The MHCC-97H HCC cell line was purchased from Cellverse Bioscience Technology Co., Ltd., and ¹²⁵I was procured from Shanghai Xinke Pharmaceutical Co., Ltd. The cells were cultured in 90% Dulbecco's modified Eagle's medium (Beijing Solarbio Science & Technology Co., Ltd.) supplemented with 10% fetal bovine serum (FBS; Cyagen Biosciences, Inc.) and 1% penicillin/streptomycin mixture (Beijing Solarbio Science & Technology Co., Ltd.), which was used to maintain cell viability and prevent contamination. The cells were cultured at 37°C and 5% CO₂. The cells were grouped as follows: Control group, normal MHCC-97H cells; and ¹²⁵I group. For ¹²⁵I radiation of MHCC-97H cells, the initial activity and dose rate were 3.0 mCi and 3.412 cGy/h, respectively. Cells were irradiated with ¹²⁵I at a dose of 0.82 Gy for 24 h 37°C (9). ¹²⁵I seeds were purchased from Shanghai Xinke Pharmaceutical Co., Ltd.

To assess the impact of ¹²⁵I intervention on MHCC-97H cells, the CCK8 assay (US Everbright, Inc.) was employed. MHCC-97H cells were seeded in 96-well plates at a density of 2,000 cells/well and were treated with ¹²⁵I. Subsequently, the cells were incubated with 20 µl CCK8 reagent for 4 h and the cell viability was determined by measuring absorbance at a wavelength of 450 nm. Each CCK8 assay was performed with five biological replicates to ensure the reliability of the results.

The 5-ethynyl-2′-deoxyuridine (EdU) assay (US Everbright, Inc.) was conducted to evaluate the effects of ¹²⁵I intervention on MHCC-97H cells. When the cell density reached 85–90%, MHCC-97H cells were incubated with 50 µM EdU reagent for 2 h at room temperature and were subsequently fixed using 4% paraformaldehyde for 0.5 h at room temperature. EdU staining was performed using 1X Apollo (fluorescent dye) and DNA was stained with 1X Hoechst 33342 solution at room temperature in the dark for 0.5 h. The stained cells were subsequently visualized under a fluorescence microscope. Each EdU assay was performed in triplicate to ensure the robustness and reproducibility of the results.

A single-cell suspension of MHCC-97H cells was inoculated into a 6-well plate at a density of 500 cells/well. The cells were incubated at 37°C for 12 days to facilitate colony formation. Subsequently, the cells were rinsed twice with PBS, fixed with 1 ml methanol at room temperature for 15 min and stained with 0.3% crystal violet (Wuhan Servicebio Technology Co., Ltd.) for 5 min at room temperature. Colonies were manually counted with those containing >50 cells counted.

MHCC-97H cells were inoculated in 6-well plates, and when the cell monolayer reached 90% confluence, a 200-µl pipette tip was used to gently scratch the monolayer across the center of the well. Subsequently, the cells were washed with PBS and cultured in complete medium (1% FBS) (13) for 24 h. Images of the scratches were captured at 0 and 24 h under an inverted light microscope.

The 24-well Transwell chambers (pore size, 8 µm) were coated in 80 µl Matrigel (Matrigel:serum-free medium, 1:8; cat. no. 356234; Corning, Inc.) in 37°C for 3 h. Subsequently, 5×10⁴ MHCC-97H cells suspended in serum-free medium were added to the upper compartments of Transwell chambers, whereas the lower compartments were filled with culture medium containing 10% FBS. After 48 h of cultivation, the cells remaining in the upper compartments were removed with cotton swabs, and the cells that had penetrated the membrane were stained with 0.3% crystal violet for 10 min at room temperature. The number of invasive cells was counted manually using a light microscope. Transwell assays were performed in triplicates.

