Machine learning model reveals roles of interferon‑stimulated genes in sorafenib‑resistant liver cancer
- Authors:
- Published online on: July 15, 2024 https://doi.org/10.3892/ol.2024.14571
- Article Number: 438
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Copyright: © Seo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
HCC is the most common form of liver cancer and the fourth leading cause of mortality worldwide (1,2). It predominates as the most common form of liver cancer, representing approximately 90% of all cases (3). Infection with Hepatitis B virus (HBV) or Hepatitis C virus (HCV) is a major risk factor for HCC development (4). Metabolic-associated steatohepatitis (MASH) and metabolic-associated fatty liver disease (MAFLD) associated with metabolic syndrome have emerged as contributing factors for HCC (5–7). Surgical treatments result in high patient survival rates. However, this approach has only been applied to patients with early stage HCC (8,9). Recently, atezolizumab plus bevacizumab has emerged as the therapy for unresectable HCC (10), sorafenib remains a treatment option for these patients.
Sorafenib, a multi-targeted tyrosine kinase inhibitor, has been approved as a therapeutic agent for advanced-stage HCC (11). Sorafenib inhibits tumor cell proliferation by suppressing the activity of BRAF, RAF1, and kinases in the MEK/ERK signaling pathways (12). In addition, the anti-angiogenesis effect of sorafenib is mediated by platelet-derived growth factor receptor (PDGFR-β), vascular endothelial growth factor receptors (VEGFR-1 and VEGFR-2), and c-KIT (13). Despite sorafenib treatment, drug resistance persists in some patients with advanced HCC (14). Only 30% of the patients with HCC benefit from sorafenib, and acquired resistance commonly occurs within 6 months (15). Therefore, the mechanisms underlying sorafenib resistance must be elucidated.
Type I interferons (IFNs) are cytokines with antiviral, anti-proliferative, and immunomodulatory effects that play crucial roles in suppressing viral infections (16). Type I IFNs bind to IFN receptors, leading to the phosphorylation of JAK1 and TYK2 (17). Subsequently, STAT1 and STAT2 are phosphorylated to form a complex with IRF9 (18). The ISGF3 (phosphorylated STAT1, phosphorylated STAT2 and IRF9) complex translocates into the nucleus and upregulates the expression of genes such as interferon-stimulated genes (ISGs) with antiviral functions (19). However, low concentrations of interferon lead to the dephosphorylation of STAT1 and STAT2. At this stage, unphosphorylated STAT1 and STAT2 bind to high levels of IRF9 to form the U-ISGF3 complex (17,20). The U-ISGF3 complex translocates to the nucleus and regulates the expression of genes such as U-ISGs, OAS1, MDA5, and BST2 (21,22). U-ISGs are associated with resistance to chemotherapy and irradiation, which are correlated with resistance to DNA damage (23).
Machine learning has become a powerful tool for the identification of diagnostic genes. We organized the data for input into the machine learning model by generating a vector of gene features that represented the influence of genes on other genes and the influence they received (24). The gene feature vector was extracted from the impact matrix generated by applying a modified PageRank algorithm to a patient-specific gene network constructed using the integrated gene network, gene variants, and gene expression data. A machine learning model was constructed by combining two autoencoders and a deep neural network. The model was trained using gene feature vectors generated from data obtained from patients with liver cancer in The Cancer Genome Atlas (TCGA) (25). These gene feature vectors are labeled based on the presence of genetic mutations and a list of known cancer driver genes. We utilized this trained machine learning model to input gene feature vectors generated from Huh-7 cells to investigate genes associated with sorafenib resistance. The model learns the patterns of influence that known cancer driver genes have on other genes; the more the input gene feature vector has feature patterns similar to the cancer driver genes, the closer the output value will be to 1. Among the genes with high model outputs in samples with sorafenib resistance, particularly those with low model outputs in samples without sorafenib resistance, we selected candidate genes involved in the mechanism of sorafenib resistance and identified the role of STAT1 in this process.
In the current study, machine learning revealed that U-ISGs were highly expressed in sorafenib-resistant liver cancer cells. We further found that the U-ISGF3 complex upregulated U-ISGs in sorafenib-resistant liver cancer cells. Our findings suggest that U-ISGs play a significant role in sorafenib resistance in liver cancer cells, and U-ISGF3 induces sorafenib resistance in liver cancer cells.
