Introduction
Conlon, liver, lung cancers are the common malignancies in most countries, and immune cells infiltration into tumour tissue and immune cells exhaustion lead to a poor prognosis [
1‐
4]. The infiltration number and exhaustion state of immune cells could exert a key role in cancer progression, and immune combination therapy is becoming a promising strategy for enhancing traditional chemotherapy. At present, the most popular immune checkpoint inhibitors are programmed death-1 (PD-1), programmed death ligand-1 (PD-L1) and cytotoxic T lymphocyte associated antigen 4 (CTLA4), which are promising antitumour therapies in multiple cancers [
5‐
7]. However, the efficacy of immunotherapies, including anti-CTLA4, anti-PD-L1 and anti-PD-1, differ from person to person. Furthermore, the study of tumour-infiltrating lymphocytes has become a hotspot, and tumour-associated macrophages (TAMs), CD8 + T cells and CD4 + T cells have effects on patient prognosis and are correlated with the efficacy of immunotherapy and chemotherapy in the clinic [
7‐
9]. Therefore, there is an urgent need to explore the recruitment types of immune cells and to discover potential targets associated with a better prognosis.
Cell death is the basic processes in the cellular life activities, which maintains homeostasis along with cell proliferation, differentiation in multicellular organisms. Apoptosis, necrosis and pyroptosis are the common cell death forms. Apoptosis is a type of programmed cell death characteristics of intact cell membrane and generally not inducing inflammation. Necrosis is a passive type of cell death caused by pathological stimuli. Similar to apoptosis, pyroptotic cells undergo nuclear condensation and chromatin DNA fragmentation, and TUNEL staining is positive. Similar to necrosis, the membrane pores formation can lead cell swelling, cell membrane rupture, and release of inflammatory factors consisting of LDH, HMGB1, IL- 1β, IL-18. Pyroptosis is mediated by the gasdermin family. The relationship between pyroptosis and tumours differs in different tissues and genetic backgrounds. It can inhibit the occurrence and development of tumours. On the other side, it can promote tumour growth due to release of proinflammatory factors. Studies have shown that DFNA5 methylation results in lower expression levels of DFNA5 in most tumour cells than in normal cells. Specific drugs can be chosen to upregulate the expression levels of DFNA5 to enhance anti-tumour effects. The pyroptosis may play key role in the antitumour efficacy.
Previous reports suggested that DFNA5 is a gene associated with deafness [
10]. Recently, DFNA5 was discovered to play an important role in pyroptosis [
11]. DFNA5 is cleaved by caspase3 after drug treatment. The N-terminal part of DFNA5 can create pores in the cell membrane, and then the cellular content can be released outside [
12‐
14]. In the tumour microenvironment, cellular components, such as cytokines, may recruit immune cells into tumour tissue to exert antitumour efficacy [
15]. Although DFNA5 is usually recognized as a tumour suppressor gene, its expression levels in diverse cancers are different. However, the underlying mechanisms of DFNA5 in tumour immunology are still unclear.
In this research, we explored the relationship of DFNA5 expression levels to diverse cancer patients based on the databases including Kaplan–Meier plotter, UALCAN, Oncomine. Furthermore, Tumour Immune Estimation Resource was used to investigate the relationship between DFNA5 levels and lymphocytes in diverse cancer. The analyses in this study illustrate the importance of DFNA5 in Conlon, liver, lung cancers and its role in lymphocytes infiltration and T cell exhaustion.
Materials and methods
Cells and cell culture
Human embryonic kidney 293T cells (HEK-293T), obtained from American Type Culture Collection, were cultured in Dulbecco’s modifified Eagle medium (Invitrogen) containing 10 v/v % fetal bovine serum (FBS, Gibco fetal bovine serum) and 1 v/v % penicillin/streptomycin (Sigma-Aldrich) at 37 °C in an atmosphere of 5% CO2.
Expression vector construction
A pcDNA3.0-flag, pcDNA3.0-PDCD1-3x-myc and pcDNA3.0-CD274-3x-myc plasmid expression vector were obtained from our lab. A pcDNA3-based expression vector for TurboID was obtained from addgene(Addgene: #107169). PLVX-Puro-DFNA5 was obtained from addgene (Addgene: #154876). A pcDNA3.0-DFNA5-flag expression vector was constructed using PLVX-Puro-DFNA5 as a template. We used overlapping RCR method to link DFNA5 gene and TurboID together (DFNA5-TurboID). Then DFNA5-TurboID was recombined into pcDNA3.0-flag vector using ClonExpress Ultra One Step Cloning Kit (Vazyme, C115-01).
