Multiomic analysis of the function of SPOCK1 across cancers: an integrated bioinformatics approach

Abstract

Objective

To investigate SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression across The Cancer Genome Atlas (TCGA) cancers, both in cancer versus normal tissues and in different stages across the cancer types.

Methods

This integrated bioinformatics study used data from several bioinformatics databases (Cancer Cell Line Encyclopedia, Genotype-Tissue Expression, TCGA, Tumor Immune Estimation Resource [TIMER]) to define the expression pattern of the SPOCK1 gene. A survival analysis was undertaken across the cancers. The search tool for retrieval of interacting genes (STRING) database was used to identify proteins that interacted with SPOCK1. Gene Set Enrichment Analysis was conducted to determine pathway enrichment. The TIMER database was used to explore the correlation between SPOCK1 and immune cell infiltration.

Results

This multiomic analysis showed that the SPOCK1 gene was expressed differently between normal tissues and tumours in several cancers and that it was involved in cancer progression. The overexpression of the SPOCK1 gene was associated with poor clinical outcomes. Analysis of gene expression and tumour-infiltrating immune cells showed that SPOCK1 correlated with several immune cells across cancers.

Conclusions

This research showed that SPOCK1 might serve as a new target for several cancer therapies in the future.

Keywords

Pan-cancer analysis multiomic analysis SPOCK1 bioinformatics

Introduction

Cancer is among the most lethal diseases worldwide and it is an increasing threat to human health.¹ The number of cancer patients continues to expand due to the growth and aging of the global population.² Over the last few decades, many efforts have been invested in cancer prevention, early diagnosis and treatments to reduce the disease burden.³ The process of carcinogenesis is extremely complex, with normal cells becoming pathological with disrupted apoptosis and dysregulation of metabolism.⁴ The accumulation of gene alterations is key to oncogenesis and closely related to the prognosis of cancer patients.⁵ Hence identification of genes that are involved can be utilized as new markers.⁴ An understanding of the mechanism of the altered expression of these genes will enable them to be exploited as novel therapeutic targets and shed new light on cancer treatments.

The SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene encodes a protein that is a member of the secreted protein acidic and rich in cysteine (SPARC) family.⁶ Other members of the gene family include SPOCK2, SPOCK3, SPARC like 1 (SPARCL1), SPARC related modular calcium binding 1 (SMOC1) and SMOC2.⁷ The proteins encoded by the members of the SPARC gene family are similar in structure, consisting of an N-terminus, follistatin-like domain and a C-terminus.⁸ They have similar functions, for example they play important roles in the process of cancer cell adhesion and cell–matrix interactions, cell proliferation, migration and apoptosis.⁹ Several studies have explored the function of SPOCK1 in light of its similar structure to SPARC and found that it participates in cancer cell invasion in oesophageal squamous cell carcinoma,¹⁰ colorectal cancer^11,12 and gallbladder cancer.¹³ Research has also shown that SPOCK1 plays an important role in prostate cancer recurrence.¹⁴ The over-expression of the SPOCK1 gene promoted glioma cell proliferation, migration and invasion via the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/protein kinase B (AKT) and Wnt/β-catenin signalling pathways; and SPOCK1 silencing reversed this process.¹⁵ Further research showed that SPOCK1 was upregulated in osimertinib-resistant lung cancer cells and knockdown of SPOCK1 inhibited osimertinib-resistant cell growth and overcame resistance.¹⁶ Recent research demonstrated that SPOCK1 could induce mesenchymal epithelial transition factor receptor-dependent epithelial–mesenchymal transition (EMT) signalling in lapatinib-resistant gastric cancer, which is responsible for gastric cancer drug resistance.¹⁷ The over-expression of SPOCK1 also promoted proliferation and invasion and blocked apoptosis of HCC cells.¹⁸ SPOCK1 can function on its own, as well as by working together with other elements like fibronectin.¹⁹ Since the expression of SPOCK1 is low among normal tissue specimens,^10–13 it could be used as a potential biomarker for the early detection and precise prognosis in a variety of cancers. The underlying oncogenic mechanisms of the SPOCK1 gene during cancer initiation and progression needs further investigation and evaluation.