Materials and methods
RNA-sequencing analysis
RNA-sequencing analysis was performed using Human liver cancer cell lines (Huh-7, sorafenib resistant Huh-7, HepG2 and sorafenib resistant HepG2). The generated RNA-sequencing libraries were sequenced using an Illumina sequencing system (Macrogen). RNA-sequencing analysis was conducted on Huh-7 (SAMN41561228), sorafenib resistant Huh-7 (SAMN41561229) HepG2 (SAMN41561230) and sorafenib resistant HepG2 (SAMN41561231), obtained from the NCBI Sequenced Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1117191, last accessed on May 27, 2024).
Overall process to identify sorafenib resistance-associated genes
The first step in identifying genes associated with sorafenib resistance using machine learning involves generating gene feature vectors from LIHC (Liver Hepatocellular Carcinoma) tumor patient samples obtained from TCGA, as well as Huh-7 samples with sorafenib resistance, along with Huh-7 samples with sorafenib resistance. These gene feature vectors represent the rows and columns of the impact matrix, calculated by applying a modified PageRank algorithm to the patient-specific gene network. This network is constructed using integrated gene network data, the patient's genetic variants, and gene expression data. Once the gene feature vector generation is complete, we construct the training data using the TCGA LIHC samples' gene feature vectors and proceed to train the model. To identify candidate genes related to sorafenib resistance, the gene feature vectors from Huh-7 samples with and without sorafenib resistance are input into the machine learning model. By comparing the sorafenib-resistant and non-sorafenib-resistant sample groups, genes exhibiting particularly high scores in the sorafenib-resistant group are considered candidate genes associated with sorafenib resistance. A visual representation of the overall process is presented in Figure 1.
Construction of patient-specific gene networks
The construction of a patient-specific gene network involves selecting relevant edges from the integrated gene network, considering the specific characteristics of each patient. The integrated gene network was created by combining directed edges from functional interaction networks obtained from Reactome (26). Additionally, gene regulation networks were incorporated from the RegNetwork and TRRUST (27).
For an edge to be included in the patient-specific gene network, it had to satisfy at least one of two conditions: (1) at least one gene from a gene pair connected by an edge exhibited a mutation, and the mutational status utilized information specific to individual patients; (2) the expression of two connected genes aligned with the overall expression pattern observed in the entire cancer sample. To determine this, the RANSAC algorithm was executed 10 times to generate 10 regression models. RANSAC uses the expression values of genes corresponding to the departure node of an edge as input and estimates the regression model parameters to predict the expression of genes corresponding to the arrival node. Regression models with regression coefficients below 0.1 were excluded from consideration. If at least one regression model identified a patient as an inlier, that particular edge was included in the patient's gene network. After acquiring a set of edges that met one or more of the previously mentioned conditions, the construction of the patient-specific gene network was accomplished using Equation (1):
W = (I – Ψ)Α + Ψ
where A is an adjacent matrix of individual patients, and each component has one value among 0, 1, and 2. Specifically, if the patient has an edge connecting genes i and j, the value of Aij becomes 2 if there is a mutation in gene i, and 1 if there is no mutation. If there are no edges connecting genes i and j, the value of Aij becomes zero. I is an identity matrix, and Ψ is a diagonal matrix meaning the weight of a self-loop in the gene network for each patient. The self-loop weight was calculated using a one-sample t-test on the gene expression data of the patient and control groups. When the self-loop weight was 0, the gene was incapable of affecting itself; conversely, when it was set to 1, the gene remained unaffected by the gene network.
Generation of gene feature vectors using modified PageRank
The modified PageRank algorithm was applied to the gene network of individual patients to produce an impact matrix. The resulting impact matrix rows and columns served as gene feature vectors, and their calculation involved iterating Equation (2):
IΜτ+1 = W̃ × IMτ
where W̃ is a stochastic matrix, which is a matrix with a column sum of 1, calculated by dividing the components of each column of the matrix W by the sum of the corresponding columns. W can be interpreted as the probability that patient gene i affects gene j. IM (impact matrix) is a square matrix of n × n, where n is the number of genes in the patient-specific gene network. The initial value matrix IM0 is a diagonal matrix in which the values of the diagonal components are all 10,000, and the impact matrix at τ + 1 is calculated as the product of the stochastic matrix W and the impact matrix at τ. If Equation (2) is repeated, each column of IM0 is a one-hot vector whose value exists only in the component of the corresponding column index; therefore, the initial value of each column spreads to other components along the patient-specific gene network. Iteration of Equation (2) ends when the impact matrix converges.
