Abstract
Objectives: Hematopoietic cell signal transducer (HCST) participates in the activation of phosphatidylinositol 3 kinase-dependent signaling pathway and in the natural killer (NK) and T cell responses, which affect cell survival and proliferation. Here, the values of HCST in kidney renal clear cell carcinoma (KIRC) are analyzed. Methods: We used GEO, TCGA, GEPIA, UALCAN and TIMER databases to profile the expression of HCST in KIRC tissues, and define its clinical roles. The biological functions and signaling mechanisms modulated by HCST and its co-expressed genes were identified and analyzed via the GO and KEGG databases. On the other hand, the potential value of HCST expression in KIRC immunity was explored using the TIMER and GEPIA databases. Results: Our analysis demonstrated that HCST is significantly overexpressed in KIRC tissues. The upregulation of HCST is associated with clinical stage, tumor grade, tissue subtype and poor prognosis of KIRC patients. Increased HCST expression might be involved in signaling pathways such as antigen processing and presentation, cell adhesion molecules, cytokine-cytokine receptor, chemokine signaling pathway, T cell receptor signaling pathway, FC gammar mediated phagocytosis and B cell receptor signaling pathway. In addition, the expression of HCST was significantly correlated with the levels of KIRC purity, B cells, CD8+ T Cell, CD4+ T cells, macrophages, neutrophils and dendritic cells (DC). Furthermore, the HCST expression is associated with levels of immune infiltration B cells, CD8+ T Cell, CD4+ T cells, macrophages, neutrophils and DC. Conclusions: Our data demonstrated that HCST could be a potential prognostic biomarker, and is related to the immune infiltration in KIRC.
Keywords: HCST, KIRC, poor prognosis, immune infiltration
Introduction
Kidney renal clear cell carcinoma (KIRC) is a common tumor of urinary system, which accounts for about 80% of the renal cell carcinoma (RCC) [1]. Presently, radical renal surgery is the mainstay treatment option for patients with KIRC. However, high blood metastasis rate presents a major hurdle leading to poor prognosis despite improvement associated with the use of radiotherapy, chemotherapy or INF-α therapy. In recent years, targeted therapy has attracted major research interests, as well as for clinical applications, and thus, improves the prognosis of cancer patients [2-4]. Therefore, discovery of new and effective prognostic biomarkers and immunotherapy targets could improve the therapeutic efficacy and long-term prognosis of KIRC patients.
Hematopoietic cell signal transducer (HCST), also referred to as DNAX-activating protein 10 (DAP10), has been shown to participate in cancer progression and immune regulation [5-8]. For instance, Sakaguchi et al. demonstrated that RAGE-DAP10 heterodimer could activate Akt and endogenous overexpression of DAP10, which leads to cell growth and survival via the RAGE-DAP10 interaction. In contrast, interference with the expression of DAP10 could activate RAGE through S100A8/A9 to block Akt phosphorylation leading to increased cell apoptosis [5]. Hernández-Caselles et al. reported that CD33 could be an inhibitory receptor in regulating the NKG2D/DAP10 cytotoxic signaling pathway, which is involved in self-tolerance, tumor and infected cell recognition [6]. Li et al. demonstrated that PD1-DAP10/NKG2D, a new type of dual targeting chimeric receptor (DTCR), could define the damage ability in solid tumor cells through activation of NKG2D receptor. The study showed that DTCR retroviral transduction increases the expression of PD1 and NKG2D on the surface of NK92 cells, which could enhance the cytotoxicity of human gastric cells, SGC-7901. Besides, DTCR stimulation was shown to increase the expression of TNF-α and TRAIL, and then trigger apoptosis of SGC-7901 cells [8]. Qi et al. reported that CSF1R and HCST have higher predictive diagnostic value compared to PDL1 in NSCLC. CSF1R and HCST were positively correlated with PDL1 expression and CD8+ T cell infiltration in the immune microenvironment and might improve the prognosis of patients with lung squamous cell carcinoma [9]. To date, the role and value of HCST in KIRC remains scanty, but the HCST was considered an immune-related factor. Here, we aimed to evaluate the prognostic value and potential mechanism of HCST in the progression of KIRC, and to investigate the potential relationship with KIRC immune cell infiltration.
Materials and methods
TCGA database
The KIRC transcriptome and clinical data were download from the Cancer Genome Atlas (TCGA) database. The transcriptome data included 72 normal kidney and 539 KIRC tissues. The 72 normal kidney tissues were matched with 72 KIRC tissues and each pair belonged to the same KIRC patient. On the other hand, the downloaded clinical data included clinicopathological characteristics and prognostic information of 537 KIRC patients.
GEPIA database
Using the Gene expression profiling interactive analysis (GEPIA) database, we analyzed and profiled the expression of HCST in both kidney and KIRC tissues with the data from the TCGA and GTEx databases. We then analyzed the relationship between the HCST expression and clinical stage, overall survival (OS) or disease-free survival (DFS) of the KIRC patients based on the KIRC data from the TCGA database. Moreover, the relationship between the expression of HCST in the KIRC tissues and KIRC immune cell markers was assessed using the correlation analysis module.
