1 Introduction

The progression of cancer has been found to be correlated with the imbalance of gene regulation programs. Searching for new candidate genes that contribute to the cancer development would be meaningful for the early screening and understanding of tumors correlated regulatory pathways [1,2,3].

Metal regulatory transcription factor 1 (MTF1) has been found to be a zinc finger-containing transcription factor that regulates subcellular metal metabolism, such as copper, iron or zinc [4]. Structurally, MTF1 consists of a α/β N-terminal domain and a tetra-α helical C-terminal domain. Of note, N-terminal domains have the function of interacting with various rRNA methyltransferases [5]. MTF1 plays a crucial role in maintaining intracellular metal homeostasis and preventing cells from excessive metal damage [6]. Furthermore, MTF1 could be translocated into the nucleus, leading to the activation of its downstream genes, such as matrix metalloproteinases (MMPs), metal binding protein metallothionein (MT1) and so on [7]. Several seminal studies have delineated the unique functions of MTF1 in the development of various diseases, especially like cancers [8]. Acetylated METTL3 could enhance MTF1 mRNA stability by binding its mRNA and reducing the m6A modification, consequently facilitating cell proliferation in liver hepatocellular carcinoma (LIHC) [9]. He et al. showed that downregulated MTF1 could serve as an independent prognostic factor for gastric cancer patients [10]. Although the aberrantly expressed MTF1 has been detected in several cancers, its potential biological functions and underlying mechanisms have not been well investigated.

Here, we employed the TCGA dataset and some other bioinformatics tools to explore the regulation roles of MTF1 in a variety of cancers (Supplementary Table S1). We not only explored the expression levels of MTF1 in cancers, but also investigated the survival values, genetic alteration, methylation and enriched signaling pathways. These explorations have elucidated that MTF1 could play a vital part in the progression of various cancers.

2 Materials and methods

2.1 Gene expression analysis

Three databases, TIMER2.0 [11] (http://timer.cistrome.org/), TNMplot [12] (https://tnmplot.com/analysis/) and Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2.0) [13] (http://gepia2.cancer-pku.cn/), were applied to evaluate the MTF1 expression between the normal groups and the tumor groups. Apart from the TCGA samples, GEPIA2.0 database also collected the data from genotype-tissue expression dataset (GTEx). In GEPIA2.0, The p value was set as: p < 0.05 and the cutoff of |Log2FC| was 0.1. Meanwhile, GEPIA2.0 was used to evaluate the expression of MTF1 and pathological stages in TCGA pan-cancer. In addition, the UALCAN [14] (https://ualcan.path.uab.edu/) was employed to explore the methylation levels of MTF1 in TCGA datasets. In addition, The GEPIA2.0 and Kaplan–Meier plotter [15] (http://kmplot.com/analysis/) have confirmed the prognostic values of MTF1 expression in several cancers.

2.2 Immunohistochemistry (IHC) staining

From Human Protein Atlas (HPA) [16] (https://www.proteinatlas.org/) database, antibody HPA028689 was applied to obtain the Immunohistochemistry (IHC) staining of MTF1 between normal tissues and tumor tissues, including 11 kidney cancer tissues, 12 testis cancer tissues and 12 colon cancer tissues. We used HPA to identify the expression profiles of MTF1 in several cancers, such as renal cancer, testicular germ cell tumors (TGCT) and colon adenocarcinoma (COAD).

2.3 Genetic alteration analysis

The cBioPortal [17] (http://www.cbioportal.org/) was applied to obtain the mutation profiles of MTF1 in TCGA pan-cancer, including alteration frequency, mutation type and mutated site. The effect of MTF1 genetic alteration on survival data for cancer patients were also downloaded from cBioPortal. The survival analysis mainly included disease-free survival (DFS), disease-specific survival (DSS), overall survival (OS) and progression-free survival (PFS). The FAQs section in cBioPortal provided the detailed mutation annotation information, including identification, sites and regions, types, and clinical prognosis. For the prognosis analysis, STATUS represents patients’ survival status with “0” meaning “living” or “1” meaning “deceased”, and MONTHS represents the time from the start of diagnosis to the end of follow-up.

2.4 Immune analysis

The TIMER2.0 database was employed to evaluate the correlation between MTF1 expression and multiple immune infiltrating cells across TCGA pan-cancer. T cell CD8 + cells, dendritic cells (DC), NK cell, T cell regulatory (Tregs), cancer-associated fibroblast (CAF), neutrophil, B cell and macrophage were searched for further evaluations.

