Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12935-024-03226-3
Title: MicroRNA expression signature as a biomarker in the diagnosis of nodal T-cell lymphomas
Authors: Bin Masroni, Muhammad Sufyan 
Eng, Gracie Wee Ling 
Jeon, Ah-Jung
Gao, Yuan
Cheng, He
Leong, Sai Mun 
Cheong, Jit Kong 
Hue, Susan Swee-Shan
Tan, Soo Yong 
Keywords: Science & Technology
Life Sciences & Biomedicine
Oncology
miRNAs
T-cell lymphoma
Biomarkers
Nodal T-cell lymphoma
FFPE
RETINOIC ACID
CLASSIFICATION
RESPONSES
GROWTH
GENES
RNAS
Issue Date: 30-Jan-2024
Publisher: BMC
Citation: Bin Masroni, Muhammad Sufyan, Eng, Gracie Wee Ling, Jeon, Ah-Jung, Gao, Yuan, Cheng, He, Leong, Sai Mun, Cheong, Jit Kong, Hue, Susan Swee-Shan, Tan, Soo Yong (2024-01-30). MicroRNA expression signature as a biomarker in the diagnosis of nodal T-cell lymphomas. CANCER CELL INTERNATIONAL 24 (1). ScholarBank@NUS Repository. https://doi.org/10.1186/s12935-024-03226-3
Abstract: Background: The diagnosis of T-cell lymphomas is typically established through a multiparameter approach that combines clinical, morphologic, immunophenotypic, and genetic features, utilizing a variety of histopathologic and molecular techniques. However, accurate diagnosis of such lymphomas and distinguishing them from reactive lymph nodes remains challenging due to their low prevalence and heterogeneous features, hence limiting the confidence of pathologists. We investigated the use of microRNA (miRNA) expression signatures as an adjunctive tool in the diagnosis and classification of T-cell lymphomas that involve lymph nodes. This study seeks to distinguish reactive lymph nodes (RLN) from two types of frequently occurring nodal T-cell lymphomas: nodal T-follicular helper (TFH) cell lymphomas (nTFHL) and peripheral T-cell lymphomas, not otherwise specified (nPTCL). Methods: From the formalin-fixed paraffin-embedded (FFPE) samples from a cohort of 88 subjects, 246 miRNAs were quantified and analyzed by differential expression. Two-class logistic regression and random forest plot models were built to distinguish RLN from the nodal T-cell lymphomas. Gene set enrichment analysis was performed on the target genes of the miRNA to identify pathways and transcription factors that may be regulated by the differentially expressed miRNAs in each subtype. Results: Using logistic regression analysis, we identified miRNA signatures that can distinguish RLN from nodal T-cell lymphomas (AUC of 0.92 ± 0.05), from nTFHL (AUC of 0.94 ± 0.05) and from nPTCL (AUC of 0.94 ± 0.08). Random forest plot modelling was also capable of distinguishing between RLN and nodal T-cell lymphomas, but performed worse than logistic regression. However, the miRNA signatures are not able to discriminate between nTFHL and nPTCL, owing to large similarity in miRNA expression patterns. Bioinformatic analysis of the gene targets of unique miRNA expression revealed the enrichment of both known and potentially understudied signalling pathways and genes in such lymphomas. Conclusion: This study suggests that miRNA biomarkers may serve as a promising, cost-effective tool to aid the diagnosis of nodal T-cell lymphomas, which can be challenging. Bioinformatic analysis of differentially expressed miRNAs revealed both relevant or understudied signalling pathways that may contribute to the progression and development of each T-cell lymphoma entity. This may help us gain further insight into the biology of T-cell lymphomagenesis.
Source Title: CANCER CELL INTERNATIONAL
URI: https://scholarbank.nus.edu.sg/handle/10635/247689
ISSN: 1475-2867
DOI: 10.1186/s12935-024-03226-3
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