SPINK1 Facilitates Tumor Progression in OSCC: Insights from Single-cell RNA Sequencing

doi:10.21203/rs.3.rs-4881606/v1

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SPINK1 Facilitates Tumor Progression in OSCC: Insights from Single-cell RNA Sequencing

https://doi.org/10.21203/rs.3.rs-4881606/v1

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Objective

This investigation aimed to delineate the role and underlying mechanism of Serine Peptidase Inhibitor Kazal Type 1 (SPINK1) in oral squamous cell carcinoma (OSCC) via single-cell RNA-seq data.

Materials and Methods

Subpopulations of OSCC cells were identified via the GEO database. Cell‒cell communication analysis, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and miRNA network construction were used to evaluate the role of SPINK1 in OSCC. The expression profile of SPINK1 in OSCC was authenticated via immunohistochemistry, quantitative polymerase chain reaction (qPCR), and Western blotting. The tumorigenic propensity of SPINK1 was evaluated through overexpression and knockdown assays employing Cell Counting Kit-8 (CCK-8), scratch assays, and transwell assays.

Results

SPINK1 was closely associated with T cells, malignant cells, and an array of immune modulators, including chemokines and immunoinhibitors, throughout OSCC progression. SPINK1 operates through pathways involving P53 and WNT signalling cascades. Relative to their normal tissue counterparts, SPINK1 is upregulated in OSCC, resulting in increased cell proliferation, invasion, and migration upon SPINK1 overexpression, whereas SPINK1 knockdown has opposite effects.

Conclusion

SPINK1 has emerged as a promising therapeutic target for the management of OSCC, offering prospective avenues for tailored therapeutic interventions and precision medicine strategies.

SPINK1

OSCC

Single-Cell RNA Sequencing

Oral squamous cell carcinoma (OSCC) is one of the most common types of cancer globally, accounting for over 90% of all malignant tumors found in the oral cavity. In 2020 alone, there was a staggering surge of 377,713 new cases, resulting in 177,757 deaths, placing OSCC in the 18th position among all malignant tumors[1, 2]. Despite continuous progress in diagnostic and therapeutic approaches, the survival rates for OSCC patients have remained unchanged, hovering around 50%-60% .[3]. Notably, invasion into adjacent tissues emerges as a pivotal determinant of poor prognosis, with nearly 40% of patients exhibiting lymph node metastasis in the neck. Furthermore, distant metastasis significantly diminishes patient survival rates, accounting for approximately 20% of all OSCC cases^{[4, 5]}.

Serine peptidase inhibitor Kazal type 1 (SPINK1) was first identified by Kazal et al. in extracts from the bovine pancreas and known as pancreatic Secretory Trypsin Inhibitor (PSTI), was first confirmed to be secreted from acinar cells in the exocrine glands of the pancreas into the pancreatic duct, where it interacts with pancreatic trypsin, inhibiting its activity both intra- and extracellularly^{[6, 7]}. Recent studies have revealed the intimate association between SPINK1 and cancer progression, which affects critical functions such as cell proliferation, metastasis, and drug resistance^{[8, 9]}. Moreover, SPINK1 significantly affects patient prognosis in lung cancer, colorectal cancer, and prostate cancer. It is also involved in modulating the tumor microenvironment through various pathways, promoting interactions between tumor cells and surrounding tissues.^[10–12].

In recent years, bioinformatics analyses have been instrumental in revealing the tumor microenvironment and intercellular relationships in OSCC, pinpointing key genes for further investigation. Previous single-cell analyses have underscored the close correlation between OSCC development and T cells and lymphocytes^{[13, 14]}. Through single-cell sequencing, SPINK1 has been detected in diverse normal tissues, including the colon, prostate, liver, and pancreas^[15]. However, research on SPINK1 in oral squamous cell carcinoma remains limited.

In this study, we employed single-cell level analysis to explore the role and preliminary mechanism of SPINK1 in OSCC, reaffirming its expression and elucidating its impact on biological behavior. Our findings hold promise for enhancing clinical management strategies for OSCC and offer novel insights into therapeutic biomarkers for its treatment.

