Comprehensive Analysis of Immune Infiltration Identifies Predictors for Immunotherapy Efficacy in Hepatocellular Carcinoma

Comprehensive Analysis of Immune Infiltration Identifies Predictors for Immunotherapy Efficacy in 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 Comprehensive Analysis of Immune Infiltration Identifies Predictors for Immunotherapy Efficacy in Hepatocellular Carcinoma Shaoru Liu, Zongfeng Wu, Yichuan Yuan, Zhu Lin, Dinglan Zuo, Yi Niu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4485605/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 aim of this study was to determine whether differences in the cellular composition of the immune infiltrate in HCC influence survival and identify predictors for immunotherapy efficacy in hepatocellular carcinoma. Methods A total of 362 patients from TCGA cohort and 204 patients from ICGC with HCC were included in the study. Two immune features were selected out of 24 immune features to construct immunotypes based on the Cox regression model. Hub genes of DEGs were identified by STRING and Cyto-scape. The role of hub genes on immunotherapy efficacy prediction was evaluated by Kaplan–Meier survival analysis in immunotherapy cohorts. The effects of LCK on HCC cell proliferation and migration were evaluated by CCK8, trans-well and wound healing assays. Results Eight immune cell subsets were associated with HCC prognosis. Two immune cells (MAIT and central-memory) were selected to construct 3 immunotypes which could predict overall survival in the TCGA cohort ( X 2 = 24.13, P < 0.0001) and ICGC cohort (validation cohort, X 2 = 10.51, P = 0.005). GO and GSEA analysis showed up-regulated immune-related pathway in Cluster3, and Cluster3 showed significantly higher immune checkpoint molecules (PD-L1, PD-1, CTLA-4, PD-L2, LAG3 and TIM3) expression. Three hub genes (CCR5, CCR7 and LCK) were identified based on the differential expression genes between Cluster3 and Cluster1. CCR5, CCR7 and LCK were efficient predictors for immune infiltration, especially CTL, and immunotherapy efficacy. We also verified that LCK conferred proliferation and metastasis of HCC cells and immunotherapy resistance of HCC patients. Conclusion Immune cell abundance and immunotypes could effectively predict prognosis of HCC. Furthermore, CCR5, CCR7 and LCK were identified as predictors for immunotherapy efficacy in hepatocellular carcinoma. Immune infiltration Immunotypes Immunotherapy CCR5 CCR7 LCK Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Liver cancer is the fourth leading cause of cancer deaths( 1 ). Hepatocellular carcinoma (HCC) is a lethal malignancy of the liver. Relative factors such as age, complications, AFP, HBV-DNA, tumor stage, vascular invasion were used to predict survival of HCC( 2 , 3 ). Immune cells constitute an important element of tumor tissue, and the amount of immune cell infiltration considerably associated with tumor types, histological subtypes and mutations( 4 – 6 ). The recent success of immune checkpoint inhibitors as treatment for unresectable hepatocellular carcinoma (HCC)( 7 ) has raised interest in the evaluation of local and systemic antitumor immunity. Immune cell subsets play pivotal roles in prognosis of many tumor types( 8 – 10 ). Specific markers and subtype classification based on immune infiltration are required to identified to predict prognosis and immunotherapy efficacy of HCC. Studies have shown that increased infiltrations of T, NK, and natural killer T (NKT) cells in HCC are positive prognostic factors( 11 – 13 ), and increased infiltration of Tregs is a negative prognostic factor. The prognostic roles of B-cell and plasma cell infiltration are controversial( 14 , 15 ). However, most studies paid attention to few subsets of immune cell but not all immune cell types. Immune cells like MAIT and central-memory, which play important roles in immune system( 16 ), also play pivotal roles in lots of cancer types( 17 , 18 ). The positive correlation between tumor mutations or neoantigen loads and immune infiltration has been reported across cancer types( 19 , 20 ). TP53 mutations frequently occur in cancer and are associated with poor prognosis in variety of cancers including HCC( 21 ). A few studies have associated TP53 with tumor immune regulation( 22 , 23 ). Cortez MA reported TP53 regulated PD-L1 expression by miR-34( 22 ). In addition, local activation of p53 in the tumor microenvironment overcomes immune suppression and enhances antitumor immunity( 23 ). However, the relationship between immune cell infiltration and TP53 mutation status and weather TP53 status is associated with immune signature in HCC are still unclear. In the present study, we estimated 24 immune cells subsets and found out prognostically relevant immune cells in HCC and TP53 subgroups. Then we constructed immunotypes and identified three predictors for immunotherapy efficacy, CCR5, CCR7 and LCK, which might enhance immune infiltration and anti-PD-1/PD-L1 therapy response through remodeling immune microenvironment. In addition, we proved that LCK conferred proliferation and migration of HCC cells and predicted immunotherapy response in HCC patients. Material and Methods Sample data collection and processing The TCGA data (TCGA-LIHC), mutations, gene expression, clinical annotations were downloaded from the TCGA data portal ( https://portal.gdc.cancer.gov/ ) in April 2020. The ICGC data (LIRI-JP), gene expression and clinical annotations were downloaded from the ICGC data portal ( https://dcc.icgc.org/ ) in April 2020. Survival analysis of patients treated with immunotherapy was obtained from the “Tumor Immune Dysfunction and Exclusion” dataset. Estimation of the abundance of immune cell populations, tumor purity and cytolytic activity. The relative abundance of 24 immune populations in tumors and healthy tissues were computed from the RNA-seq of each bulk sample. In detail, we used the ImmuCellAI( 24 ), a unique method for comprehensive T-cell subsets abundance prediction based on the enrichment score of gene signature, which was calculated using the single sample gene set enrichment analysis (ssGSEA) algorithm. The estimate score and tumor purity were calculated using “Estimate”( 25 ), a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumor samples. Immune cytolytic activity representing the geometric mean of GZMA and PRF1 is another in silico measure of immune infiltration, as described by Rooney et al( 26 ). Identification of DEGs, GO (Gene Ontology) analysis, GSEA (Gene Set Enrichment Analysis) and PPI (protein-protein interaction) network construction. DEGs were identified using “Limma” package in R (adjust 1). Gene Ontology (GO) analysis of DEGs was performed in WEB-based Gene Set Analysis Toolkit( 27 ). GSEA was used to identify the pathways that were significantly enriched between Cluster1 and Cluster3( 28 ). The STRING database was used to get the Protein–protein interaction information( 29 ). A Protein–protein interaction network (PPI) was built via Cytoscape software. Cell proliferation assay For the cell proliferation assay, 800 cells were seeded into 96-well plates, cell viability was assessed for 4 consecutive days by the Cell Counting Kit-8 (CCK-8) (Dojindo, Japan). In vitro migration and invasion assays and scratch wound healing assay Trans-well chambers (Costar, Kennebunk, USA) with polycarbonate membranes were used in migration assay. For trans-well assays, a number of 4×10 4 indicated cells were seeded into the upper chamber containing 200µL serum-free DMEM, at the same time, 800µL DMEM containing 10%FBS was added to the bottom chamber. After incubated for 20 hours, cells migrating to the lower surface of the upper chamber were fixed in methanol, stained with 0.1% crystal violet, and counted under the microscope. All experiments were triplicate and the migration and invasion cells were counted in four random optical fields of each chamber. For scratch wound healing assay, 5×10 5 cells were plated in six-well plates and incubated in the regular condition until the cells reached the full confluence of the plates. The wound was created by a sterile 100 µL pipette tip and detached cells were removed by PBS, and then the cells were incubated with serum-free DMEM for the indicated time. Images at 0, 24, and 48 h after scratching were taken, and images in the same region of each well were contrasted. Quantitative real-time PCR (qPCR) Total RNA was extracted using RNA-Quick Purification Kit (ES Science, Guangzhou, China) and cDNA synthesized using the Prime-Script cDNA synthesis kits (Invitrogen, California, USA) according to the manufacturer’s instructions. The reverse-transcribed cDNA products were used for qPCR analysis using SYBR Green PCR kit (Invitrogen, California, USA). The sequence of the used primers: Forward Sequence, AACACTCACGGCTCCTTCCTCA; Reverse Sequence, GTAGAAGCCACCGTTGTCCAGA. Small interfering RNA for LCK Small interfering RNA for LCK was purchased from Gene pharma (Shanghai, China). Reverse transfection of small interfering RNA was performed with Lipofectamine-RNAiMAX (Invitrogen, Carlsbad, CA). After 24 hours, the supernatant was replaced with fresh medium and the down-regulation efficiency was identified by qRT-PCR. The targeting sequence was 5′- UUCGUAGGUA ACCAGUGGGdTdT-3′. Statistical analysis Associations between inferred proportions of immune cell types and survival were tested using Cox regression. Kaplan‑Meier survival analysis by log rank test was used to calculate the median survival time (MST). Associations between continuous and categorical variables were tested using the Kruskal-wallis test. Correlations between immune cell subsets were evaluated using the Pearson correlation coefficient. Box plots for continuous variables were compared by unpaired t‑test. Statistical analysis was performed with GraphPad version 7.0 (GraphPad Software, Inc., La Jolla, CA, USA), SPSS software version 25.0 (SPPS, Inc., Chicago, IL, USA) and R (version 3.6.5; www.r‑project.org ). Results Immune landscape of hepatocellular carcinoma We compared immune cell abundance in HCC tumor and adjacent tissue (Figure S1 A-B). CD4-naïve T, Cytotoxic, Tfh, MAIT, Macrophage, NK, CD4 + T cells showed higher infiltration in adjacent tissue than tumor tissue, and the infiltration of Tr1, nTreg cells were lower in adjacent tissue, which indicated immune activated in adjacent tissue but immune inhibited status in HCC. However, CD8 + naïve, CD8 + T and DC cells were more abundant in tumor tissue, which showed complicated immune status in tumor microenvironment. We performed hierarchical clustering and correlation analysis of all tumor samples (Figure S1 C-D). Most immune cell types showed negative correlation and subsets like Th17, monocyte, neutrophil, CD8 + T, Th1, Exhausted, Tfh and central-memory were positively correlated with each other in HCC, suggesting complicated functional regulation and correlation among themselves. The total immune cell abundance calculated using the single sample gene set enrichment analysis (ssGSEA) algorithm was shown by stacked column (Figure S1 E) and the total immune infiltration was differential among HCC samples. As shown in Figure S1 F, the total immune cell infiltration was not correlated with overall survival (OS). Immune cell types are prognostic for HCC We then explored the association between the immune cell abundance and clinical outcomes in HCC using univariate regression and found that the infiltration of 8 subtypes were associated with overall survival (OS) (Fig. 1 A). The n-Treg, B cell were associated with worse OS, and central-memory, Tfh, CD4 + naïve, CD4+, γδ-T cells predicted better