Back

Development of an EMT-related exosomal miRNA signature that can predict prognosis in hepatocellular carcinoma

Missaghimamaghani, O.; Nehri, L. N.; Bakhshi, S.; Karaosmanoglu, O.; Sivas, H.; Acar, A. C.; Banerjee, S.

2025-09-11 cancer biology
10.1101/2025.09.06.674651 bioRxiv
Show abstract

Chemoresistance and epithelial-mesenchymal transition (EMT) are associated with failure of cancer chemotherapy and poor survival of patients. We have previously shown that chemoresistance and stemness in hepatocellular carcinoma (HCC) cells was accompanied by the development of partial EMT (p-EMT) and identified a number of EMT-associated proteins that are released from exosomes. In this study, we aimed to identify and classify the differentially expressed (DE) exosomal miRNAs from chemoresistant HuH7 cells undergoing p-EMT. Out of the fifty-four miRNAs that were enriched in the exosomes from these cells compared to controls, thirteen were identified in the exosomes isolated from the serum of HCC patients. These miRNAs targeted genes that were associated with cell-cell junctions, extracellular matrix, cytoskeleton, transcription and signal transduction. Univariate Cox regression analysis indicated that 11/13 miRNAs were associated with either favorable (n=4) or worse (n=7) prognosis. A machine learning algorithm indicated that seven miRNAs (miR 215-5p, miR 340-5p, miR 210-3p, miR 19a-3p, miR19b-3p, miR 1266-5p and miR 25-3p) could predict worse prognosis in multiple datasets with 64-68% accuracy. A Bayesian Inference network analysis with the thirteen miRNAs and their key target proteins, along with EMT and survival as the nodes indicated that the common denominator was transcription, suggesting that the exosomal miRNAs released from cells undergoing p-EMT can mediate phenotypic changes in cells through transcriptional regulation.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Cancers
213 papers in training set
Top 0.5%
9.5%
2
PeerJ
308 papers in training set
Top 0.2%
9.4%
3
Computational Biology and Chemistry
28 papers in training set
Top 0.1%
7.7%
4
Frontiers in Oncology
103 papers in training set
Top 0.5%
6.1%
5
Scientific Reports
3612 papers in training set
Top 14%
6.1%
6
Frontiers in Genetics
230 papers in training set
Top 0.5%
5.3%
7
BMC Cancer
67 papers in training set
Top 0.3%
5.3%
8
PLOS ONE
5266 papers in training set
Top 30%
5.0%
50% of probability mass above
9
International Journal of Molecular Sciences
494 papers in training set
Top 2%
4.2%
10
RNA Biology
78 papers in training set
Top 0.4%
3.1%
11
Frontiers in Bioinformatics
49 papers in training set
Top 0.2%
3.1%
12
Heliyon
152 papers in training set
Top 1%
3.1%
13
Biochemistry and Biophysics Reports
30 papers in training set
Top 0.3%
2.1%
14
Biomedicines
67 papers in training set
Top 1%
1.7%
15
eLife
5828 papers in training set
Top 51%
1.6%
16
Molecular Biology Reports
21 papers in training set
Top 0.5%
1.5%
17
iScience
1154 papers in training set
Top 23%
1.3%
18
Translational Oncology
21 papers in training set
Top 0.6%
1.1%
19
Computers in Biology and Medicine
128 papers in training set
Top 3%
1.1%
20
Genes
144 papers in training set
Top 3%
1.1%
21
Journal of Cellular Biochemistry
11 papers in training set
Top 0.2%
0.9%
22
Gene
46 papers in training set
Top 2%
0.8%
23
Frontiers in Molecular Biosciences
102 papers in training set
Top 2%
0.8%
24
Diagnostics
50 papers in training set
Top 3%
0.6%
25
Cell Death & Disease
126 papers in training set
Top 4%
0.6%
26
FEBS Open Bio
31 papers in training set
Top 1%
0.6%
27
International Journal of Cancer
49 papers in training set
Top 1%
0.6%
28
Gene Reports
14 papers in training set
Top 0.9%
0.6%