Exploring the TRIM15 protein interaction network as a drug target using machine learning in pancreatic cancer

Pancreatic cancer is among the most lethal cancers with a 5-year survival rate of only 13%. Due to its late-stage diagnosis and high metastatic potential, there is a critical need to better understand its molecular mechanisms to develop effective treatments. Tripartite motif containing 15 (TRIM15), a member of the TRIM protein family, plays a role in tumor progression, immune evasion, and lipid metabolism reprogramming, but its role in apoptosis and immune-related pathways remains unclear. Moreover, immune checkpoint proteins such as PD-L1 and VISTA have been associated with cancer progression and poor survival by contributing significantly to the tumor microenvironment. In this context, in order to better understand the effects of TRIM15 and its neighboring genes on pancreatic cancer, other genes interacting with these genes were obtained from NCBI and BioGRID databases. In the topological analysis of the protein-protein interaction network, 191 proteins in which TRIM15 is involved were identified and the expression profiles of these proteins in pancreatic cancer were analyzed using the Expression Atlas database. These analyses revealed 73 differentially expressed proteins compared to normal individuals, and high accuracy (> 0.90) was achieved with machine learning algorithms. These proteins were identified as drug targets and 50 drug candidates were identified through the L1000CSD2 database. As a result of the TF-IDF calculations performed for innovation, impartozole with a TF-IDF value of 0 was selected as a potential drug candidate for the treatment of pancreatic cancer. Importin-β1, which is overexpressed in many cancers, is associated with tumor progression and its inhibitor, importazole, exhibits antitumor activity by inducing G2/M cell cycle arrest and apoptosis. Here, TRIM15 regulation, apoptosis mechanism and immune checkpoint mechanisms were investigated to reveal the role of importazole on the underlying mechanisms of pancreatic cancer progression. The findings show that importazole administration suppressed the expression level of TRIM15 and VISTA in AsPC-1 pancreatic cancer cells and triggered the apoptosis mechanism. These results suggest that importazole may be a potential therapeutic target for TRIM15 and VISTA in pancreatic cancer and may contribute to the development of innovative strategies for cancer treatment.

Authors

Institutions

Publication Details

Journal
Discover Oncology
Published
2025-11-26
DOI
https://doi.org/10.1007/s12672-025-03847-8
Primary Topic
interferon and immune responses
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the TRIM15 protein interaction network as a drug target using machine learning in pancreatic cancer

Ercan Çaçan, Kübra Sena Baş Topcu, Kubra Temiz
Discover Oncology
interferon and immune responses
article

Exploring the TRIM15 protein interaction network as a drug target using machine learning in pancreatic cancer

Ercan Çaçan, Kübra Sena Baş Topcu, Kubra Temiz
article en

Abstract

Pancreatic cancer is among the most lethal cancers with a 5-year survival rate of only 13%. Due to its late-stage diagnosis and high metastatic potential, there is a critical need to better understand its molecular mechanisms to develop effective treatments. Tripartite motif containing 15 (TRIM15), a member of the TRIM protein family, plays a role in tumor progression, immune evasion, and lipid metabolism reprogramming, but its role in apoptosis and immune-related pathways remains unclear. Moreover, immune checkpoint proteins such as PD-L1 and VISTA have been associated with cancer progression and poor survival by contributing significantly to the tumor microenvironment. In this context, in order to better understand the effects of TRIM15 and its neighboring genes on pancreatic cancer, other genes interacting with these genes were obtained from NCBI and BioGRID databases. In the topological analysis of the protein-protein interaction network, 191 proteins in which TRIM15 is involved were identified and the expression profiles of these proteins in pancreatic cancer were analyzed using the Expression Atlas database. These analyses revealed 73 differentially expressed proteins compared to normal individuals, and high accuracy (> 0.90) was achieved with machine learning algorithms. These proteins were identified as drug targets and 50 drug candidates were identified through the L1000CSD2 database. As a result of the TF-IDF calculations performed for innovation, impartozole with a TF-IDF value of 0 was selected as a potential drug candidate for the treatment of pancreatic cancer. Importin-β1, which is overexpressed in many cancers, is associated with tumor progression and its inhibitor, importazole, exhibits antitumor activity by inducing G2/M cell cycle arrest and apoptosis. Here, TRIM15 regulation, apoptosis mechanism and immune checkpoint mechanisms were investigated to reveal the role of importazole on the underlying mechanisms of pancreatic cancer progression. The findings show that importazole administration suppressed the expression level of TRIM15 and VISTA in AsPC-1 pancreatic cancer cells and triggered the apoptosis mechanism. These results suggest that importazole may be a potential therapeutic target for TRIM15 and VISTA in pancreatic cancer and may contribute to the development of innovative strategies for cancer treatment.

Discover OncologyVol. 16(1)
Tokat Gaziosmanpaşa Üniversitesi (TR), Bartin University (TR)
Openalex Percentile: Top 58%
interferon and immune responses
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.