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Prognostic Analysis of Tumor Mutation Burden and Immune Infiltration in Hepatocellular Carcinoma Based on TCGA Data

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Prognostic Analysis of Tumor Mutation Burden and Immune Infiltration in Hepatocellular Carcinoma Based on TCGA Data www.aging-us.com AGING 2021, Vol. 13, No. 8 Research Paper Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data Shaoqing Liu1,2,*, Qianjie Tang3,*, Jianwen Huang2,*, Meixiao Zhan2, Wei Zhao2, Xiangyu Yang2, Yong Li2, Lige Qiu2, Fujun Zhang1, Ligong Lu2,&, Xu He2 1Department of Minimally Invasive and Interventional Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China 2Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital Affiliated with Jinan University, Jinan University, Zhuhai 519000, China 3Department of Pharmacy, Guangdong Women and Children Hospital, Guangzhou 511400, China *Equal contribution Correspondence to: Fujun Zhang, Ligong Lu, Xu He; email: [email protected], [email protected], https://orcid.org/0000-0002-7850-7640; [email protected], https://orcid.org/0000-0002-7565-5111 Keywords: hepatocellular carcinoma, tumor mutation burden, The Cancer Genome Atlas, immune infiltration Received: September 24, 2020 Accepted: January 14, 2021 Published: April 4, 2021 Copyright: © 2021 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT In order to explore the prognosis of tumor mutation burden (TMB) and the relationship with tumor infiltrating immune cells in hepatocellular carcinoma (HCC), we downloaded somatic mutation data and transcriptome profiles of 376 HCC patients from The Cancer Genome Atlas (TCGA) cohort. We divided the samples into high- TMB and low-TMB groups. A higher TMB level indicated improved overall survival (OS) and was associated with early pathological stages. One hundred and nine differentially expressed genes (DEGs) were identified in HCC. Moreover, based on four hub TMB-related signatures, we constructed a TMB Prognostic model (TMBPM) that possessed good predictive value with area under curve (AUC) of 0.701. HCC patients with higher TMBPM scores showed worse OS outcomes (p < 0.0001). Moreover, DCs subsets not only revealed higher infiltrating abundance in the high-TMB group, but also correlated with worse OS and hazard risk for high-TMB patients in HCC. Meanwhile, CD8+ T cells and B cells were associated with improved survival outcomes. In sum, high TMB indicates good prognosis for HCC and promotes HCC immune infiltration. Hence, DCs and the four hub TMB- related signatures can be used for predicting the prognosis in HCC as supplements to TMB. INTRODUCTION rate for HCC is about 12.5% [3, 5]. Targeted therapy has improved the OS in HCC; however, the overall Hepatocellular carcinoma (HCC) is the most common efficacy is unsatisfactory. The emergence of immuno- pathological subtype of liver cancer; it is the sixth therapy has identified new therapeutic prospects for most common type of cancer and the fourth leading HCC [6], especially immune checkpoint inhibitors cause of cancer-related death in the world [1–3]. (ICIs), such as programmed death 1 (PD-1)/ Although the main treatment of early HCC is surgery, programmed death ligand-1 (PD-L1) and cytotoxic T- 50% of the patients are at an advanced stage at the lymphocyte associated antigen-4 (CTLA-4) [7, 8]. In time of diagnosis [4]. The 5-year overall survival (OS) recent clinical studies, both nivolumab and www.aging-us.com 11257 AGING pembrolizumab have exhibited better prognosis as RESULTS second-line therapy in advanced HCC after sorafenib treatment [9, 10]. Genome-wide mutation profiling in HCC Although ICIs therapy has been shown to be effective in The somatic mutation data of 376 HCC patients were several types of malignancies [11–15], it has shown processed from the TCGA (https://tcga-data.nci.nih. varying effects in various types of cancer, even in the gov/tcga/) database and their clinical information has same cancer patients [15]. Hence, the identification of been presented in Table 1. The mean age was 61 years; accurate biomarkers is an urgent need for screening 122 (32.4%) women and 254 (59.5%) men were patients who can benefit from immunotherapy and to included. Utilizing maftools software, we classified monitor the prognosis of immunotherapy. Recently, an these mutations into various groups and exhibited increasing number of biomarkers have been identified for mutation groups in box plots using various colors in box predicting the efficacy of immunotherapy, including plots (Figure 1). The most common type was missense DNA damage repair (DDR) [16], microsatellite mutation (Figure 1A); single nucleotide polymorphism instability (MSI) [17, 18], neoantigens [19], and HLA occurred more frequently than deletion (DEL) or presentation of neoantigens against tumor [20–22]. TMB insertion (INS) (Figure 1B), and C>T transition was the is a novel biomarker that is calculated as genetic