High SPINK1 Expression Predicts Poor Prognosis and Promotes Cell Proliferation and Metastasis of Hepatocellular Carcinoma

Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma

BACKGROUND

The current study's objective is to evaluate the molecular genetic mechanisms influencing the biological behavior of hepatocellular carcinoma (HCC) by analyzing the transcriptomic and epigenetic signatures of the tumors.

METHODS

Transcriptomic data were downloaded from the NCBI GEO database. We investigated the expression differences between the GSE46444 (48 cirrhotic tissues versus 88 HCC tissues) and GSE63898 (168 cirrhotic tissues versus 228 HCC tissues) data sets using GEO2R. Differentially expressed genes were evaluated using GO and KEGG metabolic pathway analysis websites. Whole genome bisulfite sequencing (WGBS) and Methylated DNA Immunoprecipitation Sequencing (MeDIP-Seq) data sets (26 HCC tissues versus 26 adjacent non-tumoral tissues) were also downloaded from the NCBI SRA database. These data sets were analyzed using Bismark and QSEA, respectively. The methylation differences between the groups were assessed using functional enrichment analysis.

RESULTS

In the GSE46444 data set, 80 genes were upregulated, and 315 genes were downregulated in the tumor tissue (HCC tissue) compared to the non-tumor cirrhotic tissue. In the GSE63898 data set, 1261 genes were upregulated, and 458 genes were downregulated in the cirrhotic tissue compared to the tumor tissues. WGBS revealed that 20 protein-coding loci were hypermethylated. while the hypomethylated regions were non-protein-coding. The methylated residues of the tumor tissue, non-tumorous cirrhotic tissue, and healthy tissue were comparable. MeDIP-Seq, conducted on tumoral and non-tumoral tissues, identified hypermethylated or hypomethylated areas as protein-coding regions. The functional enrichment analysis indicated that these genes were related to pathways including peroxisome, focal adhesion, mTOR, RAP1, Phospholipase D, Ras, and PI3K/AKT signal transduction.

CONCLUSIONS

The investigation of transcriptomic and epigenetic mechanisms identified several genes significant in the biological behavior of HCC. These genes present potential targets for the development of targeted therapy.

High expression of serine protease inhibitor kazal type 1 predicts poor prognosis and promotes the progression and invasion of oral tongue squamous cell carcinoma

OBJECTIVE

This study aimed to investigate the expression of serine protease inhibitor kazal type 1 (SPINK1) and its carcinogenic effect in oral tongue squamous cell carcinoma (OTSCC).

DESIGN

Initially, bioinformatics analysis was conducted using data from The Cancer Genome Atlas and Gene Expression Omnibus to compare SPINK1 mRNA expression between malignant and adjacent tissues. Subsequently, the impact of differential expression on survival and other clinical variables was examined. Additionally, histology microarray analysis was performed to assess SPINK1 protein expression in 35 cases of malignant and adjacent tissues. Finally, alterations in SPINK1 expression were evaluated to determine its biological phenotypes in OTSCC, including proliferation, apoptosis, invasion, and metastasis.

RESULTS

OTSCC tissues exhibit higher levels of SPINK1 compared to surrounding cancerous tissues. Notably, increased SPINK1 expression correlates with the pathological N stage and independently predicts overall survival among patients with OTSCC.

CONCLUSION

Suppression of SPINK1 inhibited OTSCC cell proliferation, invasion, and motility while promoting apoptosis. These findings suggest that SPINK1 may serve as a prognostic biomarker as well as a potential therapeutic target for managing OTSCC.

