverage for each enriched term was calculated as the ratio of genes in our network to the total number of genes associated with that term in the database.

2.5 Differential expression and functional enrichment analysis between ATG12 high and low expression groups

To explore the molecular mechanisms underlying ATG12‘s function in HCC, we performed differential expression analysis between ATG12-high and ATG12-low expression groups using TCGA-LIHC data. Samples were divided into high (top 50%) and low (bottom 50%) expression groups based on ATG12 expression levels. Differential expression analysis was conducted using DESeq2 (version 1.36.0). Differentially expressed genes (DEGs) were identified using the following criteria: |log2FC|> 1, adjusted p < 0.05, and protein-coding gene type. The identified DEGs were subjected to functional enrichment analysis using clusterProfiler (version 4.4.4) package in R. Gene Ontology (GO) enrichment analysis was performed for biological process (BP), molecular function (MF), and cellular component (CC) categories, and KEGG pathway analysis was conducted using the org.Hs.eg.db annotation package. The enriched terms with p < 0.05 were considered significant, and the top 20 terms in each category were visualized using ggplot2 (version 3.4.4).

2.6 Immune infiltration analysis

The correlation between ATG12 expression and immune cell infiltration was analyzed using R software (version 4.2.1). Single-sample Gene Set Enrichment Analysis (ssGSEA) was performed using the GSVA package (version 1.46.0) to quantify the infiltration levels of 24 immune cell types based on their specific gene markers as defined by Bindea et al. The immune cell types included activated dendritic cells (aDC), B cells, CD8 T cells, cytotoxic cells, dendritic cells (DC), eosinophils, immature DC (iDC), macrophages, mast cells, neutrophils, NK cells (CD56bright, CD56dim, and total), plasmacytoid DC (pDC), T cells, T helper cells, T central memory (Tcm), T effector memory (Tem), T follicular helper (TFH), T gamma delta (Tgd), Th1 cells, Th17 cells, Th2 cells, and T regulatory cells (TReg). The ESTIMATE package (version 1.0.13) was used to calculate stromal and immune scores. Pearson correlation analysis was performed to evaluate relationships between ATG12 expression and immune infiltration levels. The ggplot2 package (version 3.4.4) was used to visualize the correlation results through lollipop plots and scatter plots.

2.7 Clinical samples

HCC tissues as well as adjacent normal tissues were achieved from 145 HCC patients who got surgery at Qingpu Branch of Zhongshan Hospital, Fudan University. The patients were diagnosed as HCC who were not subject to preoperative radiotherapy or chemotherapy before operation. Written consents were achieved from each patient, and this research have been approved by the Ethics Committee of Qingpu Branch of Zhongshan Hospital, Fudan University (IEC-C-007-A08-V.03, September 2022).

2.8 Immunohistochemistry (IHC)

The tissues were fixed with 4% paraformaldehyde, subsequently paraffin embedded and sectioned into 4 µm slices. After deparaffinization and rehydration, a blocking step employing 3% hydrogen peroxide in methanol and 10% goat serum (Solarbio, Beijing, China) was conducted for a period of 2 h at ambient temperature. Following this, the slides were exposed to ATG12 antibody (dilution ratio 1:500; Abcam, Cambridge, MA, USA) overnight at 4 °C, followed by incubation with HRP labeled IgG secondary antibody (dilution ratio 1:50; Abcam) for another 2 h at room temperature. Streptavidin–horseradish peroxidase was incorporated, resulting in staining with diaminobenzidine (DAB) chromogen for 3 min and subsequent counterstaining with hematoxylin for 30 s at room temperature. The slides were observed and captured using an Olympus BX50 bright-field microscope (Olympus Corporation, Tokyo, Japan).