UALCAN database
The UALCAN database was used to explore the expression of HCST in the KIRC and normal tissues. Besides, we analyzed the relationship between HCST gene expression and age, race, gender, clinical stage, tumor grade, lymph node metastasis, subtype, and OS of the KIRC patients.
GEO database
The Gene Expression Omnibus (GEO) database contains sequencing data of tissues, cells and blood samples from cancer patients. The data include gene expression, DNA methylation or mutation status. The GSE781 and GSE11151 datasets in the GEO database were used to verify the expression of HCST in the KIRC tissues.
TIMER database
The expression of HCST in pan-cancer tissues were analyzed using the differential expression module in the Tumor Immune Estimation Resource (TIMER) database, while the correlation between the expression of HCST and the level of KIRC immune infiltrating cells was evaluated in the gene module. In addition, we analyzed the correlation between the HCST expression and KIRC somatic mutations in the somatic copy number alterations (SCNA) module while the relationship between the HCST expression and KIRC immune cell markers was assessed using the correlation analysis module.
The potential value of HCST in the prognosis of KIRC patients
The clinical data of 537 KIRC patients in the TCGA database were merged with the HCST gene expression and profiled against the prognostic and clinicopathological characteristics of the KIRC patients. Kaplan-Meier survival analysis was used to analyze the effect of high- or low-expression of the HCST on the prognosis of KIRC patients. Besides, univariate Cox regression analysis was used to explore the effects of age, gender, clinical stage, tumor grade, T stage, lymph node metastasis, distant metastasis and HCST expression level on the prognosis of the KIRC patients. Thereafter, multivariate Cox regression analysis was employed to explore the effects of age, clinical stage, tumor grade, T stage, distant metastasis and HCST expression level on the prognosis of the KIRC patients.
The biological functions and signaling pathways of HCST co-expression genes
We employed the Pearson correlation analysis and R limma package to analyze the genes that co-express with HCST in the 539 KIRC tissues from the TCGA database. The genes with the P<0.001 and r>0.5 or r<-0.5 were considered as highly co-expressed genes in the HCST. Gene ontology (GO) annotation included biological process (BP), cell composition (CC) and molecular function (MF) [10,11]. We entered the HCST co-expressed genes into the DAVID database to explore the biological functions and signaling pathways that might be mediated by the HCST co-expressed genes using the GO and Kyoto Encyclopedia of Genes and Genome (KEGG).
Gene set enrichment analysis
Gene set enrichment analysis (GSEA) is commonly used to explore the signaling pathways that are modulated by a single gene [12]. In the 539 KIRC tissues downloaded, we grouped high- and low-expression groups according to the median HCST gene expression, and then the signaling mechanism involved was analyzed in the HCST overexpression group via the GSEA (version 4.0.1). This run was executed for 1000 cycles. The screening index was set at a NOM P<0.05.
Construction of the PPI network and screening of the hub genes
Using the String database, we analyzed the relationship between proteins of the HCST co-expressed genes. A combined score >0.9 was regarded as significant statistical significance [10]. Cytoscape 3.6.1 software was used for visual analysis while CytoHubba plug-in MCC method was used to screen the top 10 genes which could be defined as the hub genes.
Statistical analysis
A t-test was used to explore the difference of HCST expression between the kidney and the KIRC tissues in the data obtained from the TCGA and GEO (GSE781 and GSE11151). Kaplan-Meier survival analysis as well as the univariate and multiple Cox regression analyses were used to analyze the relationship between the HCST expression and poor prognosis of the KIRC patients. A P<0.05 was considered to be statistically significant.
Results
HCST is highly expressed in the KIRC tissues
Our analysis using the TIMER database demonstrated that HCST is differentially expressed in pan-cancer tissues (Figure S1). HCST is significantly overexpressed in ESCA, HNSC, KIRC and KIRP tissues and downregulated in BLCA, COAD, KICH, LUAD, LUSC and READ tissues.
Data from the TCGA, GEO, GEPIA and ULACAN databases showed that HCST is significantly overexpressed in the KIRC tissues (Figures 1 and S2). In the TCGA database, there was significant upregulation of the HCST expression in both the KIRC unpaired and matched tissues (Figure 1A). Similarly, the findings from the GSE781 and GSE11151 data sets in the GEO database showed that HCST was highly expressed in the KIRC tissues (Figure 1B). In the GEPIA database, HCST was highly expressed in the KIRC tissues derived from the TCGA and GTEx databases (Figure 1C). In addition, HCST was highly expressed in the KIRC tissues in the ULACAN database (Figure S2).
Figure 1.
HCST is overexpressed in KIRC tissues in multiple databases. A. TCGA unmatched and matched tissues; B. GEO GSE11151 and GSE781 tissues; C. TCGA and GTEx tissues. Note: *P<0.05; **P<0.01; ***P<0.001; TCGA, the Cancer Genome Atlas; GEO, Gene Expression Omnibus.