ECRG2/SPINK7 Tumor Suppressor as Modulator of DNA Damage Response

Esophageal Cancer-Related Gene 2 (ECRG2), also known as Serine Peptidase Inhibitor Kazal type 7 (SPINK7), is a novel tumor suppressor gene from the SPINK family of genes that exhibits anticancer potential. ECRG2 was originally identified during efforts to discover genes involved in esophageal tumorigenesis. ECRG2 was one of those genes whose expression was absent or reduced in primary human esophageal cancers. Additionally, absent or reduced ECRG2 expression was also noted in several other types of human malignancies. ECRG2 missense mutations were identified in various primary human cancers. It was reported that a cancer-derived ECRG2 mutant (valine to glutamic acid at position 30) failed to induce cell death and caspase activation triggered by DNA-damaging anticancer drugs. Furthermore, ECRG2 suppressed cancer cell proliferation in cultured cells and grafted tumors in animals and inhibited cancer cell migration/invasion and metastasis. ECRG2 also was identified as a negative regulator of Hu-antigen R (HuR), an oncogenic RNA-binding protein that is known to regulate mRNA stability and the expression of transcripts corresponding to many cancer-related genes. ECRG2 function is important also for the regulation of inflammatory responses and the maintenance of epithelial barrier integrity in the esophagus. More recently, ECRG2 was discovered as one of the newest members of the pro-apoptotic transcriptional targets of p53. Two p53-binding sites (BS-1 and BS-2) were found within the proximal region of the ECRG2 gene promoter; the treatment of DNA-damaging agents in cancer cells significantly increased p53 binding to the ECRG2 promoter and triggered a strong ECRG2 promoter induction following DNA damage. Further, the genetic depletion of ECRG2 expression significantly impeded apoptotic cell death induced by DNA damage and wild-type p53 in cancer cells. These findings suggest that the loss of ECRG2 expression, commonly observed in human cancers, could play important roles in conferring anticancer drug resistance in human cancers. Thus, ECRG2 is a novel regulator in DNA damage-induced cell death that may also be a potential target for anticancer therapeutics.

Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma

Machine learning techniques have been widely used in predicting disease prognosis, including cancer prognosis. One of the major challenges in cancer prognosis is to accurately classify cancer types and stages to optimize early screening and detection, and machine learning techniques have proven to be very useful in this regard. In this study, we aimed at identifying critical genes for diagnosis and outcomes of hepatocellular carcinoma (HCC) patients using machine learning. The HCC expression dataset was downloaded from GSE65372 datasets and TCGA datasets. Differentially expressed genes (DEGs) were identified between 39 HCC and 15 normal samples. For the purpose of locating potential biomarkers, the LASSO and the SVM-RFE assays were performed. The ssGSEA method was used to analyze the TCGA to determine whether there was an association between SPINK1 and tumor immune infiltrates. RT-PCR was applied to examine the expression of SPINK1 in HCC specimens and cells. A series of functional assays were applied to examine the function of SPINK1 knockdown on the proliferation of HCC cells. In this study, 103 DEGs were obtained. Based on LASSO and SVM-RFE analysis, we identified nine critical diagnostic genes, including C10orf113, SPINK1, CNTLN, NRG3, HIST1H2AI, GPRIN3, SCTR, C2orf40 and PITX1. Importantly, we confirmed SPINK1 as a prognostic gene in HCC. Multivariate analysis confirmed that SPINK1 was an independent prognostic factor for overall survivals of HCC patients. We also found that SPINK1 level was positively associated with Macrophages, B cells, TFH, T cells, Th2 cells, iDC, NK CD56bright cells, Th1 cells, aDC, while negatively associated with Tcm and Eosinophils. Finally, we demonstrated that SPINK1 expression was distinctly increased in HCC specimens and cells. Functionally, silence of SPINK1 distinctly suppressed the proliferation of HCC cells via regulating Wnt/β-catenin pathway. The evidence provided suggested that SPINK1 may possess oncogenic properties by inducing dysregulated immune infiltration in HCC. Additionally, SPINK1 was identified as a novel biomarker and therapeutic target for HCC.

SPINK1-induced tumor plasticity provides a therapeutic window for chemotherapy in hepatocellular carcinoma

