ELSEVIER
International Immunopharmacology
journal homepage: www.elsevier.com/locate/intimp
Z
International
Immunopharmacology
Pan-cancer analysis reveals the expression, genetic alteration and prognosis of pyroptosis key gene GSDMD
Shizheng Qiu ª, Yang Hua, ”, Siqing Dong
a School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
b Beidahuang Industry Group General Hospital, Harbin, China
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ARTICLE INFO
Keywords:
Pyroptosis
GSDMD
Cancer Genetic alteration
Prognosis Immune infiltration
ABSTRACT
Background: Gasdermins (GSDMs)-mediated pyroptosis is widely involved in activating anti-tumor immunity and suppressing tumor growth. However, whether gasdermin D (GSDMD)-mediated pyroptosis affects patient prognosis in pan-cancer remains unknown.
Methods: We performed analyses of the RNA expression, genetic alteration, prognosis and immune infiltration of GSDMD in pan-cancer. In order to explore the relationship between pyroptosis and tumors, we calculated the correlation between GSDMD and pyroptosis key genes in pan-cancer. We also investigated the enrichment pathway of GSDMD-related genes.
Results: GSDMD was differentially expressed in the vast majority of cancer, and could be used as a prognostic marker in adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), skin cutaneous melanoma (SKCM) and rectum adenocarcinoma (READ). Strong evidence indicated the significant correlation of GSDMD with almost all immune checkpoints and immune cells. Pyroptosis-related genes strongly associated with GSDMD in ACC, KIRC, LGG, LIHC and SKCM, suggesting that GSDMD-mediated pyroptosis might play a critical role in the five cancers.
Conclusion: All the evidence supported the potential role of GSDMD-mediated pyroptosis in cancer. Our results provided new insights into GSDMD as a prognostic marker and potential therapeutic target for cancer.
1. Introduction
Programmed cell death (PCD) is widely involved in tumorigenesis and tumor progression in the tumor microenvironment [1,2]. Generally, the pathways of programmed cell death include apoptosis, necroptosis, autophagy, ferroptosis, and necrosis [1,3-7]. Recent evidence highlights that pyroptosis, marked by gasdermin D (GSDMD) and its pore-forming activity, is a novel form of programmed cell death [3,8,9]. In the clas- sical pathway of pyroptosis, NF-KB is activated to induce the expression of many precursor proteins of inflammatory bodies [10]. Inflammatory bodies usually contain a cytoplasmic pattern recognition receptor (PRR), an adaptor protein and pro-caspase-1. Subsequently, caspase-1 cleaves the amino-terminal fragment of the key protein GSDMD to oli- gomerize it and form pores in the cell membrane, and contents are released through these pores, causing inflammatory reaction [7-10]. Activated caspase-1, on the other hand, cleaves cytokines pro-inter- leukin-16 (pro-IL-16) and pro-interleukin-18 (pro-IL-18) to form active IL-16 and IL-18, which are released extracellular to recruit inflammatory
cells to aggregate and amplify the inflammatory response [10,11].
Previously, gasdermins (GSDMs) were considered to be involved in cell proliferation and differentiation, and regulated the apoptosis of gastrointestinal epithelium [12,13]. GSDM family were later found to be involved in inhibiting the proliferation of a variety of cancer cells, and growing evidence indicated that GSDMD-mediated pyroptosis was related to gastric cancer, lung cancer, hepatocellular carcinoma, skin cancer and breast cance [14-19]. For instance, the low expression of GSDMD could inhibit pyroptosis, accelerate the expression of Cdk2/ cyclin A2 complex, promote the transition from S phase to G2 phase, and accelerate the proliferation of gastric cancer cells [17,20]. However, in the vast majority of cancer, the expression and prognosis of GSDMD remains unknown.
Thanks to the development of microarray technology, the expression of thousands of genes in dozens of cancers and tissues has been analyzed. Herein, we utilized the Cancer Genome Atlas (TCGA; https://portal.gdc. cancer.gov/) and the Genotype-Tissue Expression (GTEx; https://www. gtexportal.org/home/) data to analyze the expression and mutation of
* Corresponding author. E-mail address: huyang@hit.edu.cn (Y. Hu).
https://doi.org/10.1016/j.intimp.2021.108270
A
10
GSDMD expression
ns
*
ns
.
The expression of GSDMD Log2 (TPM+1)
8
-
.
6
Normal
.
..