The expression of HCST is associated with the clinical stage, tumor grade and tissue subtype of the KIRC patients
The expression of HCST was correlated with the clinical stage, tumor grade and tissue subtype of the KIRC patients (Figure 2). In the GEPIA database, the expression of HCST was associated with the clinical stage of the KIRC patients (Figure 2A). Besides, subgroup analysis showed that the clinical stage (stage 1 vs stage 2, P=4.910500E-02; stage 1 vs stage 3, P=1.967110E-04; stage 1 vs stage 4, P=1.65110000005519E-06), tumor grade (grade 1 vs grade 3, P=1.302150E-02; grade 1 vs grade 4, P=2.24739999999946E-05; grade 2 vs grade 3, P=4.350100E-04; grade 2 vs grade 4, P=3.06779999958984E-07; grade 3 vs grade 4, P=9.975900E-04) and histological subtype (ccA subtype vs ccB subtype, P=1.233850E-02) of the KIRC patients were related to the expression level of HCST (Figure 2B, 2C).
Figure 2.
HCST expression is associated with clinical stage, tumor grade and histological subtype of KIRC patients. A. Clinical stage of GEPIA database; B-D. Clinical stage, tumor grade and histological subtype of UALCAN database; E, F. Overall survival in TCGA and GEPIA databases; G. Disease free survival in GEPIA database. Note: *P<0.05; **P<0.01; ***P<0.001; GEPIA, Gene expression profiling interactive analysis; GEPIA, Gene expression profiling interactive analysis; TCGA, the Cancer Genome Atlas.
HCST expression is associated with the prognosis of KIRC patients
The data from the Kaplan-Meier survival analysis showed that the KIRC patients with high HCST expression had shorter OS (Figure 2E). The analysis of data from the GEPIA and UALCAN databases showed that elevated HCST expression was associated with shorter OS and DFS of the KIRC patients (Figures 2F, 2G and S3A). In addition, the expression of HCST was correlated with OS-related gender, race and tumor grade of the KIRC patients. However, the difference between the HCST expression level and OS-related races was not significant (Figure S3B-D). The univariate Cox regression analysis showed that age, clinical stage, tumor grade, T stage, distant metastasis as well as HCST expression level influenced the poor prognosis in the KIRC patients (Table 1). In contrast, the multivariate Cox regression analysis showed that age, clinical stage and tumor grade were independent factors influencing the poor prognosis of the KIRC patients (Figure S4).
Table 1.
Univariate Cox regression analysis showing the risk factors affecting the prognosis of KIRC patients
| Type | HR | HR.95L | HR.95H | P |
|---|---|---|---|---|
| Age | 1.816736632 | 1.311104643 | 2.517367327 | 0.00033371 |
| Gender | 0.931080776 | 0.675353696 | 1.283640581 | 0.662936583 |
| Grade | 2.293061292 | 1.854092472 | 2.835958922 | 1.94E-14 |
| Clinical stage | 1.888786162 | 1.648774014 | 2.163736894 | 4.67E-20 |
| T stage | 1.941390125 | 1.639292156 | 2.299160404 | 1.50E-14 |
| M stage | 4.2835444 | 3.10573436 | 5.908023835 | 7.45E-19 |
| N Stage | 0.864926263 | 0.739457444 | 1.011684238 | 0.06957107 |
| HCST expression | 1.021210843 | 1.010143448 | 1.032399496 | 0.000159844 |
The biological functions and signaling mechanisms of HCST and its co-expressed genes
Out of the 573 co-expressed genes of HCST, 480 were positively related genes while 93 were negatively related genes (Figure 3 and Table S1). GO annotation showed that the HCST co-expressed genes were significantly enriched in signaling transduction, inflammatory response, apoptotic processes, regulation of immune response, T cell receptor signaling pathway, intracellular signal transduction, MHC class II protein complex, positive regulation of cell proliferation or cell-cell signaling (Figure 4A-C and Table S2). On the other hand, the KEGG pathway analysis showed that the HCST co-expressed genes were significantly enriched in cell adhesion molecules (CAMs), cytokine-cytokine receptor interaction, chemokine signaling pathway, natural killer cell mediated cytotoxicity, T cell receptor signaling pathway, endocytosis, autoimmune thyroid disease, JAK-STAT signaling pathway, NF-κB signaling pathway, leukocyte trans-endothelial migration, primary immunodeficiency, toll-like receptor signaling pathway, B cell receptor signaling pathway, cytosolic DNA-sensing pathway or Fc epsilon RI signaling pathway (Figure 4D and Table 2). Moreover, the GSEA analysis showed that antigen processing and presentation, CAMs, cytokine-cytokine receptor, chemokine signaling pathway, T cell receptor signaling pathway, FC gamma r mediated phagocytosis and B cell receptor signaling pathway were significantly enriched in the HCST overexpression group (Figure S5 and Table 3).
Figure 3.
HCST is positively and negatively related to other genes. A. HCST was positively correlated with the expression of BATF, CCL5, LIMD2, LST1, TNFAIP8L2, WAS, CORO1A or IL2RG; B. HCST was negatively correlated with the expression of ARHGAP5, APOOL, FNBP1L, MPP5, NUBPL, HECTD1, LMBR1 or YIPF6.
Figure 4.
GO and KEGG analysis showing the functions and mechanisms mediated by the HCST co-expressed genes. A. BP; B. CC; C. MF; D. KEGG. Note: GO, Gene ontology; BP, biological process; CC, cell composition; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genome.
Table 2.