Tumor lineage plasticity, considered a hallmark of cancer, denotes the phenomenon in which tumor cells co-opt developmental pathways to attain cellular plasticity, enabling them to evade targeted therapeutic interventions. However, the underlying molecular events remain largely elusive. Our recent study identified CD133/Prom1 in hepatocellular carcinoma (HCC) tumors to mark proliferative tumor-propagating cells with cancer stem cell-like properties, that follow a dedifferentiation trajectory towards a more embryonic state. Here we show SPINK1 to strongly associate with CD133 + HCC, and tumor dedifferentiation. Enhanced transcriptional activity of SPINK1 is mediated by promoter binding of ELF3, which like CD133, is found to increase following 5-FU and cisplatin treatment; while targeted depletion of CD133 will reduce both ELF3 and SPINK1. Functionally, SPINK1 overexpression promotes tumor initiation, self-renewal, and chemoresistance by driving a deregulated EGFR-ERK-CDK4/6-E2F2 signaling axis to induce dedifferentiation of HCC cells into their ancestral lineages. Depleting SPINK1 function by neutralizing antibody treatment or in vivo lentivirus-mediated Spink1 knockdown dampens HCC cancer growth and their ability to resist chemotherapy. Targeting oncofetal SPINK1 may represent a promising therapeutic option for HCC treatment.

Overexpression of SH2D1A promotes cancer progression and is associated with immune cell infiltration in hepatocellular carcinoma via bioinformatics and in vitro study

BACKGROUND

SH2 domain containing 1A (SH2D1A) expression has been linked to cancer progression. However, the functions of SH2D1A in hepatocellular carcinoma (HCC) have not been reported.

METHODS

The effects of SH2D1A on the proliferation, migration, and invasion of HCC cells and the related pathways were re-explored in cell models with SH2D1A overexpression using the CCK-8, migration and invasion assays and western blotting. The functions and mechanisms of genes co-expressed with SH2D1A were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The relationship between SH2D1A expression and immune microenvironment features in HCC was explored.

RESULTS

Elevated SH2D1A expression promoted cell proliferation, migration, and invasion, which was related to the overexpression of p-Nf-κB and BCL2A1 protein levels in HCC. SH2D1A expression was related to the immune, stromal, and ESTIMATE scores, and the abundance of immune cells, such as B cells, CD8+ T cells, and T cells. SH2D1A expression was significantly related to the expression of immune cell markers, such as PDCD1, CD8A, and CTLA4 in HCC.

CONCLUSION

SH2D1A overexpression was found to promote cell growth and metastasis via the Nf-κB signaling pathway and may be related to the immune microenvironment in HCC. The findings indicate that SH2D1A can function as a biomarker in HCC.

The Carcinogenic Potential of Bisphenol A in the Liver Based on Transcriptomic Studies

Bisphenol A (BPA) is an environmental toxin widely used in the production of polycarbonate plastics. A correlation exists between BPA tissue contamination and the occurrence of pathological conditions, including cancer. First-passage detoxification of high BPA amounts in the liver promotes hepatotoxicity and morphological alterations of this organ, but there is a lack of knowledge about the molecular mechanisms underlying these phenomena. This prompted us to investigate changes in the liver transcriptomics of 3-month-old female mice exposed to BPA (50 mg/kg) in drinking water for 3 months. Five female mice served as controls. The animals were euthanized, the livers were collected, and RNA was extracted to perform RNA-seq analysis. The multistep transcriptomic bioinformatics revealed 120 differentially expressed genes (DEGs) in the BPA-exposed samples. Gene Ontology (GO) annotations indicated that DEGs have been assigned to many biological processes, including "macromolecule modification" and "protein metabolic process". Several of the revealed DEGs have been linked to the pathogenesis of severe metabolic liver disorders and malignant tumors, in particular hepatocellular carcinoma. Data from this study suggest that BPA has a significant impact on gene expression in the liver, which is predictive of the carcinogenic potential of this compound in this organ.

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.