Flag-DFNA5 plasmids co-transfected with PDCD1-3xmyc or CD274–3xmyc plasmid
DFNA5 could influence the expression of PDCD1 and CD274. 293 T Cells were seeded into 6 well plate for 24 h. The increased content of Flag-DFNA5 plasmid co-transfected with PDCD1-3xmyc or CD274-3xmyc plasmid for 48 h, then the cells protein was extracted by RIPA lysis buffer. RIPA lysis buffer was obtained from Beyotime Institute of Biotechnology.
DFNA5 could upregulate the expression levels of PDCD and CD274 (Western blot)
Monoclonal antibodies against β-actin-HRP were purchased from Proteintech. Monoclonal antibodies of Myc and FLAG was purchased from Cell Signalling Technology. The expression levels of Flag-DFNA5, CD274-3xmyc, PDCD1-3xmyc, β-actin were detected by Western blot. Protein was transferred to a PVDF membrane. After blocking with 5% nonfat milk, the membrane was incubated with Monoclonal antibodies at 4℃ overnight, followed by incubation with HRP-labeled secondary antibodies for 1 h. The protein bands were exposured by imaging system (Tangen).
DFNA5-TurboID expression vector construction for screening Instantaneous interaction proteins
TurboID system is used to identify the instantaneous or weak interaction proteins. TurboID and DFNA5-TurboID were transfected into 293 T cells, respectively. The cells were cultured for 48 h after transfection, then biotin was added to the culture medium for 10 min. TurboID was used as a control to exclude the binding itself or nonspecific binding. Then RIPA lysis buffer was used to extract the protein. Streptavidin beads were used to pull the interactive proteins. And then the proteins were identified by mass spectrometry.
Oncomine database analysis
The gene expression level of DFNA5 in diverse cancers is indicated in the Oncomine database (
https://www.oncomine.org/resource/login.html). The threshold was identified based on values: fold change of P-value of 0.001, 1.5, and all genes are ranked [
16].
Kaplan–Meier plotter database analysis
We used Kaplan–Meier plotter to assess the effect of 54 k genes on survival according to 14,912 cancer samples (
http://kmplot.com/analysis/) [
17]. The data included 1440 gastric, 2190 ovarian, 7830 breast, and 3452 lung cancer samples. The relationship of DFNA5 expression to survival in gastric cancers, ovarian cancer, breast cancer, lung cancer, and liver cancer was evaluated by Kaplan–Meier plotter. Log-rank P-values and hazard ratios (HRs) with 95% confidence intervals were also determined.
TIMER database analysis
TIMER is a combined database for analysing immune cell infiltration in tumour sites of multiple cancer types (
https://cistrome.shinyapps.io/timer/) [
18]. The abundance of tumour lymphocytes from gene expression profiles was deduced by the deconvolution method in TIMER based on a previous report [
19].
The TIMER database consists of 10,897 samples from 32 cancer types for evaluating the levels of immune cell infiltration. DFNA5 expression was analysed in multiple cancer types to investigate the relationship between DFNA5 expression and the levels of immune cell infiltration, including macrophages, dendritic cells, B cells, CD4 + T cells, CD8 + T cells, and neutrophils. Gene expression levels correlated with immune cell infiltration are shown in Table
1. Furthermore, we explored the relationship between gene markers of immune cells and DFNA5 expression by correlation analysis. The gene markers of the immune cells consist of monocytes, B cells, CD8 + T cells, T cells (general), M1 macrophages, M2 macrophages, TAMs, neutrophils, Tregs and exhausted T cells according to previous studies. DFNA5 was used with gene symbols on the x-axis and related marker genes of gene symbols on the y-axis. The gene expression level was represented as log2 RSEM.
