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Introduction

Hepatocellular carcinoma (HCC) poses a significant challenge to global health, being the third leading cause of cancer-related mortality. Although early detection can lead to a favorable prognosis through surgical resection, achieving a patient survival rate of >70% (1), most cases are diagnosed at advanced stages. In China, the 5-year survival rate is <12.5% (2). Therefore, safe and effective treatments are urgently needed.

In pursuit of innovative and targeted treatment strategies, the integration of radioisotope-based therapies has emerged as a promising field for precision medicine. Among these, iodine-125 (125I) seed brachytherapy has been widely used in the clinical treatment of various types of cancer (3). The combination of 125I radioactive particle implantation with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) has shown superior efficacy and long-term survival in patients with advanced non-small cell lung cancer compared with EGFR TKIs alone. In addition, this combination therapy has been reported to regulate the expression of T-lymphocyte subsets, natural killer cells and immune-inflammatory factors, thereby improving immune function (4). Additionally, lobaplatin-transarterial chemoembolization (TACE) combined with radioactive 125I seed therapy has been shown to enhance disease control and overall survival in patients with primary HCC (5). Furthermore, a combination therapy of 125I seed implantation with TACE can substantially prolong the median survival time, and improve the 6-, 12- and 18-month survival rates of patients with HCC plus portal vein tumor thrombosis (6). Despite these advances, the precise mechanism of action of 125I radiation particles in liver cancer treatment remains unclear.

Invasion and metastasis are the fundamental characteristics of HCC (7). Understanding the cellular signaling mechanisms driving cancer transformation, as well as those governing cell proliferation, invasion and angiogenesis, may provide valuable insights into therapeutic mechanisms (8). It has previously been indicated that 125I upregulates the PERK-eIF2a-ATF4-CHOP pathway to promote apoptosis. Notably, the ATF4-CHOP pathway is crucial in endoplasmic reticulum stress, and induces cell apoptosis by upregulating CHOP, Bcl-2 and other apoptosis-related factors (9). The inhibition of glycolysis can also enhance the inhibitory effects of radiotherapy on cancer cell proliferation, invasion and migration (10,11). Notably, 125I has been reported to inhibit glycolysis in HCC by regulating the microRNA-338/PFKL axis, thus affecting the Warburg effect (12). However, the mechanism by which 125I inhibits tumor progression remains unclear.

In the present study, human liver cancer cells (MHCC-97H) were subjected to intervention with 125I; the cells were divided into a control group and an 125I intervention group. The collected samples from both groups underwent comprehensive transcriptomics and proteomics analyses to elucidate changes in gene expression and functional alterations at the protein level. Integration of these two sets of data allowed for the identification of genes exhibiting notable differences in both transcriptional and protein expression, characterization of the biological functions of these differentially expressed genes, and delineation of potential pathways associated with tumor inhibition. This integrated approach not only provides a holistic understanding of the molecular changes induced by 125I intervention in liver cancer cells, but also offers novel insights and a theoretical foundation for the mechanism underlying the therapeutic effects of 125I in liver cancer treatment.

Materials and methods

Cell culture

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.

Cell Counting Kit-8 (CCK8) assay

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.

5-Ethynyl-2′-deoxyuridine (EdU) assay

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.

Cell colony formation assay

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.

Wound healing assay

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.

Transwell assay

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.

Flow cytometry

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

Transcriptomics analysis

For transcriptomic analysis, untreated cells served as the control group, whereas cells treated with 125I constituted the 125I intervention group. Three pairs of samples were selected for the transcriptomics analysis. Initially, total RNA was extracted from the cells using QIAzol lysis reagent (Qiagen, Inc.), RNA concentration and purity were assessed using a Nanodrop 2000 (Thermo Fisher Scientific, Inc.), and RNA integrity was confirmed by agarose gel electrophoresis (1% agarose gel; Biowest) using the SYBR™ Green I Nucleic Acid Gel Stain (Thermo Fisher Scientific, Inc.). The RNA Integrity Number value was measured using an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.). For single library construction, a total RNA volume of ≥1 µg and a concentration of ≥35 ng/µl was required. The optical density ratios OD260/280 ≥1.8 and OD260/230 ≥1.0 were used as indicators of RNA purity. These stringent criteria were implemented to ensure high quality and integrity of the RNA samples, thereby guaranteeing the reliability of subsequent transcriptomics analyses.

