Home Life Sciences Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer
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Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer

  • Ye Shen , Haibin Chen , Xiaoyang Gong , Ziyi Wang , Mengjie Chen EMAIL logo and Donghui Chen EMAIL logo
Published/Copyright: August 9, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

Lysosome dysfunction has been shown to play an important role in cancer progression. However, few research studies have reported the role of lysosomes in head and neck squamous cell carcinoma (HNSCC) progression. Lysosome-related genes (LRGs) were collected from the Molecular Signatures Database. Differentially expressed lysosome-related genes (DELRGs) were identified from the TCGA-HNSCC dataset. The least absolute shrinkage and selection operator and multivariate Cox regression analysis were used to identify the prognostic genes. The prognostic values and expression of hub DELRGs were further validated by GEO datasets. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data and the single-sample gene set enrichment analysis were applied to evaluate the correlation between cathepsin G (CTSG) and immune infiltrates. Twenty-two DELRGs were identified. Among them, CTSG was an independent prognostic biomarker for HNSCC patients. Gene set enrichment analysis indicated that the potential mechanism of CTSG in regulating HNSCC was associated with the immune- and inflammation-related pathways. CTSG expression was highly correlated with immune cell infiltration. Finally, two potential compounds (CH and MAN) targeting CTSG protein were identified, and their reliability was validated through molecular docking analysis. CTSG was associated with immune infiltration and had prognostic value in HNSCC patients, which may be a potential biomarker for predicting the outcome of immunotherapy.

Graphical Abstract

Keywords: head and neck squamous cell carcinoma; molecular docking; immune cell infiltration; lysosome

1 Introduction

Head and neck cancer is the world’s sixth leading cause of cancer [1]. It accounts for 7% of all cancers, with approximately 344,000 deaths and 693,000 new cases reported worldwide each year [1]. The majority of head and neck cancers arise in the larynx, throat, and mucous membrane epithelium of the mouth and are known as head and neck squamous cell carcinoma (HNSC) [2]. Despite the considerable advances made in the treatment of malignancies, there has been no significant improvement in head and neck cancer mortality rates in recent decades [3]. In addition, the 5-year survival rate for those diagnosed with HNSC is only about 50%. Immunotherapy has gained much attention in recent years; however, it is only effective in between 20 and 30% of patients [4]. In view of the increase in incidence and the low survival rates of HNSC, and to better tailor diagnostic, preventive, and therapeutic approaches for HNSC, there is an urgent need to develop biomarkers that can accurately predict tumor prognosis [5,6].

Lysosomes play a vital role in cellular aging, development and homeostasis as signaling hubs, and degradation centers within cells [7]. Moreover, complex transcriptional programs regulated lysosomal composition and biogenesis, tailoring their activity to changing cellular needs [8]. It is not surprising that alterations in these fundamental activities are critical to the pathophysiology of diseases, including neurodegenerative disorders, pancreatitis, autoimmune disorders, atherosclerosis, and cancer [9,10,11,12,13]. Thus, unraveling lysosome-related genes (LRGs) will help us to understand the underlying pathological mechanisms and thereby develop new therapeutic approaches relevant to human diseases. Furthermore, lysosomal dysfunction is associated with cancer-associated vascularization, tumor spread, immune escape, cancer cell metastasis, invasion, energy metabolism, and proliferation [14]. Therefore, knowing what influences lysosomes and how they might be regulated will help in cancer diagnosis and prognosis prediction. However, limited studies have reported the role of genes involved in lysosome functions in tumorigenesis and progression.

In the present study, we systematically analyzed LRGs in HNSC. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to identify the potential prognostic genes for HNSC patients. Furthermore, a series of bioinformatics approaches were used to systematically analyze the prognosis and potential functions of LRGs in HNSC patients.