The Annexin V-FITC Apoptosis Detection Kit (Biosharp Life Sciences) was used to detect the level of apoptosis. MHCC-97H cells (1×10⁵) were collected in PBS and suspended in 100 µl binding buffer. Subsequently, 5 µl Annexin V-FITC was added to the binding buffer and incubated with MHCC-97H cells for 10 min at room temperature in the dark. PI (10 µl) was then added, gently mixed, and incubated for 5 min at room temperature in the dark, followed by the addition of 400 µl PBS to resuspend the cells. Cell samples were loaded onto a flow cytometer (CytoFocus421 instrument; Beijing Zhizhen Biological Technology Co., Ltd.) for detection. Flow cytometry using the CytoFocus421 instrument and CytoFocus 3.2 software (Beijing Zhizhen Biological Technology Co., Ltd.) was performed in triplicate

To construct the database and facilitate sequencing, magnetic beads with Oligo (dT) and polyA (Thermo Fisher Scientific, Inc.) were employed for A-T base pairing to selectively isolate mRNA from total RNA samples. Paired-end sequencing was conducted using an Illumina HiSeq 4000 platform (Illumina, Inc.) owing to its advanced capabilities for high-throughput sequencing. The sequencing length was 2×150 bp, the final library concentration was 1–20 pM and its concentration was measured using the Qubit ssDNA Assay Kit (cat. no. Q10212; Thermo Fisher Scientific Inc.). Sequencing was performed using the HiSeq 3000/4000 SBS Kit (cat. no. FC-410-1003; Illumina, Inc.). Fragmentation buffer was introduced to randomly break down the mRNA, ensuring the generation of representative fragments for analysis. A small fragment of ~300 bp was selectively screened and isolated using magnetic beads, ensuring the isolation of fragments of interest. The isolated fragments were reverse transcribed to synthesize cDNA using the High-Capacity cDNA Reverse Transcription Kit (cat. no. 4368814; Thermo Fisher Scientific, Inc.) with the following steps: 25°C for 10 min, 37°C for 120 min and 85°C for 5 min, which is a crucial step for subsequent sequencing analysis. The double-stranded cDNA has sticky ends, and EndRepairMix (cat. no. Y9140; Qiagen, Inc.) was added to convert them into blunt ends, followed by the addition of an A base at the 3′ end to facilitate the subsequent ligation of the adapter sequence.

Fragment screening and library enrichment, purification and fragment sorting of the products connected to the adapter, PCR amplification of the sorted products, and purification were performed to obtain the final library.

Analysis of raw sequencing data involved a series of steps to ensure data quality and extract meaningful insights. Transcriptomics analysis was conducted using the following statistical methods: fastp (https://github.com/OpenGene/fastp) was employed to evaluate and screen the quality of the raw sequencing data obtained from Illumina sequencing, ensuring the reliability of subsequent analyses. RSEM (http://deweylab.github.io/RSEM/) was applied for the quantitative analysis of both chain-specific and non-chain-specific transcriptomics data. This approach provides an estimate of transcript abundance, contributing to an overall understanding of gene expression levels. Transcripts per million was used to standardize the gene expression levels. This normalization method allowed the comparison of gene expression across different samples, considering variations in library sizes. DESeq2 (http://bioconductor.org/packages/stats/bioc/DESeq2/), a robust tool for RNA-Seq data analysis, was employed to identify differentially expressed genes (DEGs) between the control and ¹²⁵I intervention groups. DEGs were filtered based on specific criteria, including expression difference multiples (| log2FoldChange |) ≥1, a false discovery rate (FDR) <0.5 and P<0.05. The differential gene functional enrichment analyses included Kyoto Encyclopedia of Genes and Genomes (KEGG; Version 2022.10; http://www.genome.jp/kegg/); Gene Ontology (GO) analysis, which includes Biological Process (BP), Cellular Component (CC) and Molecular Function (MF) (goatools; Version 0.6.5; http://pypi.org/project/goatools/); Reactome (Version 82; http://reactome.org); and Disease Ontology (DO; http://disease-ontology.org) enrichment analyses.

Nanoscale liquid chromatography-MS/MS technology (Easy-nLC1200 coupled with QExactive mass spectrometer; Thermo Fisher Scientific, Inc.) was used in the present study. Ionization mode, positive; nitrogen gas temperature, 350°C; nebulizer pressure, 40 psi. Peptides were dissolved in mass spectrometry (MS) loading buffer, and after loading, they were separated through a C18 chromatography column (75 µm × 25 cm; Thermo Fisher Scientific, Inc.) for 120 min at a flow rate of 300 µl/min. The EASY-nLC liquid phase gradient elution was performed as follows: Phase A, 2% acetonitrile with 0.1% formic acid; Phase B, 80% acetonitrile with 0.1% formic acid; 0–1 min, 0–5% B; 1–63 min, 5–23% B; 63–88 min, 23–48% B; 88–89 min, 48–100% B; 89–95 min, 100% B. The MS and MS/MS acquisition switched automatically, with MS resolutions of 70 and 35K, respectively. MS was used to perform a full scan (m/z 350–1300), and the top 20 parent ions were selected for secondary fragmentation with a dynamic exclusion time of 18 sec.

