MATERIALS AND METHODS: This is a retrospective analysis of histopathological records in a public tertiary health care centre in Malaysia. The computerised laboratory information system from the histopathology department of the hospital was retrieved for the period of 2005-2018. Descriptive analysis was done using Microsoft Excel.
RESULTS: There was a total of 233 histologically confirmed GC cases. The burden of GC was observed to be an increasing trend from 2016 onwards. Among them, 64% were male and 36% were female. The youngest age of diagnosis was 19, while the oldest one was 93. Malaysian Chinese were found to have the highest incidences (41.63%), followed by Malays (32.19%) and Malaysian Indians (23.61%). All cases were of adenocarcinoma cell types and were found to have poorly differentiated in majority at the time of diagnosis.
CONCLUSION: Although this report only represents one tertiary health care centre in Malaysia, the Indian Enigma was still observed, as stated in other literatures. Over time, the incidence of GC in Malays has increased. Consideration of lifestyle modifications, health education and Helicobacter pylori eradication in various nations' National Health Insurance plans, are encouraged as prevention is always better than treatment or cure, including the cost load.
METHODS: mRNA was extracted from 44 fibroadenomas and 36 giant fibroadenomas, and transcriptomic profiling was performed to identify up- and down-regulated genes in the giant fibroadenomas as compared to the fibroadenomas.
RESULTS: A total of 40 genes were significantly up-regulated and 18 genes were significantly down-regulated in the giant fibroadenomas as compared to the fibroadenomas of the breast. The top 5 up-regulated genes were FN1, IL3, CDC6, FGF8 and BMP8A. The top 5 down-regulated genes were TNR, CDKN2A, COL5A1, THBS4 and BMPR1B. The differentially expressed genes (DEGs) were found to be associated with 5 major canonical pathways involved in cell growth (PI3K-AKT, cell cycle regulation, WNT, and RAS signalling) and immune response (JAK-STAT signalling). Further analyses using 3 supervised learning algorithms identified an 8-gene signature (FN1, CDC6, IL23A, CCNA1, MCM4, FLT1, FGF22 and COL5A1) that could distinguish giant fibroadenomas from fibroadenomas with high predictive accuracy.
CONCLUSION: Our findings demonstrated that the giant fibroadenomas are biologically distinct to fibroadenomas of the breast with overexpression of genes involved in the regulation of cell growth and immune response.