Database construction and sequencing

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

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.

Proteomics analysis

For proteomics analysis, three cell samples from each group (control and 125I intervention groups) were selected. The sample preparation process involved the following steps: Total cell protein was extracted from the selected cell samples using RIPA buffer (Thermo Fisher Scientific, Inc.) to capture the complete proteomics profile. The bicinchoninic acid method was employed for protein quantification to ensure accurate measurement of protein concentrations in the samples. Subsequently, 100 µg protein sample was supplemented with lysis buffer to a final volume of 90 µl. A final concentration of 10 mmol/l TCEP reducing agent was added and the mixture was incubated at 37°C for 60 min. A final concentration of 40 mmol/l iodoacetamide was then added and was incubated in the dark at room temperature for 40 min. Precooled acetone (ratio of acetone to sample volume, 6:1) was added to each tube, followed by precipitation at −20°C for 4 h. After centrifugation at 10,000 × g for 20 min at 4°C, the precipitate was collected. The sample was fully dissolved in 50 mmol/l TEAB and trypsin was added at a mass ratio of 1:50 (enzyme:protein) for enzymatic digestion overnight at 37°C. TMT labeling and mixing were performed; the TMT reagent (cat. no. 9011; Thermo Fisher Scientific, Inc.) was brought to room temperature, followed by the addition of acetonitrile and vortexing. For every 100 µg peptide, one vial of TMT reagent was added (TMT10-126 for labeling). The mixture was incubated at room temperature for 2 h, after which, hydroxylamine was added and the reaction was carried out at room temperature for 15 min. The labeled products were mixed together in equal amounts in one tube and dried using a vacuum concentrator; this step facilitated protein identification and quantification. The peptide samples were solubilized in ultra-performance liquid chromatography buffer to ensure that the samples were suitable for subsequent analysis. A C18 column was used for high-pH liquid-phase separation, which enabled the separation of peptides based on their physicochemical properties.

Liquid chromatography-tandem mass spectrometry (MS/MS)

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.

Data analysis

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.

Comprehensive analysis

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.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

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′.

Statistical analysis

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.

Results

125I intervention suppresses MHCC-97H cell viability, proliferation, invasion and migration, and induces cell apoptosis

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.

Figure 1. 125I suppresses HCC cell viability, proliferation, invasion and migration, and induces apoptosis. Cell viability and proliferation were assessed using (A) Cell Counting Kit-8 assay, (B) EdU assay (scale bar, 50 µm) and (C) colony formation assay. Cell invasion and migration assay were assessed using (D) wound healing assay (scale bar, 50 µm) and (E) Transwell assay (scale bar, 50 µm). (F) Flow cytometric analysis of cell apoptosis and PI staining. *P<0.05, ***P<0.001, ****P<0.0001 vs. control. EdU, 5-ethynyl-2′-deoxyuridine; 125I, iodine-125.

Transcriptomics analysis of 125I intervention in MHCC-97H cells

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).

Figure 2. Transcriptomics analysis of MHCC-97H cells treated with 125I. (A) Volcano plot of differentially expressed genes. (B-D) Gene Ontology enrichment analysis: (B) BP, (C) CC and (D) MF categories. (E) Reactome enrichment analysis of the transcriptomics data. (F) DO enrichment analysis of transcriptomics data. BP, biological process; CC, cellular component; DO, Disease Ontology; FC, fold change; 125I, iodine-125; MF, molecular function.

Proteomics analysis of 125I intervention in MHCC-97H cells

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).

