Exploring plasma biomarkers for therapeutic response to tyrosine kinase inhibitors in advanced hepatocellular carcinoma: Interleukin-8 as a key prognostic indicator

Exploring plasma biomarkers for therapeutic response to tyrosine kinase inhibitors in advanced hepatocellular carcinoma: Interleukin-8 as a key prognostic indicator | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring plasma biomarkers for therapeutic response to tyrosine kinase inhibitors in advanced hepatocellular carcinoma: Interleukin-8 as a key prognostic indicator Sujin Kim, Hye Ri Ahn, Hui Gyeong Kim, Dooyeon Kim, Ji Eun Han, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8323662/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aim: Hepatocellular carcinoma (HCC) is the predominant primary liver cancer and third leading cause of cancer-related death. Despite improvements in overall survival (OS), those in HCC-specific survival rates remain modest. Tyrosine kinase inhibitors (TKIs) are crucial first-line HCC treatments when immune checkpoint inhibitors are unsuitable. To identify plasma biomarkers predicting the therapeutic response to TKIs in patients with HCC. Methods Pre-treatment plasma samples from 60 patients with advanced HCC treated with sorafenib or lenvatinib were analyzed using targeted proteomics. Differentially expressed proteins were identified based on the treatment response ( P < 0.05). Results Plasma levels of metalloproteinase 12 and vascular endothelial growth factor A were elevated in patients with progressive disease compared with those with partial response or stable disease, correlating with shorter progression-free survival (PFS) and OS. Among patients with PFS ≥ 12 months, C-C motif chemokine ligand 20, C-X-C motif chemokine ligand 1, C-X-C motif chemokine ligand 5, fibroblast growth factor 2, interleukin (IL)-7, IL-8, IL-18, latency-associated peptide transforming growth factor beta 1, and mucin 16 expressions were significantly upregulated, with IL-8 (CXCL8) levels demonstrating the highest predictive accuracy (area under the receiver operating characteristic = 0.91) and prognostic power for PFS (hazard ratio [HR] = 2.97, P = 0.0015) and OS (HR = 3.64, P = 0.001). CXCL8 expression was predominantly localized in tumor-associated myeloid cells and enriched in epithelial–mesenchymal transition- and immune modulation-related pathways, highlighting its importance in the tumor microenvironment. Conclusions Plasma biomarkers hold promise for predicting treatment response in patients with advanced HCC undergoing TKI therapy. Elevated IL-8 levels are strongly associated with poor outcomes, emphasizing their potential to guide therapeutic decisions and stratify high-risk patients. Hepatocellular carcinoma Tyrosine kinase inhibitors Olink proteomics Plasma Biomarkers Interleukin-8 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Primary liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for approximately 80% of all cases [ 1 , 2 ]. Despite advancements in overall survival (OS), improvements in HCC-specific survival rates remain modest. The five-year survival rate for advanced HCC is only 2–11%, primarily due to its frequent diagnosis at a stage where curative treatments are no longer effective [ 3 ]. In recent years, immune checkpoint inhibitor (ICI)-based therapies, such as the combination of atezolizumab and bevacizumab, have been established as first-line treatments. Tyrosine kinase inhibitors (TKIs), including sorafenib and lenvatinib, remain crucial first-line options when ICI-based regimens are unsuitable, and are used as second-line treatments for patients who fail to respond to ICI-based therapies [ 4 – 6 ]. Protein tyrosine kinases catalyze the transfer of the γ-phosphate of ATP to the tyrosine residues of protein substrates [ 7 ]. Activated tyrosine kinases drive tumor cell proliferation and growth, inhibit apoptosis, and promote angiogenesis and metastasis [ 8 , 9 ]. As these effects are initiated by receptor tyrosine kinase activation, treatment with TKIs is the primary targeted therapy. TKIs are widely used to treat non-small-cell lung cancer [ 10 ], renal cell carcinoma [ 11 ], gastrointestinal stromal tumors [ 12 ], and breast cancer [ 13 ]. Sorafenib and lenvatinib are key TKIs in the treatment of advanced-stage HCC [ 14 , 15 ]. However, their low response rates remain a significant challenge, with approximately 10–20% of patients achieving an objective response [ 16 , 17 ]. These limited response rates emphasize the need for predictive biomarkers to guide treatment decisions in HCC. Reliable biomarkers may enable clinicians to tailor therapy more effectively and identify the patients most likely to benefit from TKI treatment [ 18 , 19 ]. Several studies have suggested that the immunosuppressive tumor microenvironment (TME) plays a crucial role in influencing the TKI treatment response [ 20 – 22 ]. The immunosuppressive nature of the TME is closely associated with the expression of circulating plasma proteins, which may serve as valuable biomarkers for predicting therapeutic outcomes. To identify plasma biomarkers associated with TKI efficacy in patients with advanced HCC, we conducted a targeted proteomic analysis of pre-treatment blood samples, focusing on 96 key immuno-oncology proteins involved in immune regulation and TME interactions. By analyzing these immune-related proteins, we aimed to improve patient stratification and guide treatment planning, ultimately enhancing clinical outcomes in patients with advanced HCC. 2. Methods 2.1 Study Design and Patient Enrollment We measured the expression of 96 immuno-oncology panel proteins in pre-treatment plasma samples from patients with advanced HCC treated with TKIs. The predictive power of the identified DEPs was assessed using receiver operating characteristic (ROC) curve analysis, and survival analysis was performed based on their expression. Blood samples and clinical data from patients with advanced HCC treated with TKIs were obtained from the Biobank of Ajou University Hospital (Suwon, South Korea), a member of the Korea Biobank Network. The inclusion criteria were as follows: (1) age between 18 and 80 years; (2) modified Union for International Cancer Control (mUICC) stage IVa or IVb [ 23 ]; (3) mUICC stage II or III with extensive disease or main vascular invasion precluding curative local treatment; (4) treatment with sorafenib or lenvatinib for more than 4 weeks; and (5) availability of plasma samples collected within one month before treatment initiation. Patients were excluded if they had a history of malignancy other than HCC or if data for evaluating the treatment response after TKI administration were insufficient. The clinical data included patient demographics (age and sex), administration of TKIs (sorafenib or lenvatinib), laboratory parameters (platelet count, albumin, bilirubin, creatinine, alpha-fetoprotein, alanine aminotransferase, and aspartate aminotransferase levels; and international normalized ratio), etiology of liver disease, tumor stage (mUICC stage), treatment response to TKIs, and survival outcomes, including progression-free survival (PFS) and OS. All experiments conducted using human samples adhered to the guidelines of the Declaration of Helsinki of 1975. This study was approved by the Institutional Review Board of the Ajou University Hospital, Suwon, South Korea (AJIRB-BMR-SMP-17-188). The requirement for informed consent was waived as this study exclusively utilized anonymized data, ensuring the privacy and confidentiality of individual participants. 2.2 Definition of Terms HCC was diagnosed in accordance with the guidelines of the American Association for the Study of Liver Diseases [ 24 ]. Tumor stage was defined according to the mUICC staging system [ 23 ]. Tumor response was evaluated based on the modified Response Evaluation Criteria for Solid Tumors. The criteria were as follows: complete response (CR), characterized by the disappearance of all intratumoral arterial enhancements in the target lesions. Partial response (PR) was defined as a reduction of at least 30% in the sum of the diameters of the viable (arterially enhancing) target lesions, using the baseline sum of diameters as the reference. Stable disease (SD) referred to cases that did not meet the criteria for either PR or progressive disease (PD). PD was defined as an increase of at least 20% in the sum of the diameters of the viable target lesions compared to the smallest recorded sum after treatment [ 25 ]. PFS was defined as the time from the initiation of TKI treatment to the date of disease progression or death from any cause, whichever occurred first. OS was defined as the time from the initiation of TKI treatment to the date of death from any cause. Patients without disease progression or death at the time of analysis were censored at the date of their last follow-up. 2.3 Plasma Sample Processing and Proteomic Analysis Using the Olink® Target 96 Immuno-Oncology Panel Pre-treatment plasma samples from patients with advanced HCC treated with TKIs (n = 60) were analyzed using the Olink® Target 96 Immuno-Oncology panel, which is based on a high-sensitivity and specific proximity extension assay technology (Olink Proteomics, Uppsala, Sweden) that can simultaneously analyze 96 immuno-oncology-related protein biomarkers. The Olink® Target 96 panel process was conducted following the manufacturer's protocol. Plasma samples (1 µL) were incubated overnight at 4°C with 92 antibody pairs, each conjugated to DNA tags, to allow binding to the target proteins. The next day, extension and amplification steps were performed, where oligonucleotides brought into proximity were hybridized and extended using DNA polymerase to create DNA barcodes. These barcodes were amplified using PCR with the following thermocycling conditions: 50°C for 20 min, 95°C for 5 min, followed by 17 cycles of 95°C for 30 s, 54°C for 1 min, and 60°C for 1 min, with a final step at 10°C. The DNA reporters for each biomarker were then quantified using high-throughput real-time qPCR on the Olink® Signature Q100 system. Raw data from the Olink® Signature Q100 were imported, quality-checked, and normalized using inter-plate control to obtain normalized protein eXpression (NPX) values via Olink NPX Signature software (1.11.0). Quality control-flagged samples with deviations from the median NPX values of the internal controls and assays were filtered based on the limit of detection. DEPs were identified using a paired t- test with a P -value < 0.05, and false discovery rate control was performed using the Benjamini–Hochberg method. Data analysis and visualization were conducted using R version 4.2.2, with the OlinkAnalyze R package for the statistical analysis and visualization of DEPs. 2.4 DEP Analysis DEP analysis was performed to identify differentially expressed plasma proteins among three comparison groups: PR vs. SD/PD, PR/SD vs. PD, and disease control (DC, PFS ≥ 12 months) vs. disease progression (DP). The predictive value of significant proteins was assessed using ROC curve analysis, and Kaplan–Meier survival analysis was conducted to evaluate their association with clinical outcomes, including PFS and OS. 2.5 Public Data Acquisition Publicly available omics datasets were obtained from Gene Expression Omnibus (GSE94550, GSE248764, GSE273819, GSE140202, GSE109211, and GSE149614), GepLiver [ 26 ], and Mendeley (skrx2fz79n) Data. These datasets include transcriptomic data from TKI-treated HCC cell lines, HCC patient samples, and spatial transcriptomic data from HCC tissue sections. The analysis focused on evaluating C-X-C motif chemokine ligand 8 (CXCL8, also known as interleukin (IL)-8) expression patterns in relation to TKI resistance, liver disease progression, and the spatial localization of malignant versus non-malignant hepatocytes. 2.6 Comprehensive Liver Dataset Analysis Transcriptomic data from TKI-treated cell lines, including Huh-7, HepG2, and PLC/PRF/5, were analyzed to compare CXCL8 expression between the TKI-sensitive (Sorafenib-sensitive, Sor-Sen) and TKI-resistant (Sorafenib-resistant, Sor-Res; Lenvatinib-resistant, Len-Res) groups. Similarly, transcriptomic data from patients with HCC were used to compare CXCL8 expression between sorafenib non-responder (Sor-NR) and responder (Sor-R) groups. Additionally, transcriptomic data from the GepLiver database were used to analyze CXCL8 expression across various liver conditions, including normal liver, non-alcoholic fatty liver disease, cirrhosis, adjacent HCC (ADJ_HCC), and HCC tissues. Data were normalized to log 2 -transformed expression values and Z-score normalization was applied for consistent visualization. Statistical tests were performed to evaluate the differences in CXCL8 expression under different conditions. 2.7 Spatial Transcriptomics Analysis Spatial transcriptomic analysis was performed using Mendeley Data, which included annotated tissue sections (P7T–P11T) from patients with HCC. Tissue sections were classified into malignant and non-malignant hepatocytes, and CXCL8 expression was visualized using a color gradient representing minimal (blue) to maximal (red) expression. The proportion of CXCL8 + cells in malignant versus non-malignant hepatocytes was quantified for each tissue section to determine spatial distribution patterns. Differences in CXCL8 + cells between malignant and non-malignant regions were statistically evaluated, revealing the enrichment of CXCL8 + cells in malignant hepatocytes. 