The components of the converged impact matrix, denoted as IMij, represent the influence of gene j on gene i for a given patient. Therefore, the ith column of IM represents the impact that gene i has on all genes in the network, denoted as GIV (give impact vector). The ith row represents the impact that gene i receives from all genes in the network, denoted as the RIV (received impact vector). The gene feature vector encompasses both GIV and RIV. Each patient had a different gene network composition. Therefore, for gene i, each patient had a different gene feature vector.
Training a cancer driver gene identification model
We employed a composite model consisting of two autoencoders and one deep neural network to develop an approach for identifying cancer driver genes. In the initial phase of the model, two autoencoders were used to manage GIV and RIV separately. The encoder compresses and represents high-dimensional gene feature vectors as low-dimensional latent vectors. Subsequently, the decoder reconstructs these latent vectors back into their original input data formats. The latent vectors of the GIV and RIV were concatenated and fed into the deep neural network, where the model predicted the probability that the input gene feature vector represented a cancer driver gene. The parameters used in the machine learning model are shown in Table I.
To generate the training data, we gathered 360 tumor patient samples and 50 normal samples from TCGA using search term ‘LIHC’. During the preprocessing phase, gene expression level data excluded genes with an FPKM of zero in more than 80% of samples. Genetic mutation data were constructed by integrating somatic and gene copy number mutation data. Each gene was assigned a value of 1 if one or more mutations occurred and 0 otherwise.
After generating gene feature vectors for TCGA LIHC tumor samples, we labeled them using information from known cancer driver genes and genetic mutation data of the samples. Known cancer driver genes were sourced from the IntOGen and CGC databases (28,29). Only Tier 1 genes with substantial evidence of cancer occurrence in the CGC database were used. The list of known cancer driver genes comprised 30 genes from IntOGen and 28 genes from CGC, with eight genes common to both databases. Gene feature vectors corresponding to known cancer driver genes with mutations in individual samples were labeled true, whereas the remaining vectors were labeled false. To construct the training dataset, a falsely labeled gene feature vector was randomly selected for each truly labeled gene feature vector.
Cell lines
Human liver cancer cell lines (Huh-7 and HepG2) were obtained from American Type Culture Collection (Rockville, MD, USA). Huh-7 and Huh-7/sorafenib resistant (Huh-7-SR) cells were maintained in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (WelGENE, Daegu, Korea), 4.5 g/l glucose, L-glutamine, and 100 U/ml penicillin/streptomycin (Invitrogen, Carlsbad, CA) at 37°C with 5% CO2. HepG2, HepG2/sorafenib resistant (HepG2-SR) cells were maintained in minimum essential medium (MEM) with 10% fetal bovine serum and 100 U/ml penicillin/streptomycin. Huh-7 and HepG2 cells were obtained from American Type Culture Collection (ATCC). In order to establish sorafenib resistant cell lines, Huh-7, HepG2 cells were exposed to 1 µM sorafenib at first, and the concentration was gradually increased by 1 µM per month until reaching 6 µM.
RNA extraction, cDNA synthesis, and RT-qPCR
Total RNA was isolated using the TRIzol reagent (Invitrogen). cDNA was amplified using GoScriptTM Reverse Transcriptase (Promega). RT-qPCR was performed using specific primer sequences and SYBR based was conducted with a Light Cycler 480 (Roche Applied Science) in a total volume of 20 µl. The relative expression was analyzed using the 2-ΔΔCq method. The primer sequences for the gene were provided in Table II.
Immunoblotting
Huh-7 cells, Huh-7-SR cells, HepG2, and HepG2-SR cells were collected and lysed with RIPA buffer (20 mM Tris-HCl, 150 mM NaCl, 1% sodium deoxycholate, 1% Triton-X-100 and 0.1% SDS) containing protease inhibitors and phosphate inhibitors. SDS-PAGE (8%) was performed to separate the protein extracts. The proteins were transferred onto nitrocellulose membranes. After blocking the membrane in TBS containing 5% skim milk for 1 h. The antibodies used for immunoblotting were as follows: rabbit monoclonal anti-STAT1 (Cell signaling Technology, Cat#9176S), rabbit monoclonal anti-PY STAT1 (Cell signaling Technology, Cat#9167S), rabbit polyclonal anti-STAT2 (Cell signaling Technology, Cat#4594S), rabbit polyclonal anti-PY STAT2 (Cell signaling Technology, Cat#4441S), rabbit monoclonal IRF9 (Cell signaling Technology, Cat#28492), and horseradish peroxidase-conjugated secondary antibody (1:5,000).
siRNA transfection
Huh-7-SR cells were seeded at 2.5×105 cells per wells into a 6-well plate in DMEM. The following day, Huh-7-SR cells were transfected with siControl (Santa Cruz, Cat#sc-37007), siSTAT1 (Santa Cruz, Cat#sc-44123), siSTAT2 (Santa Cruz, Cat#sc-29492), or siIRF9 (OriGene, Cat#SR323091) at a concentration of 10 nM in 2 ml of serum-free medium containing RNAiMAX (Invitrogen, Cat#13778075). The medium was changed 4 h after transfection, and the cells were harvested after 48 h. Next, the transfected cells were treated with sorafenib for 24 h, followed by MTT assay.
