Molecular biomarkers of response to sintilimab combined with lenvatinib for locally advanced hepatitis B virus-associated hepatocellular carcinoma

Molecular biomarkers of response to sintilimab combined with lenvatinib for locally advanced hepatitis B virus-associated hepatocellular carcinoma | 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 Molecular biomarkers of response to sintilimab combined with lenvatinib for locally advanced hepatitis B virus-associated hepatocellular carcinoma Lijun Wang, Yong Cui, Longfei Huang, Hongwei Wang, Ming Liu, Kemin Jin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4368601/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: The efficacy of combining immune checkpoint inhibitors with anti-angiogenic drugs in advanced hepatocellular carcinoma (HCC) varies, and the predictive biomarkers for this therapy remain unclear. Methods: In this single-center study in China, patients with HCC ineligible for traditional resection were administered sintilimab on day 1 of a 21-day cycle, accompanied by daily oral lenvatinib. Treatment responses were assessed using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Tumor biopsies underwent RNA sequencing (RNA-seq), immune microenvironment analysis, and whole-exome sequencing (WES). Survival analyses also incorporated data from The Cancer Genome Atlas (TCGA), and a multivariate Cox regression analysis was conducted to identify potential therapeutic factors. Results: From August 1, 2018, to November 25, 2021, 33 patients with hepatitis B virus (HBV)-associated HCC were enrolled. By January 30, 2024, 13 patients had undergone potentially curative surgery or radiofrequency ablation. RNA-seq identified 94 differentially expressed genes (DEGs) between the 22 patients in the response group and the 11 in the non-response group. High expression levels of LINC01554 and WHRN were linked to prolonged progression-free survival (PFS). The analysis of immune microenvironment differences and correlation with DEGs showed a positive association with patient responses for cell types such as pro-B, class-switched memory B, plasma, CD4+ Tcm, Th1, and NKT cells. However, only the status of CD4+ Tcm approached statistical significance (P < 0.10); other immune cells did not. In the WES analysis, a significant FANCD2 mutation (P < 0.05) was found, and a CUX1 mutation was associated with shorter PFS. Neither mutation correlated with liver cancer survival in the TCGA-liver cancer dataset. The Cox regression analysis indicated that a single tumor (P = 0.02, hazard ratio (HR) = 0.31, 95% confidence interval (CI) = 0.11-0.85), high LINC01554 expression level (P = 0.01, HR = 0.16, 95% CI = 0.05-0.49) and higher CD4+ Tcm score (P = 0.05, HR = 0.29, 95% CI = 0.08-0.98) were independent predictors of prolonged PFS. Conclusions: Treatment with sintilimab plus lenvatinib can lead to varied responses in HCC patients. High levels of lncRNA LINC01554 and CD4+ Tcm, along with a single tumor, suggest improved disease control. Sintilimab Lenvatinib Hepatocellular carcinoma Molecular biomarkers Conversion therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Primary liver cancer is one of the common malignant tumors worldwide[ 1 ]. In 2018, it globally ranked sixth in terms of the incidence of malignant tumors and fourth in terms of cancer-related deaths[ 2 , 3 ]. In primary liver cancer, 75–85% of cases are diagnosed with hepatocellular carcinoma (HCC)[ 2 ]. In contrast to Europe and the United States, the main causative factor for liver cancer in China is chronic hepatitis B virus (HBV) infection, and the majority of cases have already reached the advanced stage or have metastasized, rendering them inappropriate for radical treatment, resulting in poor overall prognosis[ 4 ]. Recently, immune checkpoint inhibitors and anti-angiogenic drugs have shown promising therapeutic value in advanced HCC. The objective response rate (ORR) yielded approximately 30%, and overall survival (OS) reached 20–22 months, significantly improving the survival of patients with advanced HCC[ 5 , 6 ]. The high ORR also provides a new approach for the conversion therapy of locally advanced HCC. Our center has conducted a prospective study[ 7 ], which recruited patients with unresectable, intermediate − advanced HCC for treatment using sintilimab and lenvatinib, evaluated the ORR and surgical resection rate of the combination therapy, and explored predictive factors for the efficacy of the aforementioned combination therapy. Sintilimab is an anti-programmed cell death protein (PD-1) monoclonal antibody that has exhibited a high anti-tumor activity in a previous study[ 8 ]. Lenvatinib is an orally administered multi-targeted tyrosine kinase inhibitor (TKI) that has been approved for the first-line treatment of unresectable HCC[ 9 ]. However, the present study has indicated that there are variations in tumor regression and progression-free survival (PFS) of different patients after receiving this combination therapy, suggesting that it may be inappropriate for all patients. Given the likelihood of adverse events and the potential inefficacy of the combined treatment strategy, accurately predicting the treatment’s effectiveness in advance is of utmost importance. Previous studies have identified several gene mutations that could be associated with the efficacy of targeted therapy for HCC, including VEGFA, RAS, MET, TP53, FGF19, etc[ 10 ]. Additionally, potential molecular pathways include the PI3K/mTOR signaling pathway, HRD pathway, FGFR pathway, etc[ 11 ]. The indicators potentially associated with the efficacy of immunotherapy for HCC include PD-L1 expression, tumor mutational burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), STK11/LKB1, POLE, Wnt/CTNNB1 mutations, and others[ 12 ]. However, these indicators have not been widely applied in HCC. Furthermore, molecular biomarkers for combination of immunotherapy with targeted therapy have not yet been identified. Recently, advances in whole-exome sequencing (WES)[ 13 ] and transcriptome sequencing technologies[ 14 ] have made it possible to explore molecular characteristics of tumors in advance, assisting clinicians in making clinical decisions. The present study aimed to analyze the gene expression profiles of HCC tissues within the study cohort. It was also attempted to assess potential molecular biomarkers that could be associated with the efficacy of the combination therapy involving sintilimab and lenvatinib. Additionally, the prognostic significance of these biomarkers was investigated, providing valuable predictive information regarding treatment outcomes. Methods Study design and patients’ enrollment Totally, 36 patients with locally advanced HCC who were ineligible to undergo surgery were prospectively enrolled in a single-arm, single-center, non-randomized clinical study, and 33 of those patients with HBV-associated HCC whose tumor samples underwent molecular sequencing were included. All patients provided written informed consent prior to enrollment, and the trial was registered in the ClinicalTrials.gov database (Registration No. NCT04042805). All participants received combination of sintilimab and lenvatinib (sintilimab: 200 mg injected per 21 days; lenvatinib mesylate capsules: 12 mg orally taken for bodyweight of 60 kg and above, or 8 mg orally taken for bodyweight of less than 60 kg). The therapy continued until the occurrence of one of the following events: surgery, disease progression, intolerable toxicity, withdrawal of informed consent, loss of follow-up, death, or other situations indicated in the study protocol. The inclusion and exclusion criteria were described in our previous study[ 7 ]. Hepatectomy was performed by wedge-shaped local resection, segmental resection, hepatic lobectomy, or hemi-hepatectomy according to the location, size, and number of tumors. The total number of courses of combination treatment before and after surgery should ideally not exceed 8 cycles. If the patient received over 8 cycles of combination treatment before surgery, adjuvant therapy was not given as per the protocol. The study protocol was approved by the Ethics Committee of Peking University Cancer Hospital (Beijing, China), and the study was conducted in accordance with the principles outlined in the Declaration of Helsinki. Evaluation of tumor response Evaluation of tumor response was conducted through imaging every 9 weeks (± 7 days) starting from the initiation of therapy until the 48th week. Subsequently, the evaluation was performed every 12 weeks (± 7 days). Evaluation of tumor response was based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST). The treatment response was evaluated by assessing the sum of the diameters of the target lesion during the arterial enhancement phase throughout the treatment course. The treatment responses were categorized as follows: complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). Patients evaluated as CR and PR were assigned to the response group, and others were allocated to the non-response group. RNA-seq Total RNA was isolated from each sample (33 tumor samples) using the PureLink RNA Mini kit (Thermo Fisher Scientific), and the purity and quality were measured by the Nanodrop spectrophotometer. RNA integrity was evaluated using the RNA Nano6000 Assay kit and the Bioanalyzer 2100 system (Agilent Technologies Inc.). Sequencing was performed on the Illumina Hiseq X10 platform. Raw reads were filtered by FastQC, and reads from three samples were not used in the subsequent analysis due to poor sequencing quality. The filtered reads were then aligned to the Ensembl human genome assembly GRCh37 using the STAR (ver. 2.7.0) with default parameters. Gene expression levels were analyzed by raw count and Transcripts Per Kilobase Million (TPM). Annotations of mRNA were retrieved from the GENCODE (ver. 19) database. Identification of DEGs The differentially expressed genes (DEGs) between the response and non-response group were screened by “DESeq2” R package. The commonly used criteria for selection involved applying thresholds of |log2(foldchange)| > 1 and false discovery rate (FDR) < 0.05. Genes with mean count less than 1 among all samples were excluded. Then, the processed raw counts were imported into DESeq2, and the normalization step was integrated into DESeq2 workflow. Functional Enrichment Analysis The functional annotation for DEGs was performed by “clusterProfiler” R package. The Entrez-ID for each gene was transferred from gene symbols through “org.Hs.eg.db” for human tissues. Gene Ontology (GO) terms were annotated using default parameters. Immune Microenvironment Analysis The xCell pipeline was utilized to determine the relative abundance of transcripts (measured in TPM) for each sample. The immune cell infiltration score was calculated for down-stream analysis. The correlation analysis of DEGs was conducted using the Spearman correlation test on the R platform. Feature pairs with P-value less than 0.05 were considered statistically significant. WES Genomic DNA (gDNA) was extracted from the tumor tissues of 33 patients using the PureLink Genomic DNA kit (Thermo Fisher Scientific, Waltham, MA, USA). Exome was captured using the Agilent SureSelect Human All ExonV5 kit (Agilent Technologies Inc., Santa Clara, CA, USA) according to the manufacturer’s instructions. Subsequent paired-end sequencing was performed on Illumina Novaseq6000 sequencer (Illumina Inc., San Diego, CA, USA). Clean reads were then aligned to the reference