A potency of plasminogen activation system in long-term prognosis of endometrial cancer: a pilot study

Interleukin-1β and plasminogen activating system members in endometriotic stromal cell migration/invasion

OBJECTIVE

To study the role of interleukin (IL)-1β and the plasminogen activating (PA) system members in endometriotic stromal cell (ESC) migration/invasion.

DESIGN

Primary cultures of ESCs.

SETTING

Tertiary referral center for endometriosis and pelvic pain.

PATIENT(S)

Patients with surgically excised endometriosis.

INTERVENTION(S)

Interleukin-1β stimulation of primary cultures of ESCs and knockdown of the PA system members urokinase plasminogen activator (uPA), uPA receptor, and plasminogen activator inhibitor-1 (PAI-1).

MAIN OUTCOME MEASURE(S)

Invasion/migration assays.

RESULT(S)

In primary cultures, IL-1β-stimulated ESC production of the PA system members uPA, uPA receptor, and PAI-1. Interleukin-1β also enhanced ESC migration and invasion, and these effects were inhibited by the IL-1 receptor-1 antagonist anakinra. Knockdown of each of the 3 PA system members also inhibited ESC migration and invasion. Knockdown of these PA system members further attenuated the impact of IL-1β on migration and invasion, suggesting that they mediated the promigration and proinvasion effects of IL-1β. To supplement the cell culture work, immunohistochemistry was performed on tissue sections of endometriotic epithelium/stroma: uPA, PAI-1, and IL-1β histoscores were not found to be correlated with each other.

CONCLUSION(S)

In primary cultures of ESCs, IL-1β induces migration and invasion, which is mediated by PA system members and inhibited by the drug anakinra. However, the immunohistochemistry expression of IL-1β, urokinase plasminogen inhibitor-1, and PAI-1 were not correlated, suggesting other regulatory mechanisms for PA system members. Inhibition of IL-1β (e.g., with anakinra) may have potential as a novel treatment approach for the migration/invasion of endometriosis.

DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning

BACKGROUND

A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we wanted to expand the state-of-the-art in disease biomarker prediction from RNA-seq data using DL. However, application of DL to RNA-seq data is challenging due to absence of appropriate labels and smaller sample size as compared to number of genes. Deep learning coupled with transfer learning can improve prediction performance on novel data by incorporating patterns learned from other related data. With the emergence of new disease datasets, biomarker prediction would be facilitated by having a generalized model that can transfer the knowledge of trained feature maps to the new dataset. To the best of our knowledge, there is no Convolutional Neural Network (CNN)-based model coupled with transfer learning to predict the significant upregulating (UR) and downregulating (DR) genes from both trained and untrained datasets.

RESULTS

We implemented a CNN model, DEGnext, to predict UR and DR genes from gene expression data obtained from The Cancer Genome Atlas database. DEGnext uses biologically validated data along with logarithmic fold change values to classify differentially expressed genes (DEGs) as UR and DR genes. We applied transfer learning to our model to leverage the knowledge of trained feature maps to untrained cancer datasets. DEGnext's results were competitive (ROC scores between 88 and 99[Formula: see text]) with those of five traditional machine learning methods: Decision Tree, K-Nearest Neighbors, Random Forest, Support Vector Machine, and XGBoost. DEGnext was robust and effective in terms of transferring learned feature maps to facilitate classification of unseen datasets. Additionally, we validated that the predicted DEGs from DEGnext were mapped to significant Gene Ontology terms and pathways related to cancer.

CONCLUSIONS

DEGnext can classify DEGs into UR and DR genes from RNA-seq cancer datasets with high performance. This type of analysis, using biologically relevant fine-tuning data, may aid in the exploration of potential biomarkers and can be adapted for other disease datasets.

Prognostic significance of VEGF and components of the plasminogen activator system in endometrial cancer

Objective

The plasminogen activator system (PAS) and vascular endothelial growth factor (VEGF) are important in the carcinogenesis and play a key role in cancer invasion and mediating metastasis of carcinomas. The aim of the study was to evaluate the correlation of serum levels of VEGF and components of the PAS with clinicopathological risk factors and outcome in patients with endometrial cancer (EC).