The KEGG analysis showing the signaling pathways of HCST co-expressed genes
| Category | Count | P value |
|---|---|---|
| hsa05166: HTLV-I infection | 30 | 1.21E-07 |
| hsa04514: Cell adhesion molecules (CAMs) | 29 | 4.71E-13 |
| hsa04060: Cytokine-cytokine receptor interaction | 29 | 1.68E-07 |
| hsa04062: Chemokine signaling pathway | 27 | 8.89E-09 |
| hsa05152: Tuberculosis | 24 | 2.62E-07 |
| hsa05168: Herpes simplex infection | 24 | 4.82E-07 |
| hsa04650: Natural killer cell mediated cytotoxicity | 23 | 1.03E-09 |
| hsa04380: Osteoclast differentiation | 23 | 4.18E-09 |
| hsa04145: Phagosome | 23 | 5.44E-08 |
| hsa04660: T cell receptor signaling pathway | 21 | 9.13E-10 |
| hsa04810: Regulation of actin cytoskeleton | 21 | 1.63E-04 |
| hsa04144: Endocytosis | 21 | 9.86E-04 |
| hsa04612: Antigen processing and presentation | 20 | 4.02E-11 |
| hsa04640: Hematopoietic cell lineage | 20 | 5.01E-10 |
| hsa05323: Rheumatoid arthritis | 20 | 6.17E-10 |
| hsa05416: Viral myocarditis | 19 | 1.69E-12 |
| hsa04940: Type I diabetes mellitus | 18 | 6.22E-14 |
| hsa05150: Staphylococcus aureus infection | 18 | 7.58E-12 |
| hsa05169: Epstein-Barr virus infection | 18 | 3.85E-06 |
| hsa05164: Influenza A | 18 | 3.77E-04 |
| hsa05332: Graft-versus-host disease | 17 | 1.04E-14 |
| hsa05330: Allograft rejection | 17 | 9.93E-14 |
| hsa05320: Autoimmune thyroid disease | 17 | 4.74E-11 |
| hsa05321: Inflammatory bowel disease (IBD) | 17 | 1.47E-09 |
| hsa05140: Leishmaniasis | 17 | 7.54E-09 |
| hsa05322: Systemic lupus erythematosus | 16 | 1.90E-04 |
| hsa05162: Measles | 15 | 5.86E-04 |
| hsa04630: Jak-STAT signaling pathway | 15 | 0.001377994 |
| hsa05132: Salmonella infection | 14 | 1.51E-05 |
| hsa04064: NF-kappa B signaling pathway | 14 | 2.54E-05 |
| hsa05142: Chagas disease (American trypanosomiasis) | 14 | 1.69E-04 |
| hsa05145: Toxoplasmosis | 14 | 2.98E-04 |
| hsa04670: Leukocyte transendothelial migration | 14 | 4.61E-04 |
| hsa04672: Intestinal immune network for IgA production | 13 | 1.36E-07 |
| hsa05340: Primary immunodeficiency | 12 | 3.05E-08 |
| hsa04666: Fc gamma R-mediated phagocytosis | 12 | 3.55E-04 |
| hsa05310: Asthma | 10 | 1.18E-06 |
| hsa05131: Shigellosis | 10 | 7.19E-04 |
| hsa05133: Pertussis | 10 | 0.002265094 |
| hsa04620: Toll-like receptor signaling pathway | 10 | 0.021063179 |
| hsa05130: Pathogenic Escherichia coli infection | 9 | 6.75E-04 |
| hsa04662: B cell receptor signaling pathway | 9 | 0.004867075 |
| hsa05100: Bacterial invasion of epithelial cells | 9 | 0.01013634 |
| hsa03050: Proteasome | 8 | 0.001340673 |
| hsa04623: Cytosolic DNA-sensing pathway | 8 | 0.011292053 |
| hsa04664: Fc epsilon RI signaling pathway | 7 | 0.046848111 |
Table 3.
The GSEA analysis showing the signaling pathway associated with HCST overexpression
| Name | Size | NOM p |
|---|---|---|
| KEGG_AUTOIMMUNE_THYROID_DISEASE | 50 | 0 |
| KEGG_TYPE_I_DIABETES_MELLITUS | 41 | 0 |
| KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION | 80 | 0.001964637 |
| KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS | 54 | 0 |
| KEGG_CELL_ADHESION_MOLECULES_CAMS | 131 | 0 |
| KEGG_VIRAL_MYOCARDITIS | 68 | 0 |
| KEGG_INTESTINAL_IMMUNE_NETWORK_FOR_IGA_PRODUCTION | 46 | 0 |
| KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION | 263 | 0 |
| KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY | 131 | 0 |
| KEGG_ALLOGRAFT_REJECTION | 35 | 0 |
| KEGG_PRIMARY_IMMUNODEFICIENCY | 35 | 0.005870841 |
| KEGG_ASTHMA | 28 | 0 |
| KEGG_GRAFT_VERSUS_HOST_DISEASE | 37 | 0.001886793 |
| KEGG_HEMATOPOIETIC_CELL_LINEAGE | 85 | 0 |
| KEGG_LEISHMANIA_INFECTION | 69 | 0.003883495 |
| KEGG_CYTOSOLIC_DNA_SENSING_PATHWAY | 54 | 0 |
| KEGG_PROTEASOME | 44 | 0.005976096 |
| KEGG_CHEMOKINE_SIGNALING_PATHWAY | 187 | 0.013972056 |
| KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY | 108 | 0.025145067 |
| KEGG_FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS | 95 | 0.02970297 |
| KEGG_B_CELL_RECEPTOR_SIGNALING_PATHWAY | 75 | 0.036072146 |
PPI network and hub genes
The PPI network was visualized with Cystoscope software (Figure S6A). The main component in the PPI network was the HCST-positively related genes. In addition, using plug-ins, HLA-DRA, HLA-DRB1, HLA-DPB1, HLA-DPA1, HLA-DQA1, HLA-DQB1, HLA-A, HLA-B, HLA-F and IRF7 were shown to be hub genes (Figure S6B and Table 4). Correlation analysis showed that the expression level of HCST expression was significantly associated with the expression of hub genes in the PPI network.