human genome hg19 (Genome Reference Consortium GRCh37) using the BWA (ver. 0.7.17), and the GATK MuTect2 pipeline was used for paired tumor-normal somatic mutation calling. Common germline mutations were filtered using the Genome Aggregation Database (gnomAD). Survival Analysis Survival analyses were conducted separately for all differing factors identified in the RNA-seq, Immune microenvironment analysis, and WES analyses. Considering the significant influence of treatments following recurrence or progression on OS, this study chose to use PFS as the survival analysis metric, with definition consistent with that previously mentioned in our study[ 7 ]. Additionally, to further validate the results by expanding the sample size, sequencing data and survival outcomes from the LIHC (liver hepatocellular carcinoma) cohort were downloaded from The Cancer Genome Atlas (TCGA) database and included in this study. Kaplan-Meier analysis was employed to explore the relationship between genes and prognosis. Statistical Analysis The bioinformatics analysis was performed on the R platform (ver. 3.6.0). Fisher’s exact test and the Wilcoxon rank-sum test were utilized for comparing categorical and continuous variables, respectively. The influencing factors of PFS were analyzed by Cox univariate and multivariate regression analyses. Survival analysis was conducted using the Kaplan-Meier method. A two-tailed P-value < 0.05 was considered statistically significant. Results Patients’ baseline characteristics Between August 1, 2018 and November 25, 2021, 33 HBV-associated HCC patients were enrolled in the present study. At the data cutoff date (January 30, 2024), 12 patients underwent surgery with curative intent and 1 patient received radiofrequency ablation plus stereotactic radiotherapy due to the poor location that was inappropriate for resection. As of the data cutoff date, all remaining patients had discontinued the initial treatment. Among them, 14 discontinued due to disease progression, 2 completed the treatment after 2 years and had no tumor residue as confirmed by needle biopsy, 3 due to adverse effects, and 2 because they withdrew their informed consent. Regrettably, 12 of these patients have passed away. It is noteworthy that 22 patients were assigned to the response group and 11 patients were assigned to the non-response group according to the mRECIST. Samples were prepared for each patient for further testing. However, during the experiment, 3 RNA-seq samples from the response group failed to pass the quality control. As a result, only 30 samples (19 from response group and 11 from non-response group) were involved in the transcriptome analysis. Patients’ baseline characteristics are presented in Table 1 . The age, BCLC, Barcelona Clinic Liver Cancer (BCLC) stage, total number of tumors, size of tumors, and baseline alpha-fetoprotein (AFP) level were not significantly different between the two groups. The number of patients who underwent surgery was higher in the response group than that in the non-response group, while the difference was not statistically significant. Female patients and those with Eastern Cooperative Oncology Group (ECOG) performance status scores of 1 had a significantly higher likelihood of being non-responders (P < 0.05). Table 1 Patients’ baseline characteristics evaluated by the mRECIST standard Characteristic Response group (CR + PR, n = 22) Non-response group (SD + PD, n = 11) P Median age (years) 0.488 < 60 13 (59) 5 (46) ≥ 60 9 (41) 6 (54) Sex, n (%) 0.033 Male 21 (95) 7 (64) Female 1 (5) 4 (36) ECOG PS, n (%) 0.017 0 18 (82) 4 (36) 1 4 (18) 7 (64) BCLC Stage, n (%) 1.000 B 11 (50) 5 (45) C 11 (50) 6 (55) Tumor number, n (%) 1.000 1 12 (55) 6 (55) 2–3 6 (27) 3 (27) ≥ 4 4 (18) 2 (18) Size of the largest tumor (cm) 0.593 ˂5 3 (14) 2 (18) 5–10 10 (45) 3 (27) ≥ 10 9 (41) 6 (55) Serum alpha-fetoprotein level, n (%) 0.488 ˂400 ng/mL 13 (59) 5 (45) ≥400 ng/mL 9 (41) 6 (55) Conversion to surgery 0.132 Yes 11 (50) 2 (18) No 11 (50) 9 (82) BCLC, Barcelona Clinic Liver Cancer; ECOG PS, Eastern Cooperative Oncology Group Performance Status; HBV, hepatitis B virus; HCC, hepatocellular carcinoma Identification of DEGs To identify molecular differences of HCC samples that caused different responses to treatment, DEGs were screened from RNA-seq data. Genes with low counts were filtered out and 32,205 unique genes remained (54.01%). When comparing the response group with the non-response group, it was defined that a positive foldchange value indicated a higher expression in the response samples, while a negative value represented a lower expression. Totally 94 DEGs were found between the two groups (|log2(Foldchange)| >1, FDR < 0.05). Among them, 41 DEGs were enriched in the response group, while 53 DEGs were highly expressed in the non-response group (Fig. 1 A). Subsequently, the GO enrichment annotations for each group provided insights into the potential functions of HCC baseline DEGs. In the non-response group, highly expressed genes were identified that were enriched in functions related to development and transmembrane (Fig. 1 B). In contrast, genes, which were highly expressed in the response group, were enriched in association with apoptosis process (Fig. 1 C). Among the differentially expressed genes (DEGs), two DEGs demonstrated a significant association with progression-free survival (PFS). High expression levels of LINC01554 and WHRN were correlated with longer PFS and were predominantly expressed in the response group (Fig. 1 D-E). To further validate these findings by expanding the sample size, we analyzed the relationship between the expression levels of LINC01554 and WHRN and the prognosis in the TCGA-liver cancer dataset. Our analysis revealed that high expression of LINC01554 is significantly associated with improved disease specific survival (Fig. 1 F). In contrast, WHRN expression did not correlate with disease specific survival (Fig. 1 G). Differences in immune cells between the two groups To reveal the underlying mechanisms driving varied responses to immunotherapy, xCell algorithm was employed to calculate immune cell infiltration scores for each sample based on their baseline transcription expression. The immune micro-environment differences were described by comparing mean scores of each cell type between the response and non-response groups. The results showed that some cell types were more enriched in the response group, including pro-B cells, Class-switched memory B-cells, and plasma cells (t-test P 2), all of which predominantly exhibited enrichment in B cell subtypes (Fig. 2 A). In contrast, T cells did not show any specific preference in either group. Moreover, samples with infiltration scores greater than the median score across all samples were defined as high-score group. Unfortunately, the differences in the high or low scores of pro-B cells, class-switched memory B cells, and plasma cells did not reach statistical significance with respect to PFS (Fig. 2 B-D). Correlation of DEGs and immune cells In order to examine the role of baseline gene expression in the diversity of the immune cell microenvironment, correlation analysis was performed between DEGs and immune cell infiltration scores. In the heatmap, feature pairs with P-value smaller than 0.05 were identified using the Spearman correlation test, and these pairs were denoted with an asterisk. It was found that pro B-cells and plasma cells were positively correlated with highly expressed genes in the response group, as described earlier (Fig. 2 A). Notably, T cells were negatively correlated with these genes. In contrast, the non-response group exhibited a different pattern, where various T cell subtypes showed variable correlations. In the non-response group, most of T cell subtypes were positively associated with highly expressed genes. However, three specific subtypes, namely CD4 + central memory T cells (CD4 + Tcm), Th1 cells, and NKT cells, demonstrated a negative correlation (Fig. 3 B). Similarly, the expression differences in CD4 + Tcm, Th1 cells, and NKT cells also failed to reach statistical significance in relation to PFS. WES described mutational landscape To investigate mutational landscape of HCC baseline tissues, the WES was applied on all 33 baseline tissue samples (30 samples mentioned in RNA-seq analysis and 3 extra samples). These mutations included somatic single-nucleotide variants (SNVs), deletion, insertion, and copy number variants (CNVs) (Fig. 4 A). The most frequent mutation across all samples was TP53 mutation (60.6%), followed by APC (39.4%) and MYC (21.2%) mutations. The results of the Fisher’s exact test revealed that FANCD2 was significantly mutated in the non-response group (P < 0.05). Besides, LRP1B was mutated in the non-response group (P < 0.1). Notably, 5 samples had CTNNB1 mutation(s) in the two groups. Moreover, the immunologic markers were investigated as supplementary features. The non-response group exhibited a relatively higher tumor mutational burden (TMB), which is defined as the number of non-synonymous somatic mutations (single nucleotide variants and small insertions/deletions) per megabase in coding regions. However, this did not reach statistical significance between the two groups (Fig. 4 B). Additionally, while the results for the tumor proportion score (TPS), defined as the ratio of PD-L1 positive tumor cells to the total number of tumor cells, and the combined positive score (CPS), defined as the sum of PD-L1 positive tumor cells and PD-L1 positive immune cells divided by the total number of tumor cells, displayed higher quartiles, there were no significant differences between the groups (Fig. 4 C-D). Furthermore, PFS was examined based on the mutational status of each gene. The results revealed that samples with CUX1 mutation exhibited a relatively poorer survival. However, there was no significant association between mutational status of FANCD2 and PFS (Fig. 4 E-F). We further analyzed the relationship between FANCD2 and CUX1 mutations and prognosis in the TCGA-liver cancer dataset. We found that both FANCD2 and CUX1 mutations are not associated with liver cancer survival (Fig. 4 G-H). Univariate and multivariate regression analyses of PFS In order to delve deeper into differences in baseline gene mutations and expression changes that contribute to disease progression, prognosis analysis was conducted on the WES mutations, immune cell infiltration scores, and RNA-seq read counts. We considered infiltration scores and expression levels that exceeded the median value of all samples as positive features. Features with less than 3 positive samples in either group were filtered out. Moreover, the analysis encompassed not only molecular biology findings, but also clinical features, such as age (≥ 60 or < 60 years old), gender (male or female), ECOG score (1 or 0), BCLC stage (B or C), tumor number (multiple or single), largest tumor size (≥ 10 or < 10 cm), and AFP level (≥ 400 or < 400 ng/ml). The results of univariate Cox regression analysis showed that tumor number, AFP level, mutational status of CUX1, expression levels of LINC01554, WHRN and CD4 + Tcm score could influence duration of PFS (P ≤ 0.10). The results of multivariate Cox regression analysis indicated that single tumor (P = 0.02, hazard ratio (HR) = 0.31, 95% confidence interval (CI) = 0.11–0.85), high LINC01554 expression level (P = 0.01, HR = 0.16, 95% CI = 0.05–0.49), and higher CD4 + Tcm score (P = 0.05, HR = 0.29, 95% CI = 0.08–0.98) were independent predictors