Phospholipase A2 Group IIA Is Associated with Inflammatory Hepatocellular Adenoma.
Sadahiro IwabuchiKenta TakahashiKazunori KawaguchiAkihisa NagatsuTadashi ImafukuShigeyuki ShichinoKouji MatsushimaAkinobu TaketomiMasao HondaShinichi HashimotoPublished in: Cancers (2023)
Although benign hepatocellular adenomas (HCA) are very rare, recent observations have shown their occurrence in patients with diabetes mellitus. Consequently, most of these cases are treated by resection due to concerns regarding their potential progression to hepatocarcinoma (HCC). This decision is largely driven by the limited number of studies on HCC subtyping and the lack of molecular and biological insights into the carcinogenic potential of benign tumors. This study aimed to comprehensively investigate the subtype classification of HCA and to compare and analyze gene expression profiling between HCA and HCC tissues. One fresh inflammatory HCA (I-HCA), three non-B non-C HCCs, two hepatitis B virus-HCCs, and one normal liver tissue sample were subjected to single-cell RNA sequencing (scRNA-seq). Comparative analysis of scRNA-seq among different tissues showed that phospholipase A2 group IIA ( PLA2G2A ) mRNA was specifically expressed in I-HCA, following RNA-seq analysis in formalin-fixed paraffin-embedded tissues from other HCAs. Immunohistochemistry using the PLA2G2A antibody in these tissues indicated that the positive reaction was mainly observed in hepatocytes of I-HCAs and stromal cells surrounding the tumor tissue in HCC were also stained. According to a clinical database, PLA2G2A expression in HCC does not correlate with poor prognosis. This finding may potentially help develop a new definition for I-HCA, resulting in a significant clinical contribution, but it requires validation with other fresh HCA samples.
Keyphrases
- single cell
- rna seq
- poor prognosis
- hepatitis b virus
- gene expression
- genome wide
- long non coding rna
- high throughput
- risk assessment
- oxidative stress
- machine learning
- deep learning
- type diabetes
- dna methylation
- climate change
- human health
- binding protein
- adipose tissue
- copy number
- single molecule
- glycemic control
- case control