1. Data acquisition

The research protocol was approved by the Hunan Xiangya Stomatological Hospital, Central South University, Institutional Review Board (20240030).The full name of the GEO database (https://www.ncbi.nlm.nih.gov/geo/info/datasets.html) is GENE EXPRESSION OMNIBUS, which is a gene expression database created and maintained by the National Center for Biotechnology Information, NCBI. Created in 2000, high-throughput gene expression data submitted by research institutions around the world were collected. The Series Matrix File data file of GSE215403 was downloaded from the NCBI GEO public database, and a total of 12 cases of oral squamous cell carcinoma gingival and buccal tissue expression profile data were included.

2. Single Cell Analysis

First, the expression profile is read through the Seurat package, and low-expression genes (nFeature_RNA > 200 & percent.mt < 10 & nCount_RNA < 200000) are filtered out. Then, standardization, normalization, PCA, Harmony, and UMAP analysis are performed on the data, and ElbowPlot is used to determine the optimal number of pcs, obtain the positional relationship between each cluster through UMAP analysis, and annotate the clusters. These genes are annotated to some cells that are important in the occurrence of the disease. Finally, we extracted marker genes for each cell subtype from the single-cell expression profile by setting the logfc.threshold parameter of FindAllMarkers to 1.

3. Cell communication

By integrating intracellular and intercellular signals, CellCall is a toolkit for inferring intercellular communication networks and internal regulatory signals. It collects ligand‒receptor‒transcription factor (L‒R‒TF) axis datasets on the basis of the KEGG pathway. On the basis of prior knowledge of L-R-TF interactions, CellCall binds the expression of ligands/receptors downstream of certain L-R pairs. TF activity to infer intercellular communication.

4. GSVA (gene set difference analysis)

Gene set variation analysis (GSVA) is a nonparametric, unsupervised method for assessing gene set enrichment. GSVA converts gene-level changes into pathway-level changes by comprehensively scoring the gene set of interest and then determining the biological function of the sample. In this study, gene sets were downloaded from the Molecular Signatures Database, and the GSVA algorithm was used to score each gene set comprehensively to evaluate potential biological function changes in different samples.

5. GSEA

According to the expression of SPINK1 in patients, patients were divided into high- and low-expression groups, and GSEA was used to further analyse the differences in signalling pathways between the two groups. The background gene set is the version 7.0 annotated gene set downloaded from the MsigDB database. As an annotated gene set for subtype pathways, differential expression analysis of pathways between different groups was performed. Significantly enriched gene sets (adjusted p values) were analysed on the basis of the consistency score. less than 0.05) for sorting. GSEA is often used in research that closely combines disease classification with biological significance.

6. Cell Lines, Cell Culture Conditions, and Transfection

Human oral keratinocytes (HOKs) and the HNSC cell lines SCC9, SCC25, HN4, HN30, and CAL27 were procured from the China Center for Type Culture Collection. These cells were cultured in Dulbecco's modified Eagle's medium (DMEM; Biological Industries, Israel, C3120-0500) with 10% heat-inactivated fetal bovine serum (Biological Industries, Israel, 04-001-1C), 100 units/mL penicillin, and 100 g/mL streptomycin, maintained at 37°C in a humidified 5% CO2 atmosphere. The shSPINK1 plasmid (GeneCopoeia, China, XM_017009906) was constructed using the GV248 vector. A SPINK1 overexpression vector (GV658, CMV enhancer-MCS-polyA-EF1A-zsGreen-sv40-puromycin) was created based on the SPINK1 reference sequence (NM_001379610.1) from the National Biotechnology Information Center database. Transient transfection was performed using Lipofectamine 3000 (Invitrogen, USA, L3000015) following the manufacturer's instructions. The transfected cells were cultured and harvested for subsequent assays.

7. RNA extraction and real-time polymerase chain reaction (PCR) analysis

Total RNA was extracted using TRIzol Reagent, and cDNA was synthesized with the HiScript II Q RT SuperMix for qPCR Reagent Kit (Vazyme, China, R123-01). Real-time PCR was conducted with ChamQ Universal SYBR qPCR Master Mix (Vazyme, China, Q711-02/03). GAPDH served as the control, and the PCR products were quantified and normalized. The PCR primers for the SPINK1 gene were as follows:

forward: 5'-ATAGGATCCGCCATGAAGGTAACAGGCATC-3',

reverse: 5'-GGCGAATTCGCAAGGCCCAGATTTTTGAAT-3'.