All of the previous studies suggested that SPOCK1 plays a critical role in human tumorigenesis,^10–19 but details about SPOCK1-mediated cancer progression are lacking. This current bioinformatics analysis investigated SPOCK1 gene expression across The Cancer Genome Atlas (TCGA) cancers, both in cancer versus normal tissues and in different stages across cancers. It also explored important genes and proteins that have close interactions with SPOCK1.

Materials and methods

Expression of the SPOCK1 gene across diverse cancer types

Clinicopathological data, RNA-Seq data (HTSeq–FPKM) and immune subtypes data acquisition was achieved by downloading from TCGA²⁰ and The Genotype-Tissue Expression (GTEx).²¹ The GTEx database was used as a supplementation to the noncancerous control group for cancer types that had less than 30 adjacent normal tissues in TCGA. An online tool, the Broad Institute Cancer Cell Line Encyclopedia (CCLE),²² was used for visualization of the pan-cancer expression of SPOCK1 across diverse cancer types.²³

The cancer types that were investigated were bladder urothelial carcinoma (BLCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), sarcoma (SARC), stomach adenocarcinoma (STAD) and uveal melanoma (UVM).

Role of SPOCK1 gene over-expression in cancer prognosis

The expression levels of SPOCK1 across several types of cancers and their subtypes and its differential gene expression analysis at different pathological stages was analysed. The current study also explored the correlation between the expression of SPOCK1 and cancer prognosis in cancers. Overall survival (OS) and the disease-free survival (DFS) analysis was undertaken on the basis of SPOCK1 expression using Gene Expression Profiling Interactive Analysis.²⁴ Specifically, paired Student’s t-test was applied to compare the mRNA expression from the SPOCK1 gene in normal and tumour samples. Correlation between SPOCK1 expression and different individual cancer stages was analysed using analysis of variance. The OS and DFS of two groups (cut-off set as the median expression levels of SPOCK1) were compared using the Kaplan–Meier method. Survival maps were plotted with a significance level of P < 0.05 and false discovery rate (FDR) adjustment.

Protein–protein interaction network analysis

The search tool for retrieval of interacting genes (STRING; https://string-db.org/) database was utilized to generate protein–protein interaction (PPI) networks.²⁵ Functional interactions between SPOCK1 and other genes were analysed using Pearson's correlation coefficient. In the PPI network, nodes represent proteins and the edges represent interactions between the proteins. The median confidence score was set at 0.4. R software was used to perform Gene Set Enrichment Analysis (GSEA; https://www.gsea-msigdb.org/gsea/index.jsp) enrichment analysis where an FDR <0.05 and an enrichment score >0.65 were set as the cut-off criteria.

Correlation between SPOCK1 and tumour-infiltrating immune cells

In order to explore the correlation between SPOCK1 and tumour-infiltrating immune cells, the Tumor Immune Estimation Resource (TIMER) was utilized.²⁶ Spearman's rank correlation coefficient was used and a P-value < 0.05 was considered statistically significant.

Results

The CCLE database was used to visualize the expression of the SPOCK1 gene across diverse cancer types (Figure 1). Figure 2 illustrates the expression of the SPOCK1 gene (transcripts per million) in GTEx normal tissues, TCGA normal tissues and TCGA cancer tissues (Tables 1 and 2). In the dot plot in Figure 2, the dots represent the expression levels of the samples.

Figure 1.

The Broad Institute Cancer Cell Line Encyclopedia was used to visualize the pan-cancer expression of the SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene across diverse cancer types. Central bold horizontal lines represent the median values. Extremities of the box represent the 25th and 75th percentiles. Error bars represent minimum and maximum outliers. The colour version of this figure is available at: http://imr.sagepub.com

Figure 2.

SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression in Genotype-Tissue Expression normal tissues, The Cancer Genome Atlas (TCGA) normal tissues and TCGA cancer tissues. ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, oesophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukaemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumours; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma. The colour version of this figure is available at: http://imr.sagepub.com

Table 1.

SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression in Genotype-Tissue Expression normal tissues and The Cancer Genome Atlas normal tissues across various cancers.

Cancer type	Transcripts per million
Cancer type	Maximum	Upper quartile	Median	Lower quartile	Minimum
BLCA	23.98	11.44	8.66	6.86	4.42
BRCA	32.92	13.96	8.99	5.29	0.71
CESC	38.1	24.33	15.43	9.15	5.26
CHOL	0.01	0.05	0.02	0.01	0
COAD	11.53	3.44	1.60	0.87	0.31
ESCA	10.02	2.05	0.84	0.45	0.22
HNSC	7.99	2.29	1.35	0.49	0.13
KIRC	115.16	51.56	34.93	21.22	6.33
LIHC	0.1	0.03	0.02	0.01	0
LUAD	1.19	0.61	0.45	0.27	0.03
LUSC	2.45	1.06	0.55	0.35	0.09
PAAD	25.65	24.55	19.88	12.76	5.89
PRAD	438.28	118.18	90.46	54.56	11.16
STAD	71.76	8.86	2.33	0.44	0.11
READ	18.79	5.34	4.61	1.22	0.64

BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; ESCA, oesophageal carcinoma; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; PAAD, pancreatic adenocarcinoma; PRAD, prostate adenocarcinoma; STAD, stomach adenocarcinoma; READ, rectum adenocarcinoma.

Table 2.

SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression in The Cancer Genome Atlas cancer tissues.

Cancer type	Transcripts per million
Cancer type	Maximum	Upper quartile	Median	Lower quartile	Minimum
BLCA	131.61	5.68	5.26	1.56	0.01
BRCA	149.75	23.92	12.58	5.42	0.06
CESC	36.48	4.39	1.84	0.73	0.02
CHOL	16.03	5.63	2.31	1.2	0.14
COAD	58.22	8.45	3.40	0.75	0
ESCA	68.84	12.13	3.66	1.08	0.11
HNSC	90.14	10.08	3.52	1.44	0.04
KIRC	396.28	29.76	10.23	2.96	0
LIHC	2.87	0.53	0.14	0.04	0
LUAD	39.28	4.27	1.43	0.54	0.01
LUSC	71.55	10.79	4.86	2.50	0.05
PAAD	101.61	22.93	13.86	6.38	0.25
PRAD	874.52	219.25	127.71	73.75	15.77
STAD	235.22	16.29	7.19	2.02	0.05
READ	42.26	5.73	1.98	0.82	0.05

The SPOCK1 gene has ten splice variants: SPOCKl-201, SPOCK1-001, SPOCK1-002, SPOCK1-003, SPOCK1-004, SPOCK1-005, SPOCK1-006, SPOCK1-007, SPOCK1-008, SPOCK1-009. The predominant SPOCK1 isoforms expressed in all contexts were SPOCK1-201, SPOCK1-001 and SPOCK1-003 (Figure 3).

Figure 3.

Different isoforms of the SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene.

The levels of SPOCK1 gene expression between TCGA tumour tissues and normal tissues were significantly different in cancers such as CHOL, PAAD, PRAD and STAD (P < 0.05) (Figure 4 and Table 3). The expression of the SPOCK1 gene was significantly different among pathological stages in cancers such as BLCA, KIRC, LUAD and STAD (P < 0.05) (Figure 5 and Table 4).

Figure 4.

Comparison of SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression between tumour tissues (red boxes) and normal tissues (grey boxes). CHOL, cholangiocarcinoma; PAAD, pancreatic adenocarcinoma; PRAD, prostate adenocarcinoma; STAD, stomach adenocarcinoma. *P < 0.05, paired Student’s t-test. Central black horizontal lines represent the median values. Extremities of the box represent the 25th and 75th percentiles. Error bars represent minimum and maximum outliers. The colour version of this figure is available at: http://imr.sagepub.com

Table 3.

The levels of SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression in cancer tissues compared with normal tissues.