2.8 Analysis of CXCL8 Expression in Single-Cell RNA-Seq Data from HCC Patient Samples Single-cell RNA sequencing (scRNA-seq) data from the GSE149614 dataset were analyzed, focusing on HCC07 and HCC08 patient samples, including normal tissue (NT), primary tumor (PT), and portal vein tumor thrombus (PVTT). Raw data were processed using the Seurat package (v4.0.6) in R. Uniform manifold approximation and projection (UMAP) was used to visualize the distribution of cells, with cell clusters identified through a shared nearest-neighbor graph-based clustering approach and resolution parameters optimized to capture distinct cell populations. Cell-type annotations were assigned using canonical markers for B cells, endothelial cells, fibroblasts, hepatocytes, myeloid cells, and T/NK cells. Differential gene expression analysis was conducted to compare cell clusters in NT, PT, and PVTT groups, focusing on CXCL8 expression. Further analysis was performed on myeloid cells in the PT and PVTT. Sub-clustering of myeloid cells was conducted, followed by the identification of the C1, C2, and C3 clusters. Gene set enrichment analysis (GSEA) was performed using the ssGSEA algorithm to assess pathway enrichment in CXCL8 -positive cells within C1 and C2 clusters. Pathway databases included hallmark gene sets, focusing on epithelial–mesenchymal transition (EMT), inflammatory response, and tumor necrosis factor (TNF)-α signaling via nuclear factor (NF)-κB. 2.9 Correlation of CXCL8 with EMT-Associated Genes in TCGA-LIHC Cohort Correlation analysis was conducted to assess the relationship between CXCL8 expression and EMT-associated genes within CXCL8 + cells in C1 and C2 clusters, where ssGSEA analysis showed the highest enrichment for EMT pathways. EMT-related genes, including MMP2 , MMP9 , SNAI1 , and VIM , were selected for comparison. Bulk RNA-seq data from the The Cancer Genome Atlas (TCGA)-LIHC cohort (n = 371) were used for this analysis. Gene expression values were log 2 -transformed, and Pearson correlation coefficients (r) were calculated between CXCL8 and the selected EMT markers. The significance of correlations was determined using a two-tailed test, with values of P < 0.05 considered statistically significant. Scatter plots were generated to visualize the correlations, with regression lines fitted to depict trends in the data. 2.10 Statistical Analysis The statistical significance of the differences between the two groups was assessed using either the paired Student’s t -test or the unpaired Welch’s t -test using GraphPad Prism (version 9.0; GraphPad Software, San Diego, CA, USA). ROC analysis was conducted using the IBM SPSS software (IBM SPSS Statistics for Windows, version 22.0, released 2013, IBM, Armonk, NY, USA). ROC curves were analyzed to evaluate the diagnostic accuracy of candidate biomarkers. These curves were analyzed for sensitivity and specificity of the candidate biomarkers at various threshold values, and the area under the ROC curve (AUROC) was evaluated with 95% confidence intervals (CIs). Statistical significance was set at P < 0.05. Kaplan–Meier survival curves are statistical methods used to estimate survival function, and prognostic changes based on the expression of candidate markers were analyzed. The significance of P -value differences between the survival curves was evaluated using the log-rank test, with a P -value < 0.05 considered statistically significant. 3. Results 3.1 Baseline Characteristics of the Included Patients In a cohort of 60 patients, 39 received sorafenib and 21 received lenvatinib (Fig. 1 ). Each bar represents a patient, and the color of the bar indicates the degree of drug response. None of the patients demonstrated CR to TKI therapy. However, seven patients had a PR to treatment, 29 had SD, and 24 had PD. The distribution of patients aged older than 60 years and younger than 60 years was equal, with each group consisting of 30 individuals; the proportion of male patients was higher than that of female patients. Forty-nine patients (18.3%) had mUICC stage IV disease (Fig. 1 a). Representative computed tomography images illustrated the changes in tumor lesions before and after TKI therapy in patients who responded to treatment (Fig. 1 b). In the lung (left image) and liver (right image), the outlined tumor boundaries demonstrated a clear reduction in tumor size post-treatment, as indicated by the arrows. Notably, in the right liver lesions, a significant decrease in the viable solid portion was observed after treatment. 3.2 Identification of DEPs Based on Response to TKI Therapy Figure 2 a displays a volcano plot illustrating the DEPs according to treatment response. The proteins highlighted in red and blue indicate statistically significant differential expression. In the left plot of Fig. 2 a, five protein–placental growth factors (PGFs), adhesion G protein-coupled receptor G1 (ADGRG1), carbonic anhydrase IX (CAIX), cluster of differentiation 40 (CD40), and C-X3-C motif chemokine ligand 1 (CX3CL1), were identified as significantly upregulated proteins in the SD and PD groups compared with those in the PR group. Figure 2 b compares the expression of these five DEPs between the PR and SD/PD groups. Among them, ADGRG1 (AUROC = 0.76, 95% CI: 0.57–0.96, P = 0.02) and CAIX (AUROC = 0.76, 95% CI: 0.57–0.95, P = 0.03) demonstrated the highest AUROC value. The right plot in Fig. 2 a shows a volcano plot illustrating the DEPs between the PR/SD and PD groups. The expressions of angiopoietin 2 (ANGPT2), matrix metalloproteinase 12 (MMP-12), vascular endothelial growth factor A (VEGFA), and colony-stimulating factor 1 (CSF-1) were significantly upregulated, whereas those of IL-13 and C-X-C motif chemokine ligand 12 (CXCL12) were downregulated in the PD group (Fig. 2 c). In the ROC analysis, MMP-12 exhibited the highest AUROC value of 0.76 (95% CI: 0.6–0.88, P < 0.001, Fig. 2 c) among the six DEPs. Kaplan–Meier survival analysis was performed for the 11 plasma proteins identified in the DEP analysis (Fig. 3 ). Patients with higher plasma MMP-12 levels had significantly poorer PFS (hazard ratio = 2.10, 95% CI: 1.0–4.3, P = 0.04) and OS (HR = 2.09, 95% CI: 1.0–4.3, P = 0.04; Fig. 3 a). Patients with elevated plasma VEGFA levels also had significantly shorter PFS (HR = 2.20, 95% CI: 1.0–4.3, P = 0.02) and OS (HR = 2.05, 95% CI: 1.0–4.2, P = 0.04) than those with lower VEGFA levels (Fig. 3 b). In contrast, the remaining nine proteins showed no significant association with PFS or OS. Data for these proteins with non-significant log-rank P values are shown in Fig. S1 . 3.3 Analysis of the Targeted Plasma Proteome Based on the 12-Month PFS Criterion Additional analyses were conducted to identify plasma protein biomarkers predictive of disease control with TKI therapy (PFS ≥ 12 months). Among the 60 patients, 8 who achieved a PFS of ≥ 12 months were grouped into the DC group, whereas the remaining 52 were classified as DP. Figure 4 a presents a heatmap showing DEPs between the two groups. Hierarchical clustering in the heat map distinctly separated the DC and DP based on NPX levels, highlighting the distinct protein expression patterns between the groups. Figure 4 b displays a volcano plot highlighting the differences in pre-treatment plasma protein expression between the two groups. A total of nine proteins, namely C-C motif chemokine ligand 20 (CCL20), C-X-C motif chemokine ligand (CXCL1), C-X-C motif chemokine ligand 5 (CXCL5), fibroblast growth factor 2, IL-7, IL-8, IL-18, latency-associated peptide-transforming growth factor beta 1 (LAP TGF-beta-1), and mucin 16 (MUC-16), were significantly upregulated in the DP group (Fig. 4 c). Among these, IL-8 and MUC-16 demonstrated notably high diagnostic performance in distinguishing the DC group, with AUROC values of 0.91 (95% CI: 0.83–0.99, P < 0.001 and 95% CI: 0.83–0.98, P < 0.001; Fig. 4 d). Survival analysis was performed for nine proteins that showed significant differences between the DC and DP groups (Fig. 5 , Fig. S2). Of these, five proteins (CCL20, CXCL1, CXCL5, IL-8, and LAP TGF-beta-1) were significantly associated with poorer OS and PFS in patients with high expression levels. Among them, IL-8 exhibited the highest HR and lowest P -value in OS (HR = 3.64, 95% CI: 1.6–7.8, P = 0.001) and PFS (HR = 2.97, 95% CI: 1.4–6.2, P = 0.0015), highlighting its potential as a key prognostic biomarker. These findings suggest that IL-8 may play a pivotal role in disease progression and may serve as a critical target for therapeutic interventions aimed at improving the clinical outcomes of patients with HCC. To further validate the clinical significance of IL-8 and its role in TME, we conducted additional analyses using publicly available datasets. 3.4 Validation of Elevated CXCL8 (IL-8) Expression in TKI Resistance Using Public Transcriptomic Data We first examined CXCL8 (the gene encoding IL-8) expression in response to TKI treatment using public omics datasets. At the cell-line level, we analyzed CXCL8 expression in relation to TKI resistance. Transcriptomic data from wild-type (WT) and TKI-resistant (Res) HCC cell lines, including Huh7, HepG2, and PLC/PRF/5, were obtained from datasets GSE94550, GSE248764, and GSE273819, respectively (Fig. 6 a). Across all three cell lines, a significant increase in CXCL8 expression was observed in TKI-resistant cells compared with that in WT cells. Additionally, in another cell line dataset, GSE140202, sorafenib-resistant cell lines exhibited markedly higher CXCL8 expression levels than sorafenib-sensitive cell lines (Fig. 6 b). Next, we analyzed CXCL8 expression in pre-treated HCC tumor tissues from an HCC cohort using the transcriptomic dataset GSE109211. The analysis revealed that CXCL8 expression was significantly higher in the tumor tissues of sorafenib non-responders than in responders (Fig. 6 c). These findings collectively demonstrate that elevated CXCL8 expression is associated with TKI resistance in both HCC cell lines and the HCC cohort, corroborating the patterns observed in the pre-treatment plasma IL-8 results in our study. 3.5 Exploration of CXCL8 Expression in Liver Disease and Malignant Hepatocytes To further explore the significance of CXCL8 expression in liver disease and HCC, we analyzed the GepLiver database (DB) and Mendeley DB. These analyses were performed to compare CXCL8 expression across different liver conditions and to examine its spatial and cellular distribution within HCC tissues, providing insights into its potential role in disease progression and the TME. First, we analyzed CXCL8 expression under various liver conditions using the GepLiver DB. The analysis revealed significantly higher CXCL8 expression in HCC tissues than in non-tumor tissues, including normal liver, steatotic liver, and peri-tumoral non-cancerous tissues (Fig. 6 d). To further validate these findings, a spatial transcriptomic analysis was performed to examine the distribution of CXCL8 expression in HCC tissues. Figure 6 e shows hematoxylin and eosin-stained sections along with spatial maps of the malignant and non-malignant regions. CXCL8 expression was predominantly localized in malignant hepatocytes, with higher expression levels concentrated in tumor-dense areas, as visualized using heat maps. Quantification of CXCL8 -positive cells confirmed this trend, with a significantly higher proportion of CXCL8 -positive cells observed in the malignant regions than in the non-malignant regions across all analyzed samples (Fig. 6 f). Notably, certain samples, such as P8T and P9T, demonstrated more than a four-fold increase in CXCL8 -positive cell populations within malignant vs. non-malignant regions. These results suggest that CXCL8 expression is elevated in HCC and preferentially localized to malignant regions, highlighting its potential role in tumor progression and its relevance as a biomarker and therapeutic target in HCC. 3.6 Single-Cell Analysis Reveals CXCL8 Expression Patterns and Its Role in the Tumor Microenvironment of HCC To further elucidate the cellular origins and functional implications of CXCL8 expression in HCC, we analyzed single-cell RNA sequencing data from the GSE149614 dataset, which includes HCC and surrounding non-tumor tissues, enabling the identification of specific cell types that contribute to CXCL8 expression. Figure 7 a shows the UMAP visualization of cell clusters identified from HCC and adjacent non-tumor tissues, including hepatocytes, myeloid cells, endothelial cells, fibroblasts, B cells, and T/NK cells. Myeloid cells demonstrated the highest CXCL8 expression levels, as highlighted in the violin plot (Fig. 7 b). To investigate the functional heterogeneity within myeloid cells, a subset of myeloid cells from PTs and PVTT was recruited. This analysis revealed three distinct clusters: C1, C2, and C3 (Fig. 7 c). Differential gene expression analysis between these clusters identified CXCL8 as a marker that was highly enriched in the C1 and C2 clusters (Fig. 7 d). GSEA of CXCL8 + myeloid cells in clusters C1 and C2 revealed significant enrichment in pathways associated with EMT, inflammatory responses, and TNF-α signaling via NF-κB, suggesting a role of CXCL8 in promoting tumor progression and immune modulation (Fig. 7 e). Finally, we validated these findings using TCGA_LIHC data. CXCL8 expression was positively correlated with genes associated with EMT (e.g., SNAI1 , VIM ) and matrix remodeling (e.g., MMP-2 , MMP-9 ) (Fig. 7 f). These results further support the association between CXCL8 expression and the pathways driving tumor invasiveness and immune evasion in HCC. 4. Discussion In this study, we performed a targeted proteomic analysis using a 96-protein immuno-oncology panel to identify plasma biomarkers predictive of therapeutic responses to TKI therapy in patients with advanced HCC. Through the analysis of pre-treatment blood samples, we identified multiple differentially expressed plasma proteins associated with treatment response and clinical outcomes. Among these, IL-8 stood out as a key biomarker, demonstrating significant predictive power for distinguishing disease control from disease progression and showing strong correlations with shorter PFS and OS. These findings were further supported by analyses of public transcriptomic datasets, which consistently showed higher CXCL8 expression in TKI-resistant HCC cell lines and tumor tissues from TKI-treated HCC cohorts. Additionally, CXCL8 expression was markedly elevated in HCC tissues compared with that in other liver conditions, as revealed by GepLiver and spatial transcriptomic analyses. Single-cell RNA sequencing data provided deeper insights, showing that CXCL8 was predominantly expressed in myeloid cells within the TME, particularly in clusters enriched for pathways associated with EMT, inflammatory responses, and TNF-α signaling. Collectively, these results highlight the significant potential of IL-8 as a robust prognostic biomarker and promising therapeutic target in advanced HCC. The identification of predictive biomarkers for TKI therapy in HCC remains a challenge, hindering the improvement of therapeutic outcomes. Owing to the challenges in obtaining tumor tissues, the exploration of blood biomarkers has attracted attention as a noninvasive alternative for predicting TKI resistance in HCC. Plasma biomarkers reflect systemic changes and TME dynamics, offering practical advantages. Circulating VEGF, ANGPT2, and IL-6 levels have been associated with poor survival and resistance in patients with TKI-treated HCC [ 27 – 29 ]. Additionally, plasma secreted phosphoprotein 1 and risk models incorporating hepatocyte