Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data

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.

Tumor Mutation Burden-Associated LINC00638/miR-4732-3p/ULBP1 Axis Promotes Immune Escape via PD-L1 in Hepatocellular Carcinoma

“…Unfortunately, however, few clinical studies have revealed the role of TMB in HCC. We and others found a significant correlation between TMB and immune infiltration in HCC ( 5 – 7 ). Most importantly, the PS model was significantly associated with TMB and immune infiltration.…”

“…A detailed understanding of the mechanism underlying immune infiltration may favor precision immunotherapy in HCC. Tumor mutation burden (TMB) refers to the total number of mutations in the coding region of the evaluated gene in the tumor cell (TC) genome, which is calculated by the total number of mutations per megabase in the genome (5)(6)(7). Previous reports suggested the close association of TMB with immune infiltration, while the molecular mechanism remains inconclusive (5)(6)(7).…”

Prognostic Value and Immune Infiltration of ARMC10 in Pancreatic Adenocarcinoma Via Integrated Bioinformatics Analyses

“…Tumor microenvironment has been brought into focus in the progression of cancer for a long time, especially immune in ltrates [52]. Tumor-in ltrating immune cells (TIICs) have close connection with prognosis in various cancers [53,54]. In our study, we detected that ARMC10 expression was positively relevant to the in ltration of Th2 cells, T helper cells and negatively relevant to pDC cells in ltration.…”

Tumor mutation burden determined by a 645-cancer gene panel and compared with microsatellite instability and mismatch repair genes in colorectal cancer

“…Then, we calculated the panel-TMB for each sample as the total amount of coding variants/exons length (38 million) based on the number of non-synonymous somatic mutations, including frameshift deletion mutation, in-frame deletion mutation, frameshift insertion mutation, in-frame insertion mutation, missense mutation, nonsense mutation, nonstop mutation, and silent mutation. Using median as the threshold, patients were divided into high TMB group and low TMB group (27).…”

“…We determined the median value as the threshold for panel- KMT2C [38] FCGR2A [37] MYC [35] NCOR1 [33] CHD4 [32] HLA-B [32] KMT2D [32] SPEN [32] NOTCH2 [27] BCR [26] KMT2B [26] MAP3K1 [25] HLA-A [23] LRP1B [23] NOTCH1 [23] TP53 [23] FAT1 [22] KAT6A [21] PED4DIP [21] PRSS1 [21] ARID1A [20] ARID1B [20] CIC [20] HLA-DRB51 [20] FAT3 [ A-DRB5 [20] ClC [20] ARID1B [20] ARID1A [20] PRSS1 [21] PED4DIP [21] KAT6A [21] FAT1 [22] TP53 [23] NOTCH1 [23] LRP1B [23] HLA-A [23] MAP3K1 [25] KMT2B [26] BCR [26] NOTCH2 [27] SPEN [32] KMT2D [32] HLA-B [32] CHD4 [32] NCOR1 [33] MYC [35] FCGR2A [37] KMT2C [38]<...>…”