Cancer type	Maximum (log₂TPM+1)	Upper quartile (log₂TPM+1)	Median (log₂TPM+1)	Lower quartile (log₂TPM+1)	Minimum (log₂TPM+1)	Statistical significance^a
CHOL normal	0.212	0.071	0.033	0.019	0	P < 0.0001
CHOL tumour	4.09	2.73	1.73	1.14	0.19	P < 0.0001
COAD normal	3.647	2.151	1.382	0.902	0.387	P < 0.0001
COAD tumour	5.888	3.241	2.139	0.807	0	P < 0.0001
HNSC normal	3.168	1.72	1.232	0.577	0.175	P < 0.0001
HNSC tumour	6.51	3.47	2.177	1.284	0.056	P < 0.0001
KIRC normal	6.86	5.716	5.167	4.474	2.874	P < 0.0001
KIRC tumour	8.634	4.943	3.489	1.985	0.014	P < 0.0001
LIHC normal	0.133	0.037	0.023	0.013	0	P < 0.0001
LIHC tumour	1.953	0.614	0.187	0.059	0	P < 0.0001
LUSC normal	1.787	1.046	0.633	0.434	0.125	P < 0.0001
LUSC tumour	6.181	3.559	2.551	1.809	0.066	P < 0.0001
PRAD normal	8.779	6.897	6.515	5.796	3.604	P < 0.0001
PRAD tumour	9.774	7.783	7.008	6.224	4.068	P < 0.0001

^aStudent’s t-test.

TPM, transcripts per million; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; LIHC, liver hepatocellular carcinoma; LUSC, lung squamous cell carcinoma; PRAD, prostate adenocarcinoma.

Figure 5.

Violin plots showing the comparison of SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression between tumour tissues at different pathological stages. (a) BLCA, bladder urothelial carcinoma; (b) KIRC, kidney renal clear cell carcinoma; (c) LUAD, lung adenocarcinoma; (d) STAD, stomach adenocarcinoma. Cancer stages were compared using analysis of variance. The colour version of this figure is available at: http://imr.sagepub.com

Table 4.

The levels of SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression in tumour tissues at different pathological stages.

Cancer type	Individual cancer stage comparison	Statistical significance^a
BLCA	Stage 2 versus stage 3	P < 0.0001
BLCA	Stage 2 versus stage 4	P < 0.0001
CESC	Stage 1 versus stage 3	P < 0.0001
COAD	Stage 1 versus stage 3	P < 0.0001
ESCA	Stage 1 versus stage 2	P < 0.0001
ESCA	Stage 1 versus stage 3	P < 0.0001
HNSC	Stage 2 versus stage 4	P < 0.0001
HNSC	Stage 3 versus stage 4	P < 0.0001
KIRC	Stage 1 versus stage 3	P < 0.0001
	Stage 1 versus stage 4	P < 0.0001
	Stage 2 versus stage 3	P < 0.0001
	Stage 2 versus stage 4	P < 0.0001
LUAD	Stage 1 versus stage 3	P < 0.0001
READ	Stage 1 versus stage 2	P < 0.0001
READ	Stage 1 versus stage 3	P < 0.0001
STAD	Stage 1 versus stage 2	P < 0.0001
	Stage 1 versus stage 3	P < 0.0001
	Stage 1 versus stage 4	P < 0.0001
UCS	Stage 1 versus stage 4	P < 0.0001
UCEC	Stage 1 versus stage 4	P < 0.0001
	Stage 2 versus stage 3	P < 0.0001
	Stage 2 versus stage 4	P < 0.0001
	Stage 3 versus stage 4	P < 0.0001
UVM	Stage 3 versus stage 4	P < 0.0001

^aAnalysis of variance.

BLCA, bladder urothelial carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD, colon adenocarcinoma; ESCA, oesophageal carcinoma; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; LUAD, lung adenocarcinoma; READ, rectum adenocarcinoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.