growth factor and fibroblast growth factor have shown promise in predicting treatment responses and survival [ 30 , 31 ]. Our study builds on these insights by leveraging a high-throughput immuno-oncology panel to identify plasma biomarkers predictive of TKI response. These findings highlight the utility of comprehensive biomarker panels in the development of personalized therapeutic strategies for advanced HCC. In this study, IL-8 emerged as a pivotal plasma biomarker with significant predictive power for distinguishing disease control from disease progression in patients with advanced HCC undergoing TKI therapy. IL-8 is involved in modulating inflammatory responses [ 32 ]. Previous studies have demonstrated that IL-8 is positively correlated with the expression of drug resistance genes in human hepatoma cells and HCC tissues. This correlation is closely associated with a reduced response to anticancer therapy [ 33 , 34 ]. Herein, elevated IL-8 levels were strongly associated with shorter PFS and OS in patients with HCC treated with TKIs, highlighting its potential as a prognostic marker. Public transcriptomic datasets have consistently demonstrated higher CXCL8 expression in TKI-resistant HCC cell lines and tumor tissues from TKI-treated HCC cohorts. Spatial and single-cell transcriptomic analyses further revealed that CXCL8 was predominantly expressed in myeloid cells within the TME, particularly in clusters linked to EMT, inflammatory responses, and TNF-α signaling. Previous studies have highlighted the interplay of TME cells, including MDSCs, in TKI resistance and the critical role of IL-8 in the TME [ 35 – 37 ]. A preclinical study showed that targeting tumor-infiltrating Ly6G + myeloid cells significantly enhances the efficacy of sorafenib in a mouse model of HCC [ 38 ]. IL-8 has been implicated in the promotion of angiogenesis, immune suppression, and tumor invasiveness, correlating with poor responses to certain therapies, such as sorafenib [ 36 ]. The signaling pathways recruit immunosuppressive cells, such as MDSCs, and activate NF-κB and STAT3, thereby driving tumor progression [ 36 , 39 – 41 ]. Collectively, these findings establish IL-8 as a robust biomarker for predicting TKI efficacy and a promising therapeutic target. Its multifaceted role in TME modulation and resistance mechanisms highlights the need for further research on IL-8-targeted strategies to improve outcomes in advanced HCC. This study has some limitations that warrant consideration. First, the findings were not validated in a large prospective cohort, which restricts the generalizability of the results. Future studies with diverse and larger patient populations are required to confirm the predictive value of IL-8 and other biomarkers. Second, while IL-8 has been identified as a potential therapeutic target, this study lacks preclinical experiments showing that targeting MDSC-derived IL-8 can overcome TKI resistance in HCC. Such functional studies are essential for establishing causality and evaluating therapeutic feasibility. Additionally, this study focused on plasma biomarkers, which provide a minimally invasive approach but may not fully reflect the complexity of intratumoral heterogeneity or local TME dynamics. The incorporation of tissue-based analyses with plasma biomarkers may enhance our understanding of resistance mechanisms. Moreover, the lack of longitudinal sampling limits insight into the temporal dynamics of IL-8 expression during therapy, which may further refine its utility as a predictive biomarker. Finally, although associations with several pathways, such as EMT and inflammatory responses, were identified, a detailed exploration of downstream IL-8 signaling was beyond the scope of this study. Addressing these limitations in future research is critical for translating these findings into clinical practice. Nevertheless, this study represents a comprehensive effort to identify plasma biomarkers that are predictive of TKI response in advanced HCC. By leveraging a 96-protein immuno-oncology panel, we employed a high-throughput approach to uncover biomarkers, such as IL-8, which demonstrated robust predictive power for distinguishing disease control from disease progression and significant associations with PFS and OS. The integration of multiple datasets, including public transcriptomic, spatial transcriptomic, and single-cell RNA sequencing data, reinforces the reliability of our findings and provides mechanistic insights into the role of IL-8 in TKI resistance and tumor progression. In conclusion, this study highlights the clinical utility of plasma-based biomarker panels as a noninvasive and practical alternative to tissue-based diagnostics. Despite existing limitations, such as the need for validation in larger cohorts and preclinical models, our study highlights the potential of IL-8 as both a prognostic biomarker and a therapeutic target. Future studies focusing on IL-8-targeted interventions could lead to more personalized and effective treatments, ultimately improving outcomes in patients with advanced HCC. Declarations Ethics approval All experiments performed with human samples were performed per the 1975 Declaration of Helsinki, and the study was approved by the Institutional Review Board of the Ajou University Hospital, Suwon, South Korea (AJIRB-BMR-SMP-17-188). Consent to participate The requirement for informed consent was waived as this study exclusively utilized anonymized data, ensuring the privacy and confidentiality of individual participants. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was supported by the National Research Foundation (NRF) of Korea (Grant numbers RS-2022-NR070489 and RS-2024-00339997) and the Korea Health Technology R&D Project of the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant number HR22C1734). Author Contribution Conceptualization: Sujin Kim, Hye Ri Ahn, Jung Woo Eun, Hyo Jung Cho; Methodology: Jung Woo Eun, Hyo Jung Cho; Investigation: Sujin Kim, Hye Ri Ahn, Jung Woo Eun; Formal analysis: Jung Woo Eun; Writing - original draft preparation: Sujin Kim, Hye Ri Ahn, Hyo Jung Cho; Writing - review and editing: Sujin Kim, Hye Ri Ahn, Hui Gyeong Kim, Dooyeon Kim, Ji Eun Han, Soon Sun Kim, Jae Youn Cheong, Jung Woo Eun, Hyo Jung Cho; Funding acquisition: Jung Woo Eun, Hyo Jung Cho; Resources: Ji Eun Han, Soon Sun Kim, Jae Youn Cheong, Jung Woo Eun, Hyo Jung Cho; Supervision: Hyo Jung Cho; Project administration: Hyo Jung Cho. Acknowledgement  The biospecimens and corresponding clinical information used in this study were provided by the Biobank of Ajou University Hospital, which is a member of the Korea Biobank Network. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. The data that support the findings of this study are available from the authors without undue reservation. References Kim, D.Y., Changing etiology and epidemiology of hepatocellular carcinoma: Asia and worldwide . J Liver Cancer, 2024. 24(1): p. 62–70. Rich, N.E., Changing Epidemiology of Hepatocellular Carcinoma Within the United States and Worldwide . Surg Oncol Clin N Am, 2024. 33(1): p. 1–12. Wang, M.D., et al., Conversion therapy for advanced hepatocellular carcinoma in the era of precision medicine: Current status, challenges and opportunities . Cancer Sci, 2024. 115(7): p. 2159–2169. Gu, X.Y., Huo, J. L., Yu, Z. Y., Jiang, J. C., Xu, Y. X., & Zhao, L. J., Immunotherapy in hepatocellular carcinoma: an overview of immune checkpoint inhibitors, drug resistance, and adverse effects . Oncologie, 2024. 26(1): p. 9–25. Yang, X., et al., Precision treatment in advanced hepatocellular carcinoma . Cancer Cell, 2024. 42(2): p. 180–197. Hwang, S.Y., et al., Second-Line Treatment after Failure of Immune Checkpoint Inhibitors in Hepatocellular Carcinoma: Tyrosine Kinase Inhibitor, Retrial of Immunotherapy, or Locoregional Therapy? Liver Cancer, 2024. 13(3): p. 246–255. Hubbard, S.R. and J.H. Till, Protein tyrosine kinase structure and function . Annu Rev Biochem, 2000. 69: p. 373–98. Paul, M.K. and A.K. Mukhopadhyay, Tyrosine kinase - Role and significance in Cancer . Int J Med Sci, 2004. 1(2): p. 101–115. Tomuleasa, C., et al., Therapeutic advances of targeting receptor tyrosine kinases in cancer . Signal Transduct Target Ther, 2024. 9(1): p. 201. Huang, Y. and L. Zhang, Annual progress of clinical research on targeted therapy for nonsmall cell lung cancer in 2022 . Cancer Innov, 2023. 2(1): p. 25–35. Schoffski, P., et al., Emerging role of tyrosine kinase inhibitors in the treatment of advanced renal cell cancer: a review . Ann Oncol, 2006. 17(8): p. 1185–96. Mohammadi, M. and H. Gelderblom, Systemic therapy of advanced/metastatic gastrointestinal stromal tumors: an update on progress beyond imatinib, sunitinib, and regorafenib . Expert Opin Investig Drugs, 2021. 30(2): p. 143–152. Iancu, G., et al., Tyrosine kinase inhibitors in breast cancer (Review) . Exp Ther Med, 2022. 23(2): p. 114. de Rosamel, L. and J.F. Blanc, Emerging tyrosine kinase inhibitors for the treatment of hepatocellular carcinoma . Expert Opin Emerg Drugs, 2017. 22(2): p. 175–190. Liu, J., et al., Small molecule tyrosine kinase inhibitors approved for systemic therapy of advanced hepatocellular carcinoma: recent advances and future perspectives . Discov Oncol, 2024. 15(1): p. 259. Villanueva, A. and J.M. Llovet, Targeted therapies for hepatocellular carcinoma . Gastroenterology, 2011. 140(5): p. 1410–26. Chen, Y., et al., Lenvatinib and immune-checkpoint inhibitors in hepatocellular carcinoma: mechanistic insights, clinical efficacy, and future perspectives . J Hematol Oncol, 2024. 17(1): p. 130. Gauthier, A. and M. Ho, Role of sorafenib in the treatment of advanced hepatocellular carcinoma: An update . Hepatol Res, 2013. 43(2): p. 147–54. Pinero, F., M. Dirchwolf, and M.G. Pessoa, Biomarkers in Hepatocellular Carcinoma: Diagnosis, Prognosis and Treatment Response Assessment . Cells, 2020. 9(6). Lu, C., et al., Understanding the dynamics of TKI-induced changes in the tumor immune microenvironment for improved therapeutic effect . J Immunother Cancer, 2024. 12(6). Takashima, Y., et al., Clinicopathologic Features and Immune Microenvironment of Non-Small-cell Lung Cancer With Primary Resistance to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors . Clin Lung Cancer, 2018. 19(4): p. 352–359 e1. Zhang, J., et al., Overcoming EGFR-TKI resistance by targeting the tumor microenvironment . Chin Med J Pulm Crit Care Med, 2024. 2(3): p. 151–161. Goh, M.J., et al., Clinical practice guideline and real-life practice in hepatocellular carcinoma: A Korean perspective . Clin Mol Hepatol, 2023. 29(2): p. 197–205. Singal, A.G., et al., AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma . Hepatology, 2023. 78(6): p. 1922–1965. Llovet, J.M. and R. Lencioni, mRECIST for HCC: Performance and novel refinements . J Hepatol, 2020. 72(2): p. 288–306. Li, Z., et al., GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases . Scientific Data, 2023. 10(1): p. 376. Llovet, J.M., et al., Plasma biomarkers as predictors of outcome in patients with advanced hepatocellular carcinoma . Clin Cancer Res, 2012. 18(8): p. 2290–300. Shao, Y., Hsu, C., Huang, C., & Cheng, A., Use of plasma angiogenesis-related factors to investigate the association of interleukin 8 and interleukin 6 levels with efficacy of sorafenib-based antiangiogenic therapy in patients with advanced hepatocellular carcinoma (HCC) . Journal of Clinical Oncology, 2011. 29(4): p. 199–199. Miyahara, K., et al., Predicting the treatment effect of sorafenib using serum angiogenesis markers in patients with hepatocellular carcinoma . J Gastroenterol Hepatol, 2011. 26(11): p. 1604–11. Eun, J.W., et al., Cancer-associated fibroblast-derived secreted phosphoprotein 1 contributes to resistance of hepatocellular carcinoma to sorafenib and lenvatinib . Cancer Commun (Lond), 2023. 43(4): p. 455–479. Kim, H.Y., et al., Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study . BMC Cancer, 2018. 18(1): p. 307. Matsushima, K., D. Yang, and J.J. Oppenheim, Interleukin-8: An evolving chemokine . Cytokine, 2022. 153: p. 155828. Park, S.Y., et al., Interleukin-8 is related to poor chemotherapeutic response and tumourigenicity in hepatocellular carcinoma . Eur J Cancer, 2014. 50(2): p. 341–50. Iida-Ueno, A., et al., Changes in plasma interleukin-8 and tumor necrosis factor-alpha levels during the early treatment period as a predictor of the response to sorafenib in patients with unresectable hepatocellular carcinoma . Cancer Chemother Pharmacol, 2018. 82(5): p. 857–864. Finke, J., et al., MDSC as a mechanism of tumor escape from sunitinib mediated anti-angiogenic therapy . Int Immunopharmacol, 2011. 11(7): p. 856–61. Fousek, K., L.A. Horn, and C. Palena, Interleukin-8: A chemokine at the intersection of cancer plasticity, angiogenesis, and immune suppression . Pharmacol Ther, 2021. 219: p. 107692. Yang, Y., et al., Protein tyrosine kinase inhibitor resistance in malignant tumors: molecular mechanisms and future perspective . Signal Transduct Target Ther, 2022. 7(1): p. 329. Chang, C.J., et al., Targeting tumor-infiltrating Ly6G(+) myeloid cells improves sorafenib efficacy in mouse orthotopic hepatocellular carcinoma . Int J Cancer, 2018. 142(9): p. 1878–1889. Fang, Z., et al., Polarization of Monocytic Myeloid-Derived Suppressor Cells by Hepatitis B Surface Antigen Is Mediated via ERK/IL-6/STAT3 Signaling Feedback and Restrains the Activation of T Cells in Chronic Hepatitis B Virus Infection . J Immunol, 2015. 195(10): p. 4873–83. Mao, F.Y., et al., CD45(+)CD33(low)CD11b(dim) myeloid-derived suppressor cells suppress CD8(+) T cell activity via the IL-6/IL-8-arginase I axis in human gastric cancer . Cell Death Dis, 2018. 9(7): p. 763. Dominguez, C., et al., Neutralization of IL-8 decreases tumor PMN-MDSCs and reduces mesenchymalization of claudin-low triple-negative breast cancer. JCI Insight, 2017. 2(21). Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8323662","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559466225,"identity":"430fa53a-457a-40fc-bff4-eb0d7d825c92","order_by":0,"name":"Sujin Kim","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Sujin","middleName":"","lastName":"Kim","suffix":""},{"id":559466226,"identity":"74e2af85-a181-4a54-91ef-2ef7fa44a8f0","order_by":1,"name":"Hye Ri Ahn","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Hye","middleName":"Ri","lastName":"Ahn","suffix":""},{"id":559466227,"identity":"85719a97-13e8-4f0a-ad07-00964fe57234","order_by":2,"name":"Hui Gyeong Kim","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"Gyeong","lastName":"Kim","suffix":""},{"id":559466232,"identity":"0457a32b-669a-43e9-9377-6fb17f77fa4d","order_by":3,"name":"Dooyeon Kim","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Dooyeon","middleName":"","lastName":"Kim","suffix":""},{"id":559466234,"identity":"fe3be0fe-8eec-4b62-8284-58088c42306b","order_by":4,"name":"Ji Eun Han","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Ji","middleName":"Eun","lastName":"Han","suffix":""},{"id":559466236,"identity":"60a1be23-693f-4812-86ac-5755458493c9","order_by":5,"name":"Soon Sun Kim","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Soon","middleName":"Sun","lastName":"Kim","suffix":""},{"id":559466239,"identity":"326440fc-e867-466a-a1e9-58476200ae33","order_by":6,"name":"Jae Youn Cheong","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Jae","middleName":"Youn","lastName":"Cheong","suffix":""},{"id":559466241,"identity":"906142e3-8054-46b8-949a-5cbceb8e34b1","order_by":7,"name":"Jung Woo Eun","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Woo","lastName":"Eun","suffix":""},{"id":559466245,"identity":"25da1875-b52e-4c31-8665-d97c92303195","order_by":8,"name":"Hyo Jung Cho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACZuYGBgYDBjk2Zv6PjxEieLUwgrUY87E3GBsjRIBCuAFYAUPiPJ4DZtJIIri1mLMzNr+uKNhmzCaRkFZdUHMnmr+dsYG5oOIPTi2WzYxtlmcMbssBtRy7PePYs9wZh4FaZpzBbYvBYcY2wwaD20BbEttu87Adzm0AaeFtI6wlsU0ima2Y59/h3PlgLf/waml+CNbCc4wNaPjh3A1gLQ24tYD8wgh2GHsPszRv3+HcjUAth3mOGePUYs5/+PDHhj+35eSbeRg/83w7nDvv/OGDj3lq5HA7jIGBTQJD9ABO9RAtzB/wKRgFo2AUjIJRwAAAo+dVbMfelqMAAAAASUVORK5CYII=","orcid":"","institution":"Ajou University","correspondingAuthor":true,"prefix":"","firstName":"Hyo","middleName":"Jung","lastName":"Cho","suffix":""}],"badges":[],"createdAt":"2025-12-10 06:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8323662/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8323662/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98328069,"identity":"e655c384-b415-4e72-aa29-aa0df5c23f5e","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2340715,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/2b813eebe1cd2c52d7b365e0.docx"},{"id":98437583,"identity":"62a21800-37f7-4ee6-a6b7-29a4fb948ca7","added_by":"auto","created_at":"2025-12-17 16:57:29","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10881,"visible":true,"origin":"","legend":"","description":"","filename":"e8cdc7f15160474da451639197ef7e6b.json","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/3ef8d7ebed8f3566388a1ab2.json"},{"id":98328076,"identity":"acbe261b-426e-46f4-8aae-bd0f2f9e92d3","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1026231,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/13fdf8b632b05c63895bceb3.pdf"},{"id":98437054,"identity":"abe02ac4-c218-4285-8140-5a322f00c61a","added_by":"auto","created_at":"2025-12-17 16:56:51","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112062,"visible":true,"origin":"","legend":"","description":"","filename":"e8cdc7f15160474da451639197ef7e6b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/323b547a12ff332b09b33358.xml"},{"id":98437180,"identity":"869dc123-6568-47c3-a43e-222dd03a75cd","added_by":"auto","created_at":"2025-12-17 16:57:05","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1031682,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/ce108d687b6ea79d6eaccd48.jpeg"},{"id":98436503,"identity":"aa95c7e1-d905-4ec3-b202-43c602e91779","added_by":"auto","created_at":"2025-12-17 16:55:46","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1292339,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/54863d5f87e514ce16846f42.jpeg"},{"id":98328080,"identity":"337fa30b-3f04-4640-8b59-58ddc69f7b9e","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":337169,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/a9e4fc203f16e511c6e0ba88.jpeg"},{"id":98437329,"identity":"17668462-c92d-4db4-84ef-8958a0078b4e","added_by":"auto","created_at":"2025-12-17 16:57:13","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1138031,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/19f54124f8dda9fcd84b5cdd.jpeg"},{"id":98328074,"identity":"662a992d-3209-42f7-ac69-cd1e123e15f6","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":791392,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/debb40d58949673b4206fffa.jpeg"},{"id":98437148,"identity":"1e1884fb-e590-4c96-bd9f-0de53d37e220","added_by":"auto","created_at":"2025-12-17 16:57:03","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1184848,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/e07785be0ee601944b693c7c.jpeg"},{"id":98436973,"identity":"3a6c9619-90ea-4567-90ab-e678f237c6fa","added_by":"auto","created_at":"2025-12-17 16:56:32","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1192519,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/c02fed4355cafad89a00a8bf.jpeg"},{"id":98436873,"identity":"e0a59a2e-0bac-41f3-8012-7b4dfca58477","added_by":"auto","created_at":"2025-12-17 16:56:23","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":218683,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/189739b8fc46e0a55a89d176.png"},{"id":98436902,"identity":"a03de77c-f706-4f1d-9898-f16a4dfc91c7","added_by":"auto","created_at":"2025-12-17 16:56:26","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":256420,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/cf89cb6a70d48f80d0dc8171.png"},{"id":98437168,"identity":"c0a5de58-ba33-4a21-ba15-a212082afb6b","added_by":"auto","created_at":"2025-12-17 16:57:05","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67038,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/95d929e48bb2c42a26d3ec99.png"},{"id":98438163,"identity":"285030d1-74bd-4569-9559-ee81f7d36d5b","added_by":"auto","created_at":"2025-12-17 16:58:43","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":239022,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/a45a5a176e87613352ad901e.png"},{"id":98437529,"identity":"ab9f3435-2f98-4e11-bd17-f0591a806201","added_by":"auto","created_at":"2025-12-17 16:57:26","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160689,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/b4de99224e5581c2df775d5e.png"},{"id":98328082,"identity":"28832c43-2087-4243-aa32-0420b06e2a2a","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":308133,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/5caa28736a8b6632b801832c.png"},{"id":98328086,"identity":"ad0336f7-cb76-419e-8498-d0ff4d0e30de","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":303724,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/97a2d3669133138c679f772d.png"},{"id":98328089,"identity":"69d6d9db-0848-4a67-8bc4-2fad553e4e87","added_by":"auto","created_at":"2025-12-16 15:01:52","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111170,"visible":true,"origin":"","legend":"","description":"","filename":"e8cdc7f15160474da451639197ef7e6b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/d9380f3ae4456ac7b529b62e.xml"},{"id":98436954,"identity":"42515ab9-a97b-4411-a5af-d0d25c88daf8","added_by":"auto","created_at":"2025-12-17 16:56:30","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127126,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/b25996946207f74ac3a7b61d.html"},{"id":98328063,"identity":"bd30d41b-1169-4eac-85a7-4cb4ef586f55","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":427436,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of molecular profiles and tumor response to tyrosine kinase inhibitor (TKI) therapy in patients with hepatocellular carcinoma (HCC). \u003cstrong\u003e(a)\u003c/strong\u003e Waterfall plot illustrating tumor size reduction in individual patients following TKI therapy. Each bar represents a patient, and the colors correspond to different molecular and clinical features. Heatmap of genomic and clinical features across the patient cohort. The rows indicate individual features, whereas columns represent patients, grouped by treatment response. \u003cstrong\u003e(b)\u003c/strong\u003e Representative Computer tomography (CT) images of tumor lesions in the lung (left) and liver (right) before and after TKI therapy. Circles (purple) and arrows (yellow) highlight the tumor boundaries, demonstrating the reduction in tumor size\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/00b5df8e6a444f59892f838e.jpeg"},{"id":98328065,"identity":"2e1fb1ae-0c00-4492-80b4-b2ccc4b566f9","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":588628,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differentially expressed proteins (DEPs) and their predictive power in TKI-treated patients with HCC. \u003cstrong\u003e(a)\u003c/strong\u003e Volcano plots displaying DEPs between the partial responder (PR) and stable/progressive disease (SD/PD) groups (left) and between the PR/SD and PD groups (right). Proteins with significant fold changes and \u003cem\u003eP\u003c/em\u003e-values are highlighted in red, including key markers, such as PGF, CX3CL1, CAIX, and MMP-12. \u003cstrong\u003e(b)\u003c/strong\u003e Violin plots and ROC curves showing the predictive performance of selected proteins (PGF, ADGRG1, CAIX, CD40, and CX3CL1) in distinguishing PR from SD/PD. The \u003cem\u003eP\u003c/em\u003e-values indicate the significance of the differences between groups, and ROC curves assess the sensitivity and specificity of each marker. \u003cstrong\u003e(c)\u003c/strong\u003e Violin plots and ROC curves showing the predictive capacity of proteins (ANGPT2, CXCL12, MMP-12, VEGFA, IL-13, and CSF-1) to distinguish PR/SD from PD. The area under the curve (AUC) values and confidence intervals are provided for each ROC curve. Statistical significance is denoted as *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. HCC, hepatocellular carcinoma; PGF, placental growth factors; CX3CL1, C-X3-C motif chemokine ligand 1; CAIX, carbonic anhydrase IX; MMP-12, matrix metalloproteinase 12; ROC, receiver operating characteristic; ADGRG1, adhesion G protein-coupled receptor G1; CD40, cluster of differentiation 40; ANGPT2, angiopoietin 2; CXCL12, C-X-C motif chemokine ligand 12; VEGFA, vascular endothelial growth factor A; IL-13, interleukin 13; CSF-1, colony-stimulating factor 1; NR, non-responder; R, responder\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/46eded19066f5dea577646b0.jpeg"},{"id":98437149,"identity":"c7c02334-2ff1-4a29-9e25-6c9365613aa2","added_by":"auto","created_at":"2025-12-17 16:57:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":151442,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for MMP-12 and VEGFA expression levels in TKI-treated patients with HCC. \u003cstrong\u003e(a)\u003c/strong\u003e Kaplan–Meier survival curve comparing high and low expression of MMP-12 stratified by the median expression level. \u003cstrong\u003e(b)\u003c/strong\u003e Kaplan–Meier survival curve comparing high and low expression of VEGFA stratified by the median expression level. Statistical significance was assessed using the log-rank test (*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). HCC, hepatocellular carcinoma; MMP-12, matrix metalloproteinase 12; VEGFA, vascular endothelial growth factor A\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/b7d1c5c612febdf8666a13d0.jpeg"},{"id":98328070,"identity":"5e5372d6-e540-4b30-93d0-8929ecb9bc02","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":472427,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of selected protein markers between the disease control (DC) and disease progression (DP) groups in TKI-treated HCC patients (PFS ≥12 months for DC and PFS \u0026lt;12 months for DP). \u003cstrong\u003e(a)\u003c/strong\u003e Heatmap showing the expression levels of the nine selected proteins between the DC (PFS ≥12 months, n = 8) and DP (PFS \u0026lt;12 months, n = 52) groups. Higher protein expression is indicated in red, and lower expression in blue. The heatmap reveals distinct clusters based on protein expression (NPX values) between the two groups. \u003cstrong\u003e(b)\u003c/strong\u003e Volcano plot showing the log\u003csub\u003e2\u003c/sub\u003e fold changes and -log\u003csub\u003e10\u003c/sub\u003e \u003cem\u003eP\u003c/em\u003e-values of the nine selected proteins between DC and DP groups. Statistically significant proteins are marked in red, indicating their relevance in differentiating between two patient groups. \u003cstrong\u003e(c)\u003c/strong\u003e Violin plots comparing the expression levels of the nine selected proteins between DC and DP groups. \u003cem\u003eP\u003c/em\u003e values indicate the statistical significance of the differences in expression between the two groups. \u003cstrong\u003e(d)\u003c/strong\u003e ROC curves displaying the predictive power of the nine selected proteins in distinguishing between DC and DP groups. Higher AUC values indicate better predictive performance for the specific protein markers. Statistical significance is denoted as *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. HCC, hepatocellular carcinoma; PFS, progression-free survival; NPX, normalized protein expression; ROC, receiver operating characteristic; AUC, area under the curve; CCL20, C-C motif chemokine ligand 20; CXCL1, C-X-C motif chemokine ligand; CXCL5, C-X-C motif chemokine ligand 5; FGF2, fibroblast growth factor 2; IL-7, interleukin 7; IL-8, interleukin 8; IL-18, interleukin 18; LAP TGF-beta-1, latency-associated peptide transforming growth factor beta 1; MUC-16, mucin 16\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/9fce9aa18be600184cd68065.jpeg"},{"id":98437382,"identity":"f69b1262-53ee-44ad-8564-245bfe1afdc8","added_by":"auto","created_at":"2025-12-17 16:57:15","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":368475,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for significant prognostic markers in TKI-treated patients with HCC. Kaplan–Meier survival curves showing the overall survival of patients stratified by high (pink squares) and low (gray circles) expression levels of key prognostic markers. Each panel represents a different marker, with expression levels divided by the median values. Patients with high expression levels were compared with those with low expression levels, and survival outcomes were compared. The statistical significance of differences in survival between the two groups was assessed using the log-rank test (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). HCC, hepatocellular carcinoma; CCL20, C-C motif chemokine ligand 20; CXCL1, C-X-C motif chemokine ligand; CXCL5, C-X-C motif chemokine ligand 5; IL-8, interleukin 8; LAP TGF-beta-1, latency-associated