1
Tumor
.
O
4
·
…
2
0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
B
DNA methylation (RRBS): GSDMD
1
0.8
0.6
0.4
0.2
1
0
soft_tissue(20)
Ewings_sarcoma(12)
neuroblastoma(17)
lung_small_cell(54)
endometrium(28)
medulloblastoma(4)
stomach(39)
breast(60)
ovary(55)
lung_NSC(136)
prostate(8)
lymphoma_Hodgkin(13)
lymphoma_Burkitt(11)
chondrosarcoma(4)
glioma(66)
CML(15)
urinary_tract(28)
liver(29)
melanoma(63)
colorectal(63)
lymphoma_DLBCL(18)
esophagus(27)
mesothelioma(11)
bile_duct(8)
kidney(37)
osteosarcoma(10)
B-cell_lymphoma_other(16)
pancreas(46)
leukemia_other(5)
B-cell_ALL(13)
AML(39)
T-cell_ALL(16)
other(8)
giant_cell_tumour(3)
multiple_myeloma(29)
T-cell_lymphoma_other(11)
upper_aerodigestive(33)
thyroid(12)
A
Liver cancer
Normal
B
Renal cancer
Normal
E
C
Skin cancer
Normal
D
Glioma
Normal
GSDMD, a key gene of pyroptosis, in 30 different normal human tissues and 33 different tumors types, and determined the prognostic value of GSDMD [21]. We did a preliminary exploration of the role of GSDMD- mediated pyroptosis in cancer genesis and metastasis, as well as its potential as a therapeutic target.
2. Materials and methods
2.1. Gene expression analysis
We leveraged TCGA (cancer samples and healthy samples) and GTEx (healthy samples) from UCSC XENA (https://xenabrowser.
A
KIRC
KIRC
ACC
ACC
1.0
GSDMD
1.0
GSDMD
1.0
GSDMD
1.0
GSDMD
Low
Low
High
High
Low
Low
0.8
High
Survival probability
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
Overall Survival HR = 1.40 (1.04-1.89)
0.2
Overall Survival HR = 1.40 (1.04-1.89)
0.2
Overall Survival HR = 2.61 (1.19-5.71)
0.2
Overall Survival
HR = 2.54 (1.20-5.38)
0.0
P = 0.029
0.0
Log-rank P = 0.028
0.0
P = 0.017
0.0
Log-rank P = 0.013
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
Time (months)
Time (months)
Time (months)
Time (months)
LGG
LGG
LIHC
LIHC
1.0
GSDMD
1.0
GSDMD
1.0
GSDMD
1.0
GSDMD
Low
Low
Low
Low
High
High
High
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
Overall Survival HR = 2.29 (1.63-3.23)
0.2
Overall Survival HR = 2.41 (1.69-3.44)
0.2
Overall Survival HR = 1.42 (1.00-2.01)
0.2
Overall Survival HR = 1.42 (1.01-2.00)
0.0
Log-rank P < 0.001
0.0
P < 0.001
0.0
P = 0.048
0.0
Log-rank P = 0.047
0
50
100
150
200
0
50
100
150
200
0
30
60
90
0
30
60
90
120
Time (months)
Time (months)
Time (months)
Time (months)
READ
READ
SKCM
SKCM
1.0
GSDMD
1.0
GSDMD
1.0
GSDMD
GSDMD
Low
Low
1.0
High
Low
Low
0.8
High
0.8
0.8
High
Survival probability
Survival probability
Survival probability
Survival probability
0.8
High
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
Overall Survival
0.2
Overall Survival HR = 0.42 (0.18-1.01)
0.2
Overall Survival HR = 0.63 (0.48-0.83)
0.2
HR = 0.43 (0.20-0.92)
Overall Survival
HR = 0.64 (0.49-0.83)
0.0
Log-rank P = 0.044
0.0
P = 0.051
0.0
P = 0.001
0.0
Log-rank P = 0.001
0
25
50
75
100
125
0
25
50
75
100
125
0
100
200
300
0
100
200
300
Time (months)
Time (months)
Time (months)
Time (months)
B
LIHC
1.0
ACC
KIRC
1.0
1.0
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.4
0.4
0.4
GSDMD
GSDMD
0.2
GSDMD
0.2
1-Year (AUC = 0.740)
0.2
3-Year (AUC = 0.708)
1-Year (AUC = 0.614)
1-Year (AUC = 0.569)
3-Year (AUC = 0.564)
3-Year (AUC = 0.520)
0.0
5-Year (AUC = 0.714)
5-Year (AUC = 0.604)
0.0
5-Year (AUC = 0.542)
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.40.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
SKCM
LGG
READ
1.0
1.0
1.0
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.4
0.4
0.4
0.2
GSDMD
0.2
GSDMD
1-Year (AUC = 0.388)
1-Year (AUC = 0.781)
0.2
GSDMD
1-Year (AUC = 0.513)
3-Year (AUC = 0.414)
3-Year (AUC = 0.754)
3-Year (AUC = 0.408)
0.0
5-Year (AUC = 0.381)
0.0
5-Year (AUC = 0.680)
0.0
5-Year (AUC = 0.269)
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
net/datapages/) to determine the differential expression of GDSMD in pan-cancer [21]. The statistically significant difference was defined to be * P < 0.05; ** P < 0.01; *** P < 0.001. We also obtained DNA methylation data of different cell lines from the Cancer Cell Line Ency- clopedia (CCLE, https://portals.broadinstitute.org/ccle/) to explore the expression level of GSDMD in different cancer cell lines.