Table 4.
HCST expression level is related with the expression of hub genes
| Name | Score | r |
|---|---|---|
| HLA-DRA | 3.56E+14 | 0.561 |
| HLA-DRB1 | 3.56E+14 | 0.641 |
| HLA-DPB1 | 3.56E+14 | 0.677 |
| HLA-DPA1 | 3.56E+14 | 0.518 |
| HLA-DQA1 | 3.56E+14 | 0.563 |
| HLA-DQB1 | 3.56E+14 | 0.511 |
| HLA-A | 3.56E+14 | 0.637 |
| HLA-B | 3.56E+14 | 0.586 |
| HLA-F | 3.56E+14 | 0.621 |
| IRF7 | 3.56E+14 | 0.585 |
The expression of HCST correlates with the level of immune cell infiltration in the KIRC patients
Further analysis demonstrated that the expression of HCST was correlated with the level of KIRC immune cell infiltration (Figure 5). In details, the level of HCST was significantly associated with the KIRC purity (r=-0.386192508; P=7.00E-18), B cells (r=0.312010883; P=8.04E-12), CD8+ T cells (r=0.541116885; P=1.11E-34), CD4+ T cells (r=0.206255023; P=8.21E-06), macrophages (r=0.211924371; P=6.05E-06), neutrophils (r=0.392922766; P=2.33E-18) and DC (r=0.575509004; P=1.74E-41). In addition, HCST copy number was correlated with the arm-level gain of B cells, CD8+ T cells, CD4+ T cells, neutrophils and DCs (Figure S7).
Figure 5.
HCST expression is correlated with KIRC immune infiltration.
Besides, the expression level of HCST was significantly associated with the expression of markers of KIRC immune infiltration such as B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and DC (Figure 6 and Table 5). For instance, the expression of HCST was significantly correlated with the expression of CD8+ T cell markers such as CD8A and CD8B, B cell markers such as CD19 and CD79A, DC markers such as HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, Th1 cell markers such as TBX21, STAT4, STAT1 as well as IFNG (Figure 6). Similar results were obtained from correlation analysis based on the KIRC purity and age (Table 6).
Figure 6.
The expression of HCST is significantly associated with the marker levels of KIRC immune infiltration cells. A, B. CD8+ T cell; C, D. B cell; E-H. DCs; I-L. Th1 cell; M-P. T cell exhaustion.
Table 5.
The expression of HCST is significantly correlated with the marker levels of KIRC immune infiltration cells
| Cell | Gene | Cor | P | Cell | Gene | Cor | P |
|---|---|---|---|---|---|---|---|
| CD8+ T cell | CD8A | 0.739852523 | *** | Th1 | TBX21 | 0.516558022 | *** |
| CD8B | 0.754037027 | *** | STAT4 | 0.496597376 | *** | ||
| T cell (general) | CD3D | 0.842206601 | *** | STAT1 | 0.488052268 | *** | |
| CD3E | 0.821308286 | *** | IFNG | 0.697553365 | *** | ||
| CD2 | 0.793914929 | *** | TNF | 0.339198118 | *** | ||
| B Cell | CD19 | 0.47198925 | *** | Th2 | GATA3 | 0.373614771 | *** |
| CD79A | 0.556819138 | *** | STAT6 | -0.103192588 | * | ||
| M1 Macrophage | NOS2 | -0.042897538 | 0.323 | STAT5A | 0.496527894 | *** | |
| IRF5 | 0.358277571 | *** | IL13 | 0.101097629 | * | ||
| PTGS2 | -0.02422053 | 0.577 | Tfh | BCL6 | -0.056352542 | 0.194 | |
| M2 Macrophage | CD163 | 0.278394333 | *** | IL21 | 0.176278191 | *** | |
| VSIG4 | 0.431614549 | *** | Th17 | STAT3 | -0.028680739 | 0.509 | |
| MS4A4A | 0.380091981 | *** | IL17A | 0.047318628 | 0.276 | ||
| Neutrophils | CEACAM8 | -0.035671255 | 0.411 | Treg | FOXP3 | 0.623845919 | *** |
| ITGAM | 0.461521154 | *** | CCR8 | 0.489639827 | *** | ||
| CCR7 | 0.521288218 | *** | STAT5B | -0.159327665 | *** | ||
| Dendritic cell | HLA-DPB1 | 0.685194433 | *** | TGFB1 | 0.125360653 | ** | |
| HLA-DQB1 | 0.493948463 | *** | T cell exhaustion | PDCD1 | 0.778270323 | *** | |
| HLA-DRA | 0.618338129 | *** | CTLA4 | 0.660778269 | *** | ||
| HLA-DPA1 | 0.618497621 | *** | LAG3 | 0.777854902 | *** | ||
| CD1C | 0.274982706 | *** | HAVCR2 | 0.203663359 | *** | ||
| NRP1 | -0.140691672 | ** | GZMB | 0.603838288 | *** | ||
| ITGAX | 0.449222967 | *** |
Table 6.