8. Western blot analysis

Cells were collected when they reached 80%-90% confluence. Protein samples (30–40 µg) were separated using 6% or 8% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to 0.45 µm PVDF membranes (MERCK, Germany, IPVH00010). After blocking with 4% BSA at room temperature for 1 hour, the appropriate primary antibodies were added, and the samples were incubated overnight at 4°C. The primary antibodies used for Western blot analysis included anti-SPINK1 (Bioss, China, bs-1385R, 1:1000) and GAPDH (Bioss, China, bs-13282R, 1:1000). Secondary antibodies labelled with IR Dye (Bioss, China, bs-40295G-IRDye8, 1:10000) were used. The signals were detected via an Odyssey Infrared Imaging System (Tanon, China).

9. Cell Proliferation Analysis

Following transient transfection with SPINK1 expression vectors, tumor cells were seeded in triplicate at a density of 1000 cells per well in 96-well plates. Cell proliferation assays were conducted via the Cell Counting Kit-8 (Dojindo, Japan, CK04). At 0, 12, 24, 48, and 72 hours postseeding, the growth kinetics of the tumor cells were monitored via a microplate reader system (Tanon, China).

10. Wound-healing Assay

Tumor cells were grown in 6-well plates and transfected or pretreated according to the protocol until they were fully confluent. Once they reached 90%-100% confluency, the cells were serum-starved for 24 hours. They were then manually scratched with a 10 µL pipette tip (time, 0 h), washed twice with 2 mL of PBS for 5 minutes each, and incubated in serum-free DMEM. At 24 hours postscratching, five nonoverlapping fields were randomly imaged. The migration boundaries of the tumor cells were automatically determined via ImageJ software (NIH, USA, v1.8.0.172), and the boundaries with the highest migratory activity were manually selected and highlighted with red lines.

11. Transwell Migration and Invasion Assay

For the migration assay, Transwell chambers with a 0.8 µm pore size (Corning, USA, 3422) were used, while invasion assays employed Transwell chambers coated with Matrigel on the upper surface (BD, USA, 356234). Migrated or invaded cells were fixed, stained with crystal violet, and images from five randomly selected areas were captured. Cell counts were conducted using Image-Pro Plus software (Media Cybernetics).

12. Statistical analysis

Statistical analysis of the GEO data was performed with R software (version 4.2.2), using a significance level of p < 0.05. GraphPad Prism 7.0 was employed for analyzing and interpreting experimental data. Student’s t-test was used to compare differences between two groups, with p < 0.05 indicating significant differences.

1. Single-cell sample subtype cluster analysis

The GSE215403 dataset downloaded from the NCBI GEO public database contains 12 gingival and buccal tissue samples of oral squamous cell carcinoma. This analysis revealed the expression of SPINK1 in the original data of GSE215403 (Fig. S1A). Only cells with nFeature_RNA > 200 & percent.mt < 10 & nCount_RNA < 200000 in the expression profile were subsequently retained for this analysis, for a total of 34,297 cells. The feature expression levels were included for subsequent analysis (Fig. S1B-1C). The 10 genes with the highest standard deviations are displayed in Fig. S1D.

In this study, 2000 hypervariable genes were selected, and PCA dimensionality reduction analysis was subsequently conducted to determine that there was a batch effect among samples (Fig. S1E). Harmony analysis was further used to reduce dimensionality to eliminate batches (Fig. S1F). The optimal number of pcs was 30 according to ElbowPlot (Fig. S1G), and 13 cell subpopulations were obtained via UMAP analysis at 0.2 resolution (Fig. 1A). This study further annotated each subtype, and 13 clusters were annotated into 9 cell categories: T cells, malignant cells, myeloid cells, proliferative T cells, fibroblasts, plasma cells, endothelial cells, B cells, and mast cells. Figure 1B, 1D) and shows the proportion of these 9 cell categories in cells with high and low SPINK1 expression (Fig. 1E).