2.2. Immunohistochemical staining in cancer patients
We presented images of protein expression in cancer in the Pathology Atlas (https://www.proteinatlas.org/). The protein expression was derived from antibody-based protein profiling using immunohisto- chemistry. Information about cancer patients in the Human Protein
A
Activated B cell
Activated CD4 T cell
Activated CD8 T cell
Activated dendritic cell
CD56bright natural killer cell
CD56dim natural killer cell
Central memory CD4 T cell
Central memory CD8 T cell
Effector memeory CD4 T cell
Effector memeory CD8 T cell
Eosinophil
Gamma delta T cell
Immature B cell
Immature dendritic cell
Macrophage
Mast cell
MDSC
Memory B cell
Monocyte
Natural killer cell
Natural killer T cell
Neutrophil
Plasmacytoid dendritic cell
Regulatory T cell
T follicular helper cell
Type 1 T helper cell
Type 17 T helper cell
Type 2 T helper cell
GBM
OV
LUAD
LUSC
PRAD
UCEC
BLCA
TGCT
ESCA
PAAD
KIRP
LIHC
CESC
SARC
BRCA
MESO
COAD
STAD
SKCM
CHOL
KIRC
THCA
HNSC
LAML
READ
LGG
DLBC
KICH
UCS
ACC
PCPG
UVM
correlation
-log10(p value)
-1 -0.5 0 0.5
0
0.75
1.5
2.2
3
B
Activated B cell
Activated CD4 T cell
Activated CD8 T cell
Activated dendritic cell
CD56bright natural killer cell
CD56dim natural killer cell
Central memory CD4 T cell
Central memory CD8 T cell
Effector memeory CD4 T cell
Effector memeory CD8 T cell
Eosinophil
Gamma delta T cell
Immature B cell
Immature dendritic cell
Macrophage
Mast cell
MDSC
Memory B cell
Monocyte
Natural killer cell
Natural killer T cell
Neutrophil
Plasmacytoid dendritic cell
Regulatory T cell
T follicular helper cell
Type 1 T helper cell
Type 17 T helper cell
Type 2 T helper cell
GBM
OV
LUAD
LUSC
PRAD
UCEC
BLCA
TGCT
ESCA
PAAD
KIRP
LIHC
CESC
SARC
BRCA
MESO
COAD
STAD
SKCM
CHOL
KIRC
THCA
HNSC
LAML
READ
LGG
DLBC
KICH
UCS
ACC
PCPG
UVM
correlation
-log10(p value)
-1 -0.5 0 0.5 1 0 0.75 1.5 2.2 : 3
Atlas was shown in Table S1.
2.3. Genetic alteration analysis of GSDMD
The pore-forming ability of pyroptosis would be impaired when the N-terminal fragment of GSDMD underwent a loss of function (LOF) mutation [10]. We performed an analysis of GSDMD genetic alteration
frequency and mutation type by using cBioPortal (https://www.cbiopo rtal.org/).
2.4. Survival prognosis analysis
We analyze the overall survival of patients in 33 different cancers to reveal the impact of GSDMD on the prognosis of cancer. The patients
A
GASPI
IL18
B
2
IL18
ACC
cor - - 8.500
GOT - - 0. 54G
DOF - - 0.220
- 2159-01
p - 6.04+-02
KIRC
BOF - - 0.543
cor - 0.382
0.9
E
2
Expression Level (log2 TPM)
Expression Level (1002 TPM)
*
.
