Back

Molecular Insights into ANPEP in Gastric Adenocarcinoma

Araujo, T. M. T.; Rodrigues, B.; da Silva, J. M. C.; Remigio, M.; Moreira, F.; Moraes Casseb, S. M.; Barra, W. F.; Ishak, G.; Anaissi, A. K. M.; Magalhaes, L.; Vidal, A.; Mourao, R.; Teixeira, E.; Pereira, D.; Silva, V.; Avelar, D.; Silva, R.; Santos, A.; Marques, L.; Burbano, R. M.; Assumpcao, P. P.

2025-09-04 cancer biology
10.1101/2025.08.28.672866 bioRxiv
Show abstract

Alanyl aminopeptidase (ANPEP) has been implicated in various cancers, but its specific role in gastric adenocarcinoma (GC) remains incompletely understood. This study analyzed ANPEP gene expression in gastric cancer (GC), peritumoral tissue (PTT), metaplasia (M), and normal tissue (N). Total RNA was extracted, libraries were prepared and sequenced on the Illumina NextSeq 500. Data was processed using the nf-core/rnaseq pipeline. Transcript quantifications were imported with tximport and normalized using DESeq2. Differential expression (|log2FC| >2; adj. p < 0.05) and Kruskal-Wallis tests identified key genes. ANPEP was significantly upregulated in GC, PTT, and M compared to normal tissue (p < 0.01), suggesting its involvement in early mucosal transformation and malignant progression. Heatmap analysis revealed upregulation of genes related to immune function and oxidative stress, indicating an immunosuppressive and apoptosis-resistant tumor microenvironment. Correlation analyses identified strong positive associations between ANPEP and genes involved in cytoskeletal remodeling, immune modulation, and metabolic regulation, suggesting that ANPEP supports both the invasive potential of tumor cells and the establishment of an immunosuppressive niche. These findings position ANPEP as a promising biomarker for early detection and a candidate for targeted therapies.

Published in Genetics and Molecular Biology · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.