Predicting the prognosis of hepatocellular carcinoma based on genes related to polyamine metabolism

In conclusion, this study screened four key genes based on PMRGs and constructed a risk model to effectively predict the prognosis of HCC, providing novel potential targets and theoretical basis for the molecular subtyping and individualized treatment of HCC.

Authors

Institutions

Publication Details

Journal
PeerJ
Published
2025-09-22
DOI
https://doi.org/10.7717/peerj.19985
Citations
1
Primary Topic
Polyamine Metabolism and Applications
Type
article
Field-Weighted Citation Impact
0.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predicting the prognosis of hepatocellular carcinoma based on genes related to polyamine metabolism

Haoming Li, Meng Pu, Chengli Liu, Jingchen Zhang et al.
1 citations
PeerJ
Polyamine Metabolism and Applications
0.81
article

Predicting the prognosis of hepatocellular carcinoma based on genes related to polyamine metabolism

Haoming Li, Meng Pu, Chengli Liu, Jingchen Zhang, Yingbo Ma, Tian Xia, Qike Huang, Shuhan Zhang
article en
1 citations

Abstract

In conclusion, this study screened four key genes based on PMRGs and constructed a risk model to effectively predict the prognosis of HCC, providing novel potential targets and theoretical basis for the molecular subtyping and individualized treatment of HCC.

PeerJVol. 13
Hebei North University (CN), Anhui Medical University (CN), United States Air Force (US), Air Force Engineering University (CN), Air Force General Hospital PLA (CN)
Openalex Percentile: Top 23%
Polyamine Metabolism and Applications
0.81
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.