For data analysis, Proteome Discoverer Software 2.2 (Thermo Fisher Scientific, Inc.) was employed. Peptide identification was controlled for accuracy by setting the FDR to FDR ≤0.01, ensuring a reliable identification of peptides. Student's unpaired t-test was used to calculate the P-value of inter-sample differences and the fold change (FC) between groups. This analysis aimed to identify proteins with significant changes in expression in response to ¹²⁵I treatment. Significantly differentially expressed proteins were identified based on specific criteria: Proteins with P<0.05 and FC >1.2 were considered upregulated, whereas those with P<0.05 and FC <0.83 were considered downregulated. The differential protein functional enrichment analyses included KEGG; and GO analysis of BP, CC and MF. In addition, Evolutionary Genetics of Genes: Non superior Orthologous Groups (EggNOG; version 2020.06; http://eggnogdb.embl.de/#/app/home)was used to determine protein functional classification; and subcellular localization prediction was performed using WoLF PSORT (https://wolfpsort.hgc.jp/), which determines the location of proteins within cells.

By conducting a Venn joint analysis, the present study screened differentially expressed genes from transcriptomic and proteomic data. Subsequently, the STRING (https://string-db.org/) database was utilized to perform protein-protein interaction network analysis. In addition, the Xiantao (https://www.xiantao.love/products) software platform was used for further bioinformatics analysis, in the analysis, the samples were independent, with an equal number of adjacent normal tissues and cancer tissues. However, these samples were not from the same group of patients. Therefore, the Wilcoxon Rank Sum Test was used for statistical analysis.

Total RNA was isolated from the cells using TRIzol^® (Invitrogen; Thermo Fisher Scientific, Inc.). Subsequently, the RNA was subjected to phenol-chloroform extraction for further purification. The quantity and quality of the purified RNA were assessed by measuring the absorbance at 260/280 nm using a microplate reader (Thermo Fisher Scientific, Inc.), and the acceptable ratio for A260/A280 was considered 1.8–2.2. Subsequently, cDNA was synthesized (PrimeScript RT reagent Kit; Takara Bio, Inc.) using the following standard procedure: 37°C for 15 min and 85°C for 5 sec, followed by maintenance at 4°C. The qPCR (PowerUp SYBR Green Master Mix; Thermo Fisher Scientific, Inc.) procedure was as follows: 95°C for 1 min, followed by 40 cycles at 95°C for 10 sec and 60°C for 30 sec. Each transcript concentration was normalized to the mRNA expression levels of GAPDH using the 2^−ΔΔCq method (14). The primer sequences were as follows: GAPDH, forward 5′GGTCGGAGTCAACGGATTTG-3′, reverse 5′-GGAAGATGGTGATGGGATTTC-3′; GPNMB, forward 5′-CTTCTGCTTACATGAGGGAGC-3′, reverse 5′-GGCTGGTGAGTCACTGGTC-3′; C4BPA, forward 5′-ATGACCTTGATCGCTGCTCTG-3′, reverse 5′-GTCAACGTAATATCCATCGGGG-3′; MT2A, forward 5′-TCCTGCAAATGCAAAGAGTGC-3′, reverse 5′-GTTTGTGGAAGTCGCGTTCT-3′; CTH, forward 5′-CATGAGTTGGTGAAGCGTCAG-3′, reverse, 5′-AGCTCTCGGCCAGAGTAAATA-3′; and H1-0, forward 5′-ACTCGCAGATCAAGTTGTCCA-3′, reverse 5′-GGTTCGTCGCTCTTGGCTA-3′.

Data are presented in bar graphs, with each experiment conducted independently three times. The data from the experiments are presented as the mean ± standard error of the mean. All data calculations and statistical analyses were carried out using SPSS 23.0 (IBM Corp.) and GraphPad Prism 6.01 (Dotmatics). For the comparison of two consecutive variables, the statistical significance of normally distributed variables was analyzed using unpaired Student's t-test, whereas the differences between non-normally distributed variables were analyzed using Wilcoxon rank-sum test. P<0.05 was considered to indicate a statistically significant difference.