Table 1
Correlation analysis between DFNA5 and relate genes and markers of immune cells in TIMER
CD8 + T cell | CD8A | 0.37 | *** | 0.267 | *** | 0.182 | *** | 0.138 | 0.0102 | 0.196 | *** | 0.103 | 0.0226 |
CD8B | 0.302 | *** | 0.254 | *** | 0.165 | *** | 0.125 | 0.0201 | 0.172 | *** | 0.104 | 0.0206 |
T cell (general) | CD3D | 0.35 | *** | 0.223 | *** | 0.234 | *** | 0.198 | *** | 0.226 | *** | 0.119 | * |
CD3E | 0.426 | *** | 0.323 | *** | 0.213 | *** | 0.174 | * | 0.252 | *** | 0.144 | * |
CD2 | 0.417 | *** | 0.32 | *** | 0.216 | *** | 0.184 | *** | 0.263 | *** | 0.16 | *** |
B cell | CD19 | 0.255 | *** | 0.133 | * | 0.191 | *** | 0.139 | * | 0.112 | * | 0.002 | *** |
| CD79A | 0.318 | *** | 0.18 | *** | 0.208 | *** | 0.165 | * | 0.123 | * | 0.025 | 0.579 |
Monocyte | CD86 | 0.706 | *** | 0.659 | *** | 0.33 | *** | 0.319 | *** | 0.496 | *** | 0.45 | *** |
CD115 (CSF1R) | 0.711 | *** | 0.662 | *** | 0.265 | *** | 0.256 | *** | 0.497 | *** | 0.442 | *** |
TAM | CCL2 | 0.67 | *** | 0.623 | *** | 0.242 | *** | 0.203 | *** | 0.439 | *** | 0.391 | *** |
CD68 | 0.543 | *** | 0.489 | *** | 0.329 | *** | 0.296 | *** | 0.462 | *** | 0.412 | *** |
IL10 | 0.546 | *** | 0.516 | *** | 0.31 | *** | 0.291 | *** | 0.375 | *** | 0.303 | *** |
M1 Macrophage | INOS (NOS2) | − 0.153 | *** | − 0.208 | *** | 0.0036 | 0.486 | 0.032 | 0.557 | 0.156 | *** | 0.117 | * |
IRF5 | 0.366 | *** | 0.387 | *** | 0.196 | *** | 0,193 | *** | 0.359 | *** | 0.309 | *** |
COX2(PTGS2) | 0.255 | *** | 0.196 | *** | 0.236 | *** | 0.213 | *** | 0.164 | *** | 0.17 | *** |
M2 Macrophage | CD163 | 0.725 | *** | 0.683 | *** | 0.194 | *** | 0.17 | * | 0.427 | *** | 0.375 | *** |
VSIG4 | 0.713 | *** | 0.667 | *** | 0.284 | *** | 0.261 | *** | 0.431 | *** | 0.39 | *** |
MS4A4A | 0.697 | *** | 0.658 | *** | 0.225 | *** | 0.202 | *** | 0.433 | *** | 0.383 | *** |
Neutrophils | CD66b (CEACAM8) | − 0.101 | * | − 0.0074 | 0.136 | 0.191 | *** | 0.193 | *** | 0.098 | 0.0267 | 0.079 | 0.0787 |
CD11b (ITGAM) | 0.0737 | *** | 0.0717 | *** | 0.327 | *** | 0.328 | *** | 0.496 | *** | 0.453 | *** |
CCR7 | 0.4 | *** | 0.3 | *** | 0.197 | *** | 0.18 | *** | 0.245 | *** | 0.134 | * |
Treg | FOXP3 | 0.551 | *** | 0.487 | *** | 0.188 | *** | 0.197 | *** | 0.197 | *** | 0.26 | *** |
CCR8 | 0.562 | *** | 0.51 | *** | 0.315 | *** | 0.302 | *** | 0.283 | *** | 0.208 | *** |
STAT5B | 0.364 | *** | 0.369 | *** | 0.167 | * | 0.198 | *** | 0.18 | *** | 0.153 | *** |
TGFβ | 0.698 | *** | 0.624 | *** | 0.291 | *** | 0.268 | *** | 0.474 | *** | 0.419 | *** |
T cell exhaustion | CD274 | 0.445 | *** | 0.37 | *** | 0.192 | *** | 0.189 | *** | 0.384 | *** | 0.312 | *** |
PDCD1 | 0.386 | *** | 0.294 | *** | 0.208 | *** | 0.169 | * | 0.297 | *** | 0.19 | *** |
CTLA4 | 0.469 | *** | 0.39 | *** | 0.25 | *** | 0.224 | *** | 0.259 | *** | 0.157 | *** |
LAG3 | 0.353 | *** | 0.252 | *** | 0.161 | * | 0.136 | *** | 0.218 | *** | 0.144 | * |
TIM-3 (HAVCR2) | 0.731 | *** | 0.696 | *** | 0.358 | *** | 0.359 | *** | 0.495 | *** | 0.449 | *** |
UALCAN database analysis
UALCAN is a comprehensive web resource for analysing cancer (TCGA, MET500 and CPTAC). It is built on PERL-CGI with high-quality graphics based on JavaScript and CSS. UALCAN provides patient survival information for protein-coding genes with easy access to publicly available data (
http://ualcan.path.uab.edu/) [
20].