OS (Fig. 1 B-I). Then, we found that the infiltration of n-Treg, central-memory and MAIT cells had prognostic value in multivariate regression analysis in HCC. In addition, we analyzed the relationship between cytolytic activity and immune cell infiltration (Table S1 ). Cytolytic activity has previously been defined by Rooney et al. as the geometric mean of GZMA and PRF1 expression( 26 ). Cytolytic activity was most strongly positively correlated with the abundance of CD8 + T cells (Fig. 1 J) and Tfh (Fig. 1 K), and most strongly negatively correlated with the infiltration of Th17 (Fig. 1 L) and Neutrophil (Fig. 1 M). Identification of three immunotypes in hepatocellular carcinoma We then selected two immune cell subtypes, MAIT and central-memory cells, which showed the most significantly prognostic value to determine immunotypes. Samples were divided into four clusters: Cluster A, MAIT Low central-memony Low , Cluster B, MAIT Low central-memony High , Cluster C, MAIT High central-memony Low , Cluster D, MAIT High central-memony High . We examined the associations between the immunotypes and clinical outcomes (Fig. 2 A). However, Cluster B and Cluster C showed no difference in overall survival (P = 0.643), so we combined Cluster B with Cluster C and identified three immunotypes: Cluster1, MAIT Low central-memony Low , Cluster2, MAIT Low central-memony High or MAIT High central-memony Low , Cluster3, MAIT High central-memony High . As shown in Fig. 2 B, the immunotypes could predict prognosis of HCC patients (Log-rank X 2 = 24.13, P < 0.0001). Cluster3 showed the best prognosis and Cluster1 had the worst survival which might be caused by different immune status. The heatmap showed the immune cell infiltration signature of three immunotypes (Fig. 2 C). We also validated the immunotype in an independent cohort (ICGC-LIRI-JP) (Fig. 2 D). In addition, we found that the immunotypes were also associated with tumor stages and vascular invasion (Fig. 2 E). Cluster3 had less stage III/IV and vascular invasion HCCs, which was the very opposite of Cluster1 (Fig. 2 F). As our expectation, Cluster3 also showed highest cytolytic activity (Fig. 2 G). Variation in prognostic effect of immune cells and immunotypes by TP53 status TP53 mutation is the most common type of mutation in HCC (Figure S2 A-C). In addition, TP53 pathway plays important role in HCC development (Figure S2 D). Survival analysis demonstrated that TP53 mutation is associated with worse overall survival in patients with HCC (Figure S2 E). We then compared immune cell infiltration in TP53-WT and TP53-Mut samples (Figure S3 A-B). CD4-naïve T cells, Tfh cells, CD4 + T, Th17, Central-memory and monocyte showed higher abundance in TP53-WT HCC, and the abundance of Exhausted, n-Treg and B-cell were lower in TP53-WT subgroup, which indicated immune inhibited status in TP53-Mut samples. We conducted exploratory subgroup analyses of the prognostic effect of all 24 immune cell subsets by TP53 status. Significant heterogeneity of prognostic effect was observed for 2 subgroups. There were 8 cell types associated with clinical outcome in TP53-WT subgroup (Figure S3 C). The n-Treg and Th2 cells were associated with worse outcome, however, γδ-T, Tr1, Tfh, CD4 + T, central-memory, MAIT cells were associated with favorable outcome (Figure S3 E). B-cell and n-Treg were correlated with worse OS while CD4 + T was associated with better OS in TP53-Mut subgroup (Figure S3 D, S3F). We then explored the relationship between our immunotypes and TP53 status and weather it could be used in both subgroups. As expectation, 39% samples had TP53 mutation in Cluster1, which was only 18% in Cluster3 (Fig. 2 H). Survival plots showed that the immunotypes were associated with outcomes in both TP53-WT and TP53-Mut subgroups (Fig. 2 I). Additional prognostic impact of immunotypes on tumor stage and vascular invasion of HCC We next compared immunotypes with tumor stage and vascular invasion subgroups of HCC. Tumor stage was associated with overall survival (OS) (Fig. 3 A), Cluster3 constituted around 33% of stage I HCC patients and 20% of stage III/IV HCC patients (Fig. 3 B) and showed significantly better prognosis in stage I and stage III/IV HCC patients (Fig. 3 C). Vascular invasion including macro and micro invasion showed borderline prognostic significance (Fig. 3 D), Cluster3 constituted around 30.6% of HCC patients without vascular invasion and 22.5% of HCC patients with vascular invasion (Fig. 3 E) and showed significantly better prognosis in non-vascular invasion HCC patients (Fig. 3 F). Molecular Signature of I mmunotypes. 1083 DEGs were identified between Cluster3 and Cluster1, including 966 upregulated genes and 117 downregulated genes in Cluster3. GO analysis was performed and the top 3 biology pathway were positive regulation of pathway-restricted SMAD protein phosphorylation, negative regulation of nucleic acid-templated transcription and regulation of MAPK cascade. In addition, immune-related pathways were also differential, such as chemokine-mediated signaling pathway, chemotaxis, chemokine receptor activity, transforming growth factor beta receptor binding (Fig. 4 A). The GSEA showed that Cluster3 was highly enriched in T-cell receptor signaling pathway, B-cell receptor signaling pathway, NKT-cell–mediated immunity, CD40–mediated immunity, IL2–mediated immunity and IL17–mediated immunity (Fig. 4 B). Furthermore, we found that the expression of several immune checkpoint molecules (PD-1, PD-L1, PD-L2, CTLA-4, TIM3, LAG3) was higher in Cluster3 and Cluster2 compared to Cluster1 (Fig. 4 C). The estimate score and tumor purity were significantly different among immunotypes (Fig. 4 D), which inferred differential immune infiltration abundance. Identification of hub genes between cluster3 and cluster1 A PPI network including 1083 proteins was constructed using STRING and Cyto-scape. To identified hub genes in these DEGs, genes score was calculated and ranked by node degree. The top 10 hub genes were all associated with immunity (Fig. 5 A). The we explored prognostic value of these genes, CCR5 (C-C Motif Chemokine Receptor 5), CCR7 (C-C Motif Chemokine Receptor 7) and LCK (Lymphocyte Cell-Specific Protein-Tyrosine Kinase) were significantly associated with overall survival in HCC (Fig. 5 B) and all predicted favorable outcome (Fig. 5 C). The molecular signature showed immune activating in Cluster3 (Fig. 4 ), so we assumed CCR5, CCR7, LCK may enhanced immune cell abundance and anti-tumor immunity in tumor microenvironment. Correlation analysis showed CCR5, CCR7, LCK were strongly positively correlated with the abundance of 6 immune cells, and all negatively correlated with tumor purity (Fig. 5 D). Furthermore, we verified the correlation between hub genes and CTL infiltration in patients of immunotherapy cohorts in real world. The mRNA expression of CCR5, CCR7 and LCK was positively correlated with CTL both in anti-PD-1 and anti-CTLA4 cohorts (Fig. 5 E). CCR5, CCR7 and LCK predict efficacy of immunotherapy Given that the mRNA expression of CCR5, CCR7 and LCK exhibited strong correlation with immune infiltration, we therefore sought to investigate the potential joint utility for patient stratification and ICI treatment prediction. There was significantly prolonged OS in those with higher expression of CCR5, CCR7 and LCK compared with lower expression group patients treated with anti-PD-1 or anti-CTLA4 therapy. Similarly, there was also significantly prolonged PFS in those with higher expression of CCR5, CCR7 and LCK treated with anti-PD-1 or anti-CTLA4 compared with those of the other group (Fig. 6 A-F). Interestingly, the patients with higher expression of CCR5, CCR7 and LCK also showed predominantly prolonged OS and PFS of HCC patients treated with sorafenib (Fig. 6 G). Therefore, these hub genes might be promising predictors for both targeted therapy and immunotherapy. LCK confers proliferation and metastasis of HCC cells and immunotherapy resistance of patients We found that LCK was correlated with immune infiltration and immunotherapy response. Then we explored the effect of LCK on HCC cells. PLC-8024 cells were transfected with LCK siRNA which successfully silenced the LCK as confirmed by qRT-PCR analysis (Fig. 7 A). According to the CCK-8 assay, silencing of LCK significantly inhibited the viability of PLC-8024 (Fig. 7 B). Furthermore, trans-well assay revealed that silencing of LCK restrained the migration abilities of HCC cells (Fig. 7 C-D). To verify the role of LCK on immunotherapy, we analyzed the LCK mRNA expression of immunotherapy-response and non-response patients. As expected, the patient which was response to anti-PD-1 therapy showed higher LCK expression compared with the patient non-response to immunotherapy (Fig. 7 E). Discussion Based on a bioinformatics method, ImmuCellAI( 24 ), we estimate 24 immune cells subsets in 566 cases of HCC. We uncovered patterns of immune infiltration in HCC and their clinical implications and constructed immunotypes using the most prognostic associated immune cells (MAIT and central-memory). Then we found out differential immune cell subsets abundance and their prognostic value in different TP53 mutation status and that immunotypes were correlated with TP53 mutation status. Then we identified CCR5, CCR7 and LCK which might enhance immune infiltration and anti-PD-1/PD-L1 therapy response through remodeling immune microenvironment. Furthermore, we found that LCK promoted proliferation and migration of HCC cells and predicted immunotherapy response in HCC patients. Our immunotypes were constructed based on MAIT and central-memory cell. MAIT cells use a limited T cell antigen receptor (TCR) repertoire with public antigen specificities that are conserved across species. They can be activated by TCR-dependent and TCR-independent mechanisms and exhibit rapid, innate-like effector responses( 30 ). Previous study reported that MAIT were functionally impaired and even reprogrammed to shift away from antitumor immunity and toward a tumor-promoting direction( 17 ). However, in other studies, MAIT cells were also found to be abundant in healthy liver tissue, but diminished in number in the tumor site, and reduced MAIT cell frequencies appeared to correlate with poor prognosis in these patients, which was consistent with our findings( 18 , 31 ). Previous studies have associated central-memory with anti-tumor effect( 32 – 35 ). Maddalena Noviello reported that bone marrow central memory and memory stem T-cell exhaustion in AML patients promoted relapse After HSCT( 36 ). In present study, central-memory was also a positive prognostic factor. Therefore, characterization of the phenotype and function of infiltrating MAIT and central-memory cells in cluster3 HCCs would be important to understand the patterns of active immune reaction against HCCs. TP53 mutation is associated with higher expression of immune checkpoints, activate effector T cells and increase interferon gamma levels in lung adenocarcinoma and can be used as a predictor of anti-PD-1 immunotherapy ( 37 , 38 ). Junyu Long developed a TP53-associated immune prognostic model for hepatocellular carcinoma( 39 ), which indicated TP53 was also associated with immunity in HCC. However, the mechanism by which TP53 mutation affects the regulation of immune infiltration and immunotypes of HCC is still unknown. We found out significantly differential immune cell abundance in TP53-WT and TP53-Mut subgroups. In addition, TP53-Mut status was positively correlated with Cluster1 and negatively correlated with Cluater3 HCC, which was consistent with prognosis. Therefore, we proposed that TP53 