Cell viability assay
For cell viability assays, liver cancer cells (Huh-7, Huh-7-SR, HepG2, HepG2-SR) were seeded into 96 well plates at 1×104 cells per well and incubated with sorafenib at a concentration of 0 to 32 µM for 24 h. After the addition of 10 µl of MTT solution (Abcam, Cat#ab211091), the samples were incubated for 4 h. Subsequently, the medium was removed, and 100 µl of DMSO was added to each well. The optical density was recorded at 590 nm using an enzyme-linked immunosorbent assay reader (MDS Analytical Technologies).
Statistical analysis
Statistical analyses were performed using GraphPad Prism 8 software (GraphPad Software Inc., San Diego, CA, USA). Human liver cancer cells are presented as the mean ± SEM. Unpaired t tests were used for statistical analyses. The significance was set at P<0.05.
Results
Genes associated with sorafenib resistance
Raw RNA-seq data were processed using Cutadapt, FastQC and MultiQC (30). Kallisto was used to determine the abundance of transcripts, which were normalized using TPM (31). Genes with a TPM value of zero in >80% of the samples were excluded from the gene expression data. Gene feature vectors were derived from the processed gene expression data of Huh-7 cells. DNA-seq data were processed using the GATK pipeline v4.1.7.0 (32). Subsequently, the gene feature vectors for each sample were input into a model, and the genes were ranked based on the output of the model. For each cell line, a list of genes was obtained that ranked in the top 50 in at least two of the three samples with sorafenib resistance and outside the top 50 in at least two of the three samples without sorafenib resistance. To eliminate genes with insignificant rank differences between the groups with and without sorafenib resistance, the average rank of each group of genes was compared, and genes with an average rank difference of less than twice were excluded. This led to the identification of 21 Huh-7 genes. By intersecting the results from the Huh-7 cell lines, six common genes were identified.
Unphosphorylated ISGF3 is positively associated with sorafenib resistance in liver cancer cells
Sorafenib-resistant liver cancer cell lines were established as follow: liver cancer cells were exposed to gradually increasing concentrations ranging from 1 to 6 µM of sorafenib (increasing 0.25 µM per cycle) for ~4 months (Fig. 2A). To observe the effects of sorafenib resistance, we treated liver cancer cell lines with increasing concentrations of sorafenib for 24 h using the MTT assay. Sorafenib-resistant liver cancer cell lines were resistant to higher concentrations of sorafenib (Fig. 2B). Compared with liver cancer cells, sorafenib-resistant liver cancer cells showed markedly increased levels of IRF9, but no significant difference in STAT1 and STAT2 (Fig. 2C and D).
Sorafenib resistance increases U-ISG levels
U-ISGs, including OAS1, IFI27, BST2, and MDA5, were increased in sorafenib-resistant liver cancer cells (Fig. 3A). In contrast, ISGF3 complex-dependent ISGs did not increase in Huh-7-SR cells (Fig. 3B) or were slightly induced in HepG2-SR cells (Fig. 3B). Moreover, other U-ISGs were robustly upregulated in the sorafenib-resistant liver cancer cells (Fig. 3C). However, ISGs produced by the phosphorylated ISGF3 complex were minimally increased (Fig. 3C).
U-ISGF3 inhibition re-sensitizes sorafenib-resistant liver cancer cells to sorafenib
To explore the role of the U-ISGF3 complex, we used a transfection method to reduce U-ISGF3 levels in Huh-7-SR cells (Fig. 4A). Downregulation of the U-ISGF3 complex in Huh-7-SR cells reduced U-ISG expression (Fig. 4B). We showed that sorafenib resistance was reduced in Huh-7-SR treated with transfection and in the MTT assay. As expected, the U-ISGF3 complex increased the viability of sorafenib-resistant liver cancer cells (Fig. 4C).