2. SPINK1 expression in single-cell data and cell-call communication analysis

This study also investigated SPINK1 expression in nine cell types: T cells, malignant cells, myeloid cells, proliferative T cells, fibroblasts, plasma cells, endothelial cells, B cells, and mast cells.(Fig. 1C). We subsequently used CellCall to conduct cell communication analysis on SPINK1 high-expressing cells and low-expressing cells. The bubble chart revealed the active communication of these pathways. In the communication of highly expressing cells, signals such as the chemokine signalling pathway, the Jak-STAT signalling pathway, and osteoclast differentiation are involved. activity is particularly prominent. In low-level cell communication, the activities of signals such as the MAPK signalling pathway, TNF signalling pathway, and chemokine signalling pathway are particularly prominent. The ring interaction diagram further reveals the ligand‒receptor interaction and shows the direction and intensity of intercellular communication (Fig. 1F‒I).

Figure1. Single-cell sample subtype cluster analysis and SPINK1 expression in single-cell data and cell-to-cell communication analysis. (A) Thirteen cell subpopulations were obtained and (B) annotated into 9 cell categories

T cells, malignant cells, myeloid cells, proliferative T cells, fibroblasts, plasma cells, endothelial cells, B cells, and mast cells. (C) Expression of SPINK1 in cell subpopulations. (D) Expression of marker genes for each cell subtype. (E) Proportion of these 9 cell categories in cells with high and low SPINK1 expression. (F) Cell communication pathways and (G) their intensity and directions in high-expressing cells. (H) Cell communication pathways and (I) their intensity and directions in low-expressing cells.

3. Analysis of the correlation between SPINK1 and immune factors

This study revealed a correlation between SPINK1 and different immune factors, including immune regulatory factors, chemokines, cell receptors, and human leukocyte antigens, from the TISIDB database. SPINK1 was strongly correlated with immune factors, among which chemokines were significantly positively correlated with CCL3, CXCL2, and CXCL8; immunoinhibitors were significantly positively correlated with ADORA2A, IL10RB, TGFB1, etc.; immunostimulators were significantly positively correlated with NT5E, MICB, and CD276; MHC was significantly positively correlated with B2M, HLA-C, and HLA − A; and CXCR2, CCR8, and CXCR3 in the receptor were significantly positively correlated with CXCR2, CCR8, and CXCR3 (Fig. 2A-2E). These analyses confirmed that these key genes are closely related to the level of immune cell infiltration and play important roles in the immune microenvironment.

4. Signalling pathways in which SPINK1 participates

Next, we will study the specific signalling pathways associated with SPINK1 and explore the potential molecular mechanisms by which SPINK1 affects the progression of oral squamous cell carcinoma. The GSVA results revealed that SPINK1 was enriched in P53_PATHWAY, NOTCH_SIGNALING, WNT_BETA_CATENIN_SIGNALING, IL6_JAK_STAT3_SIGNALING and other signalling pathways (Fig. 2F). In addition, GSEA revealed that SPINK1 was enriched mainly in the apoptotic signalling pathway, extrinsic apoptotic signalling pathway, regulation of the apoptotic signalling pathway and other signalling pathways (Fig. 2G-2H). These findings suggest that SPINK1 may affect the progression of oral squamous cell carcinoma through these pathways.

Figure 2. Immunological relevance of SPINK1 and analysis of related pathways. (A) Correlations between SPINK1 and receptor-related genes. (B) Correlations between SPINK1 and MHC-related genes. (C) Correlations between SPINK1 and immunostimulator-related genes. (D) Correlations between SPINK1 and immunoinhibitor-related genes. (E) Correlations between SPINK1 and chemokine-related genes. (E) Signalling pathways involving SPINK1. (G) GSEA revealed that the signalling pathways enriched mainly by SPINK1. (H) Pathways and marker genes enriched for SPINK1. (I) ssGSEA was used to calculate the enrichment of metabolic pathways.