of prolonged PFS (Table 2 ). Table 2 Multivariate Cox regression analysis of progression-free survival (multivariate analysis was performed utilizing backward stepwise (wald) method) Number Univariate HR P-value Multivariate HR P Tumor number 18/15 0.33 (0.14–0.78) 0.01 0.31 (0.11–0.85) 0.02 (single/multiple) AFP 18/15 0.49 (0.22–1.11) 0.09 - - (< 400/≥400 ng/ml) CUX1(wild/mutant) 30/3 0.22 (0.06–0.79) 0.02 - - LINC01554 15/15 0.32 (0.13–0.79) 0.01 0.16 (0.05–0.49) 0.01 (high/low) WHRN (high/low) 15/15 0.39 (0.16–0.94) 0.04 - - CD4 + Tcm 9/21 0.43 (0.16–1.19) 0.10 0.29 (0.08–0.98) 0.05 (high/low) HR, Hazard ratio Discussion The present study revealed that the combination of sintilimab and lenvatinib was resulted in a higher ORR and a longer PFS. This makes it a noticeable treatment option for locally advanced liver cancer conversion therapy, which is widely implemented in clinical practice. Patients who have successfully undergone conversion to surgery experience two notable advantages compared with those who have not undergone conversion. First, they provide the potential for cure, and second, they facilitate avoiding prolonged drug treatments. The PFS can vary among patients who are ineligible for surgery conversion. That is why screening and identifying patients who are more likely to benefit from conversion therapy in advance or identifying the population that can achieve longer disease control through combined treatments is important. For patients who are inappropriate candidates, transcatheter arterial chemoembolization or other more intensive treatment options could be considered. The accuracy of evaluating treatment response plays a vital role in the management of liver cancer patients. The mRECIST[ 15 ], which consider tumor viability boundaries (particularly arterial-phase tumor enhancement) along with target lesion diameter, have gained widespread acceptance in post-treatment assessment of liver cancer. Previous studies have demonstrated significant advantages of the mRECIST over the RECIST (ver. 1.1) 16 in assessing treatment response in liver cancer. The mRECIST, with their stringent imaging standards, exhibited higher ORRs, improved discrimination for patients with stable disease, enhanced accuracy in evaluating study endpoints, and stronger predictive value for OS. Therefore, the mRECIST can provide a more accurate assessment for patients with HCC undergoing systemic treatment[ 16 ]. In the present study, based on these criteria, patients were assigned to the response group and the non-response group. Typically, the ORR of HCC patients receiving monotherapy with immune checkpoint inhibitors is within 20%[ 17 , 18 ]. Molecular targeted drugs[ 9 , 19 ], such as bevacizumab, sorafenib, and lenvatinib may achieve the efficacy of rapid tumor killing, alteration of the tumor microenvironment, and synergistic enhancement of immune response by inhibiting vascular endothelial growth factor (VEGF) and its mediated tumor pathways. Consequently, addition of molecular targeted drugs effectively improved the ORR of the treatment using immune checkpoint inhibitors. Due to the high heterogeneity of HCC and the presence of multiple factors influencing the response to the combined therapy, the present study comprehensively analyzed the factors affecting the efficacy through transcriptome sequencing, analysis of immune infiltration in tumor microenvironment, WES, and patients’ clinical data. The key findings of this study highlight notable variations in RNA transcription levels between the response group and non-response group. By conducting functional screening of DEGs and analyzing their correlation with survival, two genes were identified, namely LINC01554 and WHRN, which displayed a strong association with treatment efficacy. Notably, LINC01554 has been consistently reported in multiple studies. According to a previous study[ 20 ], it was found that low expression of LINC01554 has been notably linked to poor survival, advanced tumor stage, and the presence of portal vein tumor thrombus. In vitro experiments have revealed that high expression of LINC01554 partially inhibits the Wnt and PI3K-Akt pathways, resulting in reduced cell invasiveness and diminished epithelial-mesenchymal transition capability. Similarly, another study[ 21 ] highlighted LINC01554, along with three other RNAs (TMEM220-AS1, LINC02362, and LINC02499), as independent prognostic factors for HCC. These consistent results suggest that LINC01554 is an important potential therapeutic target. The WHRN[ 22 ] gene, which was also identified, is a protein-coding gene primarily associated with diseases, such as deafness, autosomal recessive, non-syndromic hearing loss, USH2D, etc. However, few studies have concentrated on its involvement in tumor occurrence and development. The efficacy of immune checkpoint inhibitors in exerting anti-tumor effects is influenced by the immune microenvironment. Preclinical studies[ 23 – 25 ] have demonstrated that blocking the interaction between PD-1 and its ligands (PD-L1 or PD-L2) can reactivate T cell proliferation and cytokine release, enhance the cytotoxicity of CD8 + T cells, and promote the presence of memory CD4 + T cells. Therefore, it is not surprising that the infiltration of CD8 + T cells in tumor tissues is closely associated with acceptable response of HCC patients receiving monotherapy (e.g., anti-PD-1 therapy)[ 26 ]. An important finding of this study is the identification of B cell subtypes, rather than T cells, as being enriched in the response group through immune microenvironment analysis. This suggests a strong correlation between B cell subtypes and a favorable response to the combination therapy. Recent research has reported the presence of tumor-infiltrating B cells as significant biomarkers of anti-tumor immune response in various types of tumors, further supporting the role of B cells in responding to immune checkpoint inhibitor therapy[ 27 , 28 ]. However, the exact mechanisms by which B cells contribute to anti-tumor immunity have not yet been fully explored. Some studies suggested that B cells may enhance T cell immunity by secreting cytokines and chemokines, as well as expressing co-stimulatory signals[ 29 , 30 ]. In certain physiological conditions, B cells may also act as primary antigen-presenting cells (APCs) to initiate CD4 + T cell responses[ 18 ]. Nevertheless, additional basic research is required to confirm these speculations. The analysis of the interaction between transcriptional RNA and immune cells in this study revealed that when tumors overexpressed specific genes (one or more of 41 DEGs) and were enriched with B cell subtypes in the immune microenvironment, a more favorable therapeutic response could be achieved. Conversely, when tumors overexpressed other specific genes (one or more of 53 DEGs), the influence of B cell subtypes on treatment efficacy was limited, and different correlations were noted with T cell subtypes (particularly a negative correlation with CD4 + Tcm). These results confirm that the relationship between tumor-specific gene expression and efficacy is more crucial, while the immune microenvironment plays a secondary role, with B cell subtypes and CD4 + Tcm indicating a close association with satisfactory treatment response. Although there were significant differences at the transcriptional RNA level between the response group and non-response group, no additional DEGs were detected in the WES except for the FANCD2 gene. Compared with the WES, which only detects exon fragments, RNA-seq sequencing includes more gene information, such as messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and non-coding RNA. Therefore, it is noteworthy that gene mutations are not consistent between transcriptional RNA level and exon expression level. This phenomenon was also observed in a previous study[ 31 ]. Previous studies have shown that the Wnt/β-catenin pathway and its important protein CTNNB1 could be associated with poor response to immunotherapy[ 26 , 32 ]. However, in the present study, CTNNB1 mutations were distributed in both groups and did not reach statistical significance. This may be related to the small sample size of mutated patients, and another possible reason is that a single gene or pathway mutation is insufficient to determine treatment efficacy. The addition of molecular targeted therapy may reverse the poor response to immunotherapy, and further large-scale studies are necessary to validate this hypothesis. In addition, tumor markers, such as TMB, TPS and CPS, which have been validated as predictors of response to immunotherapy in other tumor types, did not exhibit significant differences in this cohort. This suggests that the mechanisms underlying the therapeutic effects induced by the combination treatments are relatively complex, and a single immune-related marker may not accurately predict the response. Consequently, the efficacy of the combination therapy using sintilimab and lenvatinib may be influenced by several factors, including the genetic status of the tumor, the tumor’s immune microenvironment, and the patient’s clinical and pathological characteristics (e.g., gender, age, number and size of tumors, etc.). The results of the present study indicated a significant correlation between the high expression level of LINC01554 in tumor tissues, the high expression level of CD4 + Tcm in the immune microenvironment, and a solitary tumor (compared with multiple tumors) with the prolonged PFS. The abovementioned results strongly indicate that in cases of HCC, where specific mutations are identified through tumor biopsy and the immune microenvironment analysis shows favorable treatment response characteristics, even patients at advanced tumor stages may achieve extended disease control periods or successful conversion to surgical options. These significant findings have profound implications for guiding clinical treatment decisions. This study has certain limitations. Firstly, due to its small sample size, there might be the likelihood of selection bias among the enrolled patients. Secondly, HCC mainly consists of multiple subtypes and tissue components. Even with multiple samplings, there might be insufficient tissue volume, which could result in the inability of biopsy tissue testing to accurately reflect the overall biological characteristics of the tumor. Thirdly, the follow-up time was relatively short, which might influence the results to some extent. Finally, no further mechanistic research and large-scale validation of the DEGs were conducted, and additional reliable studies are required to validate the findings of this research. Conclusions In conclusion, this study revealed that the response and non-response observed in HCC patients undergoing treatment of sintilimab plus lenvatinib were associated with variances in gene expression within liver cancer tissues and the distribution of lymphoid immune cells in the immune microenvironment. Notably, among these factors, high expression levels of lncRNA LINC01554 and CD4 + Tcm, along with the presence of a solitary tumor, were indicative of improved disease control duration. Abbreviations HCC: hepatocellular carcinoma; ORR, objective response rate; OS: overall survival; PFS: progression-free survival; CR: complete response; PR: partial response; PD: progressive disease; SD: stable disease; mRECIST: modified Response Evaluation Criteria in Solid Tumors; RNA-seq: RNA sequencing; WES: whole-exome sequencing; DEGs: differentially expressed genes (DEGs); HBV: hepatitis B virus (HBV); HR: hazard ratio; CI: confidence interval; PD-1: anti-programmed cell death protein (PD-1); TKI: multi-targeted tyrosine kinase inhibitor; MMR: mismatch repair; MSI: microsatellite instability; TMB: tumor mutational burden; FDR: false discovery rate; GO: Gene Ontology; TCGA: The Cancer Genome Atlas (TCGA) database; BCLC: Barcelona Clinic Liver Cancer; AFP: alpha-fetoprotein; ECOG: Eastern Cooperative Oncology Group; SNVs: somatic single-nucleotide variants: CNVs: copy number variants; TPS: tumor proportion score; CPS: combined positive score; VEGF: vascular endothelial growth factor; APCs: primary antigen-presenting cells (APCs); mRNA: messenger RNA; rRNA: ribosomal RNA; tRNA: transfer RNA. Declarations Acknowledgements We thank the patients and their families who took part in this study. Author contributions LW and YC contributed equally to the work. BX contributed to the conception and design. LW was responsible for the provision of study materials, data collection, and draft writing. YC contributed to the imaging evaluation. LH contributed to bioinformatics analysis and data interpretation. HW, ML, KJ, and KW provided technical and material support. All authors read and approved the final manuscript. Funding This study was supported by grants from the Clinical Research Fund for Distinguished Young Scholars of Beijing Cancer Hospital (2019088) and Special fund for clinical research of Wu Jieping Medical Foundation (no. 320.6750.19088-38). Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Peking University Cancer Hospital (Beijing, China), and the study was conducted in accordance with the principles outlined in the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepatopancreatobiliary Surgery Unit I, Peking University Cancer Hospital & Institute, Beijing 100142, China 2 Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China 3 Research Institute, GloriousMed Clinical Laboratory Co., Ltd., Shanghai, China References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA Cancer J Clin 2018, 68 (6):394-424. Forner A, Reig M, Bruix J: Hepatocellular carcinoma . Lancet 2018, 391 (10127):1301-1314. Yang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR: A global view of hepatocellular carcinoma: trends, risk, prevention and management . Nat Rev Gastroenterol Hepatol 2019, 16 (10):589-604. Park JW, Chen M, Colombo M, Roberts LR, Schwartz M, Chen PJ, Kudo M, Johnson P, Wagner S, Orsini LS et al : Global patterns of hepatocellular carcinoma management from diagnosis to death: the BRIDGE Study . Liver Int 2015, 35 (9):2155-2166. Finn RS, Ikeda M, Zhu AX, Sung MW, Baron AD, Kudo M, Okusaka T, Kobayashi M, Kumada H, Kaneko S et al : Phase Ib Study of Lenvatinib Plus Pembrolizumab in Patients With Unresectable Hepatocellular Carcinoma . J Clin Oncol 2020, 38 (26):2960-2970. Finn RS, Qin S, Ikeda M, Galle PR, Ducreux M, Kim TY, Kudo M, Breder V, Merle P, Kaseb AO et al : Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma . N Engl J Med 2020, 382 (20):1894-1905. Wang L, Wang H, Cui Y, Liu M, Jin K, Xu D, Wang K, Xing B: Sintilimab plus Lenvatinib conversion therapy for intermediate/locally advanced hepatocellular carcinoma: A phase 2 study . Front Oncol 2023, 13 :1115109. Ren Z, Xu J, Bai Y, Xu A, Cang S, Du C, Li Q, Lu Y, Chen Y, Guo Y et al : Sintilimab plus a bevacizumab biosimilar (IBI305) versus sorafenib in unresectable hepatocellular carcinoma (ORIENT-32): a randomised, open-label, phase 2-3 study . Lancet Oncol 2021, 22 (7):977-990. Kudo M, Finn RS, Qin S, Han KH, Ikeda K, Piscaglia F, Baron A, Park JW, Han G, Jassem J et al : Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: a randomised phase 3 non-inferiority trial . Lancet 2018, 391 (10126):1163-1173. Pinero F, Dirchwolf M, Pessoa MG: Biomarkers in Hepatocellular Carcinoma: Diagnosis, Prognosis and Treatment Response Assessment . Cells 2020, 9 (6). Llovet JM, Montal R, Sia D, Finn RS: Molecular therapies and precision medicine for hepatocellular carcinoma . Nat Rev Clin Oncol 2018, 15 (10):599-616. Llovet JM, Castet F, Heikenwalder M, Maini MK, Mazzaferro V, Pinato DJ, Pikarsky E, Zhu AX, Finn RS: Immunotherapies for hepatocellular carcinoma . Nat Rev Clin Oncol 2022, 19 (3):151-172. Majewski J, Schwartzentruber J, Lalonde E, Montpetit A, Jabado N: What can exome sequencing do for you? J Med Genet 2011, 48 (9):580-589. Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics . Nat Rev Genet 2009, 10 (1):57-63. Lencioni R, Llovet JM: Modified RECIST (mRECIST) assessment for hepatocellular carcinoma . Semin Liver Dis 2010, 30 (1):52-60. Meyer T, Palmer DH, Cheng AL, Hocke J, Loembe AB, Yen CJ: mRECIST to predict survival in advanced hepatocellular carcinoma: Analysis of two randomised phase II trials comparing nintedanib vs sorafenib . Liver Int 2017, 37 (7):1047-1055. Zhu AX, Finn RS, Edeline J, Cattan S, Ogasawara S, Palmer D, Verslype C, Zagonel V, Fartoux L, Vogel A et al : Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial . Lancet Oncol 2018, 19 (7):940-952. El-Khoueiry AB, Sangro B, Yau T, Crocenzi TS, Kudo M, Hsu C, Kim TY, Choo SP, Trojan J, Welling THR et al : Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial . Lancet 2017, 389 (10088):2492-2502. Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc JF, de Oliveira AC, Santoro A, Raoul JL, Forner A et al : Sorafenib in advanced hepatocellular carcinoma . N Engl J Med 2008, 359 (4):378-390. Li L, Huang K, Lu Z, Zhao H, Li H, Ye Q, Peng G: Bioinformatics analysis of LINC01554 and its co ‑expressed genes in hepatocellular carcinoma . Oncol Rep 2020, 44 (5):2185-2197. He M, Gu W, Gao Y, Liu Y, Liu J, Li Z: Molecular subtypes and a prognostic model for hepatocellular carcinoma based on immune- and immunogenic cell death-related lncRNAs . Front Immunol 2022, 13 :1043827. Ebermann I, Scholl HP, Charbel Issa P, Becirovic E, Lamprecht J, Jurklies B, Millan JM, Aller E, Mitter D, Bolz H: A novel gene for Usher syndrome type 2: mutations in the long isoform of whirlin are associated with retinitis pigmentosa and sensorineural hearing loss . Hum Genet 2007, 121 (2):203-211. Dai S, Jia R, Zhang X, Fang Q, Huang L: The PD-1/PD-Ls pathway and autoimmune diseases . Cell Immunol 2014, 290 (1):72-79. Chikuma S, Terawaki S, Hayashi T, Nabeshima R, Yoshida T, Shibayama S, Okazaki T, Honjo T: PD-1-mediated suppression of IL-2 production induces CD8+ T cell anergy in vivo . J Immunol 2009, 182 (11):6682-6689. Bishop KD, Harris JE, Mordes JP, Greiner DL, Rossini AA, Czech MP, Phillips NE: Depletion of the programmed death-1 receptor completely reverses established clonal anergy in CD4(+) T lymphocytes via an interleukin-2-dependent mechanism . Cell Immunol 2009, 256 (1-2):86-91. Morita M, Nishida N, Sakai K, Aoki T, Chishina H, Takita M, Ida H, Hagiwara S, Minami Y, Ueshima K et al : Immunological Microenvironment Predicts the Survival of the Patients with Hepatocellular Carcinoma Treated with Anti-PD-1 Antibody . Liver Cancer 2021, 10 (4):380-393. Helmink BA, Reddy SM, Gao J, Zhang S, Basar R, Thakur R, Yizhak K, Sade-Feldman M, Blando J, Han G et al : B cells and tertiary lymphoid structures promote immunotherapy response . Nature 2020, 577 (7791):549-555. Petitprez F, de Reynies A, Keung EZ, Chen TW, Sun CM, Calderaro J, Jeng YM, Hsiao LP, Lacroix L, Bougouin A et al : B cells are associated with survival and immunotherapy response in sarcoma . Nature 2020, 577 (7791):556-560. Lund FE: Cytokine-producing B lymphocytes-key regulators of immunity . Curr Opin Immunol 2008, 20 (3):332-338. Yan J, Harvey BP, Gee RJ, Shlomchik MJ, Mamula MJ: B cells drive early T cell autoimmunity in vivo prior to dendritic cell-mediated autoantigen presentation . J Immunol 2006, 177 (7):4481-4487. Zhu AX, Abbas AR, de Galarreta MR, Guan Y, Lu S, Koeppen H, Zhang W, Hsu CH, He AR, Ryoo BY et al : Molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma . Nat Med 2022, 28 (8):1599-1611. Monga SP: beta-Catenin Signaling and Roles in Liver Homeostasis, Injury, and Tumorigenesis . Gastroenterology 2015, 148 (7):1294-1310. Additional Declarations No competing interests reported. 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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-4368601","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299182954,"identity":"47bc72ab-f871-4f17-895b-fc8ce236cfde","order_by":0,"name":"Lijun Wang","email":"","orcid":"","institution":"Peking University Cancer Hospital \u0026 Institute","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Wang","suffix":""},{"id":299182955,"identity":"1d235395-53d5-4770-a69f-5ea99c60846a","order_by":1,"name":"Yong Cui","email":"","orcid":"","institution":"Peking University Cancer Hospital \u0026 Institute","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Cui","suffix":""},{"id":299182956,"identity":"db91324e-4a2e-43b0-852f-e60c2a033d4a","order_by":2,"name":"Longfei Huang","email":"","orcid":"","institution":"Research Institute, GloriousMed Clinical Laboratory Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Longfei","middleName":"","lastName":"Huang","suffix":""},{"id":299182957,"identity":"73a7dbd1-7e70-4001-8ff3-b955a5efeb3e","order_by":3,"name":"Hongwei Wang","email":"","orcid":"","institution":"Peking University Cancer Hospital \u0026 Institute","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Wang","suffix":""},{"id":299182958,"identity":"6aedda18-49f4-4c6d-b6d0-da4d5ebd50d0","order_by":4,"name":"Ming Liu","email":"","orcid":"","institution":"Peking University Cancer Hospital \u0026 Institute","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Liu","suffix":""},{"id":299182964,"identity":"2ec393e3-4876-49bc-89e8-967ed4db8f07","order_by":5,"name":"Kemin Jin","email":"","orcid":"","institution":"Peking University Cancer Hospital \u0026 Institute","correspondingAuthor":false,"prefix":"","firstName":"Kemin","middleName":"","lastName":"Jin","suffix":""},{"id":299182965,"identity":"d744b2d7-7da4-4497-ac6f-e87b6594be1e","order_by":6,"name":"Kun Wang","email":"","orcid":"","institution":"Peking University Cancer Hospital \u0026 Institute","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Wang","suffix":""},{"id":299182966,"identity":"e57d4fb0-d7d1-4713-a84d-6363eb1233d4","order_by":7,"name":"Baocai Xing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACZjACMZgPPvjAIEGSFrZkwxlEaWGAaWHgMZPmIUa5wXHew68Lamzk+dvZko1t/ljk8TcwP3x0A48WyWa+NOsZx9IMZxxmPvg4t02iWOIAm7FxDh4t/Mw8ZsY8bIcTGA4DbcltkEhsOMDDJo1PCxtYy7/DCfKHgX6x+COROJ+QFqAtxo952w4nGIC0MLBJJG4gpEWymceMeWZfmuFGoMMMe9skEoEM/H4xOH/G+HPBNxt5ufOHDz748acucd7x5oeP8WkBeQct+pjxKwcr+UBYzSgYBaNgFIxoAACc6kVNegx5xAAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University Cancer Hospital \u0026 Institute","correspondingAuthor":true,"prefix":"","firstName":"Baocai","middleName":"","lastName":"Xing","suffix":""}],"badges":[],"createdAt":"2024-05-04 13:24:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4368601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4368601/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56542945,"identity":"be20a156-0a67-45ff-b861-ca282175c333","added_by":"auto","created_at":"2024-05-15 14:34:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1662523,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially Expressed Genes.