MTIF2 impairs 5 fluorouracil-mediated immunogenic cell death in hepatocellular carcinoma in vivo: Molecular mechanisms and therapeutic significance
Graphical abstract
Introduction
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related morbidity and mortality [1,2]. Relative to other malignancies, the incidence of HCC is fifth highest in men and the ninth highest in women [3,4]. And it is second most cancer death worldwide. It has been reported that increased infiltration of T cells and NK cells in HCC are positive prognosis factors, and increased infiltration of Treg is a negative prognosis factor [5,6].
Chemotherapy is important strategy for the treatment of HCC [7,8], natural compounds also been used for chemoprevention of cancer [9]. 5-Fluorouracil (5-FU) is widely used in the treatment of cancer, over the past 20 years, increased understanding of the mechanism of action of 5-FU has led to the development of strategies that increase its anticancer activity [[10], [11], [12]]. Apoptosis of cancer cells may result in the release of damage-associated molecular patterns (DAMP), which can then recognize by receptors on the surface of immune cells and active immunogenic response. DAMPs serve as signals to facilitate the engulfment of dying cells by macrophages and dendritic cells (DCs), leading to the activation of a potent anticancer immunity [13,14]. Calreticulin, ATP, and HMGB1 are important DAMPs that can mount an efficient immune response [15]. It has been reported that cancer cells treated with OXA and 5-FU may release DAMPs, which play important roles in immunogenic tumor cell death [16].
Mitochondrial translation initiation factor 2 (MTIF2) is nuclear-encoded and functions in the mitochondria to initiate the translation of proteins encoded by the mitochondrial genome. The protein has been isolated from bovine mitochondria.
MTIF2 is a monomeric protein that initiates translation by joining an N-formyl methionyl tRNA to the 28S mitochondrial ribosomal subunit in the presence of GTP and an mRNA template. Hydrolysis of the bound GTP is hypothesized to free the MTIF2 monomer, which allows the 28S and 39S ribosomal subunits to join, forming the 55S initiation complex [17,18]. However, the underlying role played by MTIF2 in processes of HCC progression remains unknown.
Apoptosis-inducing factor mitochondrion-associated 1 (AIFM1) possesses both NADH oxidizing and apoptosis-inducing activities, under physiological conditions, the 62-kDa AIFM1 is tethered to the inner mitochondrial matrix, where it plays an important role in regulating electron transport, ferredoxin metabolism, reactive oxygen species generation and ATP production. It has been reported that overexpression of AIFM1 can induce apoptosis, shown an important function of AIFM1 that participated in apoptosis processes [19,20].
In this study, we explore potential genes associated with immune cell infiltration in HCC by bioinformatic analysis and finally identify MTIF2 as a biomarker. Using in-vitro experiments, we confirmed that MTIF2 promotes proliferation and migration of HCC cells, the chemoresistance to 5-FU was impaired as MTIF2 was knocked down. co-IP confirmed that MTIF2 could interact with AIFM1, subsequently reduce transcription of caspase3, and finally result in suppression of apoptosis. After MTIF2 was overexpressed, secretion of calreticulin, ATP, and HMGB1 was reduced, which result in the wakening of DCs maturation and impaired CD8 + T cell proliferation. The animal experiment has shown that, under 5-FU treatment, MTIF2- overexpression HCC cells produced larger tumors compared with that of wild type HCC cells, flow cytometry confirmed that MTIF2 overexpression leads to reduced apoptosis rate of tumor cells, MTIF2 overexpression also damaged DCs maturation and CD8 + T cell activity in vivo.
Section snippets
Analysis of immune landscape of included samples
Batch effects and principal components analysis (PCA) was conducted to found there was no significant separation between three cohorts, indicating that the batch effect correction was good (Supplementary Fig. 1 A–C). A total of 442 samples and 12,892 genes were further analyzed.
The R package "CIBERSORT" was employed to calculate the scores of 22 tumor-infiltrating immune cells (TIC). The scores of seven subtypes of T cells in each sample were selected as WGCNA trait data (Fig. 1A). We clustered
Discussion
Liver cancer is estimated to be the third most common cause of cancer-related mortality globally, with incidence rates more than tripled since 1980 [21,22]. Hepatocellular carcinoma is the most frequent type of primary liver cancer, accounting for about ∼90 % of hepatic malignancies [23].
It has been reported that increased infiltration of T cells and NK cell in HCC are positive prognosis factors, and increased infiltration of Treg is a negative prognosis factor. Chemotherapy is important
Data acquisition and preprocessing
GSE133039 cohort was downloaded from Illumina HiSeq 2500 platform, standardized by Transcripts Per Kilobase of exon model per Million mapped reads (TPM), it contains 66 samples (34 tumor samples, 32 non-tumor samples), including 58,723 genes; The GSE102079 cohort was downloaded from the Affymetrix Human Genome U133 Plus 2.0 Array platform, standardized by Robust Multi-array Average (RMA), and includes 257 samples (152 tumor samples, 105 non-tumor samples), including 21,655 genes. GSE76297
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Funding
This work is supported by the Natural Science Foundation of Hainan province (819QN356; Hnky2019-53); National Natural Science Foundation(81660489; 81260367).
Availability of data and material
The data used to support the findings of this study are available from the corresponding author on reasonable request.
CRediT authorship contribution statement
Dafeng Xu: Conceptualization, Methodology, Software, Writing - review & editing. Yu Wang: Data curation, Writing - original draft. Jincai Wu: Software, Validation. Zhensheng Zhang: Software, Validation. Jiacheng Chen: Visualization, Investigation. Mingwei Xie: Visualization, Investigation. Rong Tang: Visualization, Investigation. Chen Cheng: Software, Validation. Liang Chen: Methodology, Writing - review & editing. Shiyun Lin: Methodology, Writing - review & editing. Xiangxiang Luo:
Declaration of Competing Interest
The authors report no declarations of interest.
Acknowledgements
Not applicable.
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