Back

Tracking cancer dynamics from normal tissue to malignancy using perfect N- and T-gene expression markers

Perez, G. J. G.; Perez-Rodriguez, R.; Gonzalez, A.

2026-03-08 cancer biology
10.1101/2024.11.04.621130 bioRxiv
Show abstract

Common knowledge states that the spontaneous somatic evolution of a normal tissue may lead to a tumor. Once the tumor is formed, it naturally evolves towards a state of higher malignancy. On the other hand, perfect gene expression markers for normal tissue and tumor--the so-called N-genes and T-genes--were recently introduced. We join these two pieces of knowledge in order to argue that: 1) Only N-markers participate in the spontaneous dynamics of a normal tissue. The number of active markers decreases as the tissue approaches the transition point where it becomes a tumor. 2) Only T-markers participate in the spontaneous dynamics of tumors. The number of markers increases as the tumor becomes more malignant. 3) Both sets of genes are connected by the so-called NT-genes, i.e., genes that are simultaneously N- and T-markers. They should play a crucial role at the transition point and, possibly, when the tumor is exposed to a drug or therapy. 4) The pathways or mechanisms protecting the normal tissue from becoming a tumor may be described by a small perfect panel of N-genes. 5) The pathways or mechanisms guiding the evolution of tumors in a tissue may be described by a small perfect panel of T-genes. We illustrate the above statements with the analysis of expression data for prostate adenocarcinoma, one of the most heterogeneous tumors. In this case, there are about 1000 N-genes and 6000 T-genes, and the perfect N- and T-panels contain 11 and 8 genes, respectively. Additionally, we provide examples from lung adenocarcinoma and liver hepatocarcinoma.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
PLOS ONE
4510 papers in training set
Top 14%
14.2%
2
PLOS Computational Biology
1633 papers in training set
Top 2%
12.2%
3
Cancers
200 papers in training set
Top 0.9%
6.3%
4
Scientific Reports
3102 papers in training set
Top 19%
6.3%
5
Physical Biology
43 papers in training set
Top 0.4%
3.9%
6
eLife
5422 papers in training set
Top 26%
3.6%
7
Journal of Computational Biology
37 papers in training set
Top 0.1%
3.6%
50% of probability mass above
8
Cells
232 papers in training set
Top 0.9%
2.9%
9
iScience
1063 papers in training set
Top 7%
2.7%
10
Frontiers in Molecular Biosciences
100 papers in training set
Top 0.8%
2.6%
11
Royal Society Open Science
193 papers in training set
Top 1%
2.1%
12
npj Systems Biology and Applications
99 papers in training set
Top 0.9%
1.9%
13
Heliyon
146 papers in training set
Top 2%
1.6%
14
Entropy
20 papers in training set
Top 0.2%
1.6%
15
Oncotarget
15 papers in training set
Top 0.1%
1.5%
16
PeerJ
261 papers in training set
Top 9%
1.3%
17
International Journal of Molecular Sciences
453 papers in training set
Top 10%
1.3%
18
Frontiers in Oncology
95 papers in training set
Top 3%
1.3%
19
Physical Review E
95 papers in training set
Top 0.9%
1.2%
20
Mathematical Biosciences
42 papers in training set
Top 0.9%
0.9%
21
Bulletin of Mathematical Biology
84 papers in training set
Top 2%
0.9%
22
Communications Biology
886 papers in training set
Top 17%
0.9%
23
Frontiers in Cell and Developmental Biology
218 papers in training set
Top 7%
0.9%
24
Journal of Clinical Medicine
91 papers in training set
Top 5%
0.9%
25
Mathematical Biosciences and Engineering
23 papers in training set
Top 0.5%
0.9%
26
Journal of Theoretical Biology
144 papers in training set
Top 2%
0.8%
27
Cancer Research
116 papers in training set
Top 3%
0.7%
28
Physical Review Research
46 papers in training set
Top 0.9%
0.7%
29
Expert Systems with Applications
11 papers in training set
Top 0.5%
0.7%
30
BMC Bioinformatics
383 papers in training set
Top 7%
0.7%