2 Materials and methods

2.1 Collection of microarray data

The transcriptome and relevant clinical data of HNSC patients (44 normal samples and 504 tumor samples) were obtained from the TCGA dataset. The gene expression profile and survival data from the GSE6631 (22 normal samples and 22 tumor samples) and GSE41613 (97 tumor samples) datasets were downloaded from the GEO database. Lysosome-related data sets were collected from the Molecular Signatures Database. After removing duplicates, 163 LRGs were obtained.

2.2 Identification of the differentially expressed lysosome-related genes (DELRGs) in HNSC patients

DELRGs were filtered between the normal and tumor groups using the “limma” package of R. The conditions for filtering were set to false discovery rate (FDR) <0.05 and |log FC|> 1. The results were visualized using the “ggplot2” package.

2.3 Screening of potential prognostic biomarkers in HNSC patients

The “glmnet” and “survival” packages were used to perform the LASSO analysis based on the above DELRGs. Finally, multivariate Cox regression analysis was performed to further screen signature genes. The relationship between DELRGs and overall survival was assessed and survival curves were plotted using the “ggplot2” and “survminer” packages.

2.4 Construction of nomogram

Based on the results of multivariate analysis, a nomogram was established to predict overall survival in HNSC patients. Nomogram was generated and calibration curves were plotted using the “ggplot2” and “survival” R packages.

2.5 Identification of differentially expressed genes (DEGs) and functional enrichment analysis between the low- and high-cathepsin G (CTSG) HNSC subgroups

Based on the median CTSG expression value, we divided HNSC patients into low- and high-CTSG subgroups. DEGs were identified between the two subgroups using the “limma” package of R. The conditions for the filtering were set to FDR <0.05 and |log FC|> 1. The results were visualized using the “ggplot2” package. To assess the potential biological functions and signaling pathways of DEGs, gene set enrichment analysis (GSEA) was performed using the “clusterProfiler” package of R. The “ggplot2” package was used to visualize enrichment analysis results.

2.6 Immune infiltration analysis

The Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) Score, Immune Score, and Stromal Score were compared between the low- and high-CTSG subgroups using the ESTIMATE software. In addition, we used the single-sample gene set enrichment analysis (ssGSEA) to compare the number of 24 immune cell types between the two subgroups. The “ggplot2” package was used to perform the correlation analysis and visualize the results. Tumor Immune Estimation Resource (TIMER) is an online analysis tool for comprehensive analysis of tumor-infiltrating immune cells (https://cistrome.shinyapps.io/timer/) [15]. The TISCH (http://tisch.comp-genomics.org) database is a comprehensive collection of single-cell RNA sequencing data that provide insights into the heterogeneity of the tumor microenvironment across different datasets and cell types [16]. We used this database to explore the heterogeneity of the tumor microenvironment in various cells.

2.7 Molecular docking

First, we screened the candidate drugs targeting CTSG genes by using the Dgibd database (https://dgidb.org/) [17]. The 3D protein structure of CTSG was downloaded from the PDB database (www.rcsb.org). The selection of suitable target proteins was based on the following criteria: (1) the crystal resolution was below 3.5 Å, (2) they must be obtained from Homo sapiens, and (3) the structure with the best resolution. Solvent and organics were removed from the protein conformations using PyMOL software. Subsequently, we performed molecular docking analysis using AutoDock software (1.5.6), and molecular docking results were visualized using PyMOL software.

3 Results

3.1 Identification of DELRGs in HNSC

DELRGs were presented in the volcano plot (Figure 1a). There were 11 downregulated LRGs and 11 upregulated LRGs between the normal and tumor groups. As shown in Figure 1b and c, the mRNA expression of ACP5, ATP6V0D2, CLTCL1, CTSC, CTSV, GALNS, LAMP3, LAPTM4B, SLC11A1, IFI30, and MYO7A was significantly upregulated in HNSC patients, whereas the mRNA expression of ASAH1, ATP6V0A4, CTSG, DNASE2B, SLC11A2, SORT1, MPO, NEU4, TSPAN8, and TYP was significantly downregulated in HNSC patients (p < 0.05).