2.5 The analysis of single cell sequencing

The single cell sequencing could be used for the functional analysis of candidate genes in human diseases at a single cell level [18,19,20]. The correlation heatmap between MTF1 expression and functional status were obtained from CancerSEA [21] (http://biocc.hrbmu.edu.cn/CancerSEA/). The t-SNE pictures were downloaded from the CancerSEA tool. The CancerSEA website collected 41,900 cells from 25 cancer types. Meanwhile, GSVA and Spearman’s correlations were used to analyze the functional states and correlations between the biological activities and MTF1 expressions, respectively. The significant gene-state associations were identified with FDR < 0.05 and correlation > 0.3.

2.6 MTF1-related gene enrichment evaluations

The STRING [22] (https://string-db.org/) website was applied for protein–protein network evaluation. The main settings were provided as follows: meaning of network edges (“evidence”), minimum required interaction score [“Medium confidence (0.400)”], max number of interactors to show (“no more than 10 interactors” in 1st shell and “no more than 20 interactors” in 2nd shell). And we employed the Top # similar Genes in GEPIA2.0 to download the top 100 MTF1-associated genes across TCGA pan-cancer and the corresponding normal tissues. Meantime, we performed Gene Ontology (GO) analysis to evaluate the possible pathways regulated by MTF1-associated molecules. After uploading the top 100 MTF1-associated molecules, the GO enrichment results were automatically generated by ** of immune and endometrial epithelial cells in endometrial carcinomas revealed by single-cell RNA sequencing. Aging. 2021;13(5):6565–91." href="/article/10.1007/s12672-023-00738-8#ref-CR53" id="ref-link-section-d243333465e1304">53]. The employment of single-cell sequencing in cancer could enhance the understanding of the biological functions of cancer-associated genes [54,55,56]. The further research about the view of single-cell sequencing will benefit the prognostic prediction and clinical treatment of cancer patients [57,58,59]. A study has conducted deep single-cell RNA sequencing on T cells obtained from six liver cancer patients and found that exhausted CD8 + T cells and Tregs could enrich and clonally expand in liver cancer [60]. In our study, we applied the CancerSEA to explore MTF1 expression at single cell levels across some cancers. And we found MTF1 in RB was positively associated with angiogenesis. MTF1 expression in UM was negatively associated with DNA repair. Moreover, the MTF1 expression in OV was negatively correlated with invasion. These findings suggested that MTF1 could be essential in the regulation of cancer-associated biological functions.

Cancer cells can alter the tumor immune microenvironment (TIME) through interacting with multiple cells and molecules, thereby affecting the development and treatment of cancer cells [61, 62]. Previous research has shown that different types of immune cells in the TIME have different implications for cancer pathology [63, 64]. DCs, the vital antigen-presenting cells in the immune system, play an essential role in activating the immune response. Recent research has indicated that the anti-tumor function of DCs is usually suppressed in the tumor microenvironment [65]. In addition, high infiltration degree of selected immune cells (especially cytotoxic T cells) in TIME can predict the prognosis of cancer patients [66]. A study has shown that the cuproptosis-related genes LIAS, PDK1 and BCL2L1 were strongly related to immune cells infiltrating in osteosarcoma, serving as the reliable targets for predicting the patients’ immune response [67]. Here, we used several algorithms from TIMER2.0, such as TIMER, EPIC, MCPCOUNTER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ and XCELL, to analyze the relationship between immune cell infiltration and MTF1 expression in pan-cancer. We found that the positive correlation between MTF1 expression and T cell CD8 + immune infiltration in COAD and KIRC. Additionally, MTF1 expression levels were positively correlated with DC in COAD. Correlation analysis proved the potential regulation roles of MTF1 in the infiltration of T cell CD8 + and DC cells. However, the mechanism of MTF1 on the regulation of immune cell infiltration remains to be further explored.

To sum up, through a series of pan-cancer analysis, we investigated the expression, prognosis, genetic alteration and methylation profiles of MTF1 in various cancers. Moreover, the MTF1 expression at single cell levels and the functional signaling pathways were also explored. These findings have clarified that MTF1 plays an essential role in the cancer progression, cancer metabolism and immune regulation. And this article would provide a new strategy for the MTF1-based survival prediction in several cancer patients. However, some limitations still exist in our research. Our results were mainly derived from the bioinformatics analysis and preliminary cellular experiments. Bioinformatics still limited by data quality, individual case variation and lack of temporal dynamics. The complexity of data interpretation, the paucity of currently knowledge and incomplete databases will also require more advanced algorithms and technological progress in the future. In addition, the diverse expression values and prognostic profiles in multiple cancers predicts the highly complex roles of MTF1 in human cancers. Thus, the underlying molecular mechanism of MTF1 should be further evaluated to demonstrate the functional roles of MTF1 in cancers.