The OS time between cancers with higher and lower SPOCK1 gene expression were compared in TCGA cancer types (Figure 6). Data demonstrated a shorter OS with a worse prognosis in patients with cancers with higher SPOCK1 gene expression levels compared with cancers with lower SPOCK1 gene expression levels for the following cancers: COAD, HNSC, KIRC, LUAD and UVM (P < 0.05). For DFS time in TCGA tumour types (Figure 7), data demonstrated that higher SPOCK1 gene expression levels resulted in worse DFS prognosis compared with lower levels of SPOCK1 gene expression in the following tumours: BLCA, COAD, KIRC, SARC and UVM (P < 0.05).

Figure 6.

Kaplan–Meier analysis of the overall survival (OS) time between higher SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression and lower SPOCK1 gene expression in The Cancer Genome Atlas tumour types. The other lines represent the 25% and 75% cut-offs. (a) COAD, colon adenocarcinoma; (b) HNSC, head and neck squamous cell carcinoma; (c) KIRC, kidney renal clear cell carcinoma; (d) LGG, brain lower grade glioma; (e) LUAD, lung adenocarcinoma; (f) UVM, uveal melanoma. The colour version of this figure is available at: http://imr.sagepub.com

Figure 7.

Kaplan–Meier analysis of the disease-free survival (DFS) time between higher SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene expression and lower SPOCK1 gene expression in The Cancer Genome Atlas tumour types. (a) BLCA, bladder urothelial carcinoma; (b) COAD, colon adenocarcinoma; (c) KIRC, kidney renal clear cell carcinoma; (d) LGG, brain lower grade glioma; (e) SARC, sarcoma; UVM, (f) uveal melanoma. The colour version of this figure is available at: http://imr.sagepub.com

The gene interaction network showed that the SPOCK1 gene was co-expressed with the TNFAIP6, MLLT10, MEGF11, SGCB, DCTN1, SLC24A2, CNTN1 and LSAMP genes (Figure 8). The SPOCK1 gene had physical interactions with the GTF2E2 and TNF genes. The SPOCK1 gene had shared protein domains with the TACSTD2, SPOCK3 and SPOCK2 genes; and co-localized with the SLC25A33, SFXN3, MEGF11, SGCB, GRIA4, SLC24A2 and LSAMP genes. Data from the STRING database analysis revealed that all of the proteins from these genes (CHD1L, CGREF1, SERTM1, MMP14, MMP16, SPARC, SMOC1, TG, STS, and SPARCL1) were in the same protein network with SPOCK1 (Figure 9).

Figure 8.

Genes in correlations with the SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene.

Figure 9.

The protein–protein interactions with the SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene.

The GSEA enrichment analysis revealed that the enrichment of the SPOCK1 gene was found to mediate upstream glycolysis, G2M checkpoint, mammalian target of rapamycin complex 1 signalling, mitotic spindle activities, EMT and myc targets; and to mediate downstream E2 factor targets, tumour necrosis factor-α signalling via nuclear factor kappa-light-chain-enhancer of activated B cells, oestrogen response, hypoxia and the interleukin-2-signal transducer and activator of transcription 5 signalling process (Figure 10).

Figure 10.

The Gene Set Enrichment Analysis enrichment analysis of the SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene. The colour version of this figure is available at: http://imr.sagepub.com

Figure 11 presents the correlation between the expression of the SPOCK1 gene and tumour-infiltrating immune cells in BLCA, KIRC, LUAD and STAD based on data from the TIMER database. The results showed that in BLCA, the expression of the SPOCK1 gene had significant inverse correlations with tumour purity (partial correlation = –0.352, P < 0.0001) and the infiltration of B cells (partial correlation = –0.152, P < 0.0001; and significant positive correlations with the infiltration of CD4+ T cells (partial correlation = 0.17, P = 0.001096) and macrophages (partial correlation = 0.352, P < 0.0001).

Figure 11.

Correlation analysis between tumour-infiltrating immune cell signatures and the SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 (SPOCK1) gene. Spearman's rank correlation coefficient was used and a P-value <0.05 was considered statistically significant. BLCA, bladder urothelial carcinoma; KIRC, kidney renal clear cell carcinoma; LUAD, lung adenocarcinoma; STAD, stomach adenocarcinoma. The colour version of this figure is available at: http://imr.sagepub.com

In KIRC, the expression of the SPOCK1 gene had a significant inverse correlation with tumour purity (partial correlation = −0.171, P = 0.000224); and significant positive correlations with the infiltration of B cells (partial correlation =0.175, P = 0.000164), neutrophils (partial correlation = 0.134, P = 0.003934) and dendritic cells (partial correlation = 0.123, P = 0.00889).