Methods

Preoperative blood was collected from 173 patients treated for EC between 1999 and 2009. Serum concentrations of VEGF, urokinase plasminogen activator (uPA) tissue plasminogen activator (tPA), plasminogen activator inhibitor type-1 (PAI-1) and -2 (PAI-2) were assessed by enzyme-linked immunosorbent assays (ELISA).

Results

Serum levels of VEGF and components of the PAS were significantly associated with stage of the disease, tumor histology, tumor grade, myometrial invasion (MI), presence of lymphovascular space invasion (LVSI) and lymph node metastases (LNM). Preoperative serum levels of PAI-1 and -2 and tPA were higher in patients who experienced a recurrence than in patients who remained disease free (p < 0.01). PAI-1 and -2 and tPA were significantly independent prognostic factors for DFS with a HR of 3.85 (95% CI 1.84–8.07), 3.90 (95% CI 1.75–8.66) and 2.53 (95% CI 1.16–5.55), respectively. PAI-1 and tPA turned out to be independent prognostic factors for OS, with a HR of 2.09 (95% CI 1.08–4.05) and 2.16 (95% CI 1.06–4.44), respectively.

Conclusion

Serum levels of VEGF and components of the PAS at primary diagnosis were associated with well-known clinicopathological risk factors such as; FIGO stage, tumor histology, tumor grade, MI, LVSI and LNM. High concentrations of PAI-1 and-2 and tPA are independent factors for poor prognosis in patients with endometrial cancer.

Plasminogen activator inhibitor-1 (PAI-1) expression in endometriosis

Purpose

Deep infiltrating endometriosis (DIE) is defined as an endometriotic lesion penetrating to a depth of >5 mm and is associated with pelvic pain, but the underlying mechanisms are unclear. Our objective is to investigate whether plasminogen activator inhibitor-1 expression (PAI-1) in endometriotic tissues is increased in women with DIE.

Methods

In this blinded in vitro study, immunohistochemistry and Histoscore were used to examine the expression of PAI-1 in glandular epithelium (GECs) and stroma (SCs) in a total of 62 women: deep infiltrating uterosacral/rectovaginal endometriosis (DIE; n = 13), ovarian endometrioma (OMA; n = 14), superficial peritoneal uterosacral/cul-de-sac endometriosis (SUP; n = 23), uterine (eutopic) endometrium from women with endometriosis (UE; n = 6), and non-endometriosis eutopic endometrium (UC; n = 6). The following patient characteristics were also collected: age, American Fertility Society stage, hormonal suppression, phase of menstrual cycle, dysmenorrhea score and deep dyspareunia score.

Results

PAI-1 expression in GECs and SCs of the DIE group was significantly higher than that of SUP group (p = 0.01, p = 0.01, respectively) and UE group (p = 0.03, p = 0.04, respectively). Interestingly, increased PAI-1 expression in GECs and SCs was also significantly correlated with increased dysmenorrhea (r = 0.38, p = 0.01; r = 0.34, p = 0.02, respectively).

Conclusions

We found higher expression of PAI-1 in DIE, and an association between PAI-1 and worse dysmenorrhea.

Multifaceted Role of the Urokinase-Type Plasminogen Activator (uPA) and Its Receptor (uPAR): Diagnostic, Prognostic, and Therapeutic Applications

The plasminogen activator (PA) system is an extracellular proteolytic enzyme system associated with various physiological and pathophysiological processes. A large body of evidence support that among the various components of the PA system, urokinase-type plasminogen activator (uPA), its receptor (uPAR), and plasminogen activator inhibitor-1 and -2 (PAI-1 and PAI-2) play a major role in tumor progression and metastasis. The binding of uPA with uPAR is instrumental for the activation of plasminogen to plasmin, which in turn initiates a series of proteolytic cascade to degrade the components of the extracellular matrix, and thereby, cause tumor cell migration from the primary site of origin to a distant secondary organ. The components of the PA system show altered expression patterns in several common malignancies, which have identified them as ideal diagnostic, prognostic, and therapeutic targets to reduce cancer-associated morbidity and mortality. This review summarizes the various components of the PA system and focuses on the role of uPA-uPAR in different biological processes especially in the context of malignancy. We also discuss the current state of knowledge of uPA-uPAR-targeted diagnostic and therapeutic strategies for various malignancies.