Figure 1 Identification of DELRGs in HNSC. (a) The volcano plot presents the LRGs. Blue dots indicate downregulated genes and red dots indicate upregulated genes. (b and c) The bar charts present the DELRG expression levels between the normal and tumor groups. *p < 0.05, **p < 0.01, ***p < 0.001, and ns indicates no significant difference.
Figure 1

Identification of DELRGs in HNSC. (a) The volcano plot presents the LRGs. Blue dots indicate downregulated genes and red dots indicate upregulated genes. (b and c) The bar charts present the DELRG expression levels between the normal and tumor groups. *p < 0.05, **p < 0.01, ***p < 0.001, and ns indicates no significant difference.

3.2 Screening of potential prognostic biomarkers in HNSC patients

In the present study, the above 22 DELRGs were used to perform the LASSO regression analysis. Three DELRGs (LAPTM4B, CTSG, and CLTCL1) with non-zero coefficients were screened in HNSC patients (Figure 2a and b). Subsequently, a multivariate regression analysis was performed on these three DELRGs. The results showed that M stage, CTSG, and CLTCL1 were the independent prognostic factors in HNSC patients (Table 1).

Figure 2 Screening of potential prognostic biomarkers for HNSC patients. (a and b) LASSO analysis identifies the prognostic biomarkers in the TCGA dataset. Effect of CLTCL1 genes on overall survival (c), disease-specific survival (d), and progress-free interval (e). Effect of CTSG genes on overall survival (f), disease-specific survival (g), and progress-free interval (h).
Figure 2

Screening of potential prognostic biomarkers for HNSC patients. (a and b) LASSO analysis identifies the prognostic biomarkers in the TCGA dataset. Effect of CLTCL1 genes on overall survival (c), disease-specific survival (d), and progress-free interval (e). Effect of CTSG genes on overall survival (f), disease-specific survival (g), and progress-free interval (h).

Table 1

Results of univariate and multivariate regression analyses

Characteristics Total (N) Univariate analysis Multivariate analysis
Hazard ratio (95% CI) p value Hazard ratio (95% CI) p value
T stage 486
 T1 33 Reference
 T2 143 1.086 (0.568–2.074) 0.803
 T3 131 1.461 (0.769–2.773) 0.247
 T4 179 1.249 (0.665–2.344) 0.490
N stage 479
 N0 238 Reference
 N1 80 1.058 (0.728–1.539) 0.768 1.020 (0.696–1.494) 0.919
 N2&N3 161 1.404 (1.038–1.900) 0.028 1.369 (1.005–1.865) 0.047
M stage 476
 M0 471 Reference
 M1 5 4.745 (1.748–12.883) 0.002 3.945 (1.434–10.852) 0.008
LAPTM4B 501
 Low 251 Reference
 High 250 1.161 (0.888–1.518) 0.275
CTSG 501
 Low 251 Reference
 High 250 0.656 (0.499–0.861) 0.002 0.651 (0.493–0.860) 0.002
CLTCL1 501
 Low 251 Reference
 High 250 1.316 (1.007–1.720) 0.044 1.389 (1.055–1.830) 0.019

Bold values represents a significant difference.

In addition, we performed a comprehensive survival analysis of prognostic genes. As shown in Figure 2c–h, only CLTCL1 genes could predict overall survival (p = 0.04), while CTSG genes could predict overall survival (p = 0.002), disease-specific survival (p = 0.023), and progress-free interval (p = 0.01).

3.3 Validation of expression and prognostic values of CLTCL1 and CTSG

As shown in Figure 3a and b, the gene expression of CLTCL1 and CTSG was significantly downregulated in HNSC patients in the TCGA-HNSC dataset. However, in the GSE6631 dataset, CLTCL1 expression was not significantly different between normal and tumor groups (Figure 3c). CTSG expression was significantly downregulated in the cancer group (p < 0.01, Figure 3d). We also validated the prognostic values of CLTCL1 and CTSG in the GSE41613 dataset. As shown in Figure 3e and f, CLTCL1 genes could not predict overall survival (p = 0.84), while CTSG genes could predict overall survival in HNSC patients (p = 0.0378). These findings further demonstrated that low expression of CTSG was associated with poor prognosis in HNSC patients.