peptide transforming growth factor beta 1; HR, hazard ratio\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/f0118d430e4e69ae5748a280.jpeg"},{"id":98436372,"identity":"dee24953-0c5c-4ed5-9ab3-a5532dd3df39","added_by":"auto","created_at":"2025-12-17 16:55:31","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":502320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCXCL8\u003c/em\u003eexpression is associated with TKI resistance, HCC progression, and its spatial distribution in malignant and non-malignant hepatocytes. \u003cstrong\u003e(a)\u003c/strong\u003e \u003cem\u003eCXCL8\u003c/em\u003e mRNA expression in TKI-treated HCC cell lines (Huh-7, HepG2, and PLC/PRF/5) comparing sorafenib-resistant (Sor-Res) or lenvatinib-resistant (Len-Res) cells with wild-type (WT) cells. \u003cstrong\u003e(b)\u003c/strong\u003e \u003cem\u003eCXCL8\u003c/em\u003e mRNA expression in sorafenib-sensitive (Sor-Sen) and Sor-Res Huh-7 cells from the GSE140202 dataset. \u003cstrong\u003e(c)\u003c/strong\u003e \u003cem\u003eCXCL8\u003c/em\u003e mRNA expression in the sorafenib non-responder (Sor-NR) and responder (Sor-R) groups from the GSE109211 dataset. \u003cstrong\u003e(d)\u003c/strong\u003e \u003cem\u003eCXCL8\u003c/em\u003emRNA expression across different liver conditions (normal liver, non-alcoholic fatty liver disease, cirrhosis, and HCC) analyzed using integrated GepLiver datasets. \u003cstrong\u003e(e)\u003c/strong\u003e Spatial visualization of \u003cem\u003eCXCL8\u003c/em\u003e expression in malignant and non-malignant hepatocytes from patients with HCC (Mendeley Data) with intensity represented by a color gradient. \u003cstrong\u003e(f)\u003c/strong\u003e Proportion of \u003cem\u003eCXCL8+\u003c/em\u003ecells in malignant and non-malignant hepatocytes across HCC patient samples. Statistical significance is indicated as *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001, and ****\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0001. HCC, hepatocellular carcinoma; TKI, tyrosine kinase inhibitor; CXCL8 (IL-8), C-X-C motif chemokine ligand 8 (interleukin 8); NAFLD, non-alcoholic fatty liver disease; ADJ_HCC, adjacent HCC; GepLiver, gene expression profile of liver; TPM, transcripts per million\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/b5ff11fcc608a3ca81aaaab4.jpeg"},{"id":98437917,"identity":"66d125ce-f09e-4a9e-8efa-b796e89b77eb","added_by":"auto","created_at":"2025-12-17 16:58:13","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":498270,"visible":true,"origin":"","legend":"\u003cp\u003escRNA-seq analysis of \u003cem\u003eCXCL8\u003c/em\u003e expression in NT, PT, and PVTT samples from the GSE149614 dataset. \u003cstrong\u003e(a)\u003c/strong\u003e UMAP plot showing the distribution of all cell types in HCC07 and HCC08 samples from the GSE149614 dataset, with cell types visualized as distinct clusters. \u003cstrong\u003e(b)\u003c/strong\u003e Violin plot comparing \u003cem\u003eCXCL8\u003c/em\u003e expression levels in normal tissue (NT), primary tumor (PT), and portal vein tumor thrombus (PVTT). \u003cstrong\u003e(c)\u003c/strong\u003e UMAP plot of myeloid cells in PT and PVTT, classified into three clusters (C1, C2, and C3). \u003cstrong\u003e(d)\u003c/strong\u003eHeatmap showing gene expression patterns of \u003cem\u003eCXCL8\u003c/em\u003eand related genes across C1, C2, and C3 clusters. \u003cstrong\u003e(e)\u003c/strong\u003e ssGSEA analysis of \u003cem\u003eCXCL8\u003c/em\u003e-positive myeloid cells in C1 and C2 clusters, highlighting enrichment of EMT, inflammatory response, and TNF-α signaling via NF-κB pathways. \u003cstrong\u003e(f)\u003c/strong\u003e Scatter plots using TCGA data (N = 371) showing correlations (r \u0026gt; 0.3) between \u003cem\u003eCXCL8\u003c/em\u003eexpression and EMT-related genes. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. UMAP, uniform manifold approximation and projection; HCC, hepatocellular carcinoma; ssGSEA, single-sample gene set enrichment analysis; TNF-α, tumor necrosis factor-alpha; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/7c0a93e9a8751390359da842.jpeg"},{"id":99797651,"identity":"ad619464-4119-46b1-b6a1-76e95f727351","added_by":"auto","created_at":"2026-01-08 13:46:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4072130,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/dc736acc-7e03-41f6-b566-07bb4423f167.pdf"},{"id":98328067,"identity":"81f2e28e-8c6d-447c-a8c5-88f68b766d29","added_by":"auto","created_at":"2025-12-16 15:01:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1026231,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8323662/v1/fe07e5e20fda1d0b7347aef9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring plasma biomarkers for therapeutic response to tyrosine kinase inhibitors in advanced hepatocellular carcinoma: Interleukin-8 as a key prognostic indicator","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePrimary liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for approximately 80% of all cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite advancements in overall survival (OS), improvements in HCC-specific survival rates remain modest. The five-year survival rate for advanced HCC is only 2\u0026ndash;11%, primarily due to its frequent diagnosis at a stage where curative treatments are no longer effective [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In recent years, immune checkpoint inhibitor (ICI)-based therapies, such as the combination of atezolizumab and bevacizumab, have been established as first-line treatments. Tyrosine kinase inhibitors (TKIs), including sorafenib and lenvatinib, remain crucial first-line options when ICI-based regimens are unsuitable, and are used as second-line treatments for patients who fail to respond to ICI-based therapies [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProtein tyrosine kinases catalyze the transfer of the γ-phosphate of ATP to the tyrosine residues of protein substrates [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Activated tyrosine kinases drive tumor cell proliferation and growth, inhibit apoptosis, and promote angiogenesis and metastasis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As these effects are initiated by receptor tyrosine kinase activation, treatment with TKIs is the primary targeted therapy. TKIs are widely used to treat non-small-cell lung cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], renal cell carcinoma [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], gastrointestinal stromal tumors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and breast cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Sorafenib and lenvatinib are key TKIs in the treatment of advanced-stage HCC [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, their low response rates remain a significant challenge, with approximately 10\u0026ndash;20% of patients achieving an objective response [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These limited response rates emphasize the need for predictive biomarkers to guide treatment decisions in HCC. Reliable biomarkers may enable clinicians to tailor therapy more effectively and identify the patients most likely to benefit from TKI treatment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have suggested that the immunosuppressive tumor microenvironment (TME) plays a crucial role in influencing the TKI treatment response [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The immunosuppressive nature of the TME is closely associated with the expression of circulating plasma proteins, which may serve as valuable biomarkers for predicting therapeutic outcomes.\u003c/p\u003e \u003cp\u003eTo identify plasma biomarkers associated with TKI efficacy in patients with advanced HCC, we conducted a targeted proteomic analysis of pre-treatment blood samples, focusing on 96 key immuno-oncology proteins involved in immune regulation and TME interactions. By analyzing these immune-related proteins, we aimed to improve patient stratification and guide treatment planning, ultimately enhancing clinical outcomes in patients with advanced HCC.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Patient Enrollment\u003c/h2\u003e \u003cp\u003eWe measured the expression of 96 immuno-oncology panel proteins in pre-treatment plasma samples from patients with advanced HCC treated with TKIs. The predictive power of the identified DEPs was assessed using receiver operating characteristic (ROC) curve analysis, and survival analysis was performed based on their expression.\u003c/p\u003e \u003cp\u003e Blood samples and clinical data from patients with advanced HCC treated with TKIs were obtained from the Biobank of Ajou University Hospital (Suwon, South Korea), a member of the Korea Biobank Network. The inclusion criteria were as follows: (1) age between 18 and 80 years; (2) modified Union for International Cancer Control (mUICC) stage IVa or IVb [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; (3) mUICC stage II or III with extensive disease or main vascular invasion precluding curative local treatment; (4) treatment with sorafenib or lenvatinib for more than 4 weeks; and (5) availability of plasma samples collected within one month before treatment initiation. Patients were excluded if they had a history of malignancy other than HCC or if data for evaluating the treatment response after TKI administration were insufficient.\u003c/p\u003e \u003cp\u003eThe clinical data included patient demographics (age and sex), administration of TKIs (sorafenib or lenvatinib), laboratory parameters (platelet count, albumin, bilirubin, creatinine, alpha-fetoprotein, alanine aminotransferase, and aspartate aminotransferase levels; and international normalized ratio), etiology of liver disease, tumor stage (mUICC stage), treatment response to TKIs, and survival outcomes, including progression-free survival (PFS) and OS.\u003c/p\u003e \u003cp\u003e All experiments conducted using human samples adhered to the guidelines of the Declaration of Helsinki of 1975. This study was approved by the Institutional Review Board of the Ajou University Hospital, Suwon, South Korea (AJIRB-BMR-SMP-17-188). The requirement for informed consent was waived as this study exclusively utilized anonymized data, ensuring the privacy and confidentiality of individual participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition of Terms\u003c/h2\u003e \u003cp\u003eHCC was diagnosed in accordance with the guidelines of the American Association for the Study of Liver Diseases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Tumor stage was defined according to the mUICC staging system [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Tumor response was evaluated based on the modified Response Evaluation Criteria for Solid Tumors. The criteria were as follows: complete response (CR), characterized by the disappearance of all intratumoral arterial enhancements in the target lesions. Partial response (PR) was defined as a reduction of at least 30% in the sum of the diameters of the viable (arterially enhancing) target lesions, using the baseline sum of diameters as the reference. Stable disease (SD) referred to cases that did not meet the criteria for either PR or progressive disease (PD). PD was defined as an increase of at least 20% in the sum of the diameters of the viable target lesions compared to the smallest recorded sum after treatment [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. PFS was defined as the time from the initiation of TKI treatment to the date of disease progression or death from any cause, whichever occurred first. OS was defined as the time from the initiation of TKI treatment to the date of death from any cause. Patients without disease progression or death at the time of analysis were censored at the date of their last follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Plasma Sample Processing and Proteomic Analysis Using the Olink\u0026reg; Target 96 Immuno-Oncology Panel\u003c/h2\u003e \u003cp\u003ePre-treatment plasma samples from patients with advanced HCC treated with TKIs (n\u0026thinsp;=\u0026thinsp;60) were analyzed using the Olink\u0026reg; Target 96 Immuno-Oncology panel, which is based on a high-sensitivity and specific proximity extension assay technology (Olink Proteomics, Uppsala, Sweden) that can simultaneously analyze 96 immuno-oncology-related protein biomarkers. The Olink\u0026reg; Target 96 panel process was conducted following the manufacturer's protocol. Plasma samples (1 \u0026micro;L) were incubated overnight at 4\u0026deg;C with 92 antibody pairs, each conjugated to DNA tags, to allow binding to the target proteins. The next day, extension and amplification steps were performed, where oligonucleotides brought into proximity were hybridized and extended using DNA polymerase to create DNA barcodes. These barcodes were amplified using PCR with the following thermocycling conditions: 50\u0026deg;C for 20 min, 95\u0026deg;C for 5 min, followed by 17 cycles of 95\u0026deg;C for 30 s, 54\u0026deg;C for 1 min, and 60\u0026deg;C for 1 min, with a final step at 10\u0026deg;C. The DNA reporters for each biomarker were then quantified using high-throughput real-time qPCR on the Olink\u0026reg; Signature Q100 system. Raw data from the Olink\u0026reg; Signature Q100 were imported, quality-checked, and normalized using inter-plate control to obtain normalized protein eXpression (NPX) values via Olink NPX Signature software (1.11.0). Quality control-flagged samples with deviations from the median NPX values of the internal controls and assays were filtered based on the limit of detection. DEPs were identified using a paired \u003cem\u003et-\u003c/em\u003etest with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and false discovery rate control was performed using the Benjamini\u0026ndash;Hochberg method. Data analysis and visualization were conducted using R version 4.2.2, with the OlinkAnalyze R package for the statistical analysis and visualization of DEPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 DEP Analysis\u003c/h2\u003e \u003cp\u003eDEP analysis was performed to identify differentially expressed plasma proteins among three comparison groups: PR vs. SD/PD, PR/SD vs. PD, and disease control (DC, PFS\u0026thinsp;\u0026ge;\u0026thinsp;12 months) vs. disease progression (DP). The predictive value of significant proteins was assessed using ROC curve analysis, and Kaplan\u0026ndash;Meier survival analysis was conducted to evaluate their association with