Figure 3. Proteomics analysis of MHCC-97H cells treated with 125I. (A) Volcano plot of differentially expressed genes. (B) GO enrichment analysis. (C) KEGG pathway analysis of proteomics data. (D) EggNOG classification; A, RNA processing and modification; B, Energy production and conversion; C, Cell cycle control, cell division, chromosome partitioning; D, Amino acid transport and metabolism; E, Nucleotide transport and metabolism; F, Carbohydrate transport and metabolism; G, Coenzyme transport and metabolism; H, Lipid transport and metabolism; I, Translation, ribosomal structure and biogenesis; J, Transcription; K, Replication, recombination and repair; L, Cell wall/membrane/envelope biogenesis; M, Posttranslational modification, protein turnover, chaperones; N, Inorganic ion transport and metabolism; O, Secondary metabolites biosynthesis, transport and catabolism; P, Signal transduction mechanisms; Q, Intracellular trafficking, secretion, and vesicular transport; R, Defense mechanisms; S, Cytoskeleton. (E) Subloc classification. In Fig. 3, **P<0.01, ***P<0.001. FC, fold change; GO, Gene Ontology; 125I, iodine-125; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Integrated proteomics and transcriptomics analysis

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).

Figure 4. Transcriptomics and proteomics analysis of 125I intervention in MHCC-97H cell. (A) GO classification: (B) Biological process, (C) cellular component and (D) molecular function categories. (E) GO enrichment analysis. (F) Histogram of KEGG analysis. The vertical axis represents the KEGG metabolic pathway and the horizontal axis represents the number of proteins or genes annotated under that pathway. The borderless bar chart represents proteins, while the bar chart with a border represents genes. (G) KEGG enrichment analysis. (H) Venn diagram. The graph under the Venn diagram shows the number of differentially expressed gene/protein in each group. DEG, differentially expressed gene; DEP, differentially expressed protein; GO, Gene Ontology; 125I, iodine-125; KEGG, Kyoto Encyclopedia of Genes and Genomes.

STRING analysis reveals an interaction network of differentially expressed genes in MHCC-97H cells after 125I intervention

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).

Figure 5. Network diagrams of differentially expressed genes in MHCC-97H cells after 125I intervention. Network diagrams of (A) GPNMB, (B) C4BPA, (C) MT2A, (D) CTH and (E) H1-0. 125I, iodine-125.

Pan-cancer analysis of differentially expressed genes

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).

Figure 6. Prediction analysis of differential gene expression. Pan-cancer analysis of (A) GPNMB, (B) C4BPA, (C) MT2A, (D) CTH and (E) H1-0 expression. Expression of (F) GPNMB (G) C4BPA, (H) MT2A, (I) CTH and (J) H1-0 in hepatocellular carcinoma. *P<0.05, **P<0.01, ***P<0.001.

Differential gene expression validation

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.

Figure 7. Verification of GPNMB, C4BPA, MT2A, CTH and H1-0 expression in MHCC-97 cells. mRNA expression levels of (A) GPNMB, (B) C4BPA, (C) MT2A, (D) CTH and (E) H1-0 in MHCC-97 cells. *P<0.05, **P<0.01, ***P<0.001 vs. control. 125I, iodine-125.

Discussion

Liver cancer is known for its rapid cell proliferation, invasion and metastatic capabilities. Apoptosis is a process of programmed cell death that is closely linked to the proliferative abilities of cancer cells and is key to controlling tumor growth. In recent years, transcriptomics and proteomics are emerging research fields, which have served an important role in the study of liver cancer. Based on proteogenomic characteristics, liver cancer can be precisely classified and targeted for treatment, with proteins being the main units that perform cellular functions (15). Integrating transcriptomics and proteomics data may provide a more comprehensive molecular landscape of liver cancer, helping to understand how changes in gene expression translate into changes at the protein level and affect the biological behavior of tumors, thus offering new perspectives for the early diagnosis and treatment of liver cancer (16,17).