The roles of mutated SPINK1 gene in prostate cancer cells

SPINK1-positive prostate cancer (PCa) has been identified as an aggressive PCa subtype. However, there is a lack of definite studies to elucidate the underlying mechanism of the loss of SPINK1 expression in most PCa cells except 22Rv1 cells, which are derived from a human prostatic carcinoma xenograft, CWR22R. The aim of this study was to investigate the mechanisms of SPINK1 protein positive/negative expression and its biological roles in PCa cell lines. SPINK1 mRNA was highly expressed in 22Rv1 cells compared with LNCaP, C4-2B, DU145, and PC-3 cells, and the protein was only detected in 22Rv1 cells. Among these cell lines, the wild-type SPINK1 coding sequence was only found in 22Rv1 cells, and two mutation sites, the c.194G>A missense mutation and the c.210T>C synonymous mutation, were found in other cell lines. Our further research showed that the mutations were associated with a reduction in SPINK1 mRNA and protein levels. Functional experiments indicated that SPINK1 promoted PC-3 cell proliferation, migration, and invasion, while knockdown of SPINK1 attenuated 22Rv1 cell proliferation, migration, and invasion. The wild-type SPINK1 gene can promote the malignant behaviors of cells more than the mutated ones. Cell cycle analysis by flow cytometry showed that SPINK1 decreased the percentage of cells in the G0/G1 phase and increased the percentage of S phase cells. We demonstrated that the c.194G>A and c.210T>C mutations in the SPINK1 gene decreased the mRNA and protein levels. The wild-type SPINK1 gene is related to aggressive biological behaviors of PCa cells and may be a potential therapeutic target for PCa.

A novel diagnostic four-gene signature for hepatocellular carcinoma based on artificial neural network: Development, validation, and drug screening

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers with high mortality in the world. HCC screening and diagnostic models are becoming effective strategies to reduce mortality and improve the overall survival (OS) of patients. Here, we expected to establish an effective novel diagnostic model based on new genes and explore potential drugs for HCC therapy.

Methods: The gene expression data of HCC and normal samples (GSE14811, GSE60502, GSE84402, GSE101685, GSE102079, GSE113996, and GSE45436) were downloaded from the Gene Expression Omnibus (GEO) dataset. Bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes, and functional enrichment pathways. QRT-PCR was used to validate the expression of diagnostic candidate genes. A diagnostic model based on candidate genes was established by an artificial neural network (ANN). Drug sensitivity analysis was used to explore potential drugs for HCC. CCK-8 assay was used to detect the viability of HepG2 under various presentative chemotherapy drugs.

Results: There were 82 DEGs in cancer tissues compared to normal tissue. Protein–protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and infiltrating immune cell analysis were administered and analyzed. Diagnostic-related genes of MT1M, SPINK1, AKR1B10, and SLCO1B3 were selected from DEGs and used to construct a diagnostic model. The receiver operating characteristic (ROC) curves were 0.910 and 0.953 in the training and testing cohorts, respectively. Potential drugs, including vemurafenib, LOXO-101, dabrafenib, selumetinib, Arry-162, and NMS-E628, were found as well. Vemurafenib, dabrafenib, and selumetinib were observed to significantly affect HepG2 cell viability.

Conclusion: The diagnostic model based on the four diagnostic-related genes by the ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. Also, potential chemotherapy drugs might be effective for HCC therapy.

Serine Protease Inhibitor Kazal Type 1, A Potential Biomarker for the Early Detection, Targeting, and Prediction of Response to Immune Checkpoint Blockade Therapies in Hepatocellular Carcinoma

Background

We aimed to characterize serine protease inhibitor Kazal type 1 (SPINK1) as a gene signature for the early diagnosis, molecular targeting, and prediction of immune checkpoint blockade (ICB) treatment response of hepatocellular carcinoma (HCC).

Methods

The transcriptomics, proteomics, and phenotypic analyses were performed separately or in combination.

Results

We obtained the following findings on SPINK1. Firstly, in the transcriptomic training dataset, which included 279 stage I and II tumor samples (out of 1,884 stage I–IV HCC specimens) and 259 normal samples, significantly higher area under curve (AUC) values and increased integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were demonstrated for HCC discrimination in SPINK1-associated models compared with those of alpha-fetoprotein (AFP). The calibration of both SPINK1-related curves fitted significantly better than that of AFP. In the two independent transcriptomic validation datasets, which included 201, 103 stage I-II tumor and 192, 169 paired non-tumor specimens, respectively, the obtained results were consistent with the above-described findings. In the proteomic training dataset, which included 98 stage I and II tumor and 165 normal tissue samples, the analyses also revealed better AUCs and increased IDI and NRI in the aforementioned SPINK1-associated settings. A moderate calibration was shown for both SPINK1-associated models relative to the poor results of AFP. Secondly, in the in vitro and/or in vivo murine models, the wet-lab experiments demonstrated that SPINK1 promoted the proliferation, clonal formation, migration, chemoresistance, anti-apoptosis, tumorigenesis, and metastasis of HCC cells, while the anti-SPINK1 antibody inhibited the growth of the cells, suggesting that SPINK1 has “tumor marker” and “targetable” characteristics in the management of HCC. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that SPINK1 was engaged in immunity-related pathways, including T-cell activation. Thirdly, in the transcriptomic analyses of the 368 HCC specimens from The Cancer Genome Atlas (TCGA) cohort, the high abundance of SPINK1 was positively correlated with the high levels of activated tumor-infiltrating CD4⁺ and CD8⁺ T lymphocytes and dendritic and natural killer cells, while there were also positive correlations between SPINK1 and immune checkpoints, including PD-1, LAG-3, TIM-3, TIGIT, HAVCR2, and CTLA-4. The ESTIMATE algorithm calculated positive correlations between SPINK1 and the immune and ESTIMATE scores, suggesting a close correlation between SPINK1 and the immunogenic microenvironment within HCC tissues, which may possibly help in predicting the response of patients to ICB therapy.