The urokinase-type plasminogen activator system and its role in tumor progression

In the multistage process of carcinogenesis, the key link in the growth and progression of the tumor is the invasion of malignant cells into normal tissue and their distribution and the degree of destruction of tissues. The most important role in the development of these processes is played by the system of urokinase-type plasminogen activator (uPA system), which consists of several components: serine proteinase – uPA, its receptor – uPAR and its two endogenous inhibitors – PAI-1 and PAI-2. The components of the uPA system are expressed by cancer cells to a greater extent than normal tissue cells. uPA converts plasminogen into broad spectrum, polyfunctional protease plasmin, which, in addition to the regulation of fibrinolysis, can hydrolyze a number of components of the connective tissue matrix (СTM), as well as activate the zymogens of secreted matrix metalloproteinases (MMР) – pro-MMР. MMРs together can hydrolyze all the main components of the СTM, and thus play a key role in the development of invasive processes, as well as to perform regulatory functions by activating and releasing from STM a number of biologically active molecules that are involved in the regulation of the main processes of carcinogenesis. The uPA system promotes tumor progression not only through the proteolytic cascade, but also through uPAR, PAI-1 and PAI-2, which are involved in both the regulation of uPA/uPAR activity and are involved in proliferation, apoptosis, chemotaxis, adhesion, migration and activation of epithelial-mesenchymal transition pathways. All of the above processes are aimed at regulating invasion, metastasis and angiogenesis. The components of the uPA system are used as prognostic and diagnostic markers of many cancers, as well as serve as targets for anticancer therapy.

Involvement of urokinase-type plasminogen activator system in cancer: an overview

Currently, there are several studies supporting the role of urokinase-type plasminogen activator (uPA) system in cancer. The association of uPA to its receptor triggers the conversion of plasminogen into plasmin. This process is regulated by the uPA inhibitors (PAI-1 and PAI-2). Plasmin promotes degradation of basement membrane and extracellular matrix (ECM) components as well as activation of ECM latent matrix metalloproteases. Degradation and remodeling of the surrounding tissues is crucial in the early steps of tumor progression by facilitating expansion of the tumor mass, release of tumor growth factors, activation of cytokines as well as induction of tumor cell proliferation, migration, and invasion. Hence, many tumors showed a correlation between uPA system component levels and tumor aggressiveness and survival. Therefore, this review summarizes the structure of the uPA system, its contribution to cancer progression, and the clinical relevance of uPA family members in cancer diagnosis. In addition, the review evaluates the significance of uPA system in the development of cancer-targeted therapies.

Function and clinical relevance of kallikrein-related peptidases and other serine proteases in gynecological cancers

Gynecological cancers, including malignant tumors of the ovaries, the endometrium and the cervix, account for approximately 10% of tumor-associated deaths in women of the Western world. For screening, diagnosis, prognosis, and therapy response prediction, the group of enzymes known as serine (Ser-)proteases show great promise as biomarkers. In the present review, following a summary of the clinical facts regarding malignant tumors of the ovaries, the endometrium and the cervix, and characterization of the most important Ser-proteases, we thoroughly review the current state of knowledge relating to the use of proteases as biomarkers of the most frequent gynecological cancers. Within the Ser-protease group, the kallikrein-related peptidase (KLK) family, which encompasses a subgroup of 15 members, holds particular promise, with some acting via a tumor-promoting mechanism and others behaving as protective factors. Further, the urokinase-type plasminogen activator (uPA) and its inhibitor PAI-1 (plasminogen activator inhibitor-1) seem to play an unfavorable role in gynecological tumors, while down-regulation of high-temperature requirement proteins A 1, 2 and 3 (HtrA1,2,3) is associated with malignant disease and cancer progression. Expression/activity levels of other Ser-proteases, including the type II transmembrane Ser-proteases (TTSPs) matriptase, hepsin (TMPRSS1), and the hepsin-related protease (TMPRSS3), as well as the glycosyl-phosphatidylinositol (GPI)-anchored Ser-proteases prostasin and testisin, may be of clinical relevance in gynecological cancers. In conclusion, proteases are a rich source of biomarkers of gynecological cancer, though the enzymes' exact roles and functions merit further investigation.