\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot showing differentially expressed genes between the response and non-response groups. (B) Gene Ontology (GO) annotation analysis of highly expressed genes in the non-response group. (C) GO annotation analysis of highly expressed genes in the response group. (D-E) Kaplan-Meier curves for LINC01554 and WHRN, respectively, comparing high and low expression in the study population. (F-G) Disease-specific survival for LINC01554 and WHRN, based on high and low expression, as derived from The Cancer Genome Atlas (TCGA) database.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4368601/v1/9dd054e8889fc6cb48947e13.jpg"},{"id":56542946,"identity":"5d027ac3-85d6-4a00-a396-ac2f50232453","added_by":"auto","created_at":"2024-05-15 14:34:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":768126,"visible":true,"origin":"","legend":"\u003cp\u003eImmune Cell Infiltration Scores and Survival Analysis.\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot showing the differences in immune cell infiltration scores between the response and non-response groups.\u003c/p\u003e\n\u003cp\u003e(B-D) Kaplan-Meier curves for pro-B cells, class-switched memory B cells, and plasma cells, respectively, comparing high and low expression levels in the study population.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4368601/v1/403164cddec9dc82b8e31e67.jpg"},{"id":56542949,"identity":"76977133-6637-487a-be07-2760c277cacf","added_by":"auto","created_at":"2024-05-15 14:34:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1834650,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Analysis.\u003c/p\u003e\n\u003cp\u003e(A) Correlation between upregulated genes in the response group and immune cell infiltration scores.\u003c/p\u003e\n\u003cp\u003e(B) Correlation between upregulated genes in the non-response group and immune cell infiltration scores.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4368601/v1/c56247bcf934040f1884ce78.jpg"},{"id":56542948,"identity":"06a25d46-650f-46fb-8669-2054cec70732","added_by":"auto","created_at":"2024-05-15 14:34:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1597999,"visible":true,"origin":"","legend":"\u003cp\u003eMutational Landscape and survival analysis.\u003c/p\u003e\n\u003cp\u003e(A) Oncoplot illustrating mutations detected by whole exome sequencing (WES).\u003c/p\u003e\n\u003cp\u003e(B–D) Boxplots depicting comparisons of tumor mutational burden (TMB), tumor proportion score (TPS), and combined positive score (CPS) between the response group and the non-response group.\u003c/p\u003e\n\u003cp\u003e(E–F) Kaplan-Meier curves for survival analysis based on mutations in FANCD2 and CUX1 within the study population.\u003c/p\u003e\n\u003cp\u003e(G–H) Disease-free survival analysis for mutations in FANCD2 and CUX1, utilizing data derived from The Cancer Genome Atlas (TCGA).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4368601/v1/6ea1e41db03000202d077d0e.jpg"},{"id":76950358,"identity":"b19f6072-4d85-47bf-9c0c-268f0957a00f","added_by":"auto","created_at":"2025-02-23 07:23:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8118449,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4368601/v1/8225a55a-7a78-45b0-bcda-7083e7657f09.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular biomarkers of response to sintilimab combined with lenvatinib for locally advanced hepatitis B virus-associated hepatocellular carcinoma","fulltext":[{"header":"Background","content":"\u003cp\u003ePrimary liver cancer is one of the common malignant tumors worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2018, it globally ranked sixth in terms of the incidence of malignant tumors and fourth in terms of cancer-related deaths[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In primary liver cancer, 75\u0026ndash;85% of cases are diagnosed with hepatocellular carcinoma (HCC)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In contrast to Europe and the United States, the main causative factor for liver cancer in China is chronic hepatitis B virus (HBV) infection, and the majority of cases have already reached the advanced stage or have metastasized, rendering them inappropriate for radical treatment, resulting in poor overall prognosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recently, immune checkpoint inhibitors and anti-angiogenic drugs have shown promising therapeutic value in advanced HCC. The objective response rate (ORR) yielded approximately 30%, and overall survival (OS) reached 20\u0026ndash;22 months, significantly improving the survival of patients with advanced HCC[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The high ORR also provides a new approach for the conversion therapy of locally advanced HCC. Our center has conducted a prospective study[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which recruited patients with unresectable, intermediate\u0026thinsp;\u0026minus;\u0026thinsp;advanced HCC for treatment using sintilimab and lenvatinib, evaluated the ORR and surgical resection rate of the combination therapy, and explored predictive factors for the efficacy of the aforementioned combination therapy.\u003c/p\u003e \u003cp\u003eSintilimab is an anti-programmed cell death protein (PD-1) monoclonal antibody that has exhibited a high anti-tumor activity in a previous study[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Lenvatinib is an orally administered multi-targeted tyrosine kinase inhibitor (TKI) that has been approved for the first-line treatment of unresectable HCC[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the present study has indicated that there are variations in tumor regression and progression-free survival (PFS) of different patients after receiving this combination therapy, suggesting that it may be inappropriate for all patients. Given the likelihood of adverse events and the potential inefficacy of the combined treatment strategy, accurately predicting the treatment\u0026rsquo;s effectiveness in advance is of utmost importance.\u003c/p\u003e \u003cp\u003ePrevious studies have identified several gene mutations that could be associated with the efficacy of targeted therapy for HCC, including VEGFA, RAS, MET, TP53, FGF19, etc[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, potential molecular pathways include the PI3K/mTOR signaling pathway, HRD pathway, FGFR pathway, etc[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The indicators potentially associated with the efficacy of immunotherapy for HCC include PD-L1 expression, tumor mutational burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), STK11/LKB1, POLE, Wnt/CTNNB1 mutations, and others[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, these indicators have not been widely applied in HCC. Furthermore, molecular biomarkers for combination of immunotherapy with targeted therapy have not yet been identified. Recently, advances in whole-exome sequencing (WES)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and transcriptome sequencing technologies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] have made it possible to explore molecular characteristics of tumors in advance, assisting clinicians in making clinical decisions.\u003c/p\u003e \u003cp\u003eThe present study aimed to analyze the gene expression profiles of HCC tissues within the study cohort. It was also attempted to assess potential molecular biomarkers that could be associated with the efficacy of the combination therapy involving sintilimab and lenvatinib. Additionally, the prognostic significance of these biomarkers was investigated, providing valuable predictive information regarding treatment outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u0026rsquo; enrollment\u003c/h2\u003e \u003cp\u003e Totally, 36 patients with locally advanced HCC who were ineligible to undergo surgery were prospectively enrolled in a single-arm, single-center, non-randomized clinical study, and 33 of those patients with HBV-associated HCC whose tumor samples underwent molecular sequencing were included. All patients provided written informed consent prior to enrollment, and the trial was registered in the ClinicalTrials.gov database (Registration No. NCT04042805).\u003c/p\u003e \u003cp\u003eAll participants received combination of sintilimab and lenvatinib (sintilimab: 200 mg injected per 21 days; lenvatinib mesylate capsules: 12 mg orally taken for bodyweight of 60 kg and above, or 8 mg orally taken for bodyweight of less than 60 kg). The therapy continued until the occurrence of one of the following events: surgery, disease progression, intolerable toxicity, withdrawal of informed consent, loss of follow-up, death, or other situations indicated in the study protocol. The inclusion and exclusion criteria were described in our previous study[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHepatectomy was performed by wedge-shaped local resection, segmental resection, hepatic lobectomy, or hemi-hepatectomy according to the location, size, and number of tumors. The total number of courses of combination treatment before and after surgery should ideally not exceed 8 cycles. If the patient received over 8 cycles of combination treatment before surgery, adjuvant therapy was not given as per the protocol. The study protocol was approved by the Ethics Committee of Peking University Cancer Hospital (Beijing, China), and the study was conducted in accordance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of tumor response\u003c/h2\u003e \u003cp\u003eEvaluation of tumor response was conducted through imaging every 9 weeks (\u0026plusmn;\u0026thinsp;7 days) starting from the initiation of therapy until the 48th week. Subsequently, the evaluation was performed every 12 weeks (\u0026plusmn;\u0026thinsp;7 days). Evaluation of tumor response was based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST). The treatment response was evaluated by assessing the sum of the diameters of the target lesion during the arterial enhancement phase throughout the treatment course. The treatment responses were categorized as follows: complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). Patients evaluated as CR and PR were assigned to the response group, and others were allocated to the non-response group.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eRNA-seq\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from each sample (33 tumor samples) using the PureLink RNA Mini kit (Thermo Fisher Scientific), and the purity and quality were measured by the Nanodrop spectrophotometer. RNA integrity was evaluated using the RNA Nano6000 Assay kit and the Bioanalyzer 2100 system (Agilent Technologies Inc.). Sequencing was performed on the Illumina Hiseq X10 platform. Raw reads were filtered by FastQC, and reads from three samples were not used in the subsequent analysis due to poor sequencing quality. The filtered reads were then aligned to the Ensembl human genome assembly GRCh37 using the STAR (ver. 2.7.0) with default parameters. Gene expression levels were analyzed by raw count and Transcripts Per Kilobase Million (TPM). Annotations of mRNA were retrieved from the GENCODE (ver. 19) database.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eThe differentially expressed genes (DEGs) between the response and non-response group were screened by \u0026ldquo;DESeq2\u0026rdquo; R package. The commonly used criteria for selection involved applying thresholds of |log2(foldchange)| \u0026gt; 1 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Genes with mean count less than 1 among all samples were excluded. Then, the processed raw counts were imported into DESeq2, and the normalization step was integrated into DESeq2 workflow.