mutation may induce immunosuppression in HCC. 3 hub genes (CCR5, CCR7, LCK) were upregulated in cluster3 and were associated with lymphocytic infiltration in many tumors( 40 , 41 ). We found that hub genes could predict the efficacy of immunotherapy, including anti-PD-1 and anti-CTLA4 therapy. lymphocyte-specific protein tyrosine kinase (LCK), an initiating tyrosine kinase in the T cell receptor signaling cascade( 42 ), promotes T cell activating in a variety of cancer types( 43 , 44 ) and is associated with improved patient survival( 45 , 46 ). We found out LCK not only associated with T cell activating, but also enhanced anti-tumor effect through increasing the infiltration level of immune cells. In addition, we verified LCK also promote HCC cells proliferation and migration. Therefore, CCR5, CCR7 and LCK are potential targets and promising biomarkers for immunotherapy. Several limitations should be addressed in this study. It was a retrospective study and the sizes of our cohorts were relatively small. Therefore, larger independent and prospective validation is needed in the future. Also, we estimate immune cell abundance by ImmuCellAI based on ssGSEA method, more precise and direct methods should be used for validating our conclusions. In conclusion, we found out clinical implications of immune infiltration in HCC and constructed immunotypes associated with tumor stage, vascular invasion and survival of HCCs. Three predictors for immunotherapy efficacy, CCR5, CCR7 and LCK, were identified, which might enhance immune infiltration and anti-PD-1/PD-L1 therapy response through remodeling immune microenvironment. Finally, we verified that LCK conferred proliferation and migration of HCC cells and predicted immunotherapy response in HCC patients. Abbreviations TGCA-LIHC, The Cancer Genome Atlas-Liver Hepatocellular Carcinoma; Tr1, T regulatory cells 1; n-Treg, natural-T regulatory cells; γδ, γδ-T cell; MAIT, mucosal-associated invariant T cell; TFH, T follicular helper cells; Th, T helper cells; DC, dendritic cells; NK, natural killer cells; NKT, natural killer T cells; ssGSEA, single sample gene set enrichment analysis; HR, hazard ratio; TP53-WT, TP53-wide type; TP53-Mut, TP53-mutation; PPI, Protein-protein interaction; Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions Shaoru Liu, Zongfeng Wu and Jiliang Qiu designed the study. Shaoru Liu, Zongfeng Wu, Yichuan Yuan and Zhu Lin collected and assembled the data. Shaoru Liu, Zongfeng Wu, Yi Niu, Dinglan Zuo and Binkui Li analyzed and interpreted data. Yunfei Yuan provided financial support. All authors read and approve the final manuscript. Funding This work was supported by grant from the Cancer Innovative Research Program of Sun Yat-sen University Cancer Center (No. CIRP-SYSUCC-0012). Acknowledgments We thank our colleagues for technical help and stimulating discussions. Data availability statement The data used to support the findings of this study are available from the corresponding author upon request. References Islami F, Goding Sauer A, Miller KD, Siegel RL, Fedewa SA, Jacobs EJ, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. Cancer J Clin. 2018;68(1):31–54. Lupberger J, Croonenborghs T, Roca Suarez AA, Van Renne N, Jühling F, Oudot MA, et al. Combined Analysis of Metabolomes, Proteomes, and Transcriptomes of Hepatitis C Virus-Infected Cells and Liver to Identify Pathways Associated With Disease Development. Gastroenterology. 2019;157(2):537–e519. 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Correlation between immune cell abundance and cytolytic activity Cell type Pearson Correlation p-value Th17 - 0.59844 4.34E-35 Neutrophil -0.58147 8.95E-33 Monocyte -0.51971 2.12E-25 CD8_naive -0.39898 1.09E-14 Th2 -0.16006 0.00279 Effector_memory -0.15292 0.0043 CD4_naive -0.08979 0.09492 Gamma_delta -0.06171 0.25158 nTreg 0.09024 0.09328 NKT 0.13565 0.01142 Tr1 0.14646 0.00627 MAIT 0.21312 6.29E-05 CD4_T 0.22569 2.20E-05 DC 0.33234 2.16E-10 Cytotoxic 0.36802 1.43E-12 Bcell 0.38354 1.32E-13 Macrophage 0.39009 4.65E-14 NK 0.41472 7.41E-16 Th1 0.45879 1.82E-19 iTreg 0.46102 1.16E-19 Exhausted 0.49003 2.33E-22 Central_memory 0.51066 1.93E-24 Tfh 0.52858 2.29E-26 CD8_T 0.65902 1.35E-44 Table 2. Correlation between immune phenotypes and immune check point molecule expression with TCGA cohort PDCD1 CD247 CTLA4 LAG3 HAVCR2 LGALS9 Pearson Corr. p-value Pearson Corr. p-value Pearson Corr. p-value Pearson Corr. p-value Pearson Corr. p-value Pearson Corr. p-value CD4_naive -0.10995 0.04066 -0.06389 0.23519 -0.19495 2.59E-04 -0.20342 1.36E-04 -0.12664 0.01827 -0.00878 0.87049 CD8_naive -0.22719 1.93E-05 -0.34133 6.47E-11 -0.25204 1.98E-06 -0.30631 5.68E-09 -0.27123 2.89E-07 -0.16624 0.00189 Cytotoxic 0.14967 0.00521 0.38301 1.44E-13 0.11046 0.03974 0.24759 3.04E-06 0.16034 0.00274 0.16939 0.00154 Exhausted 0.6003 2.37E-35 0.45958 1.55E-19 0.55185 4.79E-29 0.67759 5.74E-48 0.37756 3.36E-13 0.34099 6.77E-11 Tr1 0.14859 0.00555 0.11769 0.02838 0.10505 0.05056 0.23817 7.28E-06 0.15192 0.00457 0.05528 0.30454 nTreg 0.24329 4.55E-06 0.09025 0.09323 0.43915 8.59E-18 0.14828 0.00565 0.18943 3.88E-04 0.14401 0.00721 iTreg 0.4615 1.05E-19 0.45187 7.30E-19 0.58319 5.29E-33 0.2963 1.84E-08 0.50352 1.05E-23 0.31751 1.44E-09 Th1 0.44446 3.11E-18 0.42904 5.68E-17 0.49664 5.19E-23 0.48116 1.65E-21 0.32093 9.40E-10 0.25583 1.37E-06 Th2 -0.09299 0.08369 -0.14837 0.00562 -0.10951 0.04148 -0.11927 0.0263 -0.07865 0.14373 0.0544 0.31224 Th17 -0.57645 4.08E-32 -0.62855 1.50E-39 -0.605 5.09E-36 -0.58017 1.33E-32 -0.64504 3.30E-42 -0.62306 1.06E-38 Tfh 0.33832 9.72E-11 0.52137 1.40E-25 0.40468 4.18E-15 0.18267 6.28E-04 0.32518 5.47E-10 0.23208 1.26E-05 Central_memory 0.3342 1.69E-10 0.46393 6.37E-20 0.36305 3.00E-12 0.19517 2.55E-04 0.27161 2.77E-07 0.13347 0.01283 Effector_memory -0.11999 0.02541 -0.16681 0.00182 -0.10503 0.0506 -0.10548 0.04961 -0.20389 1.31E-04 -0.16135 0.00257 NKT 0.18201 6.57E-04 0.16685 0.00182 0.20272 1.43E-04 0.15083 0.00487 0.33588 1.35E-10 0.41243 1.11E-15 MAIT 0.10178 0.05823 0.26071 8.47E-07 0.12354 0.02134 -0.0312 0.56238 0.07752 0.1496 0.14425 0.00711 DC 0.2692 3.56E-07 0.37195 7.94E-13 0.4791 2.59E-21 0.2971 1.68E-08 0.60619 3.43E-36 0.46041 1.31E-19 Bcell 0.4218 2.11E-16 0.424 1.42E-16 0.43298 2.74E-17 0.36923 1.20E-12 0.45618 3.09E-19 0.42425 1.36E-16 Monocyte -0.50377 9.92E-24 -0.508 3.64E-24 -0.54247 6.10E-28 -0.51272 1.17E-24 -0.32652 4.60E-10 -0.2878 4.82E-08 Macrophage 0.25291 1.82E-06 0.3744 5.46E-13 0.34055 7.19E-11 0.27486 1.97E-07 0.71454 1.77E-55 0.58103 1.02E-32 NK 0.27961 1.19E-07 0.4611 1.14E-19 0.24556 3.68E-06 0.22935 1.60E-05 0.23655 8.43E-06 0.32238 7.82E-10 Neutrophil -0.56925 3.43E-31 -0.6099 9.91E-37 -0.61449 2.09E-37 -0.47138 1.35E-20 -0.48504 7.05E-22 -0.49203 1.48E-22 Gamma_delta -0.17705 9.25E-04 -0.10741 0.04556 -0.30216 9.29E-09 -0.0616 0.25245 -0.3223 7.90E-10 -0.33925 8.57E-11 CD4_T 0.16391 0.00219 0.26162 7.72E-07 0.13434 0.01225 0.05849 0.27727 0.06861 0.20236 0.11028 0.04006 CD8_T 0.63991 2.31E-41 0.64453 4.00E-42 0.68291 5.58E-49 0.6249 5.53E-39 0.41188 1.22E-15 0.37888 2.74E-13 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-4485605","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310704401,"identity":"9011f573-e448-4bff-8ab9-760371afb193","order_by":0,"name":"Shaoru Liu","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Shaoru","middleName":"","lastName":"Liu","suffix":""},{"id":310704402,"identity":"16024d40-b56a-40fb-9a50-7d902003c57d","order_by":1,"name":"Zongfeng Wu","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Zongfeng","middleName":"","lastName":"Wu","suffix":""},{"id":310704403,"identity":"b5d30259-9988-470f-8744-857e18565abb","order_by":2,"name":"Yichuan Yuan","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Yichuan","middleName":"","lastName":"Yuan","suffix":""},{"id":310704404,"identity":"0b0bdcc2-8450-4101-9edd-1472725c4ddd","order_by":3,"name":"Zhu Lin","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Lin","suffix":""},{"id":310704405,"identity":"2a6749da-04d8-4209-ac29-8bc7817a7726","order_by":4,"name":"Dinglan Zuo","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Dinglan","middleName":"","lastName":"Zuo","suffix":""},{"id":310704406,"identity":"d8e5f824-96bc-43e7-851c-424b1a9ad6ff","order_by":5,"name":"Yi Niu","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Niu","suffix":""},{"id":310704407,"identity":"b1dad6bd-e429-498e-9798-067af7df3a30","order_by":6,"name":"Binkui Li","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Binkui","middleName":"","lastName":"Li","suffix":""},{"id":310704408,"identity":"98f4a361-1ca9-4658-8c4a-6b4ec73ecf96","order_by":7,"name":"Yunfei Yuan","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Yunfei","middleName":"","lastName":"Yuan","suffix":""},{"id":310704409,"identity":"259c430d-aa49-4a1d-8d96-16598e2391bc","order_by":8,"name":"Jiliang Qiu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACZgYGgwcVJGtJOEOyTYltpKjmO85jUJA4ry5x/ozkhx8YKu7ZNbCfPYBXi+RhHgODxG2HEzfcSDOWYDhTnNzAk5eAV4sBRMuBxA0SOWwMjG0JyQwSPAZEaJkDchhpWhqYExtuQLTYEdQieZitwCDh2GHjDWeeGUsknElIYOPJwa+F7/zhbQYfaupk57cDQ+xDRYI9P/sZ/FoYDjCwgVQ4NoA4CUTF0QEG5gdAyh7Gt8etdBSMglEwCkYqAAAPFELYf4CvyAAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Jiliang","middleName":"","lastName":"Qiu","suffix":""}],"badges":[],"createdAt":"2024-05-27 14:09:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4485605/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4485605/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58387120,"identity":"73ac7e2b-66b6-4b9f-a6f9-a21d748e1074","added_by":"auto","created_at":"2024-06-14 18:46:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1227227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic associations of subsets of immune cells. \u003c/strong\u003e(A) HRs (boxes) and 95% confidence intervals (horizontal lines). Box size is inversely proportional to the width of the confidence interval. Asterisks denote estimates with a P-value \u0026lt; 0.05. (B-I) Survival plots of immune cell subsets. (J-M): Correlation between immune cell abundance and cytolytic activity. Abbreviation: HR, hazard ratio.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/2f3f94f73b5dbb4d3e1abb51.png"},{"id":58387122,"identity":"dc6dff4d-f7bb-4680-9936-4ac23f74b549","added_by":"auto","created_at":"2024-06-14 18:46:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":415108,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic value of Immunotypes.\u003c/strong\u003e(A-B) Survival plot of 4 clusters and 3 clusters (depicted chi-squared statistics and p-values are from log-rank tests). (C)Heatmap of immune cell infiltration abundance of 3 clusters. (D) Survival plot of an independent validating cohort: ICGC-LIRI-JP. (E-F) Correlation between clusters and HCC stage or vascular invasion. (G) Cytolytic activity of 3 clusters. (H) Proportions of TP53 status in Immunotypes. (I) Survival plots by clusters separately for TP53-WT and TP53-Mut subgroups.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/9529d469520886aa8a5a8589.png"},{"id":58387121,"identity":"f2231953-dcfb-446d-bd23-a7316ad9fd75","added_by":"auto","created_at":"2024-06-14 18:46:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional prognostic impact of Immunotypes on stages and vascular invasion of HCC.