Figure 3 Validation of expression and prognostic values of CLTCL1 and CTSG. The scatter plots present the CLTCL1 (a) and CTSG (b) expression levels between the normal and tumor groups in the TCGA database. The scatter plots present the CLTCL1 (c) and CTSG (d) expression levels between the normal and tumor groups in the GSE6631 database.*p < 0.05, **p < 0.01, ***p < 0.001, and ns indicates no significant difference. Effect of CLTCL1 (e) and CTSG (f) genes on overall survival in the GSE41613 dataset.
Figure 3

Validation of expression and prognostic values of CLTCL1 and CTSG. The scatter plots present the CLTCL1 (a) and CTSG (b) expression levels between the normal and tumor groups in the TCGA database. The scatter plots present the CLTCL1 (c) and CTSG (d) expression levels between the normal and tumor groups in the GSE6631 database.*p < 0.05, **p < 0.01, ***p < 0.001, and ns indicates no significant difference. Effect of CLTCL1 (e) and CTSG (f) genes on overall survival in the GSE41613 dataset.

As shown in Table 2, downregulation of CTSG in HNSC was significantly associated with the primary therapy outcome (p = 0.003), race (p = 0.044), anatomic neoplasm subdivision (p = 0.005), and overall survival (p = 0.015). In addition, as shown in Figure S1, the downregulation of CTSG was associated with a poor prognosis in the subgroup of CR therapy outcome (p = 0.005), radiation therapy (p = 0.008), males (p = 0.021), M0 stage (p = 0.02), N0 stage (p = 0.007), T2 stage (p = 0.041), lymph-node neck dissection (p = 0.002), smokers (p = 0.001), and age >60 (p = 0.002).