The clinical application of 125I radioactive seed implantation therapy has made significant progress in the treatment of liver cancer. For example, CT-guided 125I close-range radiotherapy is considered a safe and effective therapy, without serious adverse events, which has advantages of a high local control rate and being minimally invasive (18). The present study indicated that 125I treatment promoted the apoptosis of HCC cells, and inhibited their proliferation, invasion and migration. However, the underlying mechanisms by which 125I inhibits tumor progression are not yet clear. The present study conducted a comprehensive analysis based on transcriptomics and proteomics data, and identified differential expression of the mRNA and protein levels of GPNMB, C4BPA, H1-0, CTH and MT2A in HCC cells treated with 125I. GPNMB is a transmembrane glycoprotein that includes a long extracellular domain (ECD) and a short intracellular domain (ICD). The ICD region contains immunoreceptor tyrosine-based activation motifs and leucine-rich motifs, which are associated with intracellular signal transduction and the induction of cancer stem cell characteristics (19,20). The ECD is involved in the regulation of various signaling pathways, such as the AKT-POU2F1-ECD pathway, the RB/E2F pathway and GLUT4-dependent glycolysis, which are related to cancer cell migration, growth, differentiation and other functions. Chen et al (21) revealed that GPNMB was highly expressed in HCC cells, whereas inhibiting the expression of GPNMB could reduce the progression of HCC. The present study revealed that, after 125I intervention, GPNMB was significantly increased at both the transcriptional and protein levels, suggesting that GPNMB may not be a direct target of 125I treatment, and the ECD of GPNMB remains intact after 125I treatment.

C4BPA is an effective soluble inhibitor of the classical and lectin pathways of the complement system, composed of complement control protein domains (22). The lack of a C4b binding site leads to the loss of all inhibitory functions of C4BPA in the classical complement pathway (23). Feng et al (24) reported that C4BPA was significantly upregulated in liver cancer tissues compared with in adjacent healthy tissues, providing a mechanism for cancer cells to evade immune system attacks. Inhibiting the expression of C4BPA or blocking its interaction with the complement system may also help to enhance the response of patients with liver cancer to immunotherapy, thereby improving the therapeutic effect. In the present study, after 125I intervention, the transcriptional levels of C4BPA were significantly decreased, which is similar to previous studies (24–26); therefore, inhibiting the expression of C4BPA may affect the phenotype of liver cancer cells. However, proteomics analysis revealed that the protein expression levels of C4BPA were significantly increased, suggesting that this discrepancy in protein levels may be due to post-transcriptional regulation, such as mechanisms mediated by small RNAs. Similar results were revealed regarding the H1-0 gene. Notably, elevated expression levels of H1-0 have been shown to be positively associated with cancer recurrence and lower survival rates (27,28). In paclitaxel-resistant ovarian cancer cells, H1-0 has been reported to be upregulated, whereas knocking out H1-0 was shown to significantly downregulate the androgen receptor, enhancing the sensitivity of paclitaxel-resistant cell lines to paclitaxel (27,28).

CTH, also known as CSE, is one of the key enzymes in the production of hydrogen sulfide, which has a role in promoting tumor formation by regulating angiogenic mechanisms in tumors. Pan et al (29) revealed that the expression levels of CSE were abnormally high in HepG2 and PLC/PRF/5 liver cancer cell lines. Inhibition of CSE and its downstream signaling pathways could activate the mitochondrial-mediated apoptosis process and block the signal transduction of cell proliferation, thereby inhibiting the proliferation of liver cancer cells. In the present study, after treatment with 125I, the mRNA expression levels of CTH were significantly decreased, which is consistent with the results of Pan et al (29). However, Xiang et al (30) recently reported that, in HCC, the expression levels of CTH were significantly reduced, and high expression of CTH was revealed to be associated with the active state of various immune cells. High expression of CTH may thus be related to a better prognosis for patients with HCC. Through proteomics analysis, the present study revealed that, after intervention with 125I, the protein expression levels of CTH were significantly increased, which is in contrast to the findings of the transcriptomics analysis. The discrepancies observed in previous studies, as well as the inconsistency in the expression of CTH at the transcriptional and protein levels in the present study, indicated that the specific role and mechanism of CTH in liver cancer remain controversial. Therefore, to fully understand the role of CTH in the development of liver cancer, more in-depth research is needed in different liver cancer cell lines and clinical samples. This will help to reveal the complex mechanisms of CTH in liver cancer and to assess its potential as a therapeutic target. Future research may need to focus on the regulatory network of CTH and how it affects the response of liver cancer cells to treatment and its interaction with the immune microenvironment.

