Prediction of Lung Adenocarcinoma Driver Genes Through Protein–Protein Interaction Networks Utilizing GenePlexus

Lung adenocarcinoma, a subtype of lung cancer, is produced by uncontrolled proliferation of somatic cells affected by some tumorigenic factors. The origin of this disease can be attributed to the concept of "cancer driver," which links the occurrence of tumor with specific changes in some key genes. These key genes can be identified at various molecular levels. Our innovative method uses a groundbreaking computing technology called GenePlexus to mine new genes related to lung adenocarcinoma. Initially, a vast network was synthesized from protein-protein interactions. Utilizing GenePlexus, we traversed paths interlinking aberrant genes across different layers and pinpointed emerging candidate genes situated on these trajectories. Finally, the candidate genes that were obtained underwent a series of filtering processes, including a permutation test, interaction test, and enrichment test. Compared with the shortest path method, GenePlexus has identified previously neglected genes involved in lung adenocarcinoma. For example, genes such as EGR2, EPHA3, FGFR4, HOXB1, and HEY1 play key roles at multiple molecular levels, including methylation, microRNA, mRNA and mutation, which affect tumorigenesis and lung cancer progression. These genes regulate various processes, from gene expression and cell proliferation to drug resistance to therapeutic drugs and the progress of lung adenocarcinoma.

Authors

Institutions

Publication Details

Journal
PROTEOMICS
Published
2024-12-18
DOI
https://doi.org/10.1002/pmic.202400296
Citations
1
Primary Topic
Cancer-related gene regulation
Type
article
Field-Weighted Citation Impact
0.23

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prediction of Lung Adenocarcinoma Driver Genes Through Protein–Protein Interaction Networks Utilizing GenePlexus

Wenfeng Shen, Tao Huang, Fei Yuan, Xiaoyu Cao et al.
1 citations
PROTEOMICS
Cancer-related gene regulation
0.23
article

Prediction of Lung Adenocarcinoma Driver Genes Through Protein–Protein Interaction Networks Utilizing GenePlexus

Wenfeng Shen, Tao Huang, Fei Yuan, Xiaoyu Cao, FeiMing Huang, Jiabo Li, Yuhang Zhang, Yu‐Dong Cai, Lei Chen, Kaiyan Feng, YuSheng Bao
article en
1 citations

Abstract

Lung adenocarcinoma, a subtype of lung cancer, is produced by uncontrolled proliferation of somatic cells affected by some tumorigenic factors. The origin of this disease can be attributed to the concept of "cancer driver," which links the occurrence of tumor with specific changes in some key genes. These key genes can be identified at various molecular levels. Our innovative method uses a groundbreaking computing technology called GenePlexus to mine new genes related to lung adenocarcinoma. Initially, a vast network was synthesized from protein-protein interactions. Utilizing GenePlexus, we traversed paths interlinking aberrant genes across different layers and pinpointed emerging candidate genes situated on these trajectories. Finally, the candidate genes that were obtained underwent a series of filtering processes, including a permutation test, interaction test, and enrichment test. Compared with the shortest path method, GenePlexus has identified previously neglected genes involved in lung adenocarcinoma. For example, genes such as EGR2, EPHA3, FGFR4, HOXB1, and HEY1 play key roles at multiple molecular levels, including methylation, microRNA, mRNA and mutation, which affect tumorigenesis and lung cancer progression. These genes regulate various processes, from gene expression and cell proliferation to drug resistance to therapeutic drugs and the progress of lung adenocarcinoma.

PROTEOMICSVol. 25(5-6)
Shanghai University (CN), Brigham and Women's Hospital (US), Binzhou Medical University (CN), Shanghai Polytechnic University (CN), Harvard University (US), Shanghai University of Engineering Science (CN), Shandong University of Aeronautics (CN), Shanghai Institute of Nutrition and Health (CN), Guangdong Polytechnic Normal University (CN), University of Chinese Academy of Sciences (CN), Shanghai Maritime University (CN)
Chinese Academy of Sciences, Natural Science Foundation of Shandong Province, National Key Research and Development Program of China
Good health and well-being
Openalex Percentile: Top 40%
Cancer-related gene regulation
0.23
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.