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

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.

Shedding light on the role of LAG-3 in hepatocellular carcinoma: unraveling immunomodulatory pathways

Hepatocellular carcinoma (HCC) stands as a primary malignant liver tumor characterized by chronic inflammation and complex alterations within the tumor microenvironment (TME). Lymphocyte activation gene 3 (LAG-3), also known as CD223, has gained prominence as a potential next-generation immune checkpoint, maintaining continuous expression in response to persistent antigen exposure within the TME, warranting our attention. In patients with HCC, LAG-3 expression on T cells, regulatory T cells (Tregs), and natural killer (NK) cells contributes to immune evasion, while high expression of LAG-3 leads to increased angiogenesis and poor prognosis. By interacting with major histocompatibility complex class II molecules, LAG-3 promotes T cell exhaustion and suppresses antitumor responses, often in collaboration with other immune checkpoints like programmed cell death protein 1 (PD-1), while on Tregs and NK cells, LAG-3 modulates their suppressive functions, indirectly facilitating tumor immune escape. LAG-3 expression may offer prognostic insights, correlating with disease progression and outcomes in HCC patients, while various preclinical studies highlight the potential of LAG-3-targeted therapies in reinvigorating immune responses against HCC, with a few combination approaches targeting LAG-3 alongside other checkpoints demonstrating synergistic effects in restoring T cell function. Therefore, harnessing LAG-3 as a therapeutic target holds promise for enhancing antitumor immunity and potentially improving HCC treatment outcomes. Our narrative review aims to delve into the full spectrum of LAG-3 signaling in HCC, with the goal of a better understanding of the pathophysiological and immunological basis of its use to arrest HCC growth and development.

SPINK1 is a Potential Diagnostic and Prognostic Biomarker for Sepsis

Purpose

There are no satisfactory diagnostic biomarkers for sepsis. Accordingly, this study screened biomarkers valuable for sepsis diagnosis and prognosis using data-independent acquisition (DIA) combined with clinical data analysis.

Patients and Methods

Serine protease inhibitor Kazal-type 1 (SPINK1) is a differentially expressed protein that was screened using DIA and bioinformatics in sepsis patients (n = 22) and healthy controls (n = 10). The plasma SPINK1 levels were detected using an enzyme-linked immunosorbent assay (ELISA) in an expanded population (sepsis patients, n = 52; healthy controls, n = 10). The diagnostic value of SPINK1 in sepsis was evaluated using receiver operating characteristic (ROC) curve analysis based on clinical data. The prognostic value of SPINK1 for sepsis was evaluated using correlation and survival analyses.

Results

DIA quality control identified 78 differential proteins (72 upregulated and six downregulated), among which SPINK1 was highly expressed in sepsis. The ELISA results suggested that SPINK1 expression was significantly elevated in the sepsis group (P < 0.05). ROC analysis of SPINK1 yielded an area under the curve (AUC) of 0.9096. Combining SPINK1 with procalcitonin (PCT) for ROC analysis yielded an AUC of 1. SPINK1 expression was positively correlated with the Sequential Organ Failure Assessment (SOFA) score (r = 3497, P = 0.0053) and APACHE II score (r = 3223, P = 0.0106). High plasma SPINK1 protein expression was negatively correlated with the 28-day survival rate of patients with sepsis (P = 0.0149).

Conclusion

The plasma of sepsis patients contained increased SPINK1 protein expression. Combining SPINK1 with PCT might have a high diagnostic value for sepsis. SPINK1 was associated with the SOFA score, APACHE II score, and the 28-day survival rate in patients with sepsis.

Clinical identification of malignant pleural effusions

INTRODUCTION

Pleural effusions frequently signal disseminated cancer. Diagnostic markers of pleural malignancy at presentation that would assess cancer risk and would streamline diagnostic decisions remain unidentified.

METHODS

A consecutive cohort of 323 patients with pleural effusion (PE) from different etiologies were recruited between 2013 and 2017 and was retrospectively analyzed. Data included history, chest X-ray, and blood/pleural fluid cell counts and biochemistry. Group comparison, receiver-operator characteristics, unsupervised hierarchical clustering, binary logistic regression, and random forests were used to develop the malignant pleural effusion detection (MAPED) score. MAPED was validated in an independent retrospective UK cohort (n = 238).

RESULTS

Five variables showed significant diagnostic power and were incorporated into the 5-point MAPED score. Age > 55 years, effusion size > 50% of the most affected lung field, pleural neutrophil count 〈 2,500/mm3, effusion protein 〉 3.5 g/dL, and effusion lactate dehydrogenase > 250 U/L, each scoring one point, predicted underlying cancer with the area under curve(AUC) = 0.819 (P < 10-15) in the derivation cohort. The integrated discrimination improvement of MAPED scores showed an increase compared to cytology (p <0.001). Decision curve analysis indicated that the MAPED score generated net clinical benefit. In the validation dataset, the AUC of MAPED scores was 0.723 ( P = 3 × 10-9) for the MAPED score. Interestingly, MAPED correctly identified 33/42(79%) of cytology-negative patients that indeed had cancer.

CONCLUSIONS

The MAPED score identifies malignant pleural effusions with satisfactory accuracy and can be used complementary to cytology to streamline diagnostic procedures.

CONDENSED ABSTRACT

Diagnostic markers for malignant pleural effusions remain uncertain. The MAPED score identifies malignant pleural effusions and complements cytology and confers no additional risk to the patient or cost to the healthcare system.

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.

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.