The expression of HCST is significantly correlated with the marker levels of KIRC immune infiltration cells under the KIRC purity and age
| Gene | Purity | Age | ||
|---|---|---|---|---|
|
|
|
|||
| Cor | P | Cor | P | |
| CD8A | 0.695306707 | 7.70E-68 | 0.74088951 | 1.66E-93 |
| CD8B | 0.719145534 | 1.39E-74 | 0.754200334 | 1.07E-98 |
| CD3D | 0.81347383 | 4.20E-110 | 0.843367161 | 8.21E-145 |
| CD3E | 0.787803039 | 1.17E-98 | 0.82333865 | 3.11E-132 |
| CD2 | 0.755441673 | 2.58E-86 | 0.794982591 | 6.41E-117 |
| CD19 | 0.4226411 | 2.13E-21 | 0.473636155 | 4.80E-31 |
| CD79A | 0.504643406 | 3.71E-31 | 0.556985358 | 1.35E-44 |
| NOS2 | -0.117092532 | 0.011872284 | -0.042138835 | 0.33246247 |
| IRF5 | 0.334166624 | 1.73E-13 | 0.361550583 | 7.66E-18 |
| PTGS2 | -0.092366264 | 0.047474488 | -0.023239748 | 0.59311059 |
| CD163 | 0.212641781 | 4.10E-06 | 0.279456109 | 5.55E-11 |
| VSIG4 | 0.363852574 | 7.07E-16 | 0.430994043 | 1.98E-25 |
| MS4A4A | 0.296812684 | 7.89E-11 | 0.382419117 | 6.17E-20 |
| CEACAM8 | -0.028817032 | 0.537118436 | -0.033001033 | 0.447929218 |
| ITGAM | 0.404108931 | 1.54E-19 | 0.463038942 | 1.42E-29 |
| CCR7 | 0.462420182 | 8.38E-26 | 0.530588477 | 6.83E-40 |
| HLA-DPB1 | 0.657377131 | 2.24E-58 | 0.685992911 | 4.43E-75 |
| HLA-DQB1 | 0.436753637 | 6.78E-23 | 0.493611562 | 5.83E-34 |
| HLA-DRA | 0.580324771 | 7.52E-43 | 0.619978422 | 1.05E-57 |
| HLA-DPA1 | 0.565711099 | 2.36E-40 | 0.619889849 | 1.10E-57 |
| CD1C | 0.19297806 | 3.03E-05 | 0.285753517 | 1.95E-11 |
| NRP1 | -0.228862903 | 6.81E-07 | -0.140706513 | 0.001150371 |
| ITGAX | 0.408454739 | 5.79E-20 | 0.453382281 | 2.80E-28 |
| TBX21 | 0.477655047 | 1.19E-27 | 0.519194296 | 5.50E-38 |
| STAT4 | 0.417558545 | 7.08E-21 | 0.498336423 | 1.12E-34 |
| STAT1 | 0.432689804 | 1.86E-22 | 0.489048797 | 2.81E-33 |
| IFNG | 0.649934809 | 1.12E-56 | 0.698130456 | 8.61E-79 |
| TNF | 0.303601175 | 2.76E-11 | 0.340613437 | 6.86E-16 |
| GATA3 | 0.3595671 | 1.62E-15 | 0.373169348 | 5.46E-19 |
| STAT6 | -0.100138359 | 0.031584464 | -0.100963682 | 0.019964393 |
| STAT5A | 0.427870744 | 6.04E-22 | 0.496708327 | 1.98E-34 |
| IL13 | 0.061309244 | 0.188836819 | 0.102466343 | 0.018184887 |
| BCL6 | -0.077158106 | 0.098000681 | -0.056239829 | 0.195689887 |
| IL21 | 0.138190885 | 0.002946102 | 0.179726187 | 3.10E-05 |
| STAT3 | -0.098165456 | 0.035111426 | -0.027125543 | 0.532819697 |
| IL17A | 0.007023008 | 0.880462562 | 0.05223128 | 0.229529343 |
| FOXP3 | 0.565071703 | 3.01E-40 | 0.624445993 | 9.45E-59 |
| CCR8 | 0.406413728 | 9.20E-20 | 0.489731849 | 2.22E-33 |
| STAT5B | -0.17825941 | 0.000119153 | -0.158030612 | 0.000256197 |
| TGFB1 | 0.073838622 | 0.113366736 | 0.126021791 | 0.003628928 |
| PDCD1 | 0.751643436 | 5.43E-85 | 0.778437319 | 4.65E-109 |
| CTLA4 | 0.609306971 | 3.42E-48 | 0.661240863 | 4.78E-68 |
| LAG3 | 0.756842419 | 8.29E-87 | 0.777877907 | 8.34E-109 |
| HAVCR2 | 0.13397112 | 0.003955693 | 0.208319431 | 1.28E-06 |
| GZMB | 0.560870561 | 1.49E-39 | 0.606038161 | 1.50E-54 |
The biological functions and signaling mechanisms modulated by HCST-related immune cell infiltration markers
The DAVID database was used to analyze the biological functions and signaling mechanisms that are modulated by the HCST-related immune cell infiltration markers. The data demonstrated that the HCST-related immune cell infiltration markers were involved in T cell co-stimulation, immune responses, T cell receptor signaling pathway, antigen processing and presentation, positive regulation of T cell proliferation, negative regulation of interleukin-2 production, T cell activation and MHC class II Receptor activity (Figure 7A-C and Table S3). Besides, the HCST-related immune cell infiltration markers were involved in the signaling mechanisms of hematopoietic cell lineage, CAMs, antigen processing and presentation, T cell receptor signaling pathway, primary immunodeficiency, JAK-STAT signaling pathway, cytokine-cytokine receptor interaction as well as chemokine signaling pathway (Figure 7D and Table S4).