clinical outcomes, including PFS and OS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Public Data Acquisition\u003c/h2\u003e \u003cp\u003ePublicly available omics datasets were obtained from Gene Expression Omnibus (GSE94550, GSE248764, GSE273819, GSE140202, GSE109211, and GSE149614), GepLiver [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and Mendeley (skrx2fz79n) Data. These datasets include transcriptomic data from TKI-treated HCC cell lines, HCC patient samples, and spatial transcriptomic data from HCC tissue sections. The analysis focused on evaluating C-X-C motif chemokine ligand 8 (CXCL8, also known as interleukin (IL)-8) expression patterns in relation to TKI resistance, liver disease progression, and the spatial localization of malignant versus non-malignant hepatocytes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Comprehensive Liver Dataset Analysis\u003c/h2\u003e \u003cp\u003eTranscriptomic data from TKI-treated cell lines, including Huh-7, HepG2, and PLC/PRF/5, were analyzed to compare \u003cem\u003eCXCL8\u003c/em\u003e expression between the TKI-sensitive (Sorafenib-sensitive, Sor-Sen) and TKI-resistant (Sorafenib-resistant, Sor-Res; Lenvatinib-resistant, Len-Res) groups. Similarly, transcriptomic data from patients with HCC were used to compare \u003cem\u003eCXCL8\u003c/em\u003e expression between sorafenib non-responder (Sor-NR) and responder (Sor-R) groups. Additionally, transcriptomic data from the GepLiver database were used to analyze \u003cem\u003eCXCL8\u003c/em\u003e expression across various liver conditions, including normal liver, non-alcoholic fatty liver disease, cirrhosis, adjacent HCC (ADJ_HCC), and HCC tissues. Data were normalized to log\u003csub\u003e2\u003c/sub\u003e-transformed expression values and Z-score normalization was applied for consistent visualization. Statistical tests were performed to evaluate the differences in \u003cem\u003eCXCL8\u003c/em\u003e expression under different conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Spatial Transcriptomics Analysis\u003c/h2\u003e \u003cp\u003eSpatial transcriptomic analysis was performed using Mendeley Data, which included annotated tissue sections (P7T\u0026ndash;P11T) from patients with HCC. Tissue sections were classified into malignant and non-malignant hepatocytes, and CXCL8 expression was visualized using a color gradient representing minimal (blue) to maximal (red) expression. The proportion of \u003cem\u003eCXCL8\u003c/em\u003e\u0026thinsp;+\u0026thinsp;cells in malignant versus non-malignant hepatocytes was quantified for each tissue section to determine spatial distribution patterns. Differences in \u003cem\u003eCXCL8\u003c/em\u003e\u0026thinsp;+\u0026thinsp;cells between malignant and non-malignant regions were statistically evaluated, revealing the enrichment of \u003cem\u003eCXCL8\u003c/em\u003e\u0026thinsp;+\u0026thinsp;cells in malignant hepatocytes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Analysis of \u003cem\u003eCXCL8\u003c/em\u003e Expression in Single-Cell RNA-Seq Data from HCC Patient Samples\u003c/h2\u003e \u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) data from the GSE149614 dataset were analyzed, focusing on HCC07 and HCC08 patient samples, including normal tissue (NT), primary tumor (PT), and portal vein tumor thrombus (PVTT). Raw data were processed using the Seurat package (v4.0.6) in R. Uniform manifold approximation and projection (UMAP) was used to visualize the distribution of cells, with cell clusters identified through a shared nearest-neighbor graph-based clustering approach and resolution parameters optimized to capture distinct cell populations. Cell-type annotations were assigned using canonical markers for B cells, endothelial cells, fibroblasts, hepatocytes, myeloid cells, and T/NK cells. Differential gene expression analysis was conducted to compare cell clusters in NT, PT, and PVTT groups, focusing on \u003cem\u003eCXCL8\u003c/em\u003e expression.\u003c/p\u003e \u003cp\u003eFurther analysis was performed on myeloid cells in the PT and PVTT. Sub-clustering of myeloid cells was conducted, followed by the identification of the C1, C2, and C3 clusters. Gene set enrichment analysis (GSEA) was performed using the ssGSEA algorithm to assess pathway enrichment in \u003cem\u003eCXCL8\u003c/em\u003e-positive cells within C1 and C2 clusters. Pathway databases included hallmark gene sets, focusing on epithelial\u0026ndash;mesenchymal transition (EMT), inflammatory response, and tumor necrosis factor (TNF)-α signaling via nuclear factor (NF)-κB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Correlation of \u003cem\u003eCXCL8\u003c/em\u003e with EMT-Associated Genes in TCGA-LIHC Cohort\u003c/h2\u003e \u003cp\u003eCorrelation analysis was conducted to assess the relationship between \u003cem\u003eCXCL8\u003c/em\u003e expression and EMT-associated genes within \u003cem\u003eCXCL8\u003c/em\u003e\u0026thinsp;+\u0026thinsp;cells in C1 and C2 clusters, where ssGSEA analysis showed the highest enrichment for EMT pathways. EMT-related genes, including \u003cem\u003eMMP2\u003c/em\u003e, \u003cem\u003eMMP9\u003c/em\u003e, \u003cem\u003eSNAI1\u003c/em\u003e, and \u003cem\u003eVIM\u003c/em\u003e, were selected for comparison. Bulk RNA-seq data from the The Cancer Genome Atlas (TCGA)-LIHC cohort (n\u0026thinsp;=\u0026thinsp;371) were used for this analysis. Gene expression values were log\u003csub\u003e2\u003c/sub\u003e-transformed, and Pearson correlation coefficients (r) were calculated between \u003cem\u003eCXCL8\u003c/em\u003e and the selected EMT markers. The significance of correlations was determined using a two-tailed test, with values of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Scatter plots were generated to visualize the correlations, with regression lines fitted to depict trends in the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical significance of the differences between the two groups was assessed using either the paired Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or the unpaired Welch\u0026rsquo;s \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003et\u003c/span\u003e-test using GraphPad Prism (version 9.0; GraphPad Software, San Diego, CA, USA). ROC analysis was conducted using the IBM SPSS software (IBM SPSS Statistics for Windows, version 22.0, released 2013, IBM, Armonk, NY, USA). ROC curves were analyzed to evaluate the diagnostic accuracy of candidate biomarkers. These curves were analyzed for sensitivity and specificity of the candidate biomarkers at various threshold values, and the area under the ROC curve (AUROC) was evaluated with 95% confidence intervals (CIs). Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Kaplan\u0026ndash;Meier survival curves are statistical methods used to estimate survival function, and prognostic changes based on the expression of candidate markers were analyzed. The significance of \u003cem\u003eP\u003c/em\u003e-value differences between the survival curves was evaluated using the log-rank test, with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics of the Included Patients\u003c/h2\u003e \u003cp\u003eIn a cohort of 60 patients, 39 received sorafenib and 21 received lenvatinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each bar represents a patient, and the color of the bar indicates the degree of drug response. None of the patients demonstrated CR to TKI therapy. However, seven patients had a PR to treatment, 29 had SD, and 24 had PD. The distribution of patients aged older than 60 years and younger than 60 years was equal, with each group consisting of 30 individuals; the proportion of male patients was higher than that of female patients. Forty-nine patients (18.3%) had mUICC stage IV disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Representative computed tomography images illustrated the changes in tumor lesions before and after TKI therapy in patients who responded to treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In the lung (left image) and liver (right image), the outlined tumor boundaries demonstrated a clear reduction in tumor size post-treatment, as indicated by the arrows. Notably, in the right liver lesions, a significant decrease in the viable solid portion was observed after treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of DEPs Based on Response to TKI Therapy\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea displays a volcano plot illustrating the DEPs according to treatment response. The proteins highlighted in red and blue indicate statistically significant differential expression. In the left plot of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, five protein\u0026ndash;placental growth factors (PGFs), adhesion G protein-coupled receptor G1 (ADGRG1), carbonic anhydrase IX (CAIX), cluster of differentiation 40 (CD40), and C-X3-C motif chemokine ligand 1 (CX3CL1), were identified as significantly upregulated proteins in the SD and PD groups compared with those in the PR group. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb compares the expression of these five DEPs between the PR and SD/PD groups. Among them, ADGRG1 (AUROC\u0026thinsp;=\u0026thinsp;0.76, 95% CI: 0.57\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and CAIX (AUROC\u0026thinsp;=\u0026thinsp;0.76, 95% CI: 0.57\u0026ndash;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) demonstrated the highest AUROC value. The right plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea shows a volcano plot illustrating the DEPs between the PR/SD and PD groups. The expressions of angiopoietin 2 (ANGPT2), matrix metalloproteinase 12 (MMP-12), vascular endothelial growth factor A (VEGFA), and colony-stimulating factor 1 (CSF-1) were significantly upregulated, whereas those of IL-13 and C-X-C motif chemokine ligand 12 (CXCL12) were downregulated in the PD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). In the ROC analysis, MMP-12 exhibited the highest AUROC value of 0.76 (95% CI: 0.6\u0026ndash;0.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) among the six DEPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival analysis was performed for the 11 plasma proteins identified in the DEP analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Patients with higher plasma MMP-12 levels had significantly poorer PFS (hazard ratio\u0026thinsp;=\u0026thinsp;2.10, 95% CI: 1.0\u0026ndash;4.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) and OS (HR\u0026thinsp;=\u0026thinsp;2.09, 95% CI: 1.0\u0026ndash;4.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Patients with elevated plasma VEGFA levels also had significantly shorter PFS (HR\u0026thinsp;=\u0026thinsp;2.20, 95% CI: 1.0\u0026ndash;4.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and OS (HR\u0026thinsp;=\u0026thinsp;2.05, 95% CI: 1.0\u0026ndash;4.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) than those with lower VEGFA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In contrast, the remaining nine proteins showed no significant association with PFS or OS. Data for these proteins with non-significant log-rank \u003cem\u003eP\u003c/em\u003e values are shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of the Targeted Plasma Proteome Based on the 12-Month PFS Criterion\u003c/h2\u003e \u003cp\u003eAdditional analyses were conducted to identify plasma protein biomarkers predictive of disease control with TKI therapy (PFS\u0026thinsp;\u0026ge;\u0026thinsp;12 months). Among the 60 patients, 8 who achieved a PFS of \u0026ge;\u0026thinsp;12 months were grouped into the DC group, whereas the remaining 52 were classified as DP. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea presents a heatmap showing DEPs between the two groups. Hierarchical clustering in the heat map distinctly separated the DC and DP based on NPX levels, highlighting the distinct protein expression patterns between the groups. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb displays a volcano plot highlighting the differences in pre-treatment plasma protein expression between the two groups. A total of nine proteins, namely C-C motif chemokine ligand 20 (CCL20), C-X-C motif chemokine ligand (CXCL1), C-X-C motif chemokine ligand 5 (CXCL5), fibroblast growth factor 2, IL-7, IL-8, IL-18, latency-associated peptide-transforming growth factor beta 1 (LAP TGF-beta-1), and mucin 16 (MUC-16), were significantly upregulated in the DP group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Among these, IL-8 and MUC-16 demonstrated notably high diagnostic performance in distinguishing the DC group, with AUROC values of 0.91 (95% CI: 0.83\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 95% CI: 0.83\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurvival analysis was performed for nine proteins that showed significant differences between the DC and DP groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig. S2). Of these, five proteins (CCL20, CXCL1, CXCL5, IL-8, and LAP TGF-beta-1) were significantly associated with poorer OS and PFS in patients with high expression levels. Among them, IL-8 exhibited the highest HR and lowest \u003cem\u003eP\u003c/em\u003e-value in OS (HR\u0026thinsp;=\u0026thinsp;3.64, 95% CI: 1.6\u0026ndash;7.8, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and PFS (HR\u0026thinsp;=\u0026thinsp;2.97, 95% CI: 1.4\u0026ndash;6.