Gene correlation analysis in GEPIA
The online database Gene Expression Profiling Interactive Analysis (GEPIA) was used to further confirm the significantly correlated genes in TIMER. GEPIA is an interactive website from TCGA and the GTEx projects that analyses RNA sequencing expression (
http://gepia.cancer-pku.cn/index.html) [
21]. Gene expression correlation analysis was conducted for the given sets of TCGA expression data by the Spearman method. DFNA5 is represented on the x-axis, and related genes are displayed on the y-axis.
DAVID
TurboID system can explore the instantaneous or weak interaction molecules. In this study, we screened the chemokine and immune related Genes interacted with DFNA5 using TurboID system. Then the screened proteins were analyzed by DAVID(
https://david.ncifcrf.gov/) [
22]. Gene ontology analyses focus on three domains: biological processes (BP), cellular components (CC), and molecular functions (MF), and such analyses are commonly used to predict the functional roles of chemokine and immune related Genes interacted with DFNA5, while KEGG analysis can define the pathways related to the DFNA5 associated with immune function.
Statistical analysis
Survival curves were provided by Kaplan–Meier plots and UALCAN. The figures obtained from Oncomine are presented with fold changes, ranks and P-values. The results of GEPIA and Kaplan–Meier plots are presented with Cox P, P or HR values from a log-rank test. Spearman’s correlation and statistical significance are typical standards to assess the correlation of gene expression. The correlative strength was determined according to the following: 0.70–1.0 “strong”, 0.40–0.70 “moderate”, 0.10–0.40 “weak”, and 0.00–0.10 “very weak”. P-values < 0.05 were considered statistically significant.
Discussion
DFNA5 was originally studied as a gene linked to deafness. Then, the vital role of DFNA5 in pyroptosis was discovered. DFNA5 is cleaved by caspase-3 in response to some chemotherapeutic drugs that could induce pyroptosis [
14].
DFNA5 has been extensively studied, the role of DFNA5 in immune cell infiltration is poorly understood [
15]. Here, the relation of prognosis in patients with diverse cancer types to DFNA5 expression levels is illustrated. Elevated expression levels of DFNA5 is related to a worse prognosis in LIHC, HNSC, THYM, CHOL, and KIRC. Furthermore, the data indicate that in liver, colon and lung cancers, diverse immune markers and immune infiltration levels are related to the levels of DFNA5 expression. The novel functions of DFNA5 in immune system are introduced to this study, and suggests it may be a potential marker in cancer.
Datasets of Oncomine and UALCAN were used to analyze DFNA5 expression and prognostic level in patients with diverse cancer types. The gene expression of normal tissues was different from diverse cancer types, we analyzed DFNA5 expression in Oncomine database and it was shown that was elevatedly expressed in pancreatic cancer, kidney cancer, lymphoma, cervical cancer, head and neck cancer, melanoma and sarcoma. However, DFNA5 expression levels in TCGA data in comparison to normal adjacent tissues were elevated in HNSC, LIHC, CHOL, LUSC, STAD and THCA but decreased in UCEC, PRAD, KICH and BRCA. Increased DFNA5 expression based on analysing the TCGA database indicated that it was related to an unfavourable prognosis in HNSC, THYM, CHOL, LIHC, and KIRC, while DFNA5 expression levels had no significant effects in other cancer types. Furthermore, a higher expression level of DFNA5 based on Kaplan–Meier Plotter indicated that it was related to an unfavourable prognosis in gastric, ovarian and liver cancers. In brief, these analysis indicate that DFNA5 may be used as a biomarker for prognosis in THYM, HNSC, CHOL, LIHC, KIRC, and OVA.
