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. 2024 Aug 16;25(16):8929.
doi: 10.3390/ijms25168929.

Biomarkers Identification in the Microenvironment of Oral Squamous Cell Carcinoma: A Systematic Review of Proteomic Studies

Silvia Pomella  1 , Ombretta Melaiu  1 , Loredana Cifaldi  1 , Roberto Bei  1 , Marco Gargari  1 , Vincenzo Campanella  1 , Giovanni Barillari  1
Affiliations

Biomarkers Identification in the Microenvironment of Oral Squamous Cell Carcinoma: A Systematic Review of Proteomic Studies

Silvia Pomella et al. Int J Mol Sci. .

Abstract

An important determinant for oral squamous cell carcinoma (OSCC) onset and outcome is the composition of the tumor microenvironment (TME). Thus, the study of the interactions occurring among cancer cells, immune cells, and cancer-associated fibroblasts within the TME could facilitate the understanding of the mechanisms underlying OSCC development and progression, as well as of its sensitivity or resistance to the therapy. In this context, it must be highlighted that the characterization of TME proteins is enabled by proteomic methodologies, particularly mass spectrometry (MS). Aiming to identify TME protein markers employable for diagnosing and prognosticating OSCC, we have retrieved a total of 119 articles spanning 2001 to 2023, of which 17 have passed the selection process, satisfying all its criteria. We have found a total of 570 proteins detected by MS-based proteomics in the TME of OSCC; among them, 542 are identified by a single study, while 28 are cited by two or more studies. These 28 proteins participate in extracellular matrix remodeling and/or energy metabolism. Here, we propose them as markers that could be used to characterize the TME of OSCC for diagnostic/prognostic purposes. Noteworthy, most of the 28 individuated proteins share one feature: being modulated by the hypoxia that is present in the proliferating OSCC mass.

Keywords: CAFs; biomarkers; oral squamous cell carcinoma; proteomic; stroma; tumor microenvironment.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Schematic workflow of the systematic review process.
Figure 2
Figure 2
Representation of the PRISMA workflow (http://www.prisma-statement.org (accessed on 27 January 2024)) for the selection of articles.
Figure 3
Figure 3
A bar graph depicting the number and year of publication of the selected articles.
Figure 4
Figure 4
STRING association network of the 28 selected proteins. Network nodes represent proteins with their 3D-known structures. Blue line: known interaction from curated databases. Pink line: known interaction experimentally determined. Green line: predicted interactions from gene neighborhood. Red line: predicted interactions from gene fusions. Dark blue line: predicted interactions from gene co-occurrence. Yellow line: interactions by text mining. Black line: interactions by co-expression. Purple line: interaction by protein homology.
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