Immunohistochemical analysis of matrix metalloproteinase-9 predicts papillary thyroid carcinoma prognosis

Improvement of renal functions in mice with septic acute kidney injury using secretome of mesenchymal stem cells

Background

A potentially fatal complication of sepsis is septic acute kidney injury. Stem cell therapy is a potential new method of treating sepsis and has been applied to treat some human diseases.

Objectives

This study investigated the effects of secretome-MSCs on NGAL, CRP, NF-κB, and MMP-9 proteins, and histopathology in mice with septic AKI.

Methods

A post-test-only group design was conducted in 30 Balb/C male mice, which were randomly assigned to five groups: the control group was intraperitoneally injected with 0.5 ml of 0.9 % NaCl, the septic AKI, and the treatment groups (T1, T2, and T3) were intraperitoneally injected with 0.5 ml of 0.9 % NaCl and 0.3 mg/kg BW LPS single dose for three days. Three-day treatments of 150, 300, and 600 µl secretome-MSCs were administered intraperitoneally into the treatment groups. Furthermore, kidney and blood samples were collected for biochemical and histopathological analyses.

Results

The T1, T2, and T3 groups had lower expression of NF-κB and MMP-9 and significantly lower CRP and NGAL levels than that of septic AKI group. T1 (1.21 ± 0.19), T2 (0.75 ± 0.22), and T3 (0.38 ± 0.14) groups demonstrated lower average scores for inflammation, necrosis, hemorrhage, and degeneration compared to septic AKI group (2.17 ± 0.13).

Conclusions

Administration of 600 µl/20 g BW secretome-MSCs suppresses NF-κB and MMP-9 expression and reduces CRP and NGAL levels. Meanwhile, the 150 and 300 µl/20 g BW doses also indicated a greater improvement in renal tissue damage of mice with septic AKI.

Clinical significance of matrix metalloproteinase-9 expression in papillary thyroid carcinoma: a meta-analysis

OBJECTIVE

The purpose of this study was to investigate the relationship between the expression of matrix metalloproteinase-9 (MMP-9) and pathological indexes in papillary thyroid carcinoma (PTC).

EVIDENCE OBTAINED

The database was searched in PubMed, Embase, CNKI, and Web of Science databases for relevant clinical trials. The odds ratio (OR) and 95% confidence interval (CI) show the effect of MMP-9 expression and age, tumour size, gender, lymph node metastasis (LNM), and TNM (tumour, lymph node, metastasis) stage. Statistical analysis of the data was performed using Stata 17.0.

EVIDENCE SYNTHESIS

A total of 1433 patients with PTC were included in this meta-analysis. MMP-9 expression was significantly correlated with LNM (OR = 3.92, 95% CI = 2.71-5.65, P = 0.000), tumour size (OR = 1.69, 95% CI = 1.13-2.52, P = 0.011), and TNM stage (OR = 2.95, 95% CI = 2.10-4.13, P = 0.000), but not with gender (OR = 0.90, 95% CI = 0.66-1.22, P = 0.487) and age (OR = 1.36, 95% CI = 0.93-1.98, P = 0.115).

CONCLUSIONS

Our meta-analysis showed that MMP-9 was significantly associated with LNM, tumour size, and TNM stage; therefore, MMP-9 may be a reliable prognostic biomarker for patients with PTC. However, more high-quality studies are needed to support these findings further.

The expression of stromal biomarkers in small papillary thyroid carcinomas

Background

The importance of stroma for tumor progression is recognized for many cancer types. In this study, we aim to evaluate the expression of types I (Col1) and IV (Col4) collagens, alpha-smooth muscle actin (a-SMA), and matrix metallopeptidase 9 (MMP-9) in the tumor stroma of small papillary thyroid carcinoma (PTC).

Material and methods

Twenty-five non-metastatic small PTCs (pT1N0) and nineteen metastatic small PTCs (pT1N1b) including corresponding metastatic lateral lymph nodes were selected and paraffinized tissue blocks retrieved. The samples were stained for Col1, COL4, a-SMA, and MMP-9 antibodies using immunohistochemistry. The expression of the stromal proteins was scored and analyzed based on the location, intensity, and distribution.

Results

Col1 and Col4 expression were significantly higher in normal thyroid tissue compared to PTC tissue. On the contrary, expression of a-SMA and MMP-9 was higher in PTC tissue compared to normal thyroid tissue. Both Col1 and Col4 were significantly more highly expressed in the non-metastatic tumors compared with metastatic tumors. The expression of a-SMA and MMP9 was slightly, but not significantly, higher in the metastasized tumors and their respective lymph nodes. There was a significant correlation between the metastasized tumors and their respective lymph nodes in Col1 and MMP-9 expression.