One of key aspect of analysis is that DFNA5 expression is relate to diverse lymphocytes infiltration in cancer. The date indicate that DFNA5 expression levels are positively related to the infiltrative level of CD8 + T cells, macrophages, neutrophils, CD4 + T cells and dendritic cells in COAD, LIHC and LUAD. Furthermore, the relation of immune cells markers to DFNA5 expression indicates DFNA5 in the regulation of tumour immunology in COAD, LIHC and LUAD. M1 macrophages gene markers including INOS (NOS2), indicated no positive relation to DFNA5 expression in COAD and LIHC, but M2 macrophage markers including MS4A4A, VSIG4 and CD163, indicated positive relationships in COAD, LIHC and LUAD. These results reveal the potential polarization role of DFNA5 in tumour-associated macrophages (TAMs). In addition, the increase in DFNA5 expression was positive correlation with the expression of Tregs (FOXP3, CCR8, and STAT5B) in COAD, LIHC and LUAD, which proved that DFNA5 has the potential to trigger Tregs. Furthermore, T cell exhaustion markers, including CD274, TIM-3, LAG3, CTLA4, and PD-1, which induce T cell exhaustion [
6,
25‐
29], were increased in COAD, LUAD and LIHC. TIM3, PDCD1 and CD274, crucial surface proteins on exhausted T cells, were highly correlated with DFNA5 expression in COAD, LIHC and LUAD. The coexpression of DFNA5 and PDCD1 and CD274 indicated that DFNA5 could increase the expression levels of PDCD1 and CD274. The above relations may indicate that DFNA5 has a potential action to T cells in LIHC, COAD and LUAD. In brief, the data indicate that DFNA5 exert a key role in the regulation and recruitment of immune cells in LIHC, COAD and LUAD.
TurboID systems were used to identify instant or weak interaction proteins conveniently. Traditional methods are usually performed to investigate the interactive proteins with strong force, while it may be easy to omit the key proteins. In this research, we mainly focus on chemokines, interferon and immune checkpoint related proteins to investigate the role of DFNA5. IFIT3, IRAK1, CNOT7, EIF2AK2, TAB1, IRAK4, IFNGR1 can interactive with DFNA5, which may relate to promote immune cells infiltration and immune exhaustion. Chemokine and immune related proteins interact with DFNA5. EIF2AK2 could also exert in regulation of chemokine production. DFNA5 may regulate immune infiltration via EIF2AK2. IFNGR1 was related to the functions of PD-L1 expression and PD-1 checkpoint pathway, which may explain DFNA5 could regulate the PD-L1 and PD-1 expression levels and may improve antitumor therapy in combination to immune checkpoint inhibitors. Furthermore, Recent studies provide possible mechanisms that explain why DFNA5 expression correlates with immune infiltration and a poor prognosis. DFNA5 overexpression can cause significant enhancement of the secretion levels of many inflammatory factors, including interleukin MIP1α, MIP1β8, and IP-10, in the tumour tissue after chemotherapy treatment. These chemokines exert an important role in T-cell infiltration. Tumour cells can secrete many types of chemokines to recruit immune cells into tumour sites. In addition, DFNA activation in macrophages can also recruit NK cells into tumour tissue. However, while DFNA5 expression correlates with a poor prognosis in several cancers, it has no correlation with the prognosis of most cancers. This may be due to T cell exhaustion marker overexpression that triggers immune cells insensitive to tumour cells.
In summary, increased DFNA5 expression correlates with a poor prognosis in liver cancers and increased immune infiltration levels of CD8 + T cells, CD4 + T cells, macrophages, neutrophils and DCs of multiple cancers, especially in colon, liver and lung cancers. DFNA5 expression potentially induces polarity changes in tumour-associated macrophages, Tregs and T cell exhaustion. Therefore, DFNA5 likely plays an important role in immune cell infiltration and is a new potential target for cancer immunotherapy in the clinic.
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