The present study initially investigated the effect of ¹²⁵I on the viability and proliferation of MHCC-97H cells (Fig. 1A-C). After 24 h of ¹²⁵I treatment, cell viability was significantly reduced (Fig. 1A) and the proportion of EdU-positive cells was significantly decreased (Fig. 1B). Furthermore, the number of cell colonies significantly decreased after 12 days of ¹²⁵I treatment (Fig. 1C). The present study also examined the effects of ¹²⁵I on the invasion and migration of MHCC-97H cells (Fig. 1D and E). The wound healing area was significantly decreased after 24 h of ¹²⁵I treatment (Fig. 1D), and the number of invasive cells was significantly decreased after 48 h of ¹²⁵I intervention (Fig. 1E). Flow cytometry revealed that the percentage of PI-positive cells was significantly increased after 24 h of ¹²⁵I treatment (Fig. 1F). These results indicated that ¹²⁵I intervention may inhibit MHCC-97H cell proliferation, invasion and migration, and promote cell apoptosis.

The differential expression analysis revealed that there were 734 differentially expressed genes between the control group and the ¹²⁵I intervention group, with 455 genes being upregulated and 279 genes being downregulated. These results were visualized using a volcano plot, where the red dots represent significantly upregulated proteins, blue dots represent significantly downregulated proteins, and gray dots represent proteins with no differential expression (Fig. 2A). Compared with in the control group, the differentially expressed genes in the ¹²⁵I intervention group were primarily involved in ‘BMP signaling pathway’, ‘Regulation of programmed cell death’ and ‘Regulation of apoptotic process’ in BP terms (Fig. 2B). In terms of CC terms, the differentially expressed genes were mainly enriched in ‘Extracellular space’, ‘Gap junction’ and ‘Smooth muscle contractile fiber’ (Fig. 2C). MF term analysis indicated that the differentially expressed genes were predominantly enriched in ‘Receptor ligand activity’, ‘Signaling receptor activator activity’ and ‘Signaling receptor regulator activity’ (Fig. 2D). Reactome pathway enrichment analysis revealed that the differentially expressed genes were mainly enriched in processes such as ‘TNFs bind their physiological receptors’, ‘Activation of the AP-1 family of transcription factor’ and ‘TNFR2 non-canonical NF-kB pathway’ (Fig. 2E). DO database analysis revealed that the differentially expressed genes were related to diseases, such as ‘Bone disease’, ‘Pustulosis of palm and sole’ and ‘Urinary bladder cancer’ (Fig. 2F).

Volcano plot analysis revealed significant differences in the expression of 387 proteins between the control and ¹²⁵I intervention groups, with 277 upregulated and 110 downregulated proteins (Fig. 3A). Functional enrichment analysis of the proteins was performed using Gene Ontology to reveal the BP, CC and MF terms in which they were involved. Regarding BP terms, differentially expressed proteins participated in responses to biological stimuli, humoral immune responses, defense responses and reactions to external biological stimuli (Fig. 3B). KEGG enrichment analysis showed that the differentially expressed proteins were involved in ‘Complement and coagulation cascades’, ‘Pertussis’ and ‘Metabolic pathways’ (Fig. 3C). The EggNOG classification indicated that the differentially expressed proteins were associated with processes such as post-translational modification, protein turnover, molecular chaperones, intracellular transport, secretion, vesicular transport and transcription (Fig. 3D). Subcellular localization prediction (WoLF PSORT) suggested that the differentially expressed proteins were primarily localized in the cytoplasm, nucleus and endoplasmic reticulum (Fig. 3E).

GO classification statistics revealed that the differentially expressed genes and proteins were mainly enriched in BP terms, followed by CC and MF terms (Fig. 4A). Regarding BP terms, genes and proteins were predominantly distributed in cellular processes, biological regulation and metabolic processes (Fig. 4B). Regarding CC terms, genes were mainly distributed in cell parts and organelles, whereas proteins were primarily located in cellular anatomical entities and in complexes containing proteins (Fig. 4C). Regarding MF terms, both genes and proteins were mainly distributed in binding and catalytic activities (Fig. 4D). GO enrichment analysis revealed that genes were mainly enriched in the extracellular space, stimulus response and glycosaminoglycan binding, whereas proteins were mainly enriched in endosomes, protein activation cascades and complement activation (Fig. 4E). The KEGG histogram showed that the genes and proteins were mainly distributed in metabolism, followed by organismal systems and human diseases (Fig. 4F). Regarding metabolism, genes were mainly distributed in global and overview maps, as well as in lipid, energy and amino acid metabolism. The proteins were primarily distributed in global and overview maps, lipid metabolism and amino acid metabolism. Regarding organismal systems, genes were mainly distributed in the immune, endocrine and nervous systems, whereas proteins were primarily distributed in the immune, endocrine and digestive systems. Regarding human diseases, changes in genes after ¹²⁵I intervention were associated with viral/bacterial infectious diseases, cancer: overview, and neurodegenerative disease, leading to changes in proteins related to overview cancer, viral infectious diseases and bacterial infectious diseases. KEGG enrichment analysis indicated that the differentially expressed genes and proteins were mainly involved in cytokine-receptor interactions, pertussis and Kaposi's sarcoma-associated herpes virus infection (Fig. 4G). Transcriptomics and proteomics data were used to describe the relationships between proteins and genes. The Venn diagram showed a total of 5 proteins with differential expression at both the mRNA and protein levels (Fig. 4H).