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015), highlighting its potential as a key prognostic biomarker. These findings suggest that IL-8 may play a pivotal role in disease progression and may serve as a critical target for therapeutic interventions aimed at improving the clinical outcomes of patients with HCC. To further validate the clinical significance of IL-8 and its role in TME, we conducted additional analyses using publicly available datasets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Validation of Elevated CXCL8 (IL-8) Expression in TKI Resistance Using Public Transcriptomic Data\u003c/h2\u003e \u003cp\u003eWe first examined \u003cem\u003eCXCL8\u003c/em\u003e (the gene encoding IL-8) expression in response to TKI treatment using public omics datasets. At the cell-line level, we analyzed \u003cem\u003eCXCL8\u003c/em\u003e expression in relation to TKI resistance. Transcriptomic data from wild-type (WT) and TKI-resistant (Res) HCC cell lines, including Huh7, HepG2, and PLC/PRF/5, were obtained from datasets GSE94550, GSE248764, and GSE273819, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Across all three cell lines, a significant increase in \u003cem\u003eCXCL8\u003c/em\u003e expression was observed in TKI-resistant cells compared with that in WT cells. Additionally, in another cell line dataset, GSE140202, sorafenib-resistant cell lines exhibited markedly higher \u003cem\u003eCXCL8\u003c/em\u003e expression levels than sorafenib-sensitive cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Next, we analyzed \u003cem\u003eCXCL8\u003c/em\u003e expression in pre-treated HCC tumor tissues from an HCC cohort using the transcriptomic dataset GSE109211. The analysis revealed that \u003cem\u003eCXCL8\u003c/em\u003e expression was significantly higher in the tumor tissues of sorafenib non-responders than in responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). These findings collectively demonstrate that elevated \u003cem\u003eCXCL8\u003c/em\u003e expression is associated with TKI resistance in both HCC cell lines and the HCC cohort, corroborating the patterns observed in the pre-treatment plasma IL-8 results in our study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Exploration of \u003cem\u003eCXCL8\u003c/em\u003e Expression in Liver Disease and Malignant Hepatocytes\u003c/h2\u003e \u003cp\u003eTo further explore the significance of \u003cem\u003eCXCL8\u003c/em\u003e expression in liver disease and HCC, we analyzed the GepLiver database (DB) and Mendeley DB. These analyses were performed to compare \u003cem\u003eCXCL8\u003c/em\u003e expression across different liver conditions and to examine its spatial and cellular distribution within HCC tissues, providing insights into its potential role in disease progression and the TME. First, we analyzed \u003cem\u003eCXCL8\u003c/em\u003e expression under various liver conditions using the GepLiver DB. The analysis revealed significantly higher \u003cem\u003eCXCL8\u003c/em\u003e expression in HCC tissues than in non-tumor tissues, including normal liver, steatotic liver, and peri-tumoral non-cancerous tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). To further validate these findings, a spatial transcriptomic analysis was performed to examine the distribution of \u003cem\u003eCXCL8\u003c/em\u003e expression in HCC tissues. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee shows hematoxylin and eosin-stained sections along with spatial maps of the malignant and non-malignant regions. \u003cem\u003eCXCL8\u003c/em\u003e expression was predominantly localized in malignant hepatocytes, with higher expression levels concentrated in tumor-dense areas, as visualized using heat maps. Quantification of \u003cem\u003eCXCL8\u003c/em\u003e-positive cells confirmed this trend, with a significantly higher proportion of \u003cem\u003eCXCL8\u003c/em\u003e-positive cells observed in the malignant regions than in the non-malignant regions across all analyzed samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Notably, certain samples, such as P8T and P9T, demonstrated more than a four-fold increase in \u003cem\u003eCXCL8\u003c/em\u003e-positive cell populations within malignant vs. non-malignant regions. These results suggest that \u003cem\u003eCXCL8\u003c/em\u003e expression is elevated in HCC and preferentially localized to malignant regions, highlighting its potential role in tumor progression and its relevance as a biomarker and therapeutic target in HCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Single-Cell Analysis Reveals \u003cem\u003eCXCL8\u003c/em\u003e Expression Patterns and Its Role in the Tumor Microenvironment of HCC\u003c/h2\u003e \u003cp\u003eTo further elucidate the cellular origins and functional implications of \u003cem\u003eCXCL8\u003c/em\u003e expression in HCC, we analyzed single-cell RNA sequencing data from the GSE149614 dataset, which includes HCC and surrounding non-tumor tissues, enabling the identification of specific cell types that contribute to \u003cem\u003eCXCL8\u003c/em\u003e expression. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea shows the UMAP visualization of cell clusters identified from HCC and adjacent non-tumor tissues, including hepatocytes, myeloid cells, endothelial cells, fibroblasts, B cells, and T/NK cells. Myeloid cells demonstrated the highest \u003cem\u003eCXCL8\u003c/em\u003e expression levels, as highlighted in the violin plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). To investigate the functional heterogeneity within myeloid cells, a subset of myeloid cells from PTs and PVTT was recruited. This analysis revealed three distinct clusters: C1, C2, and C3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). Differential gene expression analysis between these clusters identified \u003cem\u003eCXCL8\u003c/em\u003e as a marker that was highly enriched in the C1 and C2 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). GSEA of \u003cem\u003eCXCL8\u003c/em\u003e\u0026thinsp;+\u0026thinsp;myeloid cells in clusters C1 and C2 revealed significant enrichment in pathways associated with EMT, inflammatory responses, and TNF-α signaling via NF-κB, suggesting a role of \u003cem\u003eCXCL8\u003c/em\u003e in promoting tumor progression and immune modulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). Finally, we validated these findings using TCGA_LIHC data. \u003cem\u003eCXCL8\u003c/em\u003e expression was positively correlated with genes associated with EMT (e.g., \u003cem\u003eSNAI1\u003c/em\u003e, \u003cem\u003eVIM\u003c/em\u003e) and matrix remodeling (e.g., \u003cem\u003eMMP-2\u003c/em\u003e, \u003cem\u003eMMP-9\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). These results further support the association between \u003cem\u003eCXCL8\u003c/em\u003e expression and the pathways driving tumor invasiveness and immune evasion in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we performed a targeted proteomic analysis using a 96-protein immuno-oncology panel to identify plasma biomarkers predictive of therapeutic responses to TKI therapy in patients with advanced HCC. Through the analysis of pre-treatment blood samples, we identified multiple differentially expressed plasma proteins associated with treatment response and clinical outcomes. Among these, IL-8 stood out as a key biomarker, demonstrating significant predictive power for distinguishing disease control from disease progression and showing strong correlations with shorter PFS and OS. These findings were further supported by analyses of public transcriptomic datasets, which consistently showed higher \u003cem\u003eCXCL8\u003c/em\u003e expression in TKI-resistant HCC cell lines and tumor tissues from TKI-treated HCC cohorts. Additionally, \u003cem\u003eCXCL8\u003c/em\u003e expression was markedly elevated in HCC tissues compared with that in other liver conditions, as revealed by GepLiver and spatial transcriptomic analyses. Single-cell RNA sequencing data provided deeper insights, showing that \u003cem\u003eCXCL8\u003c/em\u003e was predominantly expressed in myeloid cells within the TME, particularly in clusters enriched for pathways associated with EMT, inflammatory responses, and TNF-α signaling. Collectively, these results highlight the significant potential of IL-8 as a robust prognostic biomarker and promising therapeutic target in advanced HCC.\u003c/p\u003e \u003cp\u003eThe identification of predictive biomarkers for TKI therapy in HCC remains a challenge, hindering the improvement of therapeutic outcomes. Owing to the challenges in obtaining tumor tissues, the exploration of blood biomarkers has attracted attention as a noninvasive alternative for predicting TKI resistance in HCC. Plasma biomarkers reflect systemic changes and TME dynamics, offering practical advantages. Circulating VEGF, ANGPT2, and IL-6 levels have been associated with poor survival and resistance in patients with TKI-treated HCC [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, plasma secreted phosphoprotein 1 and risk models incorporating hepatocyte growth factor and fibroblast growth factor have shown promise in predicting treatment responses and survival [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our study builds on these insights by leveraging a high-throughput immuno-oncology panel to identify plasma biomarkers predictive of TKI response. These findings highlight the utility of comprehensive biomarker panels in the development of personalized therapeutic strategies for advanced HCC.\u003c/p\u003e \u003cp\u003eIn this study, IL-8 emerged as a pivotal plasma biomarker with significant predictive power for distinguishing disease control from disease progression in patients with advanced HCC undergoing TKI therapy. IL-8 is involved in modulating inflammatory responses [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Previous studies have demonstrated that IL-8 is positively correlated with the expression of drug resistance genes in human hepatoma cells and HCC tissues. This correlation is closely associated with a reduced response to anticancer therapy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Herein, elevated IL-8 levels were strongly associated with shorter PFS and OS in patients with HCC treated with TKIs, highlighting its potential as a prognostic marker. Public transcriptomic datasets have consistently demonstrated higher \u003cem\u003eCXCL8\u003c/em\u003e expression in TKI-resistant HCC cell lines and tumor tissues from TKI-treated HCC cohorts. Spatial and single-cell transcriptomic analyses further revealed that \u003cem\u003eCXCL8\u003c/em\u003e was predominantly expressed in myeloid cells within the TME, particularly in clusters linked to EMT, inflammatory responses, and TNF-α signaling. Previous studies have highlighted the interplay of TME cells, including MDSCs, in TKI resistance and the critical role of IL-8 in the TME [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A preclinical study showed that targeting tumor-infiltrating Ly6G\u0026thinsp;+\u0026thinsp;myeloid cells significantly enhances the efficacy of sorafenib in a mouse model of HCC [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. IL-8 has been implicated in the promotion of angiogenesis, immune suppression, and tumor invasiveness, correlating with poor responses to certain therapies, such as sorafenib [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The signaling pathways recruit immunosuppressive cells, such as MDSCs, and activate NF-κB and STAT3, thereby driving tumor progression [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Collectively, these findings establish IL-8 as a robust biomarker for predicting TKI efficacy and a promising therapeutic target. Its multifaceted role in TME modulation and resistance mechanisms highlights the need for further research on IL-8-targeted strategies to improve outcomes in advanced HCC.\u003c/p\u003e \u003cp\u003eThis study has some limitations that warrant consideration. First, the findings were not validated in a large prospective cohort, which restricts the generalizability of the results. Future studies with diverse and larger patient populations are required to confirm the predictive value of IL-8 and other biomarkers. Second, while IL-8 has been identified as a potential therapeutic target, this study lacks preclinical experiments showing that targeting MDSC-derived IL-8 can overcome TKI resistance in HCC. Such functional studies are essential for establishing causality and evaluating therapeutic feasibility. Additionally, this study focused on plasma biomarkers, which provide a minimally invasive approach but may not fully reflect the complexity of intratumoral heterogeneity or local TME dynamics. The incorporation of tissue-based analyses with plasma biomarkers may enhance our understanding of resistance mechanisms. Moreover, the lack of longitudinal sampling limits insight into the temporal dynamics of IL-8 expression during therapy, which may further refine its utility as a predictive biomarker. Finally, although associations with several pathways, such as EMT and inflammatory responses, were identified, a detailed exploration of downstream IL-8 signaling was beyond the scope of this study. Addressing these limitations in future research is critical for translating these findings into clinical practice.\u003c/p\u003e \u003cp\u003eNevertheless, this study represents a comprehensive effort to identify plasma biomarkers that are predictive of TKI response in advanced HCC. By leveraging a 96-protein immuno-oncology panel, we employed a high-throughput approach to uncover biomarkers, such as IL-8, which demonstrated robust predictive power for distinguishing disease control from disease progression and significant associations with PFS and OS. The integration of multiple datasets, including public transcriptomic, spatial transcriptomic, and single-cell RNA sequencing data, reinforces the reliability of our findings and provides mechanistic insights into the role of IL-8 in TKI resistance and tumor progression.\u003c/p\u003e \u003cp\u003eIn conclusion, this study highlights the clinical utility of plasma-based biomarker panels as a noninvasive and practical alternative to tissue-based diagnostics. Despite existing limitations, such as the need for validation in larger cohorts and preclinical models, our study highlights the potential of IL-8 as both a prognostic biomarker and a therapeutic target. Future studies focusing on IL-8-targeted interventions could lead to more personalized and effective treatments, ultimately improving outcomes in patients with advanced HCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003e All experiments performed with human samples were performed per the 1975 Declaration of Helsinki, and the study was approved by the Institutional Review Board of the Ajou University Hospital, Suwon, South Korea (AJIRB-BMR-SMP-17-188).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003eThe requirement for informed consent was waived as this study exclusively utilized anonymized data, ensuring the privacy and confidentiality of individual participants.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Research Foundation (NRF) of Korea (Grant numbers RS-2022-NR070489 and RS-2024-00339997) and the Korea Health Technology R\u0026amp;D Project of the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health \u0026amp; Welfare, Republic of Korea (Grant number HR22C1734).