Back

The Role of Primate-Specific Genes and Network Dynamics in Cancer Evolution

Dong, C.; Su, Y.-W.; Liao, Z.; Xu, Q.; Guo, H.-X.; Chen, J.; Wei, W.; Xia, S.; Cheng, Z.; Zhang, C.; Guo, F.-B.

2025-07-27 evolutionary biology
10.1101/2025.07.23.666306 bioRxiv
Show abstract

The atavistic evolutionary theory of cancer evolution highlights the role of ancient and unicell-originated genes, and comprehensive research demonstrated the evolutionary inference. Comparably, very few reports linked young genes with cancer development and maintenance. Here, we found that in cancer cell lines a higher proportion of core essential genes was present among primate-specific genes (PSG) than early metazoan-originated genes (Wilcoxon rank-sum test, p-value = 4.67e-12) and mammal-originated genes (Wilcoxon rank-sum test, p-value = 1.22e-16). Additionally, we found that the loss of many co-expression gene pairs in pan-cancer leads to their network becoming looser compared with normal tissue. However, PSGs particularly those being essential, exhibit a dynamic increase in co-expression, explaining their enhanced role in maintaining cancers. Furthermore, clustering of the gene co-expressions brings a cancer-exclusive module, which contains a large number of PSG connections. We also demonstrate the divergence between essential genes in healthy individuals and those in cancer cells. Our findings complement the atavistic theory in elucidating the evolutionary process of cancer.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
eLife
● 5828 papers in training set
Top 3%
18.3%
Similar papers in this journal
2
iScience
○ 1154 papers in training set
Top 0.2%
11.8%
Similar papers in this journal
3
Journal of Genetics and Genomics
○ 38 papers in training set
Top 0.1%
6.7%
Similar papers in this journal
4
Scientific Reports
○ 3612 papers in training set
Top 16%
5.4%
Similar papers in this journal
5
Molecular Biology and Evolution
◐ 542 papers in training set
Top 2%
4.8%
Similar papers in this journal
6
Genomics, Proteomics & Bioinformatics
188 papers in training set
Top 0.5%
3.8%
50% of probability mass above
7
Bioinformatics
◐ 1204 papers in training set
Top 5%
3.4%
8
Frontiers in Genetics
○ 230 papers in training set
Top 1%
2.7%
9
Genome Research
● 468 papers in training set
Top 2%
2.7%
10
Briefings in Bioinformatics
◐ 354 papers in training set
Top 3%
2.4%
11
Communications Biology
○ 993 papers in training set
Top 9%
2.4%
12
Genome Biology and Evolution
◐ 338 papers in training set
Top 2%
2.4%
13
PeerJ
◐ 308 papers in training set
Top 4%
2.1%
14
National Science Review
◐ 21 papers in training set
Top 0.1%
2.1%
15
Journal of Molecular Evolution
○ 22 papers in training set
Top 0.2%
1.7%
16
Science Bulletin
○ 21 papers in training set
Top 0.2%
1.4%
17
Heliyon
○ 152 papers in training set
Top 5%
1.3%
18
Computational Biology and Chemistry
○ 28 papers in training set
Top 0.7%
1.1%
19
Genes
○ 144 papers in training set
Top 3%
1.1%
20
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 6%
0.9%
21
Entropy
○ 21 papers in training set
Top 0.3%
0.8%
22
Cell Proliferation
○ 12 papers in training set
Top 0.3%
0.8%
23
Molecular Genetics and Genomics
○ 12 papers in training set
Top 0.2%
0.6%
24
Cancers
○ 213 papers in training set
Top 5%
0.6%
25
PLOS Computational Biology
● 1863 papers in training set
Top 21%
0.6%
26
Genome Biology
○ 637 papers in training set
Top 10%
0.6%

"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.