.
+
La
cer = 4.000
Mtiel cor - 0.152
partial.com - 0.795
Daniel cor - 0.268
Cor - 0.144
*portaloor - 0.21
partiel.cor # -0.071
sanfol gor - 0.097
pirtialoer = D.106
p= 2.180-02
p = 1.230-02
partial cor = 4.083
p - 4.246-02
p = 6.228-01
5.0
2.5
0.4
0.8
1.0
4
6
Expression Level (log2 TPM)
7.5
00
1.5
20
0
0.78
1./00
0
Expression Level (log2 TPM)
C
purity
GASPI
IL18
C4SP4
D
purty
CASPI
IL18
CASP4
CASPS
OD-
LGG
GOT = - 0.3.42
CHI - 4.147
GOT = - 0.1:21
DOP - - 0.535
cor - 4 340
cor - - 0.284
LIHC
p - 1.382-12
- 2.916-12
P - 4.378-27
P- 2.396-11
4
E
Expression Level (log2 TPM)
Expression Level (og2 TPMI
>
P
portfloor = 0.1:25
partial cor 1 445
perpol.car - 0.062
portinl cor - 0.043
pertal cor - 9.104
patoloir - 0.119
p = 2.474 01
.
Dirteloer - D.127
P- 2.450-28
2
GSOMID
0.50
0.75
1.00
0.5
1.5
20
0.25
0.50
1.00
E
Expression Level (log2 TPM)
Expression Level (log2 TPM)
6
purity
CASPI
L1B
CASPA
DASPS
F
CASPI
IL16
CASPA
CASPS
eo = - 0.237
COT -0.264
cor #: - 0.305
cor a -4.447
car w -0.481
READ
SKCM
0 -5.14-66
0 - 4 216-29
4
¥1
E
Expression Level (Jog2 TPM)
Expression Level (Jog2 TPM]
¿
- 2.420-05
partsacer - 6.184
partialcor - 001
partial cor - 0 161
parkei car - 4 50/1
partial cor - 0.694.
p = 8.850-02
B - N.870 01
p - 2.68e-01
55
8-
4.5
+
0.25
0.50
1.75
1.00
25
7.5
25
Expression Lovel (Jog2 TPM)
10.0
0.25
0.75
1.00
25
5.0
7.5
0
Expression Level (log2 TPM)
were divided into two groups with high GSDMD expression and low GSDMD expression. Hazard ratio with 95% confidence intervals and P value are calculated using cox regression model and log rank test, respectively. The survminer package (version 0.4.9) was used for visu- alization, and the survival package (version 3.2-10) was used for sta- tistical analysis of survival data. We further explored the influence of gender, mutation degree and clinical stage on the overall survival of GSDMD. In order to test the prognostic effect of the model with GSDMD as a prognostic marker, we calculated and visualized the ability of GSDMD to predict survival [22]. The ROC curve was executed and drawn using timeROC (version 0.4) and ggplot2 (version 3.3.3) [22].
2.5. Immune infiltration analysis
We performed an analysis of the association between GSDMD expression and a series of immune checkpoints, microsatellite instability (MSI), tumor mutational burden (TMB), immune microenvironment, immune cell score, immune pathways. Among them, immune cell infiltration and key cell markers were directly obtained from the TIMER (Tumor IMmune Estimation Resource) database (https://cistrome.sh inyapps.io/timer/), and other aspects were calculated by Pearson cor- relation coefficient [23].
2.6. Correlation analysis of pyroptosis
In order to explore whether GSDMD-mediated pyroptosis had an effect on cancers, we analyzed the correlation between GSDMD and the genes involved in the pyroptosis pathway in pan-cancer [23]. Two recognized pathways for GSDMD-mediated pyroptosis, namely classical pathway and non-classical pathway [10,15]. The key genes of classical pathway include CASP1, GSDMD, IL1B, IL18, and the key genes of non- classical pathway include CASP4, CASP5, GSDMD [10]. GSDMD-medi- ated pyroptosis might play a critical role in a cancer in which GSDMD was highly associated with all the genes responsible for pyroptosis. The expression scatterplots between GSDMD and other key genes in eight cancer types were shown, together with the Spearman’s rho value and estimated statistical significance [23].