Table 2

Relationship of CTSG expression with clinicopathological features in HNSC

Characteristics Low expression of CTSG High expression of CTSG p
n 251 251
T stage, n (%) 0.110
 T1 12 (2.5%) 21 (4.3%)
 T2 69 (14.2%) 75 (15.4%)
 T3 60 (12.3%) 71 (14.6%)
 T4 100 (20.5%) 79 (16.2%)
N stage, n (%) 0.396
 N0 116 (24.2%) 123 (25.6%)
 N1 34 (7.1%) 46 (9.6%)
 N2 83 (17.3%) 71 (14.8%)
 N3 3 (0.6%) 4 (0.8%)
M stage, n (%) 0.682
 M0 232 (48.6%) 240 (50.3%)
 M1 3 (0.6%) 2 (0.4%)
Clinical stage, n (%) 0.268
 Stage I 6 (1.2%) 13 (2.7%)
 Stage II 44 (9%) 51 (10.5%)
 Stage III 49 (10%) 53 (10.9%)
 Stage IV 143 (29.3%) 129 (26.4%)
Radiation therapy, n (%) 0.061
 No 68 (15.4%) 86 (19.5%)
 Yes 155 (35.1%) 132 (29.9%)
Primary therapy outcome, n (%) 0.003
 PD 29 (6.9%) 12 (2.9%)
 SD 1 (0.2%) 5 (1.2%)
 PR 5 (1.2%) 1 (0.2%)
 CR 176 (42.1%) 189 (45.2%)
Gender, n (%) 0.190
 Female 60 (12%) 74 (14.7%)
 Male 191 (38%) 177 (35.3%)
Race, n (%) 0.044
 Asian 3 (0.6%) 7 (1.4%)
 Black or African-American 31 (6.4%) 16 (3.3%)
 White 210 (43.3%) 218 (44.9%)
Age, n (%) 1.000
 ≤60 123 (24.6%) 122 (24.4%)
 >60 128 (25.5%) 128 (25.5%)
Histologic grade, n (%) 0.300
 G1 33 (6.8%) 29 (6%)
 G2 139 (28.8%) 161 (33.3%)
 G3 66 (13.7%) 53 (11%)
 G4 1 (0.2%) 1 (0.2%)
Anatomic neoplasm subdivision, n (%) 0.005
 Alveolar ridge 12 (2.4%) 6 (1.2%)
 Base of tongue 10 (2%) 13 (2.6%)
 Buccal mucosa 12 (2.4%) 10 (2%)
 Floor of mouth 39 (7.8%) 22 (4.4%)
 Hard palate 2 (0.4%) 5 (1%)
 Hypopharynx 8 (1.6%) 2 (0.4%)
 Larynx 53 (10.6%) 58 (11.6%)
 Lip 0 (0%) 3 (0.6%)
 Oral cavity 32 (6.4%) 40 (8%)
 Oral tongue 52 (10.4%) 74 (14.7%)
 Oropharynx 8 (1.6%) 1 (0.2%)
 Tonsil 23 (4.6%) 17 (3.4%)
Smoker, n (%) 1.000
 No 56 (11.4%) 55 (11.2%)
 Yes 190 (38.6%) 191 (38.8%)
Alcohol history, n (%) 0.406
 No 83 (16.9%) 75 (15.3%)
 Yes 160 (32.6%) 173 (35.2%)
Lymphovascular invasion, n (%) 0.379
 No 108 (31.7%) 111 (32.6%)
 Yes 67 (19.6%) 55 (16.1%)
Lymphnode neck dissection, n (%) 0.924
 No 46 (9.2%) 44 (8.8%)
 Yes 204 (40.9%) 205 (41.1%)
OS event, n (%) 0.015
 Alive 128 (25.5%) 156 (31.1%)
 Dead 123 (24.5%) 95 (18.9%)
 Age, median (IQR) 61 (53, 69) 61 (54, 68.75) 0.789

Bold values represents a significant difference.

3.4 Pan-cancer analysis of CTSG expression

As shown in Figure S2, we found that CTSG was significantly downregulated in a variety of cancers, such as bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangio carcinoma, colon adenocarcinoma, esophageal carcinoma, HNSC, kidney chromophobe, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, prostate adenocarcinoma, rectum adenocarcinoma, stomach adenocarcinoma, and endometrial carcinoma. CTSG was significantly upregulated in thyroid carcinoma.

3.5 Construction of a nomogram for HNSC patients

As shown in Figure 4a, we constructed a nomogram and it could predict 1-, 3-, and 5-year overall survival of HNSC patients. In addition, the prediction result of the nomogram was highly consistent with the observation of HNSC patients, based on the nomogram calibration curve (Figure 4b). The diagnostic value of CTSG was assessed by the receiver operating characteristic (ROC) curve, and the area under the curve of CTSG was 0.694 (Figure 4c).

Figure 4 Establishment of a nomogram for HNSC patients. (a) A nomogram for evaluating the survival probability of 1-, 3-, and 5 years for HNSC. (b) Calibration curve of the nomogram. (c) ROC curve of CTSG.
Figure 4

Establishment of a nomogram for HNSC patients. (a) A nomogram for evaluating the survival probability of 1-, 3-, and 5 years for HNSC. (b) Calibration curve of the nomogram. (c) ROC curve of CTSG.