In LUAD, the expression of the SPOCK1 gene had significant inverse correlations with tumour purity (partial correlation = –0.169, P = 0.000166) and the infiltration of B cells (partial correlation =–0.151, P = 0.000881); and significant positive correlations with the infiltration of neutrophils (partial correlation = 0.179, P < 0.0001) and dendritic cells (partial correlation = 0.114, P = 0.011486).

In STAD, the expression of the SPOCK1 gene had significant inverse correlations with tumour purity (partial correlation = −0.129, P = 0.011966) and the infiltration of B cells (partial correlation = –0.111, P = 0.033804); and significant positive correlations with the infiltration of CD8+ T cells (partial correlation = 0.116, P = 0.026166), CD4+ T cells (partial correlation = 0.265, P < 0.0001), macrophages (partial correlation = 0.583, P < 0.0001), neutrophils (partial correlation = 0.212, P < 0.0001) and dendritic cells (partial correlation = 0.368, P < 0.0001).

Discussions

The SPOCK1 gene is overexpressed in prostate cancer, lung cancer, ovarian cancer, gastric cancer, colorectal cancer and breast cancer and plays a key role in cancer progression,^9,17,27–32 which suggests that SPOCK1 could be a novel gene of interest in the search of new therapeutic targets. The SPOCK1 gene participates in regulating cancer cell proliferation, cell cycle regulation, apoptosis, adhesion, cell–matrix interactions, metastasis and drug resistance in these cancers.^9,17,21–32 However, the underlying mechanism of action of the SPOCK1 gene is still not completely understood. To address this lack of data, this current study implemented a comprehensive bioinformatics approach to discover the function of the SPOCK1 gene in a pan-cancer setting. Analysis of SPOCK1 gene expression was performed across all TCGA cancer types compared with TCGA normal tissues in addition to GTEx normal tissues. The results of this current study demonstrated that the SPOCK1 gene was upregulated in several types of cancer, showing that the SPOCK1 gene is functionally active in these tumours. The current analysis showed that in CHOL, PAAD, PRAD and STAD, the expression of the SPOCK1 gene was notably higher in cancer tissues than in normal tissues. In BLCA, KIRC, LUAD and STAD, the differences between the pathological stages were statistically significant. The stages of the disease and the degree of deterioration are related, indicating that the SPOCK1 gene might govern the initiation and progression processes of these cancers. In the survival analysis, cancers (COAD, HNSC, KIRC, LUAD and UVM), which have highly elevated the SPOCK1 gene expression, presented with worse overall survival than those that showed lower SPOCK1 gene expression. As for the DFS time in the TCGA tumour types, this current research showed that higher expression levels of the SPOCK1 gene resulted in worse DFS prognosis in BLCA, COAD, KIRC, SARC and UVM, emphasizing the importance of the SPOCK1 gene for prognosis. Interestingly, in lower grade glioma of the brain, lower expression of the SPOCK1 gene leads to worse OS and DFS, which might have correlations with the relatively higher expression of the SPOCK1 gene in the brain;³³ and the underlying reason for this unusual phenomena deserves further investigation.