\u003c/strong\u003e (A) (Left) Kaplan-Meyer curve of overall survival (OS) stratified by immunotypes of HCC. (Middle) Pie charts showing the fractions of immunotypes in each HCC stage. (Right) Kaplan-Meyer curves of OS of each HCC stage stratified by immunotypes. (B) (Left) Kaplan-Meyer curve of overall survival (OS) stratified by immunotypes of HCC. (Middle) Pie charts showing the fractions of immunotypes in vascular invasion or non-vascular invasion group. (Right) Kaplan-Meyer curves of OS of vascular invasion or non-vascular invasion group stratified by immunotypes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/c7e4a5b8fb07e50f5a946abd.png"},{"id":58387125,"identity":"4e096097-d7b6-4af9-a1c5-81743c9c8efd","added_by":"auto","created_at":"2024-06-14 18:46:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1282450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular Signature of Immunotypes. \u003c/strong\u003e(A) Bubble Plot for pathway enrichment of DEGs between Cluster1 and Cluster3. (B) Enrichment plot shows pathways which are significantly upregulated in Cluster3 compared to Cluster1(Pvalue\u0026lt;0.05, FDRq\u0026lt;0.1). (C) Box-plot shows six immune checkpoints expression of Immunotypes. (D) Estimate score and tumor purity of immunotypes. *P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001; ****P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/2056430bf4285932c2b6a4ed.png"},{"id":58387126,"identity":"08844e60-7b34-4d7b-b220-9782e1368e1e","added_by":"auto","created_at":"2024-06-14 18:46:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1368350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHub genes and Clinical Implications. \u003c/strong\u003e(A) 10 hub genes identified from DEGs between Cluster1 and Cluster3 using PPI and Cytoscape. Genes were ranked by degree score. (B) HRs (boxes) and 95% confidence intervals (horizontal lines). Box size is inversely proportional to the width of the confidence interval. (C, D) Correlation analysis between hub genes and tumor purity and immune cell abundance. (E) Correlation analysis between hub genes and TIL in patients treated with immunotherapy. Abbreviation: PPI, Protein-protein interaction.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/4d3f445509a9dea01e2fd4cf.png"},{"id":58387127,"identity":"d75ee86e-0319-4d40-a614-580f9972d3f2","added_by":"auto","created_at":"2024-06-14 18:46:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":403926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCCR5, CCR7 and LCK predict efficacy of immunotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-C) Kaplan-Meyer curve of overall survival (OS) or progression-free survival stratified by the mRNA expression of CCR5 (A), CCR7 (B) and LCK (C) in anti-PD-1 therapy cohorts. (D-F) Kaplan-Meyer curve of overall survival (OS) stratified by the mRNA expression of CCR5 (D), CCR7 (E) and LCK (F) in anti-CTLA4 therapy cohorts. (G) Kaplan-Meyer curve of overall survival (OS) and progression-free survival stratified by the mRNA expression of CCR5, CCR7 and LCK in sorafenib cohorts.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/8070477d568f6eec4c986438.png"},{"id":58387679,"identity":"85dfdad5-b510-468a-aa78-742cc6d26ef0","added_by":"auto","created_at":"2024-06-14 18:54:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1131338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLCK confers proliferation and metastasis of HCC cells and immunotherapy resistance of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The mRNA of LCK in PLC-8024 cells transfected with siLCK and Ctrl was determined by qRT-PCR. (B) CCK8 assay of PLC-8024 cells transfected with siLCK and Ctrl. (C) Migration ability of PLC-8024 cells transfected with siLCK and Ctrl were assessed by Trans-well assay. (D) Motility ability of PLC-8024 cells transfected with siLCK and Ctrl were assessed by wound healing assays. (E) The differential LCK mRNA expression and treatment response to anti-PD-1 immunotherapy of HCC patients.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/6ea36c7d6f453b4df61c9cc1.png"},{"id":76091012,"identity":"5db9995b-e770-4fe1-af72-c85963bc5a56","added_by":"auto","created_at":"2025-02-12 08:32:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6739518,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/42c32907-4cc1-4b66-90e3-04aa1789806e.pdf"},{"id":58387678,"identity":"c7e8fce9-d9b3-45c2-98c7-03fd9450c39d","added_by":"auto","created_at":"2024-06-14 18:54:58","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9135672,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/49b67a61f85959df41b63cab.tif"},{"id":58387123,"identity":"83ab0135-ae7f-45be-b89b-3831f88795e4","added_by":"auto","created_at":"2024-06-14 18:46:58","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8022404,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/8d59e549abb709c254cdbd27.tif"},{"id":58387131,"identity":"cedb2222-f0cd-4dee-ac70-903a99b7d7fa","added_by":"auto","created_at":"2024-06-14 18:46:59","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":8750500,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/365bac36cf52c9c7f0dafd95.tif"},{"id":58387129,"identity":"21a295e8-ae36-4c30-9276-5b8722f96d30","added_by":"auto","created_at":"2024-06-14 18:46:59","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2690800,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-4485605/v1/d6d0d9f19abeb55ee2eea33a.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive Analysis of Immune Infiltration Identifies Predictors for Immunotherapy Efficacy in Hepatocellular Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer is the fourth leading cause of cancer deaths(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Hepatocellular carcinoma (HCC) is a lethal malignancy of the liver. Relative factors such as age, complications, AFP, HBV-DNA, tumor stage, vascular invasion were used to predict survival of HCC(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Immune cells constitute an important element of tumor tissue, and the amount of immune cell infiltration considerably associated with tumor types, histological subtypes and mutations(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The recent success of immune checkpoint inhibitors as treatment for unresectable hepatocellular carcinoma (HCC)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) has raised interest in the evaluation of local and systemic antitumor immunity. Immune cell subsets play pivotal roles in prognosis of many tumor types(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Specific markers and subtype classification based on immune infiltration are required to identified to predict prognosis and immunotherapy efficacy of HCC.\u003c/p\u003e \u003cp\u003eStudies have shown that increased infiltrations of T, NK, and natural killer T (NKT) cells in HCC are positive prognostic factors(\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and increased infiltration of Tregs is a negative prognostic factor. The prognostic roles of B-cell and plasma cell infiltration are controversial(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, most studies paid attention to few subsets of immune cell but not all immune cell types. Immune cells like MAIT and central-memory, which play important roles in immune system(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), also play pivotal roles in lots of cancer types(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe positive correlation between tumor mutations or neoantigen loads and immune infiltration has been reported across cancer types(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). TP53 mutations frequently occur in cancer and are associated with poor prognosis in variety of cancers including HCC(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A few studies have associated TP53 with tumor immune regulation(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Cortez MA reported TP53 regulated PD-L1 expression by miR-34(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In addition, local activation of p53 in the tumor microenvironment overcomes immune suppression and enhances antitumor immunity(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, the relationship between immune cell infiltration and TP53 mutation status and weather TP53 status is associated with immune signature in HCC are still unclear.\u003c/p\u003e \u003cp\u003eIn the present study, we estimated 24 immune cells subsets and found out prognostically relevant immune cells in HCC and TP53 subgroups. Then we constructed immunotypes and identified three predictors for immunotherapy efficacy, CCR5, CCR7 and LCK, which might enhance immune infiltration and anti-PD-1/PD-L1 therapy response through remodeling immune microenvironment. In addition, we proved that LCK conferred proliferation and migration of HCC cells and predicted immunotherapy response in HCC patients.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample data collection and processing\u003c/h2\u003e \u003cp\u003eThe TCGA data (TCGA-LIHC), mutations, gene expression, clinical annotations were downloaded from the TCGA data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in April 2020. The ICGC data (LIRI-JP), gene expression and clinical annotations were downloaded from the ICGC data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dcc.icgc.org/\u003c/span\u003e\u003cspan address=\"https://dcc.icgc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in April 2020. Survival analysis of patients treated with immunotherapy was obtained from the \u0026ldquo;Tumor Immune Dysfunction and Exclusion\u0026rdquo; dataset.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEstimation of the abundance of immune cell populations, tumor purity and cytolytic activity.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe relative abundance of 24 immune populations in tumors and healthy tissues were computed from the RNA-seq of each bulk sample. In detail, we used the ImmuCellAI(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), a unique method for comprehensive T-cell subsets abundance prediction based on the enrichment score of gene signature, which was calculated using the single sample gene set enrichment analysis (ssGSEA) algorithm. The estimate score and tumor purity were calculated using \u0026ldquo;Estimate\u0026rdquo;(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumor samples. Immune cytolytic activity representing the geometric mean of GZMA and PRF1 is another in silico measure of immune infiltration, as described by Rooney et al(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of DEGs, GO (Gene Ontology) analysis, GSEA (Gene Set Enrichment Analysis) and PPI (protein-protein interaction) network construction.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDEGs were identified using \u0026ldquo;Limma\u0026rdquo; package in R (adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |LogFC|\u0026gt;1). Gene Ontology (GO) analysis of DEGs was performed in WEB-based Gene Set Analysis Toolkit(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). GSEA was used to identify the pathways that were significantly enriched between Cluster1 and Cluster3(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The STRING database was used to get the Protein\u0026ndash;protein interaction information(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). A Protein\u0026ndash;protein interaction network (PPI) was built via Cytoscape software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCell proliferation assay\u003c/h2\u003e \u003cp\u003eFor the cell proliferation assay, 800 cells were seeded into 96-well plates, cell viability was assessed for 4 consecutive days by the Cell Counting Kit-8 (CCK-8) (Dojindo, Japan).