most common form of single nucleotide variants (SNV) variations per million bases of the encoded genome [23, in HCC (Figure 1C). The mutation categories are shown 24]. Patients with a high TMB have a superior objective in box plots (Figure 1E). The top 10 mutated genes response rate (ORR) and prolonged OS [19, 25, 26] than were TP53 (28%), TTN (25%), CTNNB1 (24%), those with a low TMB. ORR differences can be MUC16 (16%), ABL (11%), PCLO (11%), MUC4 explained by TMB in about 55% types of tumor [20]; (10%), RYR2 (10%), ABCA13 (9%), and APOB (9%) however, TMB is not a single biomarker for predicting (Figure 1F, 1G). the efficacy of immunotherapy that may be inconsistent with specific genetic mutations in high-TMB patients. TMB was related to overall survival and clinical For example, patients with EGFR mutations and ALK stage rearrangements (EGFR+/LK+), JAK1 mutations, or JAK2 mutations are associated with low response to We calculated the number of TMB per million bases for immunotherapy [27–30]. Meanwhile patients with KRAS 363 samples and classified them into high-TMB and mutations (KRAS+) had a better ICIs response rate [27], low-TMB groups using the median value as the and STK11 mutation was found to be the main factor for threshold (Supplementary Table 1). Kaplan–Meier PD-1 inhibitor resistance in lung adenocarcinoma with survival indicated that higher TMB was associated with KRAS+ [31]. Moreover, tumor microenvironment better OS (p = 0.0004) (Figure 2A). However, high (TME) has been well identified as a molecular TMB was not in accordance with better disease-free determinant in many cancers. TUMEH [32] found that T interval (DFI) in this research (Figure 2B). Moreover, cell infiltration in TME is closely related to the efficacy we found that higher TMB was also associated with of immunotherapy. Moreover, higher infiltrating tumor stage (p = 0.035; Figure 2C), pathologic stage (p abundance of CD8+ T cell and memory activated CD4+ = 0.020; Figure 2D), and T stage (p = 0.027; Figure 2E). T cell subsets revealed prolonged OS in the high-TMB The TMB levels tended to decrease with tumor group [33]. Therefore, combined information from TMB progression (Figure 2C–2E), and clinical research with levels, gene mutations, and immune infiltration density a larger sample size is required to verify this result. can be used as a novel biomarker for predicting the efficacy of immunotherapy in HCC. Analysis of differentially expressed genes between the 2 TMB groups The prognostic role of TMB and the relationship between TMB and immune infiltration varied for The heatmap and volcano plot visualized that 109 different types of cancers [33, 34], and limited studies differentially expressed genes (DEGs) were identified have focused on TMB with immune infiltration in HCC. with limma software with |logFC|> 1.5 and false Thus, we investigated the prognostic role of TMB and discovery (FDR) < 0.05 (Figure 3A, 3B and the potential association between immune infiltration Supplementary Table 2). The Gene Ontology (GO) and hub TMB-related signature in HCC, using TCGA enrichment analysis demonstrated that in molecular HCC cohort and Gene Expression Omnibus (GEO) function group, these DEGs were mainly involved in datasets. We found that high TMB was a good extracellular matrix structural constituent, glyco- prognostic predictor for HCC, and that DCs and the four saminoglycan binding, and extracellular matrix hub TMB-related signatures could also be used for structural constituent conferring tensile strength. In the predicting the prognosis in HCC. biological process group, extracellular structure www.aging-us.com 11258 AGING Table 1. Clinical data of 376 patients with hepatocellular carcinoma (HCC) from the cancer genome atlas (TCGA) cohort in this research. Level Overall N 376 Age (median [IQR]) 61.00 [51.00, 69.00] Gender (%) female 122 (32.4) male 254 (67.6) Status (%) Alive 243 (64.6) Dead 132 (35.1) Not Reported 1 (0.3) pathologic_T (%) T1 185 (49.5) T2 94 (25.1) T3 81 (21.7) T4 13 (3.5) TX 1 (0.3) pathologic_N (%) N0 257 (68.5) N1 4 (1.1) NX 114 (30.4) pathologic_M (%) M0 272 (72.3) M1 4 (1.1) MX 100 (26.6) pathologic_stage (%) Stage I 175 (49.7) Stage II 86 (24.4) Stage III 86 (24.4) Stage IV 5 (1.4) tumor_stage (%) stage i 175 (46.5) stage ii 86 (22.9) stage iii 86 (22.9) stage iv 5 (1.3) Unknown 24 (6.4) organization, extracellular matrix organization, and cell- pathways, including fat digestion and absorption, substrate adhesion were enriched. In addition, in the cholesterol metabolism, and peroxisome proliferators- cellular component, TMB-related DEGs were mainly activated receptor (PPAR) signaling pathway (Figure involved in collagen-containing extracellular matrix, 3C and Table 2). We then selected the top gene set extracellular matrix, and extracellular matrix component enrichment analysis (GSEA) results of TMB-related (Figure 4A–4C and Supplementary Table 3).
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