Conclusions

Col1, Col4, a-SMA, and MMP-9 expression in PTCs differs significantly from that of normal thyroid tissue. The higher expression of Col1 and Col4 in normal thyroid tissue and in the non-metastasized tumors indicates that Col1 and 4 might have a potential protective role in tumor progression. The higher expression of a-SMA and MMP9 in PTCs indicates that these proteins might have a role in promoting PTC progression and aggressiveness.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02805-w.

Diagnostic and prognostic value of Stanniocalcin 1 expression in papillary thyroid cancer

PURPOSE

To evaluate the potential role of immunohistochemical changes in stanniocalcin 1 (STC1) and stanniocalcin 2 (STC2) expressions in papillary thyroid cancer (PTC) tissues in the disease's diagnosis and to investigate their relationship with classical clinicopathological prognostic factors.

METHODS

The study included 100 patients with PTC. Normal thyroid tissue adjacent to the tumor was taken as the control group. Clinicopathological prognostic features at the time of diagnosis of patients were recorded. STC1 and STC2 expressions of tumor tissue and adjacent normal tissue were determined immunohistochemically.

RESULTS

The sensitivity of STC1 in the diagnosis of PTC was 93%, the specificity was 94%, positive predictive value (PPV) 93.9%, and negative predictive value (NPV) 93.1%. It was determined that the STC1 staining score in tumor tissue was positively correlated with the disease TNM stage score (r = 0.259, p = 0.009) and the increase in STC1 staining score were independent risk factors that increased the risk of lymph node metastasis (R² = 0.398, p < 0.001). While 21% of the tumor tissues were stained with STC2, none of the normal thyroid tissues adjacent to the tumor tissue showed any staining with STC2. No correlation was found between STC2 immunohistochemical staining of tumor tissue and clinicopathological risk factors for the disease.

CONCLUSION

Increased STC1 expression in thyroid lesions may be helpful in diagnosing PTC. In addition, since increased STC1 expression in PTC tissues is associated with the risk of lymph node metastasis, it may be an efficient marker for predicting the prognosis of the disease.

Bioinformatic analysis of the LCN2–SLC22A17–MMP9 network in cancer: The role of DNA methylation in the modulation of tumor microenvironment

Several features of cancer cells such as proliferation, invasion, metastatic spreading, and drug resistance are affected by their interaction with several tumor microenvironment (TME) components, including neutrophil gelatinase-associated lipocalin (NGAL), solute carrier family 22 member 17 (SLC22A17), and matrix metallopeptidase 9 (MMP9). These molecules play a key role in tumor growth, invasion, and iron-dependent metabolism of cancer cells. However, the precise epigenetic mechanisms underlying the gene regulation of Lipocalin 2 (LCN2), SLC22A17, and MMP9 in cancer still remain unclear. To this purpose, computational analysis was performed on TCGA and GTEx datasets to evaluate the expression and DNA methylation status of LCN2, SLC22A17, and MMP9 genes in different tumor types. Correlation analysis between gene/isoforms expression and DNA methylation levels of LCN2, SLC22A17, and MMP9 was performed to investigate the role of DNA methylation in the modulation of these genes. Protein network analysis was carried out using reverse phase protein arrays (RPPA) data to identify protein–protein interactions of the LCN2–SLC22A17–MMP9 network. Furthermore, survival analysis was performed according to gene expression and DNA methylation levels. Our results demonstrated that LCN2 and MMP9 were mainly upregulated in most tumor types, whereas SLC22A17 was largely downregulated, representing a specific hallmark signature for all gastrointestinal tumors. Notably, the expression of LCN2, SLC22A17, and MMP9 genes was negatively affected by promoter methylation. Conversely, intragenic hypermethylation was associated with the overexpression of SLC22A17 and MMP9 genes. Protein network analysis highlighted the role of the LCN2–SLC22A17–MMP9 network in TME by the interaction with fibronectin 1 and claudin 7, especially in rectal tumors. Moreover, the impact of expression and methylation status of LCN2, SLC22A17, and MMP9 on overall survival and progression free interval was tumor type–dependent. Overall, our analyses provide a detailed overview of the expression and methylation status of LCN2, SLC22A17, and MMP9 in all TCGA tumors, indicating that the LCN2–SLC22A17–MMP9 network was strictly regulated by DNA methylation within TME. Our findings pave the way for the identification of novel DNA methylation hotspots with diagnostic and prognostic values and suitable for epi-drug targeting.