2.9 Cell culture

Human HCC cells Hep3b and Huh-7 were bought from BeNa Culture Collection (BNCC, Beijing, China), and were routinely used at passages four and five. Huh-7 cells were cultured in DMEM (ThermoFisher Scientific, Waltham, MA, USA) with 10% fetal bovine serum (FBS, ThermoFisher), while Hep3b cells were seeded in EMEM (ThermoFisher) containing 10% FBS under 37 ℃ and 5% CO2.

2.10 Cell transfection

Huh-7 and Hep3b cells were cultivated in 6-well plates with optimum density and then incubated overnight 24 h before transfection. ATG12 siRNA, negative control siRNA, pcDNA 3.1 ATG12 and empty vector were designed by GenePharma (Shanghai, China), and transfected by lipofectamine 2000 reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s guidance. The expression level of ATG12 mRNA was calculated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) 48 h after transfection. si-NC served as the control for si-ATG12, and the empty vector functioned as the control for pcDNA3.1 ATG12.

2.11 qRT-PCR

Total cellular RNA was isolated utilizing TRIzol reagents (ThermoFisher), in accordance with the prescribed manufacturer's guidelines. Reversed transcription into cDNA proceeded under conditions of 37 °C for 15 min followed by 80 °C for 5 s, employing a PrimeScript RT reagent kit (Takara, Dalian, Liaoning, China). Real-time quantitative fluorescent PCR was carried out using SYBR GreenPCR reagents and the ABI7500FAST real-time PCR. The 2-ΔΔCt method was applied to calculate the profile of ATG12 mRNA[17]. β-actin was adopted as the internal parameter of ATG12.

2.12 Cell counting kit 8 (CCK-8) assay

Cell proliferation was accessed using Cell Counting Kit 8 (MedChemExpress, Monmouth Junction, NJ, USA) according to user‘s guidance. After transfection, 2 × 103 Huh-7 or Hep3b were seeded into 96-well-plates (100 μL/well) and pre-incubated for 24 h. Subsequently, CCK-8 reagent was serially introduced into each well, and cell counts were performed biochemically via Epoch Microplate Spectrophotometer (BioTek, Winooski, VT, USA) operating at wavelength 450 nm, during a span of three consecutive days.

2.13 Transwell assay

Hep3b and Huh-7 cells (2 × 105) were cultivated within the upper wells of 24-well Transwell plates incorporated with Matrigel (BD Biosciences, San Jose, CA, USA) in DMEM without FBS. The subjacent wells contained complete medium supplemented with 10% FBS. Following a 48 h period of incubation under a humidified environment enriched with 5% carbon dioxide, the overlying culture was gently wiped away using cotton swabs. Subsequently, according to the manufacturer‘s guidelines, the Transwell chambers underwent processing while the cells that had traversed the membrane were labelled with 0.1% crystal violet dye. Three arbitrary fields from each assay were chosen for cell enumeration through use of phase-contrast microscopy.

2.14 Flow cytometry assay

The apoptosis rate was estimated as per the guidelines provided by Annexin V-FITC/PI Apoptosis Detection Kit (KeyGEN Biotech, Nanjing, China). Huh-7 and Hep3b cells were consecutively stained with Annexin V-FITC and propidium iodide (PI), followed by 15 min of incubation in an obscurity. Post that, the apoptotic condition of these cells was examined using a MoFlow flow cytometer (Beckman Coulter, Atlanta, GA, USA).

2.15 Statistical analysis

All cell experiments were performed thrice. GraphPad Prism 8 and SPSS 24.0 were used for statistical analysis. All data of cell experiments are described as the mean ± standard error of the mean (SD). Student’s t test was utilized to examine the statistical difference between groups. Clinicopathological features of HCC patients were calculated through Chi-square test. The survival curve was plotted by log rank (Mantel-Cox) using SPSS. Univariate analysis was done through the Kaplan–Meier method (the log-rank test). Multivariate analysis was performed by the Cox multivariate proportional hazard regression model with stepwise manner (forward, conditional likelihood ratio). Significance was evaluated at a p-value threshold of < 0.05.