Conclusions

SPINK1 could be a potential biomarker for the early detection, targeted therapy, and prediction of ICB treatment response in the management of HCC.

Identification and Validation of a Novel Immune Infiltration-Based Diagnostic Score for Early Detection of Hepatocellular Carcinoma by Machine-Learning Strategies

Objective

To investigate the diagnostic gene biomarkers for hepatocellular carcinoma (HCC) and identify the immune cell infiltration characteristics in this pathology.

Methods

Five gene expression datasets were obtained through Gene Expression Omnibus (GEO) portal. After batch effect removal, differentially expressed genes (DEGs) were conducted between 209 HCC and 146 control tissues and functional correlation analyses were performed. Two machine learning algorithms were used to develop diagnostic signatures. The discriminatory ability of the gene signature was measured by AUC. The expression levels and diagnostic value of the identified biomarkers in HCC were further validated in three independent external cohorts. CIBERSORT algorithm was adopted to explore the immune infiltration of HCC. A correlation analysis was carried out between these diagnostic signatures and immune cells.

Results

A total of 375 DEGs were identified. GPC3, ACSM3, SPINK1, COL15A1, TP53I3, RRAGD, and CLDN10 were identified as the early diagnostic signatures of HCC and were all validated in external cohorts. The corresponding results of AUC presented excellent discriminatory ability of these feature genes. The immune cell infiltration analysis showed that multiple immune cells associated with these biomarkers may be involved in the development of HCC.

Conclusion

This study indicates that GPC3, ACSM3, SPINK1, COL15A1, TP53I3, RRAGD, and CLDN10 are potential biomarkers associated with immune infiltration in HCC. Combining these genes can be used for early detection of HCC and evaluating immune cell infiltration. Further studies are needed to explore their roles underlying the occurrence of HCC.

Isolation of a Human Betaretrovirus from Patients with Primary Biliary Cholangitis

A human betaretrovirus (HBRV) has been linked with the autoimmune liver disease, primary biliary cholangitis (PBC), and various cancers, including breast cancer and lymphoma. HBRV is closely related to the mouse mammary tumor virus, and represents the only exogenous betaretrovirus characterized in humans to date. Evidence of infection in patients with PBC has been demonstrated through the identification of proviral integration sites in lymphoid tissue, the major reservoir of infection, as well as biliary epithelium, which is the site of the disease process. Accordingly, we tested the hypothesis that patients with PBC harbor a transmissible betaretrovirus by co-cultivation of PBC patients’ lymph node homogenates with the HS578T breast cancer line. Because of the low level of HBRV replication, betaretrovirus producing cells were subcloned to optimize viral isolation and production. Evidence of infection was provided by electron microscopy, RT-PCR, in situ hybridization, cloning of the HBRV proviral genome and demonstration of more than 3400 integration sites. Further evidence of viral transmissibility was demonstrated by infection of biliary epithelial cells. While HBRV did not show a preference for integration proximal to specific genomic features, analyses of common insertion sites revealed evidence of integration proximal to cancer associated genes. These studies demonstrate the isolation of HBRV with features similar to mouse mammary tumor virus and confirm that patients with PBC display evidence of a transmissible viral infection.