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Sujin Kim, Hye Ri Ahn, Jung Woo Eun, Hyo Jung Cho; Methodology: Jung Woo Eun, Hyo Jung Cho; Investigation: Sujin Kim, Hye Ri Ahn, Jung Woo Eun; Formal analysis: Jung Woo Eun; Writing - original draft preparation: Sujin Kim, Hye Ri Ahn, Hyo Jung Cho; Writing - review and editing: Sujin Kim, Hye Ri Ahn, Hui Gyeong Kim, Dooyeon Kim, Ji Eun Han, Soon Sun Kim, Jae Youn Cheong, Jung Woo Eun, Hyo Jung Cho; Funding acquisition: Jung Woo Eun, Hyo Jung Cho; Resources: Ji Eun Han, Soon Sun Kim, Jae Youn Cheong, Jung Woo Eun, Hyo Jung Cho; Supervision: Hyo Jung Cho; Project administration: Hyo Jung Cho.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The biospecimens and corresponding clinical information used in this study were provided by the Biobank of Ajou University Hospital, which is a member of the Korea Biobank Network.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information. The data that support the findings of this study are available from the authors without undue reservation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim, D.Y., \u003cem\u003eChanging etiology and epidemiology of hepatocellular carcinoma: Asia and worldwide\u003c/em\u003e. J Liver Cancer, 2024. 24(1): p. 62\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRich, N.E., \u003cem\u003eChanging Epidemiology of Hepatocellular Carcinoma Within the United States and Worldwide\u003c/em\u003e. Surg Oncol Clin N Am, 2024. 33(1): p. 1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, M.D., et al., \u003cem\u003eConversion therapy for advanced hepatocellular carcinoma in the era of precision medicine: Current status, challenges and opportunities\u003c/em\u003e. Cancer Sci, 2024. 115(7): p. 2159\u0026ndash;2169.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu, X.Y., Huo, J. L., Yu, Z. Y., Jiang, J. C., Xu, Y. X., \u0026amp; Zhao, L. J., \u003cem\u003eImmunotherapy in hepatocellular carcinoma: an overview of immune checkpoint inhibitors, drug resistance, and adverse effects\u003c/em\u003e. Oncologie, 2024. 26(1): p. 9\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, X., et al., \u003cem\u003ePrecision treatment in advanced hepatocellular carcinoma\u003c/em\u003e. Cancer Cell, 2024. 42(2): p. 180\u0026ndash;197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang, S.Y., et al., \u003cem\u003eSecond-Line Treatment after Failure of Immune Checkpoint Inhibitors in Hepatocellular Carcinoma: Tyrosine Kinase Inhibitor, Retrial of Immunotherapy, or Locoregional Therapy?\u003c/em\u003e Liver Cancer, 2024. 13(3): p. 246\u0026ndash;255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHubbard, S.R. and J.H. Till, \u003cem\u003eProtein tyrosine kinase structure and function\u003c/em\u003e. Annu Rev Biochem, 2000. 69: p. 373\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul, M.K. and A.K. Mukhopadhyay, \u003cem\u003eTyrosine kinase - Role and significance in Cancer\u003c/em\u003e. Int J Med Sci, 2004. 1(2): p. 101\u0026ndash;115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomuleasa, C., et al., \u003cem\u003eTherapeutic advances of targeting receptor tyrosine kinases in cancer\u003c/em\u003e. Signal Transduct Target Ther, 2024. 9(1): p. 201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, Y. and L. Zhang, \u003cem\u003eAnnual progress of clinical research on targeted therapy for nonsmall cell lung cancer in 2022\u003c/em\u003e. Cancer Innov, 2023. 2(1): p. 25\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoffski, P., et al., \u003cem\u003eEmerging role of tyrosine kinase inhibitors in the treatment of advanced renal cell cancer: a review\u003c/em\u003e. Ann Oncol, 2006. 17(8): p. 1185\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammadi, M. and H. Gelderblom, \u003cem\u003eSystemic therapy of advanced/metastatic gastrointestinal stromal tumors: an update on progress beyond imatinib, sunitinib, and regorafenib\u003c/em\u003e. Expert Opin Investig Drugs, 2021. 30(2): p. 143\u0026ndash;152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIancu, G., et al., \u003cem\u003eTyrosine kinase inhibitors in breast cancer (Review)\u003c/em\u003e. Exp Ther Med, 2022. 23(2): p. 114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Rosamel, L. and J.F. Blanc, \u003cem\u003eEmerging tyrosine kinase inhibitors for the treatment of hepatocellular carcinoma\u003c/em\u003e. Expert Opin Emerg Drugs, 2017. 22(2): p. 175\u0026ndash;190.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, J., et al., \u003cem\u003eSmall molecule tyrosine kinase inhibitors approved for systemic therapy of advanced hepatocellular carcinoma: recent advances and future perspectives\u003c/em\u003e. Discov Oncol, 2024. 15(1): p. 259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillanueva, A. and J.M. Llovet, \u003cem\u003eTargeted therapies for hepatocellular carcinoma\u003c/em\u003e. Gastroenterology, 2011. 140(5): p. 1410\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y., et al., \u003cem\u003eLenvatinib and immune-checkpoint inhibitors in hepatocellular carcinoma: mechanistic insights, clinical efficacy, and future perspectives\u003c/em\u003e. J Hematol Oncol, 2024. 17(1): p. 130.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGauthier, A. and M. Ho, \u003cem\u003eRole of sorafenib in the treatment of advanced hepatocellular carcinoma: An update\u003c/em\u003e. Hepatol Res, 2013. 43(2): p. 147\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinero, F., M. Dirchwolf, and M.G. Pessoa, \u003cem\u003eBiomarkers in Hepatocellular Carcinoma: Diagnosis, Prognosis and Treatment Response Assessment\u003c/em\u003e. Cells, 2020. 9(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, C., et al., \u003cem\u003eUnderstanding the dynamics of TKI-induced changes in the tumor immune microenvironment for improved therapeutic effect\u003c/em\u003e. J Immunother Cancer, 2024. 12(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakashima, Y., et al., \u003cem\u003eClinicopathologic Features and Immune Microenvironment of Non-Small-cell Lung Cancer With Primary Resistance to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors\u003c/em\u003e. Clin Lung Cancer, 2018. 19(4): p. 352\u0026ndash;359 e1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, J., et al., \u003cem\u003eOvercoming EGFR-TKI resistance by targeting the tumor microenvironment\u003c/em\u003e. Chin Med J Pulm Crit Care Med, 2024. 2(3): p. 151\u0026ndash;161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoh, M.J., et al., \u003cem\u003eClinical practice guideline and real-life practice in hepatocellular carcinoma: A Korean perspective\u003c/em\u003e. Clin Mol Hepatol, 2023. 29(2): p. 197\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingal, A.G., et al., \u003cem\u003eAASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma\u003c/em\u003e. Hepatology, 2023. 78(6): p. 1922\u0026ndash;1965.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlovet, J.M. and R. Lencioni, \u003cem\u003emRECIST for HCC: Performance and novel refinements\u003c/em\u003e. J Hepatol, 2020. 72(2): p. 288\u0026ndash;306.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Z., et al., \u003cem\u003eGepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases\u003c/em\u003e. Scientific Data, 2023. 10(1): p. 376.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlovet, J.M., et al., \u003cem\u003ePlasma biomarkers as predictors of outcome in patients with advanced hepatocellular carcinoma\u003c/em\u003e. Clin Cancer Res, 2012. 18(8): p. 2290\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao, Y., Hsu, C., Huang, C., \u0026amp; Cheng, A., \u003cem\u003eUse of plasma angiogenesis-related factors to investigate the association of interleukin 8 and interleukin 6 levels with efficacy of sorafenib-based antiangiogenic therapy in patients with advanced hepatocellular carcinoma (HCC)\u003c/em\u003e. Journal of Clinical Oncology, 2011. 29(4): p. 199\u0026ndash;199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiyahara, K., et al., \u003cem\u003ePredicting the treatment effect of sorafenib using serum angiogenesis markers in patients with hepatocellular carcinoma\u003c/em\u003e. J Gastroenterol Hepatol, 2011. 26(11): p. 1604\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEun, J.W., et al., \u003cem\u003eCancer-associated fibroblast-derived secreted phosphoprotein 1 contributes to resistance of hepatocellular carcinoma to sorafenib and lenvatinib\u003c/em\u003e. Cancer Commun (Lond), 2023. 43(4): p. 455\u0026ndash;479.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, H.Y., et al., \u003cem\u003eNovel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study\u003c/em\u003e. BMC Cancer, 2018. 18(1): p. 307.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsushima, K., D. Yang, and J.J. Oppenheim, \u003cem\u003eInterleukin-8: An evolving chemokine\u003c/em\u003e. Cytokine, 2022. 153: p. 155828.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, S.Y., et al., \u003cem\u003eInterleukin-8 is related to poor chemotherapeutic response and tumourigenicity in hepatocellular carcinoma\u003c/em\u003e. Eur J Cancer, 2014. 50(2): p. 341\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIida-Ueno, A., et al., \u003cem\u003eChanges in plasma interleukin-8 and tumor necrosis factor-alpha levels during the early treatment period as a predictor of the response to sorafenib in patients with unresectable hepatocellular carcinoma\u003c/em\u003e. Cancer Chemother Pharmacol, 2018. 82(5): p. 857\u0026ndash;864.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinke, J., et al., \u003cem\u003eMDSC as a mechanism of tumor escape from sunitinib mediated anti-angiogenic therapy\u003c/em\u003e. Int Immunopharmacol, 2011. 11(7): p. 856\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFousek, K., L.A. Horn, and C. Palena, \u003cem\u003eInterleukin-8: A chemokine at the intersection of cancer plasticity, angiogenesis, and immune suppression\u003c/em\u003e. Pharmacol Ther, 2021. 219: p. 107692.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, Y., et al., \u003cem\u003eProtein tyrosine kinase inhibitor resistance in malignant tumors: molecular mechanisms and future perspective\u003c/em\u003e. Signal Transduct Target Ther, 2022. 7(1): p. 329.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, C.J., et al., \u003cem\u003eTargeting tumor-infiltrating Ly6G(+) myeloid cells improves sorafenib efficacy in mouse orthotopic hepatocellular carcinoma\u003c/em\u003e. Int J Cancer, 2018. 142(9): p. 1878\u0026ndash;1889.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang, Z., et al., \u003cem\u003ePolarization of Monocytic Myeloid-Derived Suppressor Cells by Hepatitis B Surface Antigen Is Mediated via ERK/IL-6/STAT3 Signaling Feedback and Restrains the Activation of T Cells in Chronic Hepatitis B Virus Infection\u003c/em\u003e. J Immunol, 2015. 195(10): p. 4873\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao, F.Y., et al., \u003cem\u003eCD45(+)CD33(low)CD11b(dim) myeloid-derived suppressor cells suppress CD8(+) T cell activity via the IL-6/IL-8-arginase I axis in human gastric cancer\u003c/em\u003e. Cell Death Dis, 2018. 9(7): p. 763.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominguez, C., et al., \u003cem\u003eNeutralization of IL-8 decreases tumor PMN-MDSCs and reduces mesenchymalization of claudin-low triple-negative breast cancer.\u003c/em\u003e JCI Insight, 2017. 2(21).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, Tyrosine kinase inhibitors, Olink proteomics, Plasma, Biomarkers, Interleukin-8","lastPublishedDoi":"10.21203/rs.3.rs-8323662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8323662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aim:\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC) is the predominant primary liver cancer and third leading cause of cancer-related death. Despite improvements in overall survival (OS), those in HCC-specific survival rates remain modest. Tyrosine kinase inhibitors (TKIs) are crucial first-line HCC treatments when immune checkpoint inhibitors are unsuitable. To identify plasma biomarkers predicting the therapeutic response to TKIs in patients with HCC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePre-treatment plasma samples from 60 patients with advanced HCC treated with sorafenib or lenvatinib were analyzed using targeted proteomics. Differentially expressed proteins were identified based on the treatment response (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePlasma levels of metalloproteinase 12 and vascular endothelial growth factor A were elevated in patients with progressive disease compared with those with partial response or stable disease, correlating with shorter progression-free survival (PFS) and OS. Among patients with PFS\u0026thinsp;\u0026ge;\u0026thinsp;12 months, C-C motif chemokine ligand 20, C-X-C motif chemokine ligand 1, C-X-C motif chemokine ligand 5, fibroblast growth factor 2, interleukin (IL)-7, IL-8, IL-18, latency-associated peptide transforming growth factor beta 1, and mucin 16 expressions were significantly upregulated, with IL-8 (CXCL8) levels demonstrating the highest predictive accuracy (area under the receiver operating characteristic\u0026thinsp;=\u0026thinsp;0.91) and prognostic power for PFS (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;2.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015) and OS (HR\u0026thinsp;=\u0026thinsp;3.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). \u003cem\u003eCXCL8\u003c/em\u003e expression was predominantly localized in tumor-associated myeloid cells and enriched in epithelial\u0026ndash;mesenchymal transition- and immune modulation-related pathways, highlighting its importance in the tumor microenvironment.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePlasma biomarkers hold promise for predicting treatment response in patients with advanced HCC undergoing TKI therapy. Elevated IL-8 levels are strongly associated with poor outcomes, emphasizing their potential to guide therapeutic decisions and stratify high-risk patients.\u003c/p\u003e","manuscriptTitle":"Exploring plasma biomarkers for therapeutic response to tyrosine kinase inhibitors in advanced hepatocellular carcinoma: Interleukin-8 as a key prognostic indicator","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 15:01:46","doi":"10.21203/rs.3.rs-8323662/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00aed146-054d-4e00-ba17-8b7204c50802","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T02:39:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 15:01:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8323662","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8323662","identity":"rs-8323662","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}