3.6 Screening of CTSG-associated DEGs in HNSC patients

As shown in Figure 5a, a total of 641 CTSG-related DEGs were identified, which included 62 downregulated genes and 579 upregulated genes. The heatmap showed the expression of the top 50 DEGs (Figure 5b). Furthermore, the GSEA was performed to further explore the function of DEGs associated with CTSG expression in HNSC patients. CTSG-related DEGs were involved in immune-related pathways, including neutrophil degranulation (NES = 2.08, p-adj < 0.05), signaling by interleukins (NES = 1.74, p -adj < 0.05), cytokine–cytokine receptor interaction (NES = 2.16, p-adj < 0.05), signaling by B cell receptor BCR (NES = 2.7, p-adj < 0.05), natural killer cell-mediated cytotoxicity (NES = 1.72, p.adj < 0.05), leukocyte transendothelial migration (NES = 2.16, p-adj < 0.05), T-cell receptor signaling pathway (NES = 1.87, p-adj < 0.05), B-cell receptor signaling pathway (NES = 1.92, p-adj < 0.05), acute myeloid leukemia (NES = 1.65, p-adj < 0.05), inflammatory response pathway (NES = 1.92, p-adj < 0.05), cancer immunotherapy by PD1 blockade (NES = 1.86, p-adj < 0.05), etc. (Figure 5c).

Figure 5 Screening of CTSG-associated DEGs in HNSC. (a) The volcano plot presents the DEGs. Blue dots indicate down-regulated genes and red dots indicate up-regulated genes. (b) Co-expression heatmap of top 50 DEGs. (c) Enrichment analysis of DEGs between the low- and high-CTSG subgroups.
Figure 5

Screening of CTSG-associated DEGs in HNSC. (a) The volcano plot presents the DEGs. Blue dots indicate down-regulated genes and red dots indicate up-regulated genes. (b) Co-expression heatmap of top 50 DEGs. (c) Enrichment analysis of DEGs between the low- and high-CTSG subgroups.

3.7 Correlation between the CTSG expression and the infiltration of immune cells

The ESTIMATE algorithm results showed that the low-CTSG subgroup had a lower ESTIMATE score (Figure 6a, p < 0.001), immune score (Figure 6b, p < 0.001), and Sromal score (Figure 6c, p < 0.001) compared to those in the high-CTSG subgroup. In addition, the ssGESA algorithm results indicated that the proportion of dendritic cells (DCs) (p < 0.001), aDC (p < 0.01), B cells (p < 0.001), CD8 T cells (p < 0.05), cytotoxic cells (p < 0.001), eosinophils (p < 0.001), iDC (p < 0.001), macrophages (p < 0.001), mast cells (p < 0.001), neutrophils (p < 0.001), NK CD56dim cells (p < 0.01), NK cells (p < 0.001), pDC (p < 0.001), and T cells (p < 0.001) was significantly decreased in the low-CTSG subgroup compared with those in the high-CTSG subgroup (Figure 6d). The correlation analysis revealed positive correlations between CTSG expression and most immune cells, including mast cells, DC, eosinophils, pDC, B cells, TFH, macrophages, Th17 cells, Th1 cells, T cells, TReg, Tem, cytotoxic cells, NK cells, T helper cells, aDC, NK CD56dim cells, Th2 cells, and CD8 T cells (Figure 6e).

Figure 6 Correlation between the expression of CTSG and the infiltration of immune cells. ESTIMATE algorithm shows the relationship between CTSG expression and ESTIMATE score (a), immune score (b), and Stromal score (c). (d) ssGSEA algorithm showed the relationship between CTSG expression and immune cell infiltration. (e) The correlation results between CTSG genes and immune cell infiltration were visualized by a lollipop plot. *p < 0.05, **p < 0.01, and ***p < 0.001.
Figure 6

Correlation between the expression of CTSG and the infiltration of immune cells. ESTIMATE algorithm shows the relationship between CTSG expression and ESTIMATE score (a), immune score (b), and Stromal score (c). (d) ssGSEA algorithm showed the relationship between CTSG expression and immune cell infiltration. (e) The correlation results between CTSG genes and immune cell infiltration were visualized by a lollipop plot. *p < 0.05, **p < 0.01, and ***p < 0.001.

