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. 2025 Sep 29;15(10):455.
doi: 10.3390/jpm15100455.

Molecular Profiling of SYT-SSX Fusion Transcripts for Enhanced Diagnosis of Synovial Sarcomas

Affiliations

Molecular Profiling of SYT-SSX Fusion Transcripts for Enhanced Diagnosis of Synovial Sarcomas

Sara Louati et al. J Pers Med. .

Abstract

Background/Objectives: Synovial sarcoma (SS) is an aggressive soft-tissue tumor characterized by the chromosomal translocation t(X;18) (p11.2;q11.2), most commonly involving the fusion of the SYT gene on chromosome 18 with the SSX1 or SSX2 genes on chromosome X. This study aims to explore the clinicopathological and molecular characteristics of synovial sarcoma in a cohort of Moroccan patients. Methods: We analyzed 48 cases of synovial sarcoma using formalin-fixed, paraffin-embedded (FFPE) tissue samples. Histological grading was performed according to the FNCLCC system. Immunohistochemical staining was employed to detect cytokeratin (CK) and epithelial membrane antigen (EMA). Molecular analysis included fluorescence in situ hybridization (FISH) to identify SS18 gene rearrangements and reverse transcription-polymerase chain reaction (RT-PCR) to detect SYT-SSX fusion transcripts. Results: Among the cohort, 56% of cases showed SS18 gene rearrangements via FISH, while RT-PCR confirmed the presence of SS18-SSX1 and SS18-SSX2 transcripts in 60% and 32% of cases, respectively. The remainder was classified as undifferentiated sarcoma. Notably, no significant associations were observed between SYT-SSX fusion type and clinicopathological features. Conclusions: These findings underscore the importance of integrating molecular techniques for precise diagnosis in synovial sarcoma. The results align with global patterns, emphasizing the necessity for molecular testing to enhance diagnostic accuracy and informing potential therapeutic advancements.

Keywords: SYT-SSX fusion transcripts; molecular diagnostics; synovial sarcoma.

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Conflict of interest statement

Authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
H&E staining (×250): (a) diffuse proliferation of spindle cells in monophasic synovial sarcoma; (b) and biphasic synovial sarcoma.
Figure 2
Figure 2
This bar graph illustrates the distribution of synovial sarcoma cases based on age, sex (F = female, M = male), histomorphology, tumor grade, and IHC markers. The chart highlights that males constitute 56% of the cohort, with the highest representation in the 20–29 age group (25%) and a notable presence of paediatric cases represented in the age category 11–19 (19%). Histomorphologically, 62.5% of tumors were fusiform, 25% were round, and 12.5% were epithelial. The majority of tumors were grade 2 (58%), while 42% were grade 3. Immunohistochemical analysis revealed EMA positivity in 35 cases and CK positivity in 21 cases. Error bars represent the standard error of the mean.
Figure 3
Figure 3
Cytoplasmic expression of antibodies observed in a synovial sarcoma (×400): (a) anti-CK; (b) anti-EMA.
Figure 4
Figure 4
FISH technique using the Vysis SS18 break-apart probe in synovial sarcoma. (×1000): (a) In patient number 3, two break-apart red and green signals (red and green arrowheads) and one fused signal (yellow arrowhead) are observed, indicative of rearrangement in the SS18 gene; (b) In patient number 27, two fused signals (yellow arrowhead) are observed, thus demonstrating no SS18 gene rearrangement. The presence of separated signals in at least 20% of the nuclei indicates a positive result for the chromosomal alteration associated with this malignancy. This result confirms the presence of the chromosomal alteration associated with synovial sarcoma, highlighting the utility of FISH in diagnosing this malignancy.
Figure 5
Figure 5
This bar graph presents the distribution of tumor locations (lower limbs, upper limbs, thorax, and head and neck) for patients with SYT-SSX1 and SYT-SSX2 fusion types. SYT-SSX1 fusion type is more common in tumors located in the lower limbs, while SYT-SSX2 fusion type exhibits a more even distribution across various anatomical locations. Error bars represent the standard error of the mean (SEM) for each group, indicating variability within the sample.
Figure 6
Figure 6
This combined heatmap illustrates the expression of the epithelial membrane antigen (EMA) marker in synovial sarcoma patients, categorized by tumor morphology (spindle-shaped, round, and epithelial) and sex (male and female). The color intensity represents the proportion of patients with positive EMA expression, with darker shades indicating higher positivity, allowing for comparisons across morphological subtypes and highlighting potential sex-specific variations.
Figure 7
Figure 7
This combined heatmap illustrates the expression of CK marker in synovial sarcoma patients, categorized by tumor morphology (spindle-shaped, round, and epithelial) and sex (male and female). The color gradients represent the proportion of patients with CK positivity, with darker shades indicating greater expression. The distribution across tumor morphologies and sexes offers insights into potential diagnostic or prognostic implications related to marker expression patterns.
Figure 8
Figure 8
MDS plots illustrating the relationships between key clinical features in synovial sarcoma patients: (a) represents the relationship between fusion type (SYT-SSX1, SYT-SSX2, and Unknown) and tumor grade (Grade 2 and Grade 3). The color of each point corresponds to the patient fusion type, and the location of the points in two-dimensional space reflects the similarity or dissimilarity between patients based on both fusion type and tumor grade. This reduction into two dimensions allows for visualization of potential clustering patterns based on these variables; (b) displays the relationship between sex (male and female) and fusion type. The color of each point represents the patient’s sex, while different shapes denote the fusion types. The position of each point indicates the similarity or dissimilarity between patients in terms of both sex and fusion type, enabling a visual exploration of any potential clustering or grouping patterns in the data.

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