Single-cell Transcriptomic Architecture Unraveling the Complexity of Tumor Heterogeneity in Distal Cholangiocarcinoma

BACKGROUND & AIMS

Distal cholangiocarcinoma (dCCA) are a group of epithelial cell malignancies that occurs at the distal common bile duct, and account for approximately 40% of all cholangiocarcinoma cases. dCCA remains a highly lethal disease as it typically features remarkable cellular heterogeneity. A comprehensive exploration of cellular diversity and the tumor microenvironment is essential to depict the mechanisms driving dCCA progression.

METHODS

Single-cell RNA sequencing was used here to dissect the heterogeneity landscape and tumor microenvironment composition of human dCCAs. Seven human dCCAs and adjacent normal bile duct samples were included in the current study for single-cell RNA sequencing and subsequent validation approaches. Additionally, the results of the analyses were compared with bulk transcriptomic datasets from extrahepatic cholangiocarcinoma and single-cell RNA data from intrahepatic cholangiocarcinoma.

RESULTS

We sequenced a total of 49,717 single cells derived from human dCCAs and adjacent tissues, identifying 11 distinct cell types. Malignant cells displayed remarkable inter- and intra-tumor heterogeneity with 5 distinct subsets were defined in tumor samples. The malignant cells displayed variable degree of aneuploidy, which can be classified into low- and high-copy number variation groups based on either amplification or deletion of chr17q12 - chr17q21.2. Additionally, we identified 4 distinct T lymphocytes subsets, of which cytotoxic CD8+ T cells predominated as effectors in tumor tissues, whereas tumor infiltrating FOXP3+ CD4+ regulatory T cells exhibited highly immunosuppressive characteristics.

CONCLUSION

Our single-cell transcriptomic dataset depicts the inter- and intra-tumor heterogeneity of human dCCAs at the expression level.

SPINKs in Tumors: Potential Therapeutic Targets

The serine protease inhibitor Kazal type (SPINK) family includes SPINK1-14 and is the largest branch in the serine protease inhibitor family. SPINKs play an important role in pancreatic physiology and disease, sperm maturation and capacitation, Nager syndrome, inflammation and the skin barrier. Evidence shows that the unregulated expression of SPINK1, 2, 4, 5, 6, 7, and 13 is closely related to human tumors. Different SPINKs exhibit various regulatory modes in different tumors and can be used as tumor prognostic markers. This article reviews the role of SPINK1, 2, 4, 5, 6, 7, and 13 in different human cancer processes and helps to identify new cancer treatment targets.

circRPS16 Promotes Proliferation and Invasion of Hepatocellular Carcinoma by Sponging miR-876-5p to Upregulate SPINK1

The roles of serine protease inhibitor Kazal type 1 (SPINK1) in multiple types of cancers have been significantly documented. However, its specific roles in hepatocellular carcinoma (HCC) remain to be investigated. This study found that SPINK1 is upregulated in HCC and its upregulation correlates with poor prognosis. Besides, functional assays revealed that SPINK1 promotes cell proliferation, cell cycle, and invasion in vitro. Through bioinformatics analysis, we speculate that circRPS16 regulates SPINK1 expression by sponging miR-876-5p. This was further verified by the dual-luciferase reporter and fluorescent in situ hybridization (FISH) assays. Subsequently, rescue assays verified that circRPS16 promotes cell proliferation, cell cycle, and invasion through miR-876-5p. Importantly, silencing circRPS16 inhibited tumor growth by downregulating SPINK1 expression in vivo. Collectively, our results confirm that SPINK1 is a downstream target of circRPS16. Besides, circRPS16 and SPINK1 are oncogenic factors in HCC progression; they provide novel diagnostic and therapeutic targets for HCC patients.