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003emigration and invasion assays and scratch wound healing assay\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTrans-well chambers (Costar, Kennebunk, USA) with polycarbonate membranes were used in migration assay. For trans-well assays, a number of 4\u0026times;10\u003csup\u003e4\u003c/sup\u003e indicated cells were seeded into the upper chamber containing 200\u0026micro;L serum-free DMEM, at the same time, 800\u0026micro;L DMEM containing 10%FBS was added to the bottom chamber. After incubated for 20 hours, cells migrating to the lower surface of the upper chamber were fixed in methanol, stained with 0.1% crystal violet, and counted under the microscope. All experiments were triplicate and the migration and invasion cells were counted in four random optical fields of each chamber.\u003c/p\u003e \u003cp\u003eFor scratch wound healing assay, 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells were plated in six-well plates and incubated in the regular condition until the cells reached the full confluence of the plates. The wound was created by a sterile 100 \u0026micro;L pipette tip and detached cells were removed by PBS, and then the cells were incubated with serum-free DMEM for the indicated time. Images at 0, 24, and 48 h after scratching were taken, and images in the same region of each well were contrasted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real-time PCR (qPCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using RNA-Quick Purification Kit (ES Science, Guangzhou, China) and cDNA synthesized using the Prime-Script cDNA synthesis kits (Invitrogen, California, USA) according to the manufacturer\u0026rsquo;s instructions. The reverse-transcribed cDNA products were used for qPCR analysis using SYBR Green PCR kit (Invitrogen, California, USA). The sequence of the used primers: Forward Sequence, AACACTCACGGCTCCTTCCTCA; Reverse Sequence, GTAGAAGCCACCGTTGTCCAGA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSmall interfering RNA for LCK\u003c/h2\u003e \u003cp\u003eSmall interfering RNA for LCK was purchased from Gene pharma (Shanghai, China). Reverse transfection of small interfering RNA was performed with Lipofectamine-RNAiMAX (Invitrogen, Carlsbad, CA). After 24 hours, the supernatant was replaced with fresh medium and the down-regulation efficiency was identified by qRT-PCR. The targeting sequence was 5\u0026prime;- UUCGUAGGUA ACCAGUGGGdTdT-3\u0026prime;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAssociations between inferred proportions of immune cell types and survival were tested using Cox regression. Kaplan‑Meier survival analysis by log rank test was used to calculate the median survival time (MST). Associations between continuous and categorical variables were tested using the Kruskal-wallis test. Correlations between immune cell subsets were evaluated using the Pearson correlation coefficient. Box plots for continuous variables were compared by unpaired t‑test. Statistical analysis was performed with GraphPad version 7.0 (GraphPad Software, Inc., La Jolla, CA, USA), SPSS software version 25.0 (SPPS, Inc., Chicago, IL, USA) and R (version 3.6.5; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://portal.gdc.cancer.gov/\" target=\"_blank\"\u003ewww.r‑project.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.r‑project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImmune landscape of hepatocellular carcinoma\u003c/h2\u003e \u003cp\u003eWe compared immune cell abundance in HCC tumor and adjacent tissue (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). CD4-na\u0026iuml;ve T, Cytotoxic, Tfh, MAIT, Macrophage, NK, CD4\u0026thinsp;+\u0026thinsp;T cells showed higher infiltration in adjacent tissue than tumor tissue, and the infiltration of Tr1, nTreg cells were lower in adjacent tissue, which indicated immune activated in adjacent tissue but immune inhibited status in HCC. However, CD8\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve, CD8\u0026thinsp;+\u0026thinsp;T and DC cells were more abundant in tumor tissue, which showed complicated immune status in tumor microenvironment. We performed hierarchical clustering and correlation analysis of all tumor samples (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC-D). Most immune cell types showed negative correlation and subsets like Th17, monocyte, neutrophil, CD8\u0026thinsp;+\u0026thinsp;T, Th1, Exhausted, Tfh and central-memory were positively correlated with each other in HCC, suggesting complicated functional regulation and correlation among themselves. The total immune cell abundance calculated using the single sample gene set enrichment analysis (ssGSEA) algorithm was shown by stacked column (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE) and the total immune infiltration was differential among HCC samples. As shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF, the total immune cell infiltration was not correlated with overall survival (OS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmune cell types are prognostic for HCC\u003c/h2\u003e \u003cp\u003eWe then explored the association between the immune cell abundance and clinical outcomes in HCC using univariate regression and found that the infiltration of 8 subtypes were associated with overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The n-Treg, B cell were associated with worse OS, and central-memory, Tfh, CD4\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve, CD4+, γδ-T cells predicted better OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-I). Then, we found that the infiltration of n-Treg, central-memory and MAIT cells had prognostic value in multivariate regression analysis in HCC. In addition, we analyzed the relationship between cytolytic activity and immune cell infiltration (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Cytolytic activity has previously been defined by Rooney et al. as the geometric mean of GZMA and PRF1 expression(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Cytolytic activity was most strongly positively correlated with the abundance of CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ) and Tfh (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK), and most strongly negatively correlated with the infiltration of Th17 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eL) and Neutrophil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eM).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of three immunotypes in hepatocellular carcinoma\u003c/h2\u003e \u003cp\u003eWe then selected two immune cell subtypes, MAIT and central-memory cells, which showed the most significantly prognostic value to determine immunotypes. Samples were divided into four clusters: Cluster A, MAIT\u003csup\u003eLow\u003c/sup\u003e central-memony\u003csup\u003eLow\u003c/sup\u003e, Cluster B, MAIT\u003csup\u003eLow\u003c/sup\u003e central-memony\u003csup\u003eHigh\u003c/sup\u003e, Cluster C, MAIT\u003csup\u003eHigh\u003c/sup\u003e central-memony\u003csup\u003eLow\u003c/sup\u003e, Cluster D, MAIT\u003csup\u003eHigh\u003c/sup\u003e central-memony\u003csup\u003eHigh\u003c/sup\u003e. We examined the associations between the immunotypes and clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). However, Cluster B and Cluster C showed no difference in overall survival (P\u0026thinsp;=\u0026thinsp;0.643), so we combined Cluster B with Cluster C and identified three immunotypes: Cluster1, MAIT\u003csup\u003eLow\u003c/sup\u003e central-memony\u003csup\u003eLow\u003c/sup\u003e, Cluster2, MAIT\u003csup\u003eLow\u003c/sup\u003e central-memony\u003csup\u003eHigh\u003c/sup\u003e or MAIT\u003csup\u003eHigh\u003c/sup\u003e central-memony\u003csup\u003eLow\u003c/sup\u003e, Cluster3, MAIT\u003csup\u003eHigh\u003c/sup\u003e central-memony\u003csup\u003eHigh\u003c/sup\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, the immunotypes could predict prognosis of HCC patients (Log-rank \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;24.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Cluster3 showed the best prognosis and Cluster1 had the worst survival which might be caused by different immune status. The heatmap showed the immune cell infiltration signature of three immunotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). We also validated the immunotype in an independent cohort (ICGC-LIRI-JP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). In addition, we found that the immunotypes were also associated with tumor stages and vascular invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Cluster3 had less stage III/IV and vascular invasion HCCs, which was the very opposite of Cluster1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). As our expectation, Cluster3 also showed highest cytolytic activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eVariation in prognostic effect of immune cells and immunotypes by TP53 status\u003c/h2\u003e \u003cp\u003eTP53 mutation is the most common type of mutation in HCC (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-C). In addition, TP53 pathway plays important role in HCC development (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD). Survival analysis demonstrated that TP53 mutation is associated with worse overall survival in patients with HCC (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eE). We then compared immune cell infiltration in TP53-WT and TP53-Mut samples (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA-B). CD4-na\u0026iuml;ve T cells, Tfh cells, CD4\u0026thinsp;+\u0026thinsp;T, Th17, Central-memory and monocyte showed higher abundance in TP53-WT HCC, and the abundance of Exhausted, n-Treg and B-cell were lower in TP53-WT subgroup, which indicated immune inhibited status in TP53-Mut samples.\u003c/p\u003e \u003cp\u003eWe conducted exploratory subgroup analyses of the prognostic effect of all 24 immune cell subsets by TP53 status. Significant heterogeneity of prognostic effect was observed for 2 subgroups. There were 8 cell types associated with clinical outcome in TP53-WT subgroup (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC). The n-Treg and Th2 cells were associated with worse outcome, however, γδ-T, Tr1, Tfh, CD4\u0026thinsp;+\u0026thinsp;T, central-memory, MAIT cells were associated with favorable outcome (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eE). B-cell and n-Treg were correlated with worse OS while CD4\u0026thinsp;+\u0026thinsp;T was associated with better OS in TP53-Mut subgroup (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eD, S3F).\u003c/p\u003e \u003cp\u003eWe then explored the relationship between our immunotypes and TP53 status and weather it could be used in both subgroups. As expectation, 39% samples had TP53 mutation in Cluster1, which was only 18% in Cluster3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Survival plots showed that the immunotypes were associated with outcomes in both TP53-WT and TP53-Mut subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAdditional prognostic impact of immunotypes on tumor stage and vascular invasion of HCC\u003c/h2\u003e \u003cp\u003eWe next compared immunotypes with tumor stage and vascular invasion subgroups of HCC. Tumor stage was associated with overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), Cluster3 constituted around 33% of stage I HCC patients and 20% of stage III/IV HCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and showed significantly better prognosis in stage I and stage III/IV HCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Vascular invasion including macro and micro invasion showed borderline prognostic significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), Cluster3 constituted around 30.6% of HCC patients without vascular invasion and 22.5% of HCC patients with vascular invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) and showed significantly better prognosis in non-vascular invasion HCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular Signature of\u003c/b\u003e I\u003cb\u003emmunotypes.