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe functional annotation for DEGs was performed by \u0026ldquo;clusterProfiler\u0026rdquo; R package. The Entrez-ID for each gene was transferred from gene symbols through \u0026ldquo;org.Hs.eg.db\u0026rdquo; for human tissues. Gene Ontology (GO) terms were annotated using default parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmune Microenvironment Analysis\u003c/h2\u003e \u003cp\u003e The xCell pipeline was utilized to determine the relative abundance of transcripts (measured in TPM) for each sample. The immune cell infiltration score was calculated for down-stream analysis. The correlation analysis of DEGs was conducted using the Spearman correlation test on the R platform. Feature pairs with P-value less than 0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eWES\u003c/h2\u003e \u003cp\u003eGenomic DNA (gDNA) was extracted from the tumor tissues of 33 patients using the PureLink Genomic DNA kit (Thermo Fisher Scientific, Waltham, MA, USA). Exome was captured using the Agilent SureSelect Human All ExonV5 kit (Agilent Technologies Inc., Santa Clara, CA, USA) according to the manufacturer\u0026rsquo;s instructions. Subsequent paired-end sequencing was performed on Illumina Novaseq6000 sequencer (Illumina Inc., San Diego, CA, USA). Clean reads were then aligned to the reference human genome hg19 (Genome Reference Consortium GRCh37) using the BWA (ver. 0.7.17), and the GATK MuTect2 pipeline was used for paired tumor-normal somatic mutation calling. Common germline mutations were filtered using the Genome Aggregation Database (gnomAD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003eSurvival analyses were conducted separately for all differing factors identified in the RNA-seq, Immune microenvironment analysis, and WES analyses. Considering the significant influence of treatments following recurrence or progression on OS, this study chose to use PFS as the survival analysis metric, with definition consistent with that previously mentioned in our study[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, to further validate the results by expanding the sample size, sequencing data and survival outcomes from the LIHC (liver hepatocellular carcinoma) cohort were downloaded from The Cancer Genome Atlas (TCGA) database and included in this study. Kaplan-Meier analysis was employed to explore the relationship between genes and prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe bioinformatics analysis was performed on the R platform (ver. 3.6.0). Fisher\u0026rsquo;s exact test and the Wilcoxon rank-sum test were utilized for comparing categorical and continuous variables, respectively. The influencing factors of PFS were analyzed by Cox univariate and multivariate regression analyses. Survival analysis was conducted using the Kaplan-Meier method. A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; baseline characteristics\u003c/h2\u003e \u003cp\u003eBetween August 1, 2018 and November 25, 2021, 33 HBV-associated HCC patients were enrolled in the present study. At the data cutoff date (January 30, 2024), 12 patients underwent surgery with curative intent and 1 patient received radiofrequency ablation plus stereotactic radiotherapy due to the poor location that was inappropriate for resection. As of the data cutoff date, all remaining patients had discontinued the initial treatment. Among them, 14 discontinued due to disease progression, 2 completed the treatment after 2 years and had no tumor residue as confirmed by needle biopsy, 3 due to adverse effects, and 2 because they withdrew their informed consent. Regrettably, 12 of these patients have passed away.\u003c/p\u003e \u003cp\u003eIt is noteworthy that 22 patients were assigned to the response group and 11 patients were assigned to the non-response group according to the mRECIST. Samples were prepared for each patient for further testing. However, during the experiment, 3 RNA-seq samples from the response group failed to pass the quality control. As a result, only 30 samples (19 from response group and 11 from non-response group) were involved in the transcriptome analysis. Patients\u0026rsquo; baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The age, BCLC, Barcelona Clinic Liver Cancer (BCLC) stage, total number of tumors, size of tumors, and baseline alpha-fetoprotein (AFP) level were not significantly different between the two groups. The number of patients who underwent surgery was higher in the response group than that in the non-response group, while the difference was not statistically significant. Female patients and those with Eastern Cooperative Oncology Group (ECOG) performance status scores of 1 had a significantly higher likelihood of being non-responders (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients\u0026rsquo; baseline characteristics evaluated by the mRECIST standard\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse group (CR\u0026thinsp;+\u0026thinsp;PR, n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-response\u003c/p\u003e \u003cp\u003egroup (SD\u0026thinsp;+\u0026thinsp;PD, n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG PS, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLC Stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor number, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize of the largest tumor (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e˂5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum alpha-fetoprotein level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e˂400 ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;400 ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConversion to surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBCLC, Barcelona Clinic Liver Cancer; ECOG PS, Eastern Cooperative Oncology Group Performance Status; HBV, hepatitis B virus; HCC, hepatocellular carcinoma\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eTo identify molecular differences of HCC samples that caused different responses to treatment, DEGs were screened from RNA-seq data. Genes with low counts were filtered out and 32,205 unique genes remained (54.01%). When comparing the response group with the non-response group, it was defined that a positive foldchange value indicated a higher expression in the response samples, while a negative value represented a lower expression. Totally 94 DEGs were found between the two groups (|log2(Foldchange)| \u0026gt;1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among them, 41 DEGs were enriched in the response group, while 53 DEGs were highly expressed in the non-response group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Subsequently, the GO enrichment annotations for each group provided insights into the potential functions of HCC baseline DEGs. In the non-response group, highly expressed genes were identified that were enriched in functions related to development and transmembrane (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In contrast, genes, which were highly expressed in the response group, were enriched in association with apoptosis process (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the differentially expressed genes (DEGs), two DEGs demonstrated a significant association with progression-free survival (PFS). High expression levels of LINC01554 and WHRN were correlated with longer PFS and were predominantly expressed in the response group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-E). To further validate these findings by expanding the sample size, we analyzed the relationship between the expression levels of LINC01554 and WHRN and the prognosis in the TCGA-liver cancer dataset. Our analysis revealed that high expression of LINC01554 is significantly associated with improved disease specific survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). In contrast, WHRN expression did not correlate with disease specific survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in immune cells between the two groups\u003c/h2\u003e \u003cp\u003eTo reveal the underlying mechanisms driving varied responses to immunotherapy, xCell algorithm was employed to calculate immune cell infiltration scores for each sample based on their baseline transcription expression. The immune micro-environment differences were described by comparing mean scores of each cell type between the response and non-response groups. The results showed that some cell types were more enriched in the response group, including pro-B cells, Class-switched memory B-cells, and plasma cells (t-test P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, log2 FC\u0026thinsp;\u0026gt;\u0026thinsp;2), all of which predominantly exhibited enrichment in B cell subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, T cells did not show any specific preference in either group. Moreover, samples with infiltration scores greater than the median score across all samples were defined as high-score group. Unfortunately, the differences in the high or low scores of pro-B cells, class-switched memory B cells, and plasma cells did not reach statistical significance with respect to PFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of DEGs and immune cells\u003c/h2\u003e \u003cp\u003eIn order to examine the role of baseline gene expression in the diversity of the immune cell microenvironment, correlation analysis was performed between DEGs and immune cell infiltration scores. In the heatmap, feature pairs with P-value smaller than 0.05 were identified using the Spearman correlation test, and these pairs were denoted with an asterisk. It was found that pro B-cells and plasma cells were positively correlated with highly expressed genes in the response group, as described earlier (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Notably, T cells were negatively correlated with these genes. In contrast, the non-response group exhibited a different pattern, where various T cell subtypes showed variable correlations. In the non-response group, most of T cell subtypes were positively associated with highly expressed genes. However, three specific subtypes, namely CD4\u0026thinsp;+\u0026thinsp;central memory T cells (CD4\u0026thinsp;+\u0026thinsp;Tcm), Th1 cells, and NKT cells, demonstrated a negative correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Similarly, the expression differences in CD4\u0026thinsp;+\u0026thinsp;Tcm, Th1 cells, and NKT cells also failed to reach statistical significance in relation to PFS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWES described mutational landscape\u003c/h2\u003e \u003cp\u003eTo investigate mutational landscape of HCC baseline tissues, the WES was applied on all 33 baseline tissue samples (30 samples mentioned in RNA-seq analysis and 3 extra samples). These mutations included somatic single-nucleotide variants (SNVs), deletion, insertion, and copy number variants (CNVs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The most frequent mutation across all samples was TP53 mutation (60.6%), followed by APC (39.4%) and MYC (21.2%) mutations. The results of the Fisher\u0026rsquo;s exact test revealed that FANCD2 was significantly mutated in the non-response group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Besides, LRP1B was mutated in the non-response group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Notably, 5 samples had CTNNB1 mutation(s) in the two groups. Moreover, the immunologic markers were investigated as supplementary features. The non-response group exhibited a relatively higher tumor mutational burden (TMB), which is defined as the number of non-synonymous somatic mutations (single nucleotide variants and small insertions/deletions) per megabase in coding regions. However, this did not reach statistical significance between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Additionally, while the results for the tumor proportion score (TPS), defined as the ratio of PD-L1 positive tumor cells to the total number of tumor cells, and the combined positive score (CPS), defined as the sum of PD-L1 positive tumor cells and PD-L1 positive immune cells divided by the total number of tumor cells, displayed higher quartiles, there were no significant differences between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, PFS was examined based on the mutational status of each gene. The results revealed that samples with CUX1 mutation exhibited a relatively poorer survival. However, there was no significant association between mutational status of FANCD2 and PFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F). We further analyzed the relationship between FANCD2 and CUX1 mutations and prognosis in the TCGA-liver cancer dataset. We found that both FANCD2 and CUX1 mutations are not associated with liver cancer survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-H).