Functional Roles of SPINK1 in Cancers

Serine Peptidase Inhibitor Kazal Type 1 (SPINK1) is a secreted protein known as a protease inhibitor of trypsin in the pancreas. However, emerging evidence shows its function in promoting cancer progression in various types of cancer. SPINK1 modulated tumor malignancies and induced the activation of the downstream signaling of epidermal growth factor receptor (EGFR) in cancer cells, due to the structural similarity with epidermal growth factor (EGF). The discoverable SPINK1 somatic mutations, expressional signatures, and prognostic significances in various types of cancer have attracted attention as a cancer biomarker in clinical applications. Emerging findings further clarify the direct and indirect biological effects of SPINK1 in regulating cancer proliferation, metastasis, drug resistance, transdifferentiation, and cancer stemness, warranting the exploration of the SPINK1-mediated molecular mechanism to identify a therapeutic strategy. In this review article, we first integrate the transcriptomic data of different types of cancer with clinical information and recent findings of SPINK1-mediated malignant phenotypes. In addition, a comprehensive summary of SPINK1 expression in a pan-cancer panel and individual cell types of specific organs at the single-cell level is presented to indicate the potential sites of tumorigenesis, which has not yet been reported. This review aims to shed light on the roles of SPINK1 in cancer and provide guidance and potential directions for scientists in this field.

SOCS2 is a potential prognostic marker that suppresses the viability of hepatocellular carcinoma cells

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-associated mortality worldwide. Thus, there is an urgent requirement to identify novel diagnostic and prognostic biomarkers for this disease. The present study aimed to identify the hub genes associated with the progression and prognosis of patients with HCC. A total of three expression profiles of HCC tissues were extracted from the Gene Expression Omnibus (GEO) database, followed by the identification of differentially expressed genes (DEGs) using the GEO2R method. The identified DEGs were assessed for survival significance using Kaplan-Meier analysis. Among the 15 identified DEGs in HCC tissues [cytochrome P450 family 39 subfamily A member 1, cysteine rich angiogenic inducer 61, Fos proto-oncogene, forkhead transcription factor 1 (FOXO1), growth arrest and DNA damage inducible β, Inhibitor of DNA binding 1, interleukin-1 receptor accessory protein, metallothionein-1M, pleckstrin homology-like domain family A member 1, Rho family GTPase 3, serine dehydratase, suppressor of cytokine signaling 2 (SOCS2), tyrosine aminotransferase (TAT), S100 calcium-binding protein P and serine protease inhibitor Kazal-type 1 (SPINK1)]. Low expression levels of FOXO1, SOCS2 and TAT and high SPINK1 expression indicated poor survival outcomes for patients with HCC. In addition, SOCS2 was associated with distinct stages of HCC progression in patients and presented optimal diagnostic value. In vitro functional experiments indicated that overexpression of SOCS2 inhibited HCC cell proliferation and migration. Taken together, the results of the present study suggest that SOCS2 may act as a valuable prognostic marker that is closely associated with HCC progression.

DDX3X is Epigenetically Repressed in Renal Cell Carcinoma and Serves as a Prognostic Indicator and Therapeutic Target in Cancer Progression

DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, X-linked (DDX3X) is a member of the DEAD-box family of RNA helicases whose function has been revealed to be involved in RNA metabolism. Recent studies further indicate the abnormal expression in pan-cancers and the relevant biological effects on modulating cancer progression. However, DDX3X’s role in renal cell carcinoma (RCC) progression remains largely unknown. In this study, a medical informatics-based analysis using The Cancer Genome Atlas (TCGA) dataset was performed to evaluate clinical prognoses related to DDX3X. The results suggest that DDX3X is epigenetically repressed in tumor tissue and that lower DDX3X is correlated with the poor overall survival of RCC patients and high tumor size, lymph node metastasis, and distant metastasis (TNM staging system). Furthermore, knowledge-based transcriptomic analysis by Ingenuity Pathway Analysis (IPA) revealed that the SPINK1-metallothionein pathway is a top 1-repressed canonical signaling pathway by DDX3X. Furthermore, SPINK1 and the metallothionein gene family all serve as poor prognostic indicators, and the expression levels of those genes are inversely correlated with DDX3X in RCC. Furthermore, digoxin was identified via Connectivity Map analysis (L1000) for its capability to reverse gene signatures in patients with low DDX3X. Importantly, cancer cell proliferation and migration were decreased upon digoxin treatment in RCC cells. The results of this study indicate the significance of the DDX3X_low/SPINK1_high/metallothionein_high axis for predicting poor survival outcome in RCC patients and suggest digoxin as a precise and personalized compound for curing those patients with low DDX3X expression levels.