2.7. GSDMD-related gene enrichment analysis
We first explored the proteins that interact with GSDMD using Search Tool for the Retrieval of Interacting Genes (STRING) (https://string-db. org/) [24]. We then calculated 50 genes with similar expression pattern with GSDMD in each cancer from Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/index.html), and con- ducted an over-representation enrichment analysis in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), respec- tively [25,26]. Bonferroni correction was performed for enrichment analysis, and statistically significant association is defined to be P < 0.05.
3. Results
3.1. GSDMD expression in normal and tumor tissues
GSDMD was differentially expressed in most cancers, and was highly expressed in bladder urothelial carcinoma (BLCA), breast invasive car- cinoma (BRCA), cholangio carcinoma (CHOL), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), liver he- patocellular carcinoma (LIHC), pancreatic adenocarcinoma (PAAD), skin cutaneous melanoma (SKCM), testicular germ cell tumors (TGCT), thymoma (THYM), and lowly expressed in adrenocortical carcinoma (ACC), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), kidney chromophobe (KICH), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), pheochromocytoma and paraganglioma (PCPG), prostate adeno- carcinoma (PRAD), rectum adenocarcinoma (READ), stomach adeno- carcinoma (STAD), thyroid carcinoma (THCA), uterine carcinosarcoma (UCS) (Fig. 1A). Moreover, we used immunohistochemistry to validate GSDMD expression. Compared with normal tissues, GSDMD was highly expressed in KIRC, LGG and LIHC, but differential expression of GSDMD was not significant in SKCM (Fig. 2). Finally, we investigated the genetic alteration of GSDMD of 10953 patients and 10967 controls in 32 studies and found that the genetic alteration frequency of GSDMD was as high as
PPFIA1
A
6%
CASP4
GSDMD
IL18
NLRC4
CASP5
NLRP3
MLKL
AIM2
CASP1
MEFV
B
GSDMD-related gene enrichment
Proteasome
mRNA 3’-UTR AU-rich region binding
threonine-type peptidase activity
threonine-type endopeptidase activity
BP
peptidase complex
CC
MF
endopeptidase complex
KEGG
proteasome complex
regulation of mRNA catabolic process
regulation of RNA stability
regulation of mRNA stability
0
1
2
3
4
-Log10 (p.adjust)
3.2. Prognostic significance of GSDMD in six human tumors
Under the cox regression model and log rank test, GSDMD could be used as a potential prognostic marker for six cancers. High expression of GSDMD improved the survival time of patients with READ and SKCM (Fig. 3A). In KIRC, ACC, LGG and LIHC, high expression of GSDMD led to poor prognosis (Fig. 3A). In addition, we divided the samples into sub- groups by genders, mutation burden and stages. Interestingly, high expression of GSDMD in male patients was a risk factor for KIRC and LIHC, but a protective factor in female (Table S2). GSDMD expression had a good prognosis in LIHC with high mutation burden (Table S3).
Importantly, in some cancer, GSDMD expression had the dual function of promoting tumor and inhibiting tumor. For instance, the expression of GSDMD might be an inhibitor of death in the early stage of cancer, but it turned into a risk factor in the late stage in KIRC (Table S4).
Using the GSDMD model to predict the survival of tumor patients had the best effect in ACC and LGG, with the one-year AUC reaching 0.740 and 0.781, respectively (Fig. 3B). Overall, the short-term forecasting effect was better than the long-term forecast (Fig. 3B).
3.3. The correlation of GSDMD expression with immune infiltration
We conducted the correlation of GSDMD expression with tumor immune checkpoints and immune infiltration levels in 33 different
tumors types. GSDMD was associated with immunotherapy markers such as microsatellite instability (MSI), tumor mutational burden (TMB) and immune cell markers in a large number of cancers (Fig. 4A, Fig. S4). Among them, in KIRC, GSDMD was significantly correlated with MSI and TMB at the same time (PMSI = 0.0023, PTMB = 0.0089). Immune infil- tration analysis highlighted the interaction between GSDMD and tumor microenvironment, especially with CD8+ and CD4+ T cell (Fig. 4B, Fig. S4). In BLCA, SKCM, LGG, PCPG and UVM, GSDMD was associated with almost all immune cell infiltration levels (Fig. 5). The occurrence of pyroptosis usually accompanied by recruitment of large numbers of Tumor-infiltrating lymphocytes (TIL) and macrophages, which associ- ated with GSDMD strongly in tumors with significant prognosis (Fig. 4B) [27].