In addition, we used the TIMER analysis tool to further validate the correlation between CTSG expression and immune cell infiltration. Consistent with the above results, the expression of CTSG was positively correlated with DC (p = 3.02 × 10−8), neutrophil (p = 4.41 × 10−5), macrophage (p = 1.44 × 10−8), CD4+ T cell (p = 2.81 × 10−8), CD8+ T cell (1.91 × 10−6), and B cell (p = 9.5 × 10−5) (Figure 7a). Furthermore, as shown in Figure 7b, the results of copy number alterations in CTSG expression could inhibit immune cell infiltration. We analyzed the level of CTSG genes in tumor immune microenvironment-associated cells, utilizing the TISCH database. Our findings revealed that CTSG showed a higher degree of infiltration in mast cells (Figure S3).

Figure 7 TIMER analysis tool was used to assess the relationship between CTSG and immune cell infiltration. (a) The correlation results between CTSG genes and the proportion of DC, neutrophils, macrophage, CD4+ T cells, CD8+ T cells, and tumor purity. (b) The relationship between somatic copy number alterations of CTSG and immune cell infiltration. *p < 0.05, **p < 0.01, and ***p < 0.001.
Figure 7

TIMER analysis tool was used to assess the relationship between CTSG and immune cell infiltration. (a) The correlation results between CTSG genes and the proportion of DC, neutrophils, macrophage, CD4+ T cells, CD8+ T cells, and tumor purity. (b) The relationship between somatic copy number alterations of CTSG and immune cell infiltration. *p < 0.05, **p < 0.01, and ***p < 0.001.

3.8 Screening of potential drugs and molecular docking

In the present study, the Dgibd database was applied to screen the potential small molecular compounds for CTSG genes. Our findings showed that CHEMBL374027 (CH) and MANNITOL (MAN) were the targeted drugs of CTSG. Subsequently, the molecular docking analysis was performed to further confirm the above results. Our findings showed that CTSG binds to CH with a binding energy of −4.83 kcal/mol and MAN at −7.05 kcal/mol. As presented in Figure 8, there were interactions and binding modes between the CTSG protein and small molecules (CH and MAN). All results showed that CH and MAN had a good potential to target CTSG genes.

Figure 8 Molecular docking analysis. CTSG protein binding to CH (a and b) and MAN (c and d) was visualized with PyMOL software. A 3D surface structure of the CTSG receptor and the small molecular ligand is presented on the left side of the figure (a and c). The binding pattern of the small molecular ligand to the CTSG receptor is presented on the right side of the figure (b and d).
Figure 8

Molecular docking analysis. CTSG protein binding to CH (a and b) and MAN (c and d) was visualized with PyMOL software. A 3D surface structure of the CTSG receptor and the small molecular ligand is presented on the left side of the figure (a and c). The binding pattern of the small molecular ligand to the CTSG receptor is presented on the right side of the figure (b and d).

4 Discussion

Despite advancements in cancer research and treatment, HNSC still has a bleak outlook with little improvement in prognosis over the last few decades [18]. Thus, the identification of new biomarkers and a deeper understanding of the molecular mechanisms involved in HNSC are essential for the development of more effective diagnostic and treatment approaches. In the present study, three LRGs (CLTCL1, LAPTM4B, and CTSG) were identified for predicting the prognosis of HNSC patients. Among these prognostic genes, only the CTSG gene proved to be an independent prognostic factor for HNSC patients. In addition, our findings indicated that low CTSG expression was associated with poor overall survival, disease-specific survival, and progress-free interval in HNSC patients. CTSG is a serine protease mainly found in azurophilic granules of neutrophils [19]. Few studies have shown that CTSG genes were associated with tumor progression and prognosis. However, a recent study has demonstrated that CTSG was a prognostic biomarker for oral squamous cell carcinoma patients [20]. The aim of this study was to examine the biological function and molecular mechanism of CTSG and to determine its potential as a cancer biomarker.