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1083 DEGs were identified between Cluster3 and Cluster1, including 966 upregulated genes and 117 downregulated genes in Cluster3. GO analysis was performed and the top 3 biology pathway were positive regulation of pathway-restricted SMAD protein phosphorylation, negative regulation of nucleic acid-templated transcription and regulation of MAPK cascade. In addition, immune-related pathways were also differential, such as chemokine-mediated signaling pathway, chemotaxis, chemokine receptor activity, transforming growth factor beta receptor binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The GSEA showed that Cluster3 was highly enriched in T-cell receptor signaling pathway, B-cell receptor signaling pathway, NKT-cell\u0026ndash;mediated immunity, CD40\u0026ndash;mediated immunity, IL2\u0026ndash;mediated immunity and IL17\u0026ndash;mediated immunity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, we found that the expression of several immune checkpoint molecules (PD-1, PD-L1, PD-L2, CTLA-4, TIM3, LAG3) was higher in Cluster3 and Cluster2 compared to Cluster1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The estimate score and tumor purity were significantly different among immunotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), which inferred differential immune infiltration abundance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of hub genes between cluster3 and cluster1\u003c/h2\u003e \u003cp\u003eA PPI network including 1083 proteins was constructed using STRING and Cyto-scape. To identified hub genes in these DEGs, genes score was calculated and ranked by node degree. The top 10 hub genes were all associated with immunity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The we explored prognostic value of these genes, CCR5 (C-C Motif Chemokine Receptor 5), CCR7 (C-C Motif Chemokine Receptor 7) and LCK (Lymphocyte Cell-Specific Protein-Tyrosine Kinase) were significantly associated with overall survival in HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and all predicted favorable outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The molecular signature showed immune activating in Cluster3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), so we assumed CCR5, CCR7, LCK may enhanced immune cell abundance and anti-tumor immunity in tumor microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis showed CCR5, CCR7, LCK were strongly positively correlated with the abundance of 6 immune cells, and all negatively correlated with tumor purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Furthermore, we verified the correlation between hub genes and CTL infiltration in patients of immunotherapy cohorts in real world. The mRNA expression of CCR5, CCR7 and LCK was positively correlated with CTL both in anti-PD-1 and anti-CTLA4 cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCCR5, CCR7 and LCK predict efficacy of immunotherapy\u003c/h2\u003e \u003cp\u003eGiven that the mRNA expression of CCR5, CCR7 and LCK exhibited strong correlation with immune infiltration, we therefore sought to investigate the potential joint utility for patient stratification and ICI treatment prediction. There was significantly prolonged OS in those with higher expression of CCR5, CCR7 and LCK compared with lower expression group patients treated with anti-PD-1 or anti-CTLA4 therapy. Similarly, there was also significantly prolonged PFS in those with higher expression of CCR5, CCR7 and LCK treated with anti-PD-1 or anti-CTLA4 compared with those of the other group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-F). Interestingly, the patients with higher expression of CCR5, CCR7 and LCK also showed predominantly prolonged OS and PFS of HCC patients treated with sorafenib (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Therefore, these hub genes might be promising predictors for both targeted therapy and immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLCK confers proliferation and metastasis of HCC cells and immunotherapy resistance of patients\u003c/h2\u003e \u003cp\u003eWe found that LCK was correlated with immune infiltration and immunotherapy response. Then we explored the effect of LCK on HCC cells. PLC-8024 cells were transfected with LCK siRNA which successfully silenced the LCK as confirmed by qRT-PCR analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). According to the CCK-8 assay, silencing of LCK significantly inhibited the viability of PLC-8024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Furthermore, trans-well assay revealed that silencing of LCK restrained the migration abilities of HCC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). To verify the role of LCK on immunotherapy, we analyzed the LCK mRNA expression of immunotherapy-response and non-response patients. As expected, the patient which was response to anti-PD-1 therapy showed higher LCK expression compared with the patient non-response to immunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on a bioinformatics method, ImmuCellAI(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), we estimate 24 immune cells subsets in 566 cases of HCC. We uncovered patterns of immune infiltration in HCC and their clinical implications and constructed immunotypes using the most prognostic associated immune cells (MAIT and central-memory). Then we found out differential immune cell subsets abundance and their prognostic value in different TP53 mutation status and that immunotypes were correlated with TP53 mutation status. Then we identified CCR5, CCR7 and LCK which might enhance immune infiltration and anti-PD-1/PD-L1 therapy response through remodeling immune microenvironment. Furthermore, we found that LCK promoted proliferation and migration of HCC cells and predicted immunotherapy response in HCC patients.\u003c/p\u003e \u003cp\u003eOur immunotypes were constructed based on MAIT and central-memory cell. MAIT cells use a limited T cell antigen receptor (TCR) repertoire with public antigen specificities that are conserved across species. They can be activated by TCR-dependent and TCR-independent mechanisms and exhibit rapid, innate-like effector responses(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Previous study reported that MAIT were functionally impaired and even reprogrammed to shift away from antitumor immunity and toward a tumor-promoting direction(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, in other studies, MAIT cells were also found to be abundant in healthy liver tissue, but diminished in number in the tumor site, and reduced MAIT cell frequencies appeared to correlate with poor prognosis in these patients, which was consistent with our findings(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Previous studies have associated central-memory with anti-tumor effect(\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Maddalena Noviello reported that bone marrow central memory and memory stem T-cell exhaustion in AML patients promoted relapse After HSCT(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In present study, central-memory was also a positive prognostic factor. Therefore, characterization of the phenotype and function of infiltrating MAIT and central-memory cells in cluster3 HCCs would be important to understand the patterns of active immune reaction against HCCs.\u003c/p\u003e \u003cp\u003eTP53 mutation is associated with higher expression of immune checkpoints, activate effector T cells and increase interferon gamma levels in lung adenocarcinoma and can be used as a predictor of anti-PD-1 immunotherapy (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Junyu Long developed a TP53-associated immune prognostic model for hepatocellular carcinoma(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), which indicated TP53 was also associated with immunity in HCC. However, the mechanism by which TP53 mutation affects the regulation of immune infiltration and immunotypes of HCC is still unknown. We found out significantly differential immune cell abundance in TP53-WT and TP53-Mut subgroups. In addition, TP53-Mut status was positively correlated with Cluster1 and negatively correlated with Cluater3 HCC, which was consistent with prognosis. Therefore, we proposed that TP53 mutation may induce immunosuppression in HCC.\u003c/p\u003e \u003cp\u003e3 hub genes (CCR5, CCR7, LCK) were upregulated in cluster3 and were associated with lymphocytic infiltration in many tumors(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). We found that hub genes could predict the efficacy of immunotherapy, including anti-PD-1 and anti-CTLA4 therapy. lymphocyte-specific protein tyrosine kinase (LCK), an initiating tyrosine kinase in the T cell receptor signaling cascade(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), promotes T cell activating in a variety of cancer types(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) and is associated with improved patient survival(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). We found out LCK not only associated with T cell activating, but also enhanced anti-tumor effect through increasing the infiltration level of immune cells. In addition, we verified LCK also promote HCC cells proliferation and migration. Therefore, CCR5, CCR7 and LCK are potential targets and promising biomarkers for immunotherapy.\u003c/p\u003e \u003cp\u003eSeveral limitations should be addressed in this study. It was a retrospective study and the sizes of our cohorts were relatively small. Therefore, larger independent and prospective validation is needed in the future. Also, we estimate immune cell abundance by ImmuCellAI based on ssGSEA method, more precise and direct methods should be used for validating our conclusions.\u003c/p\u003e \u003cp\u003eIn conclusion, we found out clinical implications of immune infiltration in HCC and constructed immunotypes associated with tumor stage, vascular invasion and survival of HCCs. Three predictors for immunotherapy efficacy, CCR5, CCR7 and LCK, were identified, which might enhance immune infiltration and anti-PD-1/PD-L1 therapy response through remodeling immune microenvironment. Finally, we verified that LCK conferred proliferation and migration of HCC cells and predicted immunotherapy response in HCC patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTGCA-LIHC, The Cancer Genome Atlas-Liver Hepatocellular Carcinoma; Tr1, T regulatory cells 1; n-Treg, natural-T regulatory cells; \u0026gamma;\u0026delta;, \u0026gamma;\u0026delta;-T cell; MAIT, mucosal-associated invariant T cell; TFH, T follicular helper cells; Th, T helper cells; DC, dendritic cells; NK, natural killer cells; NKT, natural killer T cells; ssGSEA, single sample gene set enrichment analysis; HR, hazard ratio; TP53-WT, TP53-wide type; TP53-Mut, TP53-mutation; PPI, Protein-protein interaction;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShaoru Liu, Zongfeng Wu and Jiliang Qiu designed the study. Shaoru Liu, Zongfeng Wu, Yichuan Yuan and Zhu Lin collected and assembled the data. Shaoru Liu, Zongfeng Wu, Yi Niu, Dinglan Zuo and Binkui Li analyzed and interpreted data. Yunfei Yuan provided financial support. All authors read and approve the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grant from the Cancer Innovative Research Program of Sun Yat-sen University Cancer Center (No. CIRP-SYSUCC-0012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank our colleagues for technical help and stimulating discussions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIslami F, Goding Sauer A, Miller KD, Siegel RL, Fedewa SA, Jacobs EJ, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. 