Discussion
Several studies have revealed a critical role of the U-ISGF3 complex in cancer (33). Thus, the inhibition of U-ISGF3 is emerging as an attractive therapeutic strategy for cancer. However, the relationship between the U-ISGF3 complex and sorafenib resistance in HCC remains poorly understood. Here, we confirmed that the U-ISGF3 complex promotes sorafenib resistance and that inhibition of the U-ISGF3 complex reduces sorafenib resistance.
Sorafenib is a multityrosine kinase inhibitor used to treat HCC (34). However, its sensitivity appears in only 30% of the patients, and within 6 months, sorafenib resistance is acquired in HCC (35). The first mechanism occurs when there is no initial response to sorafenib treatment and is mainly associated with altered activation of signaling pathways. In contrast, the second mechanism refers to the development of resistance to sorafenib after following an initial response. Sorafenib resistance targets multiple cellular pathways that contribute to tumor survival and proliferation (36,37). The PI3K/Akt/mTOR signaling pathway is strongly activated by prolonged exposure to sorafenib, leading to the development of resistance (38). Machine learning was used to elucidate the mechanisms underlying sorafenib resistance. We employed a deep neural network to identify candidate genes responsible for sorafenib resistance. However, machine learning methods, including deep neural networks, require a substantial number of samples. Therefore, we trained the model using TCGA data and applied it to the data obtained from Huh-7 cells. While attempting to identify driver genes exhibiting distinct patterns in samples with and without resistance, an inherent limitation arises owing to factors such as batch effects between TCGA and Huh-7 cell data, potentially causing a decrease in accuracy.
Using ML, we determined the significance of STAT1 expression in sorafenib resistance. In this study, we found no differences in STAT1 expression in sorafenib-resistant cell lines, whereas only IRF9 was differentially expressed. The U-ISGF3 complex binds to the promoter regions of U-ISGs, particularly IRF9, which contributes to most of the U-ISG promotor region. This suggests that IRF9 is important for U-ISG expression. Fig. 5 shows that in sorafenib-resistant cell lines, the U-ISGF3 complex translocates into the nucleus to regulate the expression of U-ISGs, leading to the acquisition of resistance. Several studies have suggested that U-ISGs are critical regulators of irradiation or chemotherapy. The knockdown of STAT1, STAT2, and IRF9 significantly enhanced the antitumor activity of sorafenib in vitro.
In conclusion, our results indicate that the U-ISGF3 complex plays a crucial role in mediating sorafenib resistance in liver cancer cells. These results suggest that this mechanism may have clinical relevance and could potentially be applicable to patients. The current study is limited by the lack of patient samples, and future research should be verified using patient samples.
Acknowledgements
Not applicable.
Funding
This work was supported by the Basic Science Research Program of the National Research Foundation of Korea through the Ministry of Science Information and Communication Technology (grant no. NRF-2019R1A2C3005212) and the Research Fund of Seoul St. Mary's Hospital (grant no. 2022-001). This work was also supported by the Basic Science Research Program of the National Research Foundation of Korea through the Ministry of Science Information and Communication Technology (grant no. RS-2024-00337298).
Availability of data and materials
The datasets have been deposited in the Sequenced Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1117191) under accession no. PRJNA1117191. The rest of data generated in the present study may be requested from the corresponding author.
Authors' contributions
DHS contributed to collecting raw data, analyzing data interpretation, designing the research and writing the draft of the paper and revision of the manuscript. JWP, HWJ, MWK and BYK contributed to collecting raw data and data analysis. DYL and JJL contributed to the study conception and data interpretation. SKY and JWJ interpreted the data and revised the manuscript. PSS designed the research and supervised the study. JGA contributed to the study design, data interpretation, drafting the paper, and manuscript revision. PSS and JGA confirm the authenticity of the raw data. All authors have reviewed and approved the final 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.
Glossary
Abbreviations
Abbreviations:
|
HCC |
hepatocellular carcinoma |
|
U-ISGs |
unphosphorylated interferon-stimulated genes |
|
U-ISGF3 |
unphosphorylated interferon-stimulated gene factor-3 |
|
HBV |
hepatitis B virus |
|
HCV |
hepatitis C virus |
|
MASH |
metabolic-associated steatohepatitis |
|
MAFLD |
metabolic-associated fatty liver disease |
|
PDGFR-β |
platelet-derived growth factor receptor |
|
VEGFR |
vascular endothelial growth factor receptors |
|
IFNs |
type I interferons |
|
ISGs |
interferon-stimulated genes |
|
LIHC |
liver hepatocellular carcinoma |
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