The CHD1L, CGREF1, SERTM1, MMP14, MMP16, SPARC, SMOC1, TG, STS, and SPARCL1 genes correlated with the SPOCK1 gene in the protein–protein network. Among the SPOCK1 gene protein partners, matrix metalloproteinase 14 (MMP14) and MMP16 are involved in the breakdown of the extracellular matrix.³⁴ The PPI network showed interactions between the SPOCK1 gene and general transcription factor IIE subunit 2 (GTF2E2), tumour necrosis factor (TNF), lysyl oxidase like 2 (LOXL2) and multiple EGF like domrix in cancer metastasis (MEGF11); and with CHD1L, which is involved in DNA replication, repair and transcription processes.³⁵ The GSEA enrichment analysis showed that the SPOCK1 gene might have positive interactions with the EMT process. Previous research also indicated that the SPOCK1 gene mediates EMT signalling to regulate cancer cell progression and drug resistance.³⁶ Checkpoint immunotherapy has been studied comprehensively in recent years.³⁷ However, the underlying mechanisms of how tumour cells and immune cells interact remains elusive. This current study showed that the correlation between the SPOCK1 gene and tumour-infiltrating immune cells might be of novel therapeutic value to the immunotherapy of cancers.

In conclusion, the SPOCK1 gene has been demonstrated to be an oncogene that is involved in major oncogenic processes including cell-cycle control, DNA repair, apoptosis and metastasis. It may affect the migration and invasion of cancer cells in several cancers through the EMT process. It has been confirmed that the overexpression of the SPOCK1 gene can promote the occurrence and development of tumours, suggesting that it may become a new anti-tumour therapeutic target. This current systematic pan-cancer analysis of SPOCK1 gene expression in several cancer databases has provided evidence of the relationship between the altered expression of the SPOCK1 gene and clinical outcomes. This current study uncovered the importance of SPOCK1 gene expression and possible SPOCK1-related proteins and pathways in cancer progression. Therefore, this current analysis may provide valuable insights into the SPOCK1 gene as a potential biomarker and therapeutic target for various human cancers.

Footnotes

Declaration of conflicting interest

The authors declare that there are no conflicts of interest.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

Siegel

Miller

Jemal

Cancer statistics, 2019. CA Cancer J Clin 2019; 69: 7–34.

Sleeman

de Brito

Etkind

, et al. The escalating global burden of serious health-related suffering: projections to 2060 by world regions, age groups, and health conditions. Lancet Glob Health 2019; 7: e883–e892.

Biemar

Foti

Global progress against cancer – challenges and opportunities. Cancer Biol Med 2013; 10: 183–186.

Tang

Kang

Berghe

, et al. The molecular machinery of regulated cell death. Cell Res 2019; 29: 347–364.

Plati

Bucur

Khosravi-Far

Dysregulation of apoptotic signaling in cancer: molecular mechanisms and therapeutic opportunities. J Cell Biochem 2008; 104: 1124–1149.

Bradshaw

AD.

Diverse biological functions of the SPARC family of proteins.

Int J Biochem Cell Biol 2012; 44: 480–488.

Chen

Zou

Chen

, et al. Regulation of SPARC family proteins in disorders of the central nervous system. Brain Res Bull 2020; 163: 178–189.

Tai

Tang

MJ.

SPARC in cancer biology: Its role in cancer progression and potential for therapy.

Drug Resist Updat 2008; 11: 231–246.

Huang

Zhang

Zhao

, et al. SPARC expression and prognostic value in non-small cell lung cancer. Chin J Cancer 2012; 31: 541–548.

10.

Osako

Seki

Koshizuka

, et al. Regulation of SPOCK1 by dual strands of pre-miR-150 inhibit cancer cell migration and invasion in esophageal squamous cell carcinoma. J Hum Genet 2017; 62: 935–944.

11.

Zhao

Guan

Dai

, et al. Knockdown of SPOCK1 Inhibits the Proliferation and Invasion in Colorectal Cancer Cells by Suppressing the PI3K/Akt Pathway. Oncol Res 2016; 24: 437–445.

12.

Zhang

Zhi

Shi

, et al. SPOCK1 is up-regulated and promotes tumor growth via the PI3K/AKT signaling pathway in colorectal cancer. Biochem Biophys Res Commun 2017; 482: 870–876.

13.

Shu

Weng

, et al. SPOCK1 as a potential cancer prognostic marker promotes the proliferation and metastasis of gallbladder cancer cells by activating the PI3K/AKT pathway. Mol Cancer 2015; 14: 12.

14.