MT2A is a protein belonging to the metallothionein family. In co-expression gene analysis of microarray data in HCC, the transcription factor FOS and its target gene MT2A were both revealed to be upregulated in the HCC cell line HepG2, having an important role in the pathogenesis of HCC and potentially serving as a therapeutic target for HCC (31,32). According to a previous study, the levels of MT-1 and MT-2A have been reported to be markedly reduced in primary human HCC and diethylnitrosamine-induced mouse liver tumors, mainly due to transcriptional repression (33). In addition, it has been reported that in HCC the expression levels of MT-1 and MT-2 in the nucleus and cytoplasm are closely related to the occurrence of liver cancer and the degree of tumor differentiation and invasiveness, and may be important biomarkers for predicting the prognosis of patients with HCC (34). In colorectal cancer, the inhibition of MAT2A/MAT2B expression has been shown to inhibit the migration and invasion of cancer cells (35). In the present study, it was revealed that the protein expression of MAT2A in liver cancer cells was significantly decreased after 125I intervention, which is consistent with previous research results. Therefore, it could be hypothesized that 125I may inhibit the proliferation, invasion and migration of liver cancer cells by regulating the expression of the MAT2A protein. The increase in its transcriptional mRNA levels and the decrease in its protein expression levels after 125I intervention may be due to the regulation of the translation process, leading to a reduction in protein synthesis, but further experimental verification is required.

To the best of our knowledge, the present study is the first to link 125I intervention with changes in specific gene expression patterns, providing new insights into the potential roles of CTH and MT2A in HCC. Additionally, the study explored the possible impact of these gene expression changes on disease progression and response to treatment. However, there are some limitations in the current study. Although the integrated analysis revealed differential expression of five proteins at both the mRNA and protein levels, these identified proteins lack a protein-protein interaction network, highlighting the limitations of our current understanding. Furthermore, the study was conducted using a single HCC cell line, necessitating further experiments to validate the results of the omics analysis and elucidate the specific mechanisms of 125I action. Addressing these issues in future research will enhance the robustness and applicability of the study results.

In conclusion, the present study identified the inhibitory effect of 125I on HCC, manifested as suppression of proliferation, invasion and migration, and promotion of cell apoptosis. The integration of transcriptomics and proteomics data implicated MT2A and CTH in the antitumor effects of 125I. RT-qPCR validated some of the results. However, further investigation is required to ascertain whether MT2A and CTH act as pivotal mediators in the suppression of tumor progression by 125I. The present findings provide novel insights into the potential mechanisms of 125I radiation particle therapy in liver cancer, and offer new therapeutic targets for the management of this disease.

Acknowledgements

Not applicable.

Funding

This study was supported by the Tianjin Health Science and Technology Project (grant no. TJWJ2021MS013) and by the Tianjin Applied Basic Research Diversified Investment Fund Project (grant no. 21JCYBJC01060).

Availability of data and materials

The transcriptomic data generated in the present study may be found in the NCBI Sequence Read Archive by using the following URL: https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1135734. The proteomic data generated in this study can be found in the iprox with login number PXD054130 or by visiting the following website: https://www.iprox.cn/page/PSV023.html;?url=17294821861659WCR, password: Oqts. Other data generated in this study can be requested from the corresponding author.

Authors' contributions

JS and ED conceived and designed the study. YY participated in the research design, conducted experiments and wrote the manuscript. WY assisted with the experiments, analyzed the data and revised the manuscript. YY and JS confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References