5. Activity differences and coexpression analysis between SPINK1 and metabolic pathways

This study used the gsva function of the GSVA package to perform ssGSEA to calculate the enrichment scores of different metabolic pathways for each cell. These scores were subsequently normalized, and a heatmap was generated to display the enrichment scores of different cells. The results revealed that the enrichment score of amino acid metabolism pathways was greater (Fig. 2I). We obtained oral squamous cell carcinoma disease regulatory genes from the GeneCards database and selected the five genes with the highest relevance scores (IL-6, NOTCH1, TP53, and WNT1) to analyse gene coexpression with SPINK1, as well as the correlation of coexpressed genes (Fig. 3A-D).

Figure 3. Correlation of SPINK1 with related pathways. (A-D) Coexpression of SPINK1 with IL-6, NOTCH1, TP53, and WNT1 and the correlation of coexpressed genes.

6. SPINK1 is upregulated in OSCC

This study aimed to validate the expression of SPINK1 in OSCC. The TCGA database revealed that SPINK1 was highly expressed in OSCC (Fig. 4A) and had good diagnostic performance (Fig. 4B). Real-time PCR analysis was conducted to assess SPINK1 gene expression in HOK normal oral keratinocytes and OSCC cells (SCC9, SCC25, CAL27, HN4, and HN30), revealing a notable increase in SPINK1 gene expression in OSCC (Fig. 4c). Further validation of SPINK1 protein expression in OSCC cell lines was performed via Western blot analysis, which revealed elevated expression of SPINK1 in OSCC cell lines (Fig. 4D).

Figure 4. SPINK1 was upregulated in OSCC. (A) Expression of SPINK1 in the TCGA-OSCC cohort. (B) Better diagnostic ROC performance of SPINK1. (C) High expression of SPINK1 mRNA in OSCC cell lines. (D) High expression of the SPINK1 protein in OSCC cell lines. (E) Real-time PCR showing reduced expression of spink1 after transfection with the shRNA vector. (F) Knockdown of SPINK1 reduces the proliferation ability of CAL27 cells. (G) A scratch assay demonstrated that the knockdown of SPINK1 reduces the healing rate and (H) invasion and migration ability of CAL27 cells.

7. Effect of SPINK1 on Biological Behavior

To further explore the influence of SPINK1 on the biological behavior of OSCC cells, the CAL27 and HN4 cell lines were selected for subsequent experiments on the basis of their expression levels. Following transfection with SPINK1-specific shRNA vectors for 48 hours, real-time PCR revealed a significant reduction in SPINK1 mRNA expression in CAL27 cells (Fig. 4E). Subsequent SPINK1 knockdown led to a notable decrease in the proliferation of CAL27 cells (Fig. 4F). The wound-healing assay results demonstrated an approximately 40% reduction in the migration of SPINK1-knockdown CAL27 cells (Fig. 4G). Transwell migration and invasion assays further validated the significantly diminished migration and invasion abilities of SPINK1-knockdown CAL27 cells (Fig. 4H), underscoring the pivotal role of SPINK1 in mediating the proliferation, migration, and invasion of OSCC tumors.

Following transfection with SPINK1 overexpression vectors for 48 hours, real-time PCR revealed a marked increase in SPINK1 mRNA expression in HN4 cells (Fig. 5A). The overexpression of SPINK1 strongly increased the proliferation ability of HN4 cells (Fig. 5B). Moreover, SPINK1 overexpression in HN4 cells markedly promoted their migration and invasion abilities (Fig. 5D). Additionally, the wound-healing assay results corroborated these findings (Fig. 5C).

8. SPINK1 interacts with the IL6-STAT3 signalling pathway

We further analysed the signalling pathways potentially involving SPINK1. We found that the overexpression of SPINK1 in HN4 cells can increase IL-6 expression and increase STAT3 phosphorylation. Similarly, SPINK1 knockdown in CAL27 cells reduced IL-6 expression and decreased STAT3 phosphorylation (Fig. 5E).

Figure 5. SPINK1 is a promoter of carcinogenesis in OSCC. (A) Real-time PCR results showing increased expression of spink1 after transfection with the SPINK1 overexpression vector. (B) Overexpression of SPINK1 enhances the proliferation ability of HN4 cells. (C) A scratch assay demonstrated that the overexpression of SPINK1 increased the healing rate and (D) invasion and migration ability of HN4 cells. (E) Western blotting results indicating that SPINK1 may be associated with the activation of the IL-6/STAT3 pathway.