Figure 7.
HCST-related immune cell infiltration markers are involved in biological functions and signaling mechanisms. A. BP; B. CC; C. MF; D. KEGG. Note: GO, Gene ontology; BP, biological process; CC, cell composition; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genome.
The expression of HCST correlates with the levels of immune cell markers in KIRC tissues
The relationship between the expression of HCST and the levels of KIRC immune cell markers was further assessed in KIRC tissues (Figure 8 and Table 7). The results showed that the expression of HCST was positively correlated with the levels of CD3D (r=0.86), CD3E (r=0.85), LAG3 (r=0.79), CD2 (r=0.79), CD8A (r=0.77), CD8B (r=0.76), HLA-DPB1 (r=0.69), IFNG (r=0.67), PDCD1 (r=0.67), HLA-DRA (r=0.64), CTLA4 (r=0.62), HLA-DPA1 (r=0.6), HLA-DQB1 (r=0.59), STAT1 (r=0.56), FOXP3 (r=0.55), GZMB (r=0.53), STAT5A (r=0.52), TBX21 (r=0.47), VSIG4 (r=0.4), CCR8 (r=0.39), STAT4 (r=0.39), IL21 (r=0.36), ITGAX (r=0.35), CD163 (r=0.35), MS4A4A (r=0.33), CCR7 (r=0.3), IRF5 (r=0.29), TNF (r=0.21), CD79A (r=0.19), ITGAM (r=0.13) and CD1C (r=0.13). In contrast, the expression of HCST was negatively correlated with the levels of STAT5B (r=-0.11) and NRP1 (r=-0.17).
Figure 8.
HCST expression is correlated with the levels of KIRC immune cell markers. A. CD3D; B. CD3E; C. LAG3; D. CD2; E. CD8A; F. CD8B; G. HLA-DPB1; H. IFNG; I. PDCD1; J. HLA-DRA; K. CTLA4; L. HLA-DPA1; M. HLA-DQB1; N. STAT1; O. FOXP3; P. GZMB.
Table 7.
The relationship between the expression of HCST and the levels of KIRC immune cell markers in the GEPIA KIRC tissues
| Gene | Cor | P | Gene | Cor | P | Gene | Cor | P |
|---|---|---|---|---|---|---|---|---|
| CD3D | 0.86 | 0 | HLA-DPA1 | 0.6 | 0 | ITGAX | 0.35 | 0 |
| CD3E | 0.85 | 0 | HLA-DQB1 | 0.59 | 0 | CD163 | 0.35 | 2.2e-16 |
| LAG3 | 0.79 | 0 | STAT1 | 0.56 | 0 | MS4A4A | 0.33 | 3.8e-15 |
| CD2 | 0.79 | 0 | FOXP3 | 0.55 | 0 | CCR7 | 0.3 | 1e-12 |
| CD8A | 0.77 | 0 | GZMB | 0.53 | 0 | IRF5 | 0.29 | 6.1e-12 |
| CD8B | 0.76 | 0 | STAT5A | 0.52 | 0 | TNF | 0.21 | 2.2e-06 |
| HLA-DPB1 | 0.69 | 0 | TBX21 | 0.47 | 0 | CD79A | 0.19 | 1.4e-05 |
| IFNG | 0.67 | 0 | VSIG4 | 0.4 | 0 | ITGAM | 0.13 | 0.0032 |
| PDCD1 | 0.67 | 0 | CCR8 | 0.39 | 0 | CD1C | 0.13 | 0.0022 |
| HLA-DRA | 0.64 | 0 | STAT4 | 0.39 | 0 | NRP1 | -0.17 | 0.00014 |
| CTLA4 | 0.62 | 0 | IL21 | 0.36 | 0 | STAT5B | -0.11 | 0.012 |
Discussion
In the recent years, the application of immunotherapy in disease treatment has received widespread attention [13-16]. For instance, circMET (hsa_circ_0082002) was shown to be overexpressed in hepatocellular carcinoma (HCC) tissues, and the expression level of the circMET was related to the survival and tumor recurrence in HCC patients. The overexpression of circMET mediated tumor microenvironment through miR-30-5p/Snail/DPP4/CXCL10 signaling mechanism and induced epithelial-mesenchymal transition (EMT), thereby fueling the progression of HCC. A combination treatment with sitagliptin, a DPP4 inhibitor, and anti-PD1 antibody could improve the anti-tumor immunity in mice models. Besides, tissues from diabetic HCC patients under sitagliptin treatment had higher CD8 T cell infiltration [16]. Previous studies have reported that HCST could participate in tumorigenesis, development, and immune regulation [5-8]. However, there is no available data defining the effect of abnormally expressed HCST on the progression of KIRC. In this study, we used the TCGA, GEO, GEPIA and ULACAN databases to analyze the expression of in KIRC samples. The results robustly demonstrated that HCST is upregulated in KIRC. The overexpressed HCST was associated with clinical stage, tumor grade and the tissue subtype of the KIRC patients. Results from the Kaplan-Meier survival analysis showed that KIRC patients with high HCST expression had a shorter OS and DFS. In addition, elevated HCST expression was also related to the gender, race and tumor grade associated with OS in the KIRC patients. Moreover, results from the Cox regression analysis showed that HCST expression influenced poor prognosis in the KIRC patients. These results preliminarily indicated that HCST is a carcinogenic factor mediating the progression of KIRC, and it is a promising prognostic biomarker for the KIRC patients.