The tumor microenvironment plays a crucial role in the development and progression of cancer as it contains tumor cells that interact with surrounding cells through the lymphatic systems. This interaction influences tumorigenesis and highlights the importance of studying the tumor microenvironment in cancer research [21]. The progression of cancer is heavily influenced by changes in the tumor microenvironment, which includes non-immune and immune cells as well as the extracellular matrix. These changes can have a significant impact on tumor progression [22,23]. In the present study, we also performed DEG analysis and functional enrichment analysis to assess the potential function of CTSG expression in subgroups of HNSC patients. The GSEA results showed CTSG-associated DEGs were mainly implicated in inflammation- and immune-related pathways, such as neutrophil degranulation, signaling by interleukins, natural killer cell-mediated cytotoxicity, B-cell receptor signaling pathway, inflammatory response pathway, etc. Previous studies have revealed that CTSG could activate matrix metalloproteinases and regulate inflammation, and play an important role in a variety of inflammatory diseases such as acute respiratory distress syndrome, rheumatoid arthritis, chronic obstructive pulmonary disease, and periodontitis [19,24,25]. In addition, CTSG genes were shown to be important for maintaining neutrophil-medicated acute histopathology and fibrosis after renal ischemia/reperfusion injury [26]. CTSG could regulate the level of soluble forms of TNF-α and promote the pro-inflammatory effect of neutrophils [27]. Moreover, immune inflammation played a primary role in drug resistance, clinical outcomes, and tumor microenvironment in HNSC [28]. Specifically, interleukin 2 immunotherapy showed promising results in the treatment of HNSC [29,30].

Another important finding was that CTSG expression was positively correlated with the level of infiltration of immune cells (mast cells, DC, eosinophils, neutrophils, B cells, macrophages, CD4 T cells, and CD8 T cells) in HNSC patients. Our findings imply that CTSG plays a significant role in promoting tumor progression by facilitating communication between tumor cells and their inflammatory microenvironment. Increasing evidence suggests that immune cells and their biomarkers can act as therapeutic targets or prognostic markers or participate in tumor progression. An immune-related gene prognostic index differentiated prognosis and served as a potential biomarker in HNSC patients [31]. A recent study revealed the importance of B cells in prognosis and responses to PD-1 blockade in HPV-associated HNSC patients [32]. CD8 T cells were independent prognostic biomarkers in HNSC patients, and patients with low levels of B cells, CD8 T cells, and endothelial cells had a worse prognosis [33]. The poor prognosis of HNSC patients was associated with the presence of tumor-associated macrophages [34]. Neutrophils increased the invasiveness of oral squamous cell carcinoma by activating invadopodia [35]. All these pieces of evidence further showed that CTSG genes impacted the prognosis of HNSC patients by affecting tumor immune cell infiltration. However, the relationship between immunoregulatory cells and CTSG expression requires further investigation.

In our study, two potential drugs (CH and MAN) with high affinity to CTSG protein were identified. Among them, MAN exerted the nephroprotective effect in HNSC patients with cisplatin therapy [36]. Although these potential compounds should be investigated in depth for specific mechanisms, our findings implied their potential in tumor immunotherapy.

5 Conclusion

Our findings suggested that CTSG was an independent prognostic biomarker in HNSC patients and was associated with tumor immune cell infiltration in patients. These results laid a theoretical foundation for further exploration of the role of CTSG in HNSC patients.

# Ye Shen and Haibin Chen contributed equally to this work.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: Ye Shen and Haibin Chen wrote the main manuscript. Xiaoyang Gong and Ziyi Wang prepared figures and performed bioinformatics analysis. Mengjie Chen and Donghui Chen edited the article. All authors reviewed the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-02-19
Revised: 2023-05-28
Accepted: 2023-06-23
Published Online: 2023-08-09

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
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  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
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  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0660
Keywords for this article
head and neck squamous cell carcinoma; molecular docking; immune cell infiltration; lysosome
Creative Commons
BY 4.0

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  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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