Single-cell RNA sequencing (scRNA-seq) is a powerful method for analyzing complex gene structures and expression patterns at the single-cell level, enabling accurate cell type differentiation and a deeper understanding of molecular mechanisms.^[16]. In this investigation, we embarked on a comprehensive exploration of the interplay between SPINK1 and OSCC. By clustering cells on the basis of SPINK1 expression levels, we explored the involvement of SPINK1 in intercellular communication, immune modulation, metabolic activity, and molecular pathways. Finally, we revealed the expression and functional significance of SPINK1 in OSCC cells.

The immune system is a pivotal player in cancer initiation and progression. Our findings underscore the enrichment of SPINK1 across diverse cell populations, including T cells, malignant cells, and myeloid cells. Furthermore, the SPINK1 gene is positively correlated with numerous immune factors, implying an intimate association between SPINK1, immune regulation, and evasion strategies employed by tumors. Similar findings have been observed in stem cell cancers, where SPINK1 is involved in several immune pathways, including T-cell activation, and is positively correlated with various immune checkpoints. ^[17].

While the precise mechanisms underlying the role of SPINK1 in cancer remain elusive, studies suggest that the SPINK1 gene harbors IL-6 response elements, indicating that it functions as an acute-phase reactant. In colorectal cancer, IL-6 increases SPINK1 expression through STAT3 activation.^[18].Similarly, in clear cell carcinoma, IL-6 is believed to enhance cancer cell spread and therapy resistance via SPINK1. ^[19].In prostate cancer, the KIT tyrosine kinase signaling pathway regulates SPINK1-mediated tumorigenesis and may interact with the WNT pathway.^[20]. Consistently, our findings revealed enrichment of the SPINK1 gene in pathways such as the P53, Notch, Wnt, and IL-6 pathways. Aberrant activation of these pathways may intricately contribute to tumorigenesis and progression, with SPINK1 potentially serving as a regulatory nexus in these cascades.

The dysregulated expression of the SPINK1 gene across various cancers closely correlates with the modulation of tumor biological behaviors. Understanding the impact of SPINK1 on cancer biology is paramount for deciphering its mechanistic underpinnings and identifying therapeutic targets. SPINK1 activates signaling through EGFR to boost pancreatic cancer cell proliferation, a pattern also observed in prostate and liver cancers.^[21–23].In prostate cancer, activation of the androgen receptor raises SPINK1 levels, enhancing cancer cell plasticity.y[24]. In our study, the inhibition of SPINK1 in CAL27 cells substantially inhibited tumor proliferation and growth, whereas the overexpression of SPINK1 in HN4 cells intensified these malignant traits.

In summary, we first analysed the expression of SPINK1 in various cell subgroups of OSCC and the potential mechanisms involved. We successfully validated the high expression of SPINK1 and its impact on the proliferation, invasion, and migration of OSCC cells. This discovery not only enriches cancer treatment targets but also provides new perspectives and strategies for personalized therapy and precision medicine.

SPINK1	Serine Peptidase Inhibitor Kazal Type 1
OSCC	oral squamous cell carcinoma
GSVA	gene set variation analysis
GSEA	gene set enrichment analysis
qPCR	quantitative polymerase chain reaction
CCK-8	Cell Counting Kit-8
PSTI	Pancreatic Secretory Trypsin Inhibitor

Acknowledgements：All the authors are thankful to the kindly sharing from Xiangya Stomatological Hospital.

Ethics approval and consent to participate：Not applicable.

Clinical trial number: not applicable.

Consent for publication：Not applicable.

Availability of data and materials：The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests：The authors declare no competing interests.

Funding：This study was funded by National Natural Science Foundation of China, Grant/Award Number:82170972.

Authors' contributions：Mingyan Bao completed the experiment and wrote the manuscript.Zhangui Tang guided the experimental design and methods, reviewed the experimental data, and made revisions to the manuscript.

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SPINK1 Facilitates Tumor Progression in OSCC: Insights from Single-cell RNA Sequencing

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