Okato

Arai

Kojima

, et al. Dual strands of pre-miR‑150 (miR‑150‑5p and miR‑150‑3p) act as antitumor miRNAs targeting SPOCK1 in naïve and castration-resistant prostate cancer. Int J Oncol 2017; 51: 245–256.

15.

Gao

, et al. SPOCK1 is upregulated in recurrent glioblastoma and contributes to metastasis and Temozolomide resistance. Cell Prolif 2016; 49: 195–206.

16.

Gao

, et al. SPOCK1 contributes to the third-generation EGFR tyrosine kinase inhibitors resistance in lung cancer. J Cell Biochem 2019; 120: 12566–12573.

17.

Chen

Zhou

Liu

, et al. SPOCK1 promotes the invasion and metastasis of gastric cancer through Slug‐induced epithelial‐mesenchymal transition. J Cell Mol Med 2018; 22: 797–807.

18.

Chen

Chan

THM

, et al. SPOCK1 is regulated by CHD1L and blocks apoptosis and promotes HCC cell invasiveness and metastasis in mice. Gastroenterology 2013; 144: 179–191.

19.

Wang

Liu

Tian

, et al. Reduced SPOCK1 expression inhibits non-small cell lung cancer cell proliferation and migration through Wnt/β-catenin signaling. Eur Rev Med Pharmacol Sci 2018; 22: 637–644.

20.

Weinstein

Collisson

Mills

, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 2013; 45: 1113–1120.

21.

GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 2017; 550: 204–213.

22.

Ghandi

Huang

Jané-Valbuena

, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 2019; 569: 503–508.

23.

Campbell

Getz

Korbel

, et al. Pan-cancer analysis of whole genomes. Nature 2020; 578: 82–93.

24.

Tang

Kang

, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017; 45: W98–W102.

25.

Szklarczyk

Franceschini

Wyder

, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015; 43: D447–D452.

26.

Fan

Wang

, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 2017; 77: e108–e110.

27.

Chen

Yao

, et al. SPOCK1 promotes tumor growth and metastasis in human prostate cancer. Drug Des Devel Ther 2016; 10: 2311–2321.

28.

Perurena

Zandueta

Martínez-Canarias

, et al. EPCR promotes breast cancer progression by altering SPOCK1/testican 1-mediated 3D growth. J Hematol Oncol 2017; 10: 23.

29.

Zhang

Wang

Zhang

Effects of shRNA-mediated knockdown of SPOCK1 on ovarian cancer growth and metastasis.

Cell Mol Biol (Noisy-le-grand) 2015; 61: 102–109.

30.

Yan

Sun

Liu

, et al. MiR-129-5p influences the progression of gastric cancer cells through interacting with SPOCK1. Tumour Biol 2017; 39: DOI: 10.1177/1010428317706916.

31.

Fan

Jeng

, et al. SPOCK1 Is a Novel Transforming Growth Factor-β-Induced Myoepithelial Marker That Enhances Invasion and Correlates with Poor Prognosis in Breast Cancer. PLoS One 2016; 11: e0162933.

32.

Miao

Wang

Xia

, et al. SPOCK1 is a novel transforming growth factor-β target gene that regulates lung cancer cell epithelial-mesenchymal transition. Biochem Biophys Res Commun 2013; 440: 792–797.

33.

Singh

Venugopal

Tokar

, et al. RNAi screen identifies essential regulators of human brain metastasis-initiating cells. Acta Neuropathol 2017; 134: 923–940.

34.

Page-McCaw

Ewald

Werb

, Matrix metalloproteinases and the regulation of tissue remodelling. Nat Rev Mol Cell Biol 2007; 8: 221–233.

35.

Cheng

CHD1L: a novel oncogene. Mol Cancer 2013; 12: 170.

36.

Chien

Lin

Wen

, et al. Targeting the SPOCK1-snail/slug axis-mediated epithelial-to-mesenchymal transition by apigenin contributes to repression of prostate cancer metastasis. J Exp Clin Cancer Res 2019; 38: 246.

37.

Karachaliou

Gonzalez-Cao

Sosa

, et al. The combination of checkpoint immunotherapy and targeted therapy in cancer. Ann Transl Med 2017; 5: 388.