Other studies have reported that HCST was associated with the T cells and NK cells [17-21]. For example, DAP10-deficient mice showed antigen-specific CD8 T cell recruitment, activation and development following aerosol infection. The loss of cytotoxicity in the DAP10-deficient CD8 T cells was related to impaired release of cytotoxic particles [18]. NKG2D was an important activation receptor that triggers the cytotoxic activity of the NK cells, and in conjunction with specific ligands, it could induce damage to NK cell function. Besides, the NKG2D/DAP10 receptor complex has been associated with the activation of the NK cells [20]. In our study, we showed that the HCST expression level was significantly correlated with the markers of KIRC immune infiltration such as B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and DC. In addition, the correlation analysis showed that the HCST is significantly correlated with the levels of CD8+ T cell markers including CD8A and CD8B, the levels of B cell markers such as CD19 and CD79A, the levels of DC markers such as HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, as well as the levels of Th1 cell markers such as TBX21, STAT4, STAT1 or IFNG.
The occurrence and development of tumors not only involve abnormal immune regulation, but also change in multiple signaling pathways [22-24]. For example, up-regulation of lncRNA RP11-468E2.5 has been shown to inhibit the JAK/STAT signaling pathway by targeting STAT5 and STAT6 in inhibiting colorectal cancer (CRC) cell proliferation and promotion of cell apoptosis [22]. Besides, the overexpression of chemokine receptor 7 (CCR7) was closely associated with gastric cancer (GC) metastasis, staging, differentiation and poor prognosis. CCL19 could increase the expression of p-ERK, p-AKT, Snail and MMP9 in GC cells, and decrease the expression of E-cadherin. CCR7 was shown to induce ERK and PI3K signaling pathways to regulate Snail signaling [24]. HCST-related immune cell infiltration markers involve T cell co-stimulation, immune response, T cell receptor signaling pathway, antigen processing and presentation, positive regulation of T cell proliferation, negative regulation of interleukin-2 production, T cell activation, MHC class II receptor activity, CAMs, antigen processing and presentation, T cell receptor signaling pathway, primary immunodeficiency, Jak-STAT signaling pathway, cytokine-cytokine receptor interaction as well as chemokine signaling pathway. In addition, the KEGG analysis showed that HCST is involved in signaling pathways such as antigen processing and presentation, CAMs, cytokine-cytokine receptor, chemokine signaling pathway, T cell and B cell receptor signaling pathways. This demonstrated that HCST plays an important role in tumor immune infiltration.
This study used a larger sample size with extensive data, and we showed that HCST was significantly overexpressed in KIRC tissues. Increased HCST was related to the clinical stage, tumor grade, tissue subtype and poor prognosis of KIRC patients, and it influenced poor prognosis in the KIRC patients. Increased HCST mediate signaling mechanisms such as antigen processing and presentation, CAMs, cytokine-cytokine receptor, chemokine signaling pathway, T cell receptor signaling pathway, FC gammar mediated phagocytosis as well as B cell receptor signaling pathway. In addition, the expression of HCST was significantly correlated with the levels of KIRC immune cell infiltration purity, B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and DC. Furthermore, the expression of HCST was significantly associated with markers of KIRC immune infiltration B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and DC. Taken together, the HCST upregulation is associated with poor prognosis and the levels of immune infiltration in KIRC. HCST might be a potential prognostic biomarker, and is related to the immune infiltration in KIRC.
Disclosure of conflict of interest
None.
Supporting Information
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