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Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015;160(1\u0026ndash;2):48\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41(Web Server issue):W77\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):D362\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodfrey DI, Koay HF, McCluskey J, Gherardin NA. The biology and functional importance of MAIT cells. Nat Immunol. 2019;20(9):1110\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng C, Zheng L, Yoo JK, Guo H, Zhang Y, Guo X, et al. Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Cell. 2017;169(7):1342\u0026ndash;e5616.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlebanoff CA, Scott CD, Leonardi AJ, Yamamoto TN, Cruz AC, Ouyang C, et al. Memory T cell-driven differentiation of naive cells impairs adoptive immunotherapy. J Clin Investig. 2016;126(1):318\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeiger R, Rieckmann JC, Wolf T, Basso C, Feng Y, Fuhrer T, et al. L-Arginine Modulates T Cell Metabolism and Enhances Survival and Anti-tumor Activity. Cell. 2016;167(3):829\u0026ndash;e4213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraietta JA, Nobles CL, Sammons MA, Lundh S, Carty SA, Reich TJ, et al. Disruption of TET2 promotes the therapeutic efficacy of CD19-targeted T cells. Nature. 2018;558(7709):307\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar I, Pati S, Dutta A, Basak U, Sa G. T-memory cells against cancer: Remembering the enemy. Cell Immunol. 2019;338:27\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoviello M, Manfredi F, Ruggiero E, Perini T, Oliveira G, Cortesi F, et al. Bone marrow central memory and memory stem T-cell exhaustion in AML patients relapsing after HSCT. Nat Commun. 2019;10(1):1065.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong ZY, Zhong WZ, Zhang XC, Su J, Xie Z, Liu SY, et al. Potential Predictive Value of TP53 and KRAS Mutation Status for Response to PD-1 Blockade Immunotherapy in Lung Adenocarcinoma. Clin cancer research: official J Am Association Cancer Res. 2017;23(12):3012\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkoulidis F, Goldberg ME, Greenawalt DM, Hellmann MD, Awad MM, Gainor JF, et al. STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma. Cancer Discov. 2018;8(7):822\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong J, Wang A, Bai Y, Lin J, Yang X, Wang D, et al. Development and validation of a TP53-associated immune prognostic model for hepatocellular carcinoma. EBioMedicine. 2019;42:363\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBan Y, Mai J, Li X, Mitchell-Flack M, Zhang T, Zhang L, et al. Targeting Autocrine CCL5-CCR5 Axis Reprograms Immunosuppressive Myeloid Cells and Reinvigorates Antitumor Immunity. Cancer Res. 2017;77(11):2857\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFankhauser M, Broggi MAS, Potin L, Bordry N, Jeanbart L, Lund AW et al. Tumor lymphangiogenesis promotes T cell infiltration and potentiates immunotherapy in melanoma. Sci Transl Med. 2017;9(407).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLo WL, Shah NH, Ahsan N, Horkova V, Stepanek O, Salomon AR, et al. Lck promotes Zap70-dependent LAT phosphorylation by bridging Zap70 to LAT. Nat Immunol. 2018;19(7):733\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng S, Cheng X, Zhang L, Lu X, Chaudhary S, Teng R, et al. Myeloid-derived suppressor cells inhibit T cell activation through nitrating LCK in mouse cancers. Proc Natl Acad Sci USA. 2018;115(40):10094\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuryadevara CM, Desai R, Farber SH, Choi BD, Swartz AM, Shen SH, et al. Preventing Lck Activation in CAR T Cells Confers Treg Resistance but Requires 4-1BB Signaling for Them to Persist and Treat Solid Tumors in Nonlymphodepleted Hosts. Clin cancer research: official J Am Association Cancer Res. 2019;25(1):358\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenomic Classification of Cutaneous Melanoma. Cell. 2015;161(7):1681\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinchcliff E, Paquette C, Roszik J, Kelting S, Stoler MH, Mok SC, et al. Lymphocyte-specific kinase expression is a prognostic indicator in ovarian cancer and correlates with a prominent B cell transcriptional signature. Cancer Immunol immunotherapy: CII. 2019;68(9):1515\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Correlation between immune cell abundance and cytolytic activity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eCell type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eTh17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e- 0.59844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e4.34E-35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eNeutrophil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e-0.58147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e8.95E-33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eMonocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e-0.51971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e2.12E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eCD8_naive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e-0.39898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e1.09E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eTh2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e-0.16006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e0.00279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eEffector_memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e-0.15292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eCD4_naive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e-0.08979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e0.09492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eGamma_delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e-0.06171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e0.25158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003enTreg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.09024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e0.09328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eNKT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.13565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e0.01142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eTr1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.14646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e0.00627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eMAIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.21312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e6.29E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eCD4_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.22569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e2.20E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.33234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e2.16E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eCytotoxic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.36802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e1.43E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eBcell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.38354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e1.32E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eMacrophage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.39009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e4.65E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.41472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e7.41E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eTh1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.45879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e1.82E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eiTreg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.46102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e1.16E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eExhausted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.49003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e2.33E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eCentral_memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.51066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e1.93E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eTfh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.52858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e2.29E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.613207547169814%\"\u003e\n \u003cp\u003eCD8_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08490566037736%\"\u003e\n \u003cp\u003e0.65902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.30188679245283%\"\u003e\n \u003cp\u003e1.35E-44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Correlation between immune phenotypes and immune check point molecule expression with TCGA cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.211573236889693%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" colspan=\"2\"\u003e\n \u003cp\u003ePDCD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" colspan=\"2\"\u003e\n \u003cp\u003eCD247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" colspan=\"2\"\u003e\n \u003cp\u003eCTLA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" colspan=\"2\"\u003e\n \u003cp\u003eLAG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\" colspan=\"2\"\u003e\n \u003cp\u003eHAVCR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.009041591320072%\" colspan=\"2\"\u003e\n \u003cp\u003eLGALS9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003ePearson Corr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003ePearson Corr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003ePearson Corr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003ePearson Corr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003ePearson Corr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003ePearson