STRING analysis (https://string-db.org/) was used to generate a network of the interactions between differentially expressed genes (GPNMB, C4BPA, MT2A, CTH and H1-0) in MHCC-97H cells after ¹²⁵I intervention, highlighting their close interactions (Fig. 5A-E). GPNMB was highly expressed at both the transcriptomic and proteomic levels in MHCC-97H cells after ¹²⁵I treatment; GPNMB may be associated with growth delay and reduced metabolic potential. In the network diagram, the interaction network has 6 nodes, 8 edges, an average node degree of 2.67, an average local clustering coefficient of 0.883, 6 expected edges, and a PPI enrichment P-value of 0.223 (Fig. 5A). C4BPA, CTH and H1-0 showed low transcript and high protein expression levels after ¹²⁵I intervention in MHCC-97H cells. C4BPA is involved in the positive regulation of the complement cascade, immune response, lectin-induced complement pathway and apoptotic cell clearance. The interaction network of this network diagram had 6 nodes, 11 edges, an average node degree of 3.67, an average local clustering coefficient of 0.933, an expected number of edges of 5, and a PPI enrichment P-value of 0.0165 (Fig. 5B). CTH is involved in sulfur amino acid metabolism, one-carbon metabolism and other related pathways. The network diagram showed an interaction network with 6 nodes, 15 edges, an average node degree of 5, an average local clustering coefficient of 1, an expected number of edges of 5, and a PPI enrichment P-value of 0.000247 (Fig. 5D). H1-0 is involved in the response to stimuli and programmed cell death pathways. The interaction network of the H1-0 network diagram had 6 nodes, 9 edges, an average node degree of 3, an average local clustering coefficient of 0.844, 7 expected edges, and a PPI enrichment P-value of 0.275 (Fig. 5E). MT2A in MHCC-97H cells showed high expression at the transcriptional level and low expression at the protein level after ¹²⁵I treatment. It is involved in metal ion SLC transport and interferon-γ signaling. The interaction network of this network diagram had 6 nodes, 11 edges, an average node degree of 3.67, an average local clustering coefficient of 0.933, an expected number of edges of 5 and a PPI enrichment P-value of 0.0138 (Fig. 5C).

Xiantao predicts the differential expression of genes (GPNMB, C4BPA, MT2A, CTH and H1-0) in cancer. In the pan-cancer analysis, the expression levels of GPNMB were significantly upregulated in various types of cancer, particularly in lung and kidney cancer (Fig. 6A). The expression levels of C4BPA were downregulated various types of cancer (Fig. 6B), whereas the expression levels of MT2A, CTH and H1-0 varied among different types of cancer (Fig. 6C-E). Notably, it was predicted that the expression levels of GPNMB (Fig. 6F) and H1-0 (Fig. 6J) would be significantly higher, whereas the expression levels of C4BPA (Fig. 6G), MT2A (Fig. 6H) and CTH (Fig. 6I) would be significantly lower in HCC tissues compared with those in normal tissues (adjacent healthy tissues).

RT-qPCR was used to detect the mRNA expression levels of GPNMB, C4BPA, MT2A, CTH and H1-0 in MHCC-97 cells. The results showed that compared with in the control group, the expression levels of GPNMB (Fig. 7A) and MT2A (Fig. 7C) were significantly increased in cells treated with ¹²⁵I, whereas the expression levels of C4BPA (Fig. 7B), CTH (Fig. 7D) and H1-0 (Fig. 7E) were significantly reduced.