3.4. GSDMD-mediated pyroptosis in cancer
GSDMD was strongly associated with five pyroptosis markers in LGG and SKCM concurrently, suggesting that GSDMD-mediated pyroptosis might be present (Fig. 5). GSDMD was associated with CASP4 and CASP5 in KIRC, ACC and LIHC, suggesting that the non-classical pyroptosis pathway might be present in these cancers (Fig. 5).
3.5. GSDMD-related gene enrichment analysis
Most of the GSDMD-related genes were correlated with pyroptosis, which has been previously verified in biological experiments (Fig. 6). Therefore, we further searched for the top 50 genes that were most significantly associated with GSDMD and observed the pathways they were involved in. We found that GSDMD-related genes were mainly involved in regulation of mRNA stability, proteasome and threonine - type peptidase activity, suggesting that a variety of enzymes played a key role in regulating GSDMD lysis and cell membrane perforation in pyroptosis (Fig. 6).
4. Discussion
GSDMD mediates pyroptosis, and is widely found in immune cells, placenta, oesophagus and gastrointestinal tract epithelium [10,28]. Meanwhile, it is widely believed that GSDMD is a tumor suppressor gene that plays play a part in oesophageal and gastric, melanoma, pancreatic and prostate cancers [10,11]. However, whether GSDMD-associated pyroptosis affects patient prognosis in pan-cancer remains unknown.
In this study, we found that GSDMD was significantly differentially expressed in the vast majority of cancers compared with normal sam- ples, and survival of patients with six types of cancers was significantly associated with GSDMD. Importantly, strong evidence highlighted the significant correlation of GSDMD with almost all immune checkpoints and immune cells. In previous studies, glioma, kidney cancer, skin cutaneous melanoma and liver cancer have been shown to be related to pyroptosis [10,11,15,29-32]. For ACC and READ, the pathway that pyroptosis mediated tumors was novel. However, it should not be ignored that GSDMD might play different roles in some tumors or in different stages of tumors. For instance, in KIRC and READ, GSDMD protected patients from death in the pre tumor stage, but increased the risk of poor prognosis in the fourth tumor stage. It was consistent with the potential dual role of pyroptosis in tumorigenesis and metastasis. Under the stimulation of danger signals, inflammatory bodies assemble, activate caspases, cleave GSDMD, trigger pyroptosis and induce immune response. In this way, tumor proliferation could be inhibited by the immune response caused by pyroptosis and release of cytokines such as IL16 and IL18. However, excessive pyrosis would lead to sepsis, cytokine release syndrome (CRS), severe inflammation and tissue damage, which were the risk factors of tumorigenesis [10,33]. Therefore, the selective activation of GSDMD in tumor cells rather than in normal cells is essential for the development of cancer therapies that induce pyroptosis.
However, we acknowledged certain limitations in our study. In LGG
and SKCM, GSDMD was strongly associated with both of the key genes of pyroptosis classical and non-classical pathways, suggesting that GSDMD-mediated pyroptosis might play a critical role in the two can- cers. To our best knowledge, the mechanism of pyroptosis involved in tumorigenesis and metastasis is still unclear, so we could hardly verify our conclusion from the known pathways [10,11,15]. In addition, some tumor samples were too small to form a statistically significant analysis.
In conclusion, we confirmed that GSDMD could be used as a prog- nostic marker in six cancers, and might promote or inhibit tumor pro- liferation and metastasis by mediating pyroptosis. We provided theoretical support and new insights for the treatment of tumors through the pyroptosis pathway targeting GSDMD.
CRediT authorship contribution statement
Shizheng Qiu: Conceptualization, Investigation, Visualization, Writing - original draft, Writing - review & editing. Yang Hu: Conceptualization, Writing - review & editing, Supervision. Siqing Dong: Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
Shizheng Qiu and Siqing Dong contributed equally to this manu- script. This work was supported by the National Key R&D Program of China (2017YFC1201201, 2018YFC0910504 and 2017YFC0907503), the Natural Science Foundation of China (61801147 and 82003553) and Heilongjiang Postdoctoral Science Foundation (LBH-Z6064). We acknowledge TCGA, CCLE and GTEx for providing RNA-seq data.
Appendix A. Supplementary material
Supplementary data to this article can be found online at https://doi. org/10.1016/j.intimp.2021.108270.
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