Corr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eCD4_naive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.10995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.04066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.06389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.23519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.19495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e2.59E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.20342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.36E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-0.12664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.01827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-0.00878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.87049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eCD8_naive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.22719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.93E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.34133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e6.47E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.25204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.98E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.30631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e5.68E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-0.27123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e2.89E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-0.16624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.00189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eCytotoxic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.14967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.00521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.38301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.44E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.11046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.03974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.24759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e3.04E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.16034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.00274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.16939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.00154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eExhausted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.6003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e2.37E-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.45958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.55E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.55185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e4.79E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.67759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e5.74E-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.37756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3.36E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.34099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e6.77E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eTr1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.14859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.00555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.11769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.02838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.10505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.05056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.23817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e7.28E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.15192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.00457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.05528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.30454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003enTreg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.24329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e4.55E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.09025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.09323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.43915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e8.59E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.14828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.00565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.18943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3.88E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.14401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.00721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eiTreg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.4615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.05E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.45187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e7.30E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.58319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e5.29E-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.2963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.84E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.50352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e1.05E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.31751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e1.44E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eTh1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.44446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e3.11E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.42904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e5.68E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.49664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e5.19E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.48116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n 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width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-0.62306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e1.06E-38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eTfh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.33832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e9.72E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.52137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.40E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.40468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e4.18E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n 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width=\"11.11111111111111%\"\u003e\n \u003cp\u003eEffector_memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.11999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.02541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.16681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.00182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.10503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.0506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.10548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.04961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-0.20389\u003c/p\u003e\n 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width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.43E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.15083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.00487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.33588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e1.35E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.41243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e1.11E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eMAIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.10178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n 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width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.00711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.2692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e3.56E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.37195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e7.94E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.4791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e2.59E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.2971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n 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width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-0.2878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e4.82E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eMacrophage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.25291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e1.82E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.3744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e5.46E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.34055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e7.19E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n 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width=\"7.526881720430108%\"\u003e\n \u003cp\u003e7.05E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-0.49203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e1.48E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eGamma_delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.17705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e9.25E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.10741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e0.04556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n \u003cp\u003e-0.30216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.347670250896058%\"\u003e\n 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width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.41188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e1.22E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.37888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e2.74E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Immune infiltration, Immunotypes, Immunotherapy, CCR5, CCR7, LCK","lastPublishedDoi":"10.21203/rs.3.rs-4485605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4485605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe aim of this study was to determine whether differences in the cellular composition of the immune infiltrate in HCC influence survival and identify predictors for immunotherapy efficacy in hepatocellular carcinoma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 362 patients from TCGA cohort and 204 patients from ICGC with HCC were included in the study. Two immune features were selected out of 24 immune features to construct immunotypes based on the Cox regression model. Hub genes of DEGs were identified by STRING and Cyto-scape. The role of hub genes on immunotherapy efficacy prediction was evaluated by Kaplan\u0026ndash;Meier survival analysis in immunotherapy cohorts. The effects of LCK on HCC cell proliferation and migration were evaluated by CCK8, trans-well and wound healing assays.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEight immune cell subsets were associated with HCC prognosis. Two immune cells (MAIT and central-memory) were selected to construct 3 immunotypes which could predict overall survival in the TCGA cohort (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;24.13, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and ICGC cohort (validation cohort, \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.51, P\u0026thinsp;=\u0026thinsp;0.005). GO and GSEA analysis showed up-regulated immune-related pathway in Cluster3, and Cluster3 showed significantly higher immune checkpoint molecules (PD-L1, PD-1, CTLA-4, PD-L2, LAG3 and TIM3) expression. Three hub genes (CCR5, CCR7 and LCK) were identified based on the differential expression genes between Cluster3 and Cluster1. CCR5, CCR7 and LCK were efficient predictors for immune infiltration, especially CTL, and immunotherapy efficacy. We also verified that LCK conferred proliferation and metastasis of HCC cells and immunotherapy resistance of HCC patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eImmune cell abundance and immunotypes could effectively predict prognosis of HCC. Furthermore, CCR5, CCR7 and LCK were identified as predictors for immunotherapy efficacy in hepatocellular carcinoma.\u003c/p\u003e","manuscriptTitle":"Comprehensive Analysis of Immune Infiltration Identifies Predictors for Immunotherapy Efficacy in Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-14 18:46:53","doi":"10.21203/rs.3.rs-4485605/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":"27b545f9-4842-436f-a394-c84e7639aad4","owner":[],"postedDate":"June 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-12T08:23:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-14 18:46:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4485605","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4485605","identity":"rs-4485605","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}