Proteoglycans as potential biomarkers in odontogenic tumors

Surrogate Immunohistochemical Markers of Proliferation and Embryonic Stem Cells in Distinguishing Ameloblastoma from Ameloblastic Carcinoma

PURPOSE

The current study aimed to investigate the use of surrogate immunohistochemical (IHC) markers of proliferation and stem cells to distinguish ameloblastoma (AB) from ameloblastic carcinoma (AC).

METHODS

The study assessed a total of 29 ACs, 6 ABs that transformed into ACs, and a control cohort of 20 ABs. The demographics and clinicopathologic details of the included cases of AC were recorded. The Ki-67 proliferation index was scored through automated methods with the QuPath open-source software platform. For SOX2, OCT4 and Glypican-3 IHC, each case was scored using a proportion of positivity score combined with an intensity score to produce a total score.

RESULTS

All cases of AC showed a relatively high median proliferation index of 41.7%, with statistically significant higher scores compared to ABs. ABs that transformed into ACs had similar median proliferation scores to the control cohort of ABs. Most cases of AC showed some degree of SOX2 expression, with 58.6% showing high expression. OCT4 expression was not seen in any case of AC. GPC-3 expression in ACs was limited, with high expression in 17.2% of ACs. Primary ACs showed higher median proliferation scores and degrees of SOX2 and GPC-3 expression than secondary cases. Regarding SOX2, OCT4 and GPC-3 IHC expression, no statistically significant differences existed between the cohort of ABs and ACs.

CONCLUSION

Ki-67 IHC as a proliferation marker, particularly when assessed via automated methods, was helpful in distinguishing AC from AB cases. In contrast to other studies, surrogate IHC markers of embryonic stem cells, SOX2, OCT4 and GPC-3, were unreliable in distinguishing the two entities.

Exploring protein profiles and hub genes in ameloblastoma

Ameloblastoma (AM) is a prominent benign odontogenic tumor characterized by aggressiveness, likely originating from tooth-generating tissue or the dental follicle (DF). However, proteomic distinctions between AM and DF remain unclear. In the present study, the aim was to identify the distinction between AM and DF in terms of their proteome and to determine the associated hub genes. Shotgun proteomics was used to compare the proteomes of seven fresh-frozen AM tissues and five DF tissues. Differentially expressed proteins (DEPs) were quantified and subsequently analyzed through Gene Ontology-based functional analysis, protein-protein interaction (PPI) analysis and hub gene identification. Among 7,550 DEPs, 520 and 216 were exclusive to AM and DF, respectively. Significant biological pathways included histone H2A monoubiquitination and actin filament-based movement in AM, as well as pro-B cell differentiation in DF. According to PPI analysis, the top-ranked upregulated hub genes were ubiquitin C (UBC), breast cancer gene 1 (BRCA1), lymphocyte cell-specific protein-tyrosine kinase (LCK), Janus kinase 1 and ATR serine/threonine kinase, whereas the top-ranked downregulated hub genes were UBC, protein kinase, DNA-activated, catalytic subunit (PRKDC), V-Myc avian myelocytomatosis viral oncogene homolog (MYC), tumor protein P53 and P21 (RAC1) activated kinase 1. When combining upregulated and downregulated genes, UBC exhibited the highest degree and betweenness values, followed by MYC, BRCA1, PRKDC, embryonic lethal, abnormal vision, Drosophila, homolog-like 1, myosin heavy chain 9, amyloid beta precursor protein, telomeric repeat binding factor 2, LCK and filamin A. In summary, these findings contributed to the knowledge on AM protein profiles, potentially aiding future research regarding AM etiopathogenesis and leading to AM prevention and treatment.

Role of Microenvironmental Components in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell cancer (HNSCC) is one of the ten most common malignant neoplasms, characterized by an aggressive course, high recurrence rate, poor response to treatment, and low survival rate. This creates the need for a deeper understanding of the mechanisms of the pathogenesis of this cancer. The tumor microenvironment (TME) of HNSCC consists of stromal and immune cells, blood and lymphatic vessels, and extracellular matrix. It is known that HNSCC is characterized by complex relationships between cancer cells and TME components. TME components and their dynamic interactions with cancer cells enhance tumor adaptation to the environment, which provides the highly aggressive potential of HNSCC and resistance to antitumor therapy. Basic research aimed at studying the role of TME components in HNSCC carcinogenesis may serve as a key to the discovery of both new biomarkers-predictors of prognosis and targets for new antitumor drugs. This review article focuses on the role and interaction with cancer of TME components such as newly formed vessels, cancer-associated fibroblasts, and extracellular matrix.

The Tumor Microenvironment: Focus on Extracellular Matrix

The extracellular matrix (ECM) regulates the development and maintains tissue homeostasis. The ECM is composed of a complex network of molecules presenting distinct biochemical properties to regulate cell growth, survival, motility, and differentiation. Among their components, proteoglycans (PGs) are considered one of the main components of ECM. Its composition, biomechanics, and anisotropy are exquisitely tuned to reflect the physiological state of the tissue. The loss of ECM's homeostasis is seen as one of the hallmarks of cancer and, typically, defines transitional events in tumor progression and metastasis. In this chapter, we discuss the types of proteoglycans and their roles in cancer. It has been observed that the amount of some ECM components is increased, while others are decreased, depending on the type of tumor. However, both conditions corroborate with tumor progression and malignancy. Therefore, ECM components have an increasingly important role in carcinogenesis and this leads us to believe that their understanding may be a key in the discovery of new anti-tumor therapies. In this book, the main ECM components will be discussed in more detail in each chapter.