Assessment the value of Pyroptosis-Associated Gasdermin family genes in hepatocellular carcinoma: A Multi-Omics Comprehensive Analysis

Abstract

Background: Hepatocellular carcinoma (HCC) is one of the common primary cancers of the liver worldwide and leading cause of mortality. Gasdermins (GSDMs) family genes play an important role in the regulation of the normal physiological processes and have been implicated in multiple diseases. However, little is known about the relationship between different GSDMs proteins and HCC. The aim of this study was to explore the potential relationship between the expression, prognosis, genetic variation and immune infiltration of GSDMs family genes and HCC. Methods: We used different bioinformatics common public databases such as GSCA, GEPIA, UALCAN, HPA, Kaplan-Meier Plotter, LinkedOmics, GeneMANIA, STRING, cBioPortal, TIMER and TISIDB to analyze the differential expression of the different GSDMs, prognostic value, genetic alterations, immune cell infiltration and their functional networks in HCC patients. Results: All the members of the GSDMs family exhibited elevated mRNA expression levels in LIHC compared to the normal tissues, while only GSDMB, GSDMD and GSDME showed enhanced protein expression. The mRNA expression of most GSDMs members was found to be elevated in HCC patients at stages I-III (clinical stage) compared to the normal subjects. The expression of GSDMD was correlated with OS and DSS of patients, whereas GSDME was correlated with OS, DSS and RFS of patients. Gene amplification was observed to be main mode of variation in members of the GSDMs family. KEGG pathway analysis showed that genes associated with different members of the GSDMs family were enriched in the pathways of S. aureus infection, intestinal immunity, ribosome and protein assembly, oxidative phosphorylation, osteoclast differentiation and Fc gamma (γ) R-mediated phagocytosis. In addition, expression of both GSDMA and GSDME were found to be correlated most significantly with infiltration of immune cells, while GSDMA and GSDME somatic cell copy number alteration (CAN) were correlated significantly with the infiltration of immune cells. All GSDMs were noted to be associated with distinct subtypes of immune cells, except GSDMC. Conclusions: Our findings have provided useful insights to better understand the roles and functions of GSDMs in HCC that can provide novel direction for developing therapeutic modalities for HCC, including immunotherapy.

Keywords: Bioinformatics analysis; GSDME; GSDMs; Hepatocellular carcinoma; Prognosis; immune infiltration.

Figure 1

mRNA expression of GSDMs in the different cancers (GSCA).

Figure 2

mRNA expression levels of GSDMs in LIHC. A: Scatter diagram (GEPIA), B: Box plot (GEPIA), C: Box plot (UALCAN).

Figure 3

The protein expression levels of GSDMs in LIHC (A: UALCAN, B: HPA, C: GSDME protein levels in 2 LIHC cell lines and human normal liver cell. * P<0.05, **P<0.01, ***P<0.001).

Figure 4

Correlation between GSDMs expression and the tumor stage in HCC patients (A: GEPIA, B: UALCAN).

Figure 5

The survival analysis of the patients of LIHC by analyzing the expression levels of GSDMs (Kaplan-Meier plotter).

Figure 6

Genetic alterations related to the GSDMs in LIHC. The total frequency of GSDMs alterations (A). Details of the genetic alterations of each member of GSDMs in each sample of LIHC (B). K-M comparing OS of patients with and without GSDMs alterations (C). KM plot comparing DFS in the patients with and without GSDMs alterations (D). KM comparing DSS in patients with and without GSDMs alterations (E). KM comparing PFS in the patients with and without GSDMs alterations (F).

Figure 7

Interaction network diagram of GSDMs family genes and the proteins in LIHC. (A) Interaction network map between proteins encoded by GSDMs (STRING). (B) PPI network of GSDMs (GeneMANIA).

Figure 8

Volcano map of the top 50 genes co-expressed with GSDMs (LinkedOmics).

Figure 9

Heat map of the top 50 genes co-expressed with GSDMs in LIHC (LinkedOmics).

Figure 10

KEGG pathway analysis of the various genes associated with GSDMs in LIHC (LinkedOmics).

Figure 11

Relationship between GSDMs expression in LIHC and immune infiltration (TIMER). Correlation between the abundance of immune cells and the expression of GSDMs (A). Effect of CNV of GSDMs on the distribution of the various immune cells (B). (*p < 0.05; **p < 0.01; ***p < 0.001).

Figure 12

Correlation between the expression of GSDMs in LIHC and the level of immune infiltration. (A) Correlation between the expression of GSDMs in LIHC and immune subtypes (C1: Wound healing; C2: IFN-γ dominant; C3: inflammatory; C4: lymphocyte depleted; C5: immunologically quiet; C6: TGF- β dominant). (B) Association of GSDMs expression with different molecular subtypes of HCC (TISIDB).