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and multivariate regression analyses of PFS\u003c/h2\u003e \u003cp\u003eIn order to delve deeper into differences in baseline gene mutations and expression changes that contribute to disease progression, prognosis analysis was conducted on the WES mutations, immune cell infiltration scores, and RNA-seq read counts. We considered infiltration scores and expression levels that exceeded the median value of all samples as positive features. Features with less than 3 positive samples in either group were filtered out. Moreover, the analysis encompassed not only molecular biology findings, but also clinical features, such as age (\u0026ge;\u0026thinsp;60 or \u0026lt;\u0026thinsp;60 years old), gender (male or female), ECOG score (1 or 0), BCLC stage (B or C), tumor number (multiple or single), largest tumor size (\u0026ge;\u0026thinsp;10 or \u0026lt;\u0026thinsp;10 cm), and AFP level (\u0026ge;\u0026thinsp;400 or \u0026lt;\u0026thinsp;400 ng/ml).\u003c/p\u003e \u003cp\u003eThe results of univariate Cox regression analysis showed that tumor number, AFP level, mutational status of CUX1, expression levels of LINC01554, WHRN and CD4\u0026thinsp;+\u0026thinsp;Tcm score could influence duration of PFS (P\u0026thinsp;\u0026le;\u0026thinsp;0.10). The results of multivariate Cox regression analysis indicated that single tumor (P\u0026thinsp;=\u0026thinsp;0.02, hazard ratio (HR)\u0026thinsp;=\u0026thinsp;0.31, 95% confidence interval (CI)\u0026thinsp;=\u0026thinsp;0.11\u0026ndash;0.85), high LINC01554 expression level (P\u0026thinsp;=\u0026thinsp;0.01, HR\u0026thinsp;=\u0026thinsp;0.16, 95% CI\u0026thinsp;=\u0026thinsp;0.05\u0026ndash;0.49), and higher CD4\u0026thinsp;+\u0026thinsp;Tcm score (P\u0026thinsp;=\u0026thinsp;0.05, HR\u0026thinsp;=\u0026thinsp;0.29, 95% CI\u0026thinsp;=\u0026thinsp;0.08\u0026ndash;0.98) were independent predictors of prolonged PFS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox regression analysis of progression-free survival (multivariate analysis was performed utilizing backward stepwise (wald) method)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnivariate HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariate HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33 (0.14\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31 (0.11\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(single/multiple)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49 (0.22\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;400/\u0026ge;400 ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUX1(wild/mutant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22 (0.06\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32 (0.13\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16 (0.05\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(high/low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHRN (high/low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39 (0.16\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;Tcm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9/21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.16\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29 (0.08\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(high/low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eHR, Hazard ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study revealed that the combination of sintilimab and lenvatinib was resulted in a higher ORR and a longer PFS. This makes it a noticeable treatment option for locally advanced liver cancer conversion therapy, which is widely implemented in clinical practice. Patients who have successfully undergone conversion to surgery experience two notable advantages compared with those who have not undergone conversion. First, they provide the potential for cure, and second, they facilitate avoiding prolonged drug treatments. The PFS can vary among patients who are ineligible for surgery conversion. That is why screening and identifying patients who are more likely to benefit from conversion therapy in advance or identifying the population that can achieve longer disease control through combined treatments is important. For patients who are inappropriate candidates, transcatheter arterial chemoembolization or other more intensive treatment options could be considered.\u003c/p\u003e \u003cp\u003eThe accuracy of evaluating treatment response plays a vital role in the management of liver cancer patients. The mRECIST[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which consider tumor viability boundaries (particularly arterial-phase tumor enhancement) along with target lesion diameter, have gained widespread acceptance in post-treatment assessment of liver cancer. Previous studies have demonstrated significant advantages of the mRECIST over the RECIST (ver. 1.1)\u003csup\u003e16\u003c/sup\u003e in assessing treatment response in liver cancer. The mRECIST, with their stringent imaging standards, exhibited higher ORRs, improved discrimination for patients with stable disease, enhanced accuracy in evaluating study endpoints, and stronger predictive value for OS. Therefore, the mRECIST can provide a more accurate assessment for patients with HCC undergoing systemic treatment[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the present study, based on these criteria, patients were assigned to the response group and the non-response group.\u003c/p\u003e \u003cp\u003eTypically, the ORR of HCC patients receiving monotherapy with immune checkpoint inhibitors is within 20%[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Molecular targeted drugs[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], such as bevacizumab, sorafenib, and lenvatinib may achieve the efficacy of rapid tumor killing, alteration of the tumor microenvironment, and synergistic enhancement of immune response by inhibiting vascular endothelial growth factor (VEGF) and its mediated tumor pathways. Consequently, addition of molecular targeted drugs effectively improved the ORR of the treatment using immune checkpoint inhibitors. Due to the high heterogeneity of HCC and the presence of multiple factors influencing the response to the combined therapy, the present study comprehensively analyzed the factors affecting the efficacy through transcriptome sequencing, analysis of immune infiltration in tumor microenvironment, WES, and patients\u0026rsquo; clinical data.\u003c/p\u003e \u003cp\u003eThe key findings of this study highlight notable variations in RNA transcription levels between the response group and non-response group. By conducting functional screening of DEGs and analyzing their correlation with survival, two genes were identified, namely LINC01554 and WHRN, which displayed a strong association with treatment efficacy. Notably, LINC01554 has been consistently reported in multiple studies. According to a previous study[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], it was found that low expression of LINC01554 has been notably linked to poor survival, advanced tumor stage, and the presence of portal vein tumor thrombus. In vitro experiments have revealed that high expression of LINC01554 partially inhibits the Wnt and PI3K-Akt pathways, resulting in reduced cell invasiveness and diminished epithelial-mesenchymal transition capability. Similarly, another study[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] highlighted LINC01554, along with three other RNAs (TMEM220-AS1, LINC02362, and LINC02499), as independent prognostic factors for HCC. These consistent results suggest that LINC01554 is an important potential therapeutic target. The WHRN[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] gene, which was also identified, is a protein-coding gene primarily associated with diseases, such as deafness, autosomal recessive, non-syndromic hearing loss, USH2D, etc. However, few studies have concentrated on its involvement in tumor occurrence and development.\u003c/p\u003e \u003cp\u003eThe efficacy of immune checkpoint inhibitors in exerting anti-tumor effects is influenced by the immune microenvironment. Preclinical studies[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] have demonstrated that blocking the interaction between PD-1 and its ligands (PD-L1 or PD-L2) can reactivate T cell proliferation and cytokine release, enhance the cytotoxicity of CD8\u0026thinsp;+\u0026thinsp;T cells, and promote the presence of memory CD4\u0026thinsp;+\u0026thinsp;T cells. Therefore, it is not surprising that the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells in tumor tissues is closely associated with acceptable response of HCC patients receiving monotherapy (e.g., anti-PD-1 therapy)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. An important finding of this study is the identification of B cell subtypes, rather than T cells, as being enriched in the response group through immune microenvironment analysis. This suggests a strong correlation between B cell subtypes and a favorable response to the combination therapy. Recent research has reported the presence of tumor-infiltrating B cells as significant biomarkers of anti-tumor immune response in various types of tumors, further supporting the role of B cells in responding to immune checkpoint inhibitor therapy[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the exact mechanisms by which B cells contribute to anti-tumor immunity have not yet been fully explored. Some studies suggested that B cells may enhance T cell immunity by secreting cytokines and chemokines, as well as expressing co-stimulatory signals[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In certain physiological conditions, B cells may also act as primary antigen-presenting cells (APCs) to initiate CD4\u0026thinsp;+\u0026thinsp;T cell responses[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nevertheless, additional basic research is required to confirm these speculations. The analysis of the interaction between transcriptional RNA and immune cells in this study revealed that when tumors overexpressed specific genes (one or more of 41 DEGs) and were enriched with B cell subtypes in the immune microenvironment, a more favorable therapeutic response could be achieved. Conversely, when tumors overexpressed other specific genes (one or more of 53 DEGs), the influence of B cell subtypes on treatment efficacy was limited, and different correlations were noted with T cell subtypes (particularly a negative correlation with CD4\u0026thinsp;+\u0026thinsp;Tcm). These results confirm that the relationship between tumor-specific gene expression and efficacy is more crucial, while the immune microenvironment plays a secondary role, with B cell subtypes and CD4\u0026thinsp;+\u0026thinsp;Tcm indicating a close association with satisfactory treatment response.