Can We Predict Differentiated Thyroid Cancer Behavior? Role of Genetic and Molecular Markers

Thyroid cancer is ranked in ninth place among all the newly diagnosed cancer cases in 2020. Differentiated thyroid cancer behavior can vary from indolent to extremely aggressive. Currently, predictions of cancer prognosis are mainly based on clinicopathological features, which are direct consequences of cell and tissue microenvironment alterations. These alterations include genetic changes, cell cycle disorders, estrogen receptor expression abnormalities, enhanced epithelial-mesenchymal transition, extracellular matrix degradation, increased hypoxia, and consecutive neovascularization. All these processes are represented by specific genetic and molecular markers, which can further predict thyroid cancer development, progression, and prognosis. In conclusion, evaluation of cancer genetic and molecular patterns, in addition to clinicopathological features, can contribute to the identification of patients with a potentially worse prognosis. It is essential since it plays a crucial role in decision-making regarding initial surgery, postoperative treatment, and follow-up. To date, there is a large diversity in methodologies used in different studies, frequently leading to contradictory results. To evaluate the true significance of predictive markers, more comparable studies should be conducted.

PPARγ Targets-Derived Diagnostic and Prognostic Index for Papillary Thyroid Cancer

Simple Summary

Through targeted next-generation sequencing of thyroid cancer-related genes in monozygotic twins with papillary thyroid cancer (PTC), we identified common variants of the gene encoding peroxisome proliferator activated receptor gamma (PPARG). Notably, the expression levels of PPARγ target genes were frequently deregulated in PTC compared to benign tissues and were closely associated with disease-specific survival (DSS) outcomes in a TCGA-PTC cohort. Machine learning-powered personalized scoring index comprising 10 PPARγ targets, termed as PPARGi, achieved a near-perfect accuracy in distinguishing cancers from benign tissues, and further identified a small subpopulation of patients at high-risk across different profiling platforms.

Abstract

In most cases, papillary thyroid cancer (PTC) is highly curable and associated with an excellent prognosis. Yet, there are several clinicopathological features that lead to a poor prognosis, underscoring the need for a better genomic strategy to refine prognostication and patient management. We hypothesized that PPARγ targets could be potential markers for better diagnosis and prognosis due to the variants found in PPARG in three pairs of monozygotic twins with PTC. Here, we developed a 10-gene personalized prognostic index, designated PPARGi, based on gene expression of 10 PPARγ targets. Through scRNA-seq data analysis of PTC tissues derived from patients, we found that PPARGi genes were predominantly expressed in macrophages and epithelial cells. Machine learning algorithms showed a near-perfect performance of PPARGi in deciding the presence of the disease and in selecting a small subset of patients with poor disease-specific survival in TCGA-THCA and newly developed merged microarray data (MMD) consisting exclusively of thyroid cancers and normal tissues.

Identification of soluble tissue-derived biomarkers from human thyroid tissue explants maintained on a microfluidic device

Although a large cohort of potential biomarkers for thyroid cancer aggressiveness have been tested in various formats in recent years, to the best of our knowledge, thyroglobulin and calcitonin remain the only two established biomarkers associated with thyroid cancer management. Our group has recently validated a novel means of maintaining live, human ex vivo thyroid tissue within a tissue-on-chip format. The present pilot study aimed to interrogate the tissue effluent, containing all the soluble markers released by the tissue samples maintained within the devices' tissue chamber, for the presence of markers potentially associated with thyroid cancer aggressiveness. Culture effluent from tissue samples harvested from 19 individual patients who had undergone thyroidectomy for the treatment of suspected thyroid cancer was assessed, first using a proteome profiler™ angiogenesis array kit. Patients were subcategorised as 'aggressive' if they possessed a minimum of N1b level metastases, whilst 'non-aggressive' samples were T3 or lower without evidence of multifocality; and contralateral healthy thyroid tissue was harvested for comparative studies. Levels of Serpin-F1, vascular endothelial growth factor, Thrombospondin-1 and chemokine (C-C motif) ligand were significantly altered and, thus, were further investigated using ELISA to allow for quantitative analysis. The concentration of serpin-F1 was significantly increased in the effluent of aggressive thyroid cancer tissue when compared with levels released by both non-aggressive and benign samples. The present study demonstrated the usability of microfluidic technology for the analysis of the ex vivo tissue secretome in order to identify novel biomarkers.