\u003c/p\u003e \u003cp\u003eAlthough there were significant differences at the transcriptional RNA level between the response group and non-response group, no additional DEGs were detected in the WES except for the FANCD2 gene. Compared with the WES, which only detects exon fragments, RNA-seq sequencing includes more gene information, such as messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and non-coding RNA. Therefore, it is noteworthy that gene mutations are not consistent between transcriptional RNA level and exon expression level. This phenomenon was also observed in a previous study[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Previous studies have shown that the Wnt/β-catenin pathway and its important protein CTNNB1 could be associated with poor response to immunotherapy[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, in the present study, CTNNB1 mutations were distributed in both groups and did not reach statistical significance. This may be related to the small sample size of mutated patients, and another possible reason is that a single gene or pathway mutation is insufficient to determine treatment efficacy. The addition of molecular targeted therapy may reverse the poor response to immunotherapy, and further large-scale studies are necessary to validate this hypothesis. In addition, tumor markers, such as TMB, TPS and CPS, which have been validated as predictors of response to immunotherapy in other tumor types, did not exhibit significant differences in this cohort. This suggests that the mechanisms underlying the therapeutic effects induced by the combination treatments are relatively complex, and a single immune-related marker may not accurately predict the response.\u003c/p\u003e \u003cp\u003eConsequently, the efficacy of the combination therapy using sintilimab and lenvatinib may be influenced by several factors, including the genetic status of the tumor, the tumor\u0026rsquo;s immune microenvironment, and the patient\u0026rsquo;s clinical and pathological characteristics (e.g., gender, age, number and size of tumors, etc.). The results of the present study indicated a significant correlation between the high expression level of LINC01554 in tumor tissues, the high expression level of CD4\u0026thinsp;+\u0026thinsp;Tcm in the immune microenvironment, and a solitary tumor (compared with multiple tumors) with the prolonged PFS. The abovementioned results strongly indicate that in cases of HCC, where specific mutations are identified through tumor biopsy and the immune microenvironment analysis shows favorable treatment response characteristics, even patients at advanced tumor stages may achieve extended disease control periods or successful conversion to surgical options. These significant findings have profound implications for guiding clinical treatment decisions.\u003c/p\u003e \u003cp\u003eThis study has certain limitations. Firstly, due to its small sample size, there might be the likelihood of selection bias among the enrolled patients. Secondly, HCC mainly consists of multiple subtypes and tissue components. Even with multiple samplings, there might be insufficient tissue volume, which could result in the inability of biopsy tissue testing to accurately reflect the overall biological characteristics of the tumor. Thirdly, the follow-up time was relatively short, which might influence the results to some extent. Finally, no further mechanistic research and large-scale validation of the DEGs were conducted, and additional reliable studies are required to validate the findings of this research.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study revealed that the response and non-response observed in HCC patients undergoing treatment of sintilimab plus lenvatinib were associated with variances in gene expression within liver cancer tissues and the distribution of lymphoid immune cells in the immune microenvironment. Notably, among these factors, high expression levels of lncRNA LINC01554 and CD4\u0026thinsp;+\u0026thinsp;Tcm, along with the presence of a solitary tumor, were indicative of improved disease control duration.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHCC:\u0026nbsp;hepatocellular carcinoma; ORR, objective response rate; OS: overall survival; PFS: progression-free survival; CR: complete response; PR: partial response; PD: progressive disease; SD: stable disease; mRECIST: modified Response Evaluation Criteria in Solid Tumors; RNA-seq: RNA sequencing; WES: whole-exome sequencing; DEGs: differentially expressed genes (DEGs); HBV: hepatitis B virus (HBV); HR: hazard ratio; CI: confidence interval; PD-1: anti-programmed cell death protein (PD-1); TKI: multi-targeted tyrosine kinase inhibitor; MMR: mismatch repair; MSI: microsatellite instability; TMB: tumor mutational burden; FDR: false discovery rate; GO: Gene Ontology; TCGA: The Cancer Genome Atlas (TCGA) database;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBCLC: Barcelona Clinic Liver Cancer; AFP: alpha-fetoprotein; ECOG: Eastern Cooperative Oncology Group; SNVs: somatic single-nucleotide variants: CNVs: copy number variants; TPS: tumor proportion score; CPS: combined positive score; VEGF: vascular endothelial growth factor; APCs: primary antigen-presenting cells (APCs); mRNA: messenger RNA; rRNA: ribosomal RNA; tRNA: transfer RNA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the patients and their families who took part in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLW and YC contributed equally to the work. BX contributed to the conception and design. LW was responsible for the provision of study materials, data collection, and draft writing. YC contributed to the imaging evaluation. LH contributed to bioinformatics analysis and data interpretation. HW, ML, KJ, and KW provided technical and material support. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Clinical Research Fund for Distinguished Young Scholars of Beijing Cancer Hospital (2019088) and Special fund for clinical research of Wu Jieping Medical Foundation (no. 320.6750.19088-38).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Peking University Cancer Hospital (Beijing, China), and the study was conducted in accordance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepatopancreatobiliary Surgery Unit I, Peking University Cancer Hospital \u0026amp; Institute, Beijing 100142, China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital \u0026amp; Institute, Beijing 100142, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eResearch Institute, GloriousMed Clinical Laboratory Co., Ltd., Shanghai, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A: \u003cstrong\u003eGlobal cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2018, \u003cstrong\u003e68\u003c/strong\u003e(6):394-424.\u003c/li\u003e\n\u003cli\u003eForner A, Reig M, Bruix J: \u003cstrong\u003eHepatocellular carcinoma\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2018, \u003cstrong\u003e391\u003c/strong\u003e(10127):1301-1314.\u003c/li\u003e\n\u003cli\u003eYang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR: \u003cstrong\u003eA global view of hepatocellular carcinoma: trends, risk, prevention and management\u003c/strong\u003e. \u003cem\u003eNat Rev Gastroenterol Hepatol \u003c/em\u003e2019, 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\u003cstrong\u003e148\u003c/strong\u003e(7):1294-1310.\u003c/li\u003e\n\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":"Sintilimab, Lenvatinib, Hepatocellular carcinoma, Molecular biomarkers, Conversion therapy","lastPublishedDoi":"10.21203/rs.3.rs-4368601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4368601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe efficacy of combining immune checkpoint inhibitors with anti-angiogenic drugs in advanced hepatocellular carcinoma (HCC) varies, and the predictive biomarkers for this therapy remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this single-center study in China, patients with HCC ineligible for traditional resection were administered sintilimab on day 1 of a 21-day cycle, accompanied by daily oral lenvatinib. Treatment responses were assessed using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Tumor biopsies underwent RNA sequencing (RNA-seq), immune microenvironment analysis, and whole-exome sequencing (WES). Survival analyses also incorporated data from The Cancer Genome Atlas (TCGA), and a multivariate Cox regression analysis was conducted to identify potential therapeutic factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFrom August 1, 2018, to November 25, 2021, 33 patients with hepatitis B virus (HBV)-associated HCC were enrolled. By January 30, 2024, 13 patients had undergone potentially curative surgery or radiofrequency ablation. RNA-seq identified 94 differentially expressed genes (DEGs) between the 22 patients in the response group and the 11 in the non-response group. High expression levels of LINC01554 and WHRN were linked to prolonged progression-free survival (PFS).\u003c/p\u003e\n\u003cp\u003eThe analysis of immune microenvironment differences and correlation with DEGs showed a positive association with patient responses for cell types such as pro-B, class-switched memory B, plasma, CD4+ Tcm, Th1, and NKT cells. However, only the status of CD4+ Tcm approached statistical significance (P \u0026lt; 0.10); other immune cells did not. In the WES analysis, a significant FANCD2 mutation (P \u0026lt; 0.05) was found, and a CUX1 mutation was associated with shorter PFS. Neither mutation correlated with liver cancer survival in the TCGA-liver cancer dataset. The Cox regression analysis indicated that a single tumor (P = 0.02, hazard ratio (HR) = 0.31, 95% confidence interval (CI) = 0.11-0.85), high LINC01554 expression level (P = 0.01, HR = 0.16, 95% CI = 0.05-0.49) and higher CD4+ Tcm score (P = 0.05, HR = 0.29, 95% CI = 0.08-0.98) were independent predictors of prolonged PFS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eTreatment with sintilimab plus lenvatinib can lead to varied responses in HCC patients. High levels of lncRNA LINC01554 and CD4+ Tcm, along with a single tumor, suggest improved disease control.\u003c/p\u003e","manuscriptTitle":"Molecular biomarkers of response to sintilimab combined with lenvatinib for locally advanced hepatitis B virus-associated hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 14:34:18","doi":"10.21203/rs.3.rs-4368601/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":"9e363b67-4dcd-40ba-be31-fc9f3493efdd","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-23T07:23:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 14:34:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4368601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4368601","identity":"rs-4368601","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}