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. 2024 Feb 4:18:11779322241227722.
doi: 10.1177/11779322241227722. eCollection 2024.

Functional Enrichment Analysis of Tumor Microenvironment-Driven Molecular Alterations That Facilitate Epithelial-to-Mesenchymal Transition and Distant Metastasis

Mahnaz Abdolahi  1 , Parnian Ghaedi Talkhounche  2 , Mohammad Hossein Derakhshan Nazari  2 , Haniyeh Sadat Hosseininia  3   4 , Niloofar Khoshdel-Rad  5 , Amin Ebrahimi Sadrabadi  4   6
Affiliations

Functional Enrichment Analysis of Tumor Microenvironment-Driven Molecular Alterations That Facilitate Epithelial-to-Mesenchymal Transition and Distant Metastasis

Mahnaz Abdolahi et al. Bioinform Biol Insights. .

Abstract

Nowadays, hepatocellular carcinoma (HCC) is the second leading cause of cancer deaths, and identifying the effective factors in causing this disease can play an important role in its prevention and treatment. Tumors provide effective agents for invasion and metastasis to other organs by establishing appropriate communication between cancer cells and the microenvironment. Epithelial-to-mesenchymal transition (EMT) can be mentioned as one of the effective phenomena in tumor invasion and metastasis. Several factors are involved in inducing this phenomenon in the tumor microenvironment, which helps the tumor survive and migrate to other places. It can be effective to identify these factors in the use of appropriate treatment strategies and greater patient survival. This study investigated the molecular differences between tumor border cells and tumor core cells or internal tumor cells in HCC for specific EMT genes. Expression of NOTCH1, ID1, and LST1 genes showed a significant increase at the HCC tumor border. Targeting these genes can be considered as a useful therapeutic strategy to prevent distant metastasis in HCC patients.

Keywords: Hepatocellular carcinoma; epithelial-to-mesenchymal transition; metastasis; tumor microenvironment.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Discovering DEGs and dDEGs and associated GOs: (A) value distribution of selected samples in both groups; (B) volcano plot showing significant downregulated (purple dots) and upregulated (green dots) genes considering P < .05 and |log2FC| ⩾ 0.5; and (C) GO analysis results from DAVID. BP indicates biological process; CC, cellular component; DAVID, Database for Annotation, Visualization and Integrated Discovery; dDEG, differentiation differentially expressed gene; DEG, differentially expressed gene; FC, fold change; GO, gene ontology; MF, molecular function.
Figure 2.
Figure 2.
Pathway analysis results using KEGG and WikiPathways. (A) KEGG and (B) WikiPathways significant pathways considering P < .05. Gene ratio refers to the relative number of the involved genes in a pathway and all genes that were identified in significant pathways. KEGG indicates Kyoto Encyclopedia of Genes and Genomes.
Figure 3.
Figure 3.
Protein network construction and analysis in Cytoscape. (A) PPI network that was constructed by STRING and visualized by Cytoscape. Purple and green nodes represented downregulated and upregulated dDEGs, respectively. (B) Degree and (C) radiality models constructed by CytoHubba indicated NOTCH1 as the critical protein. The darker the color, the higher the value. dDEGs indicates differentiation differentially expressed genes; PPI, protein-protein interaction.
Figure 4.
Figure 4.
GEPIA analysis results: (A) stage-dependent expression analysis correlated LST1, ID1, and NOTCH1 higher expression and distant metastasis in LIHC, and (B) Kaplan-Meier plot was drawn and demonstrated the correlation of higher expression of correlated LST1, ID1, and NOTCH1 and lower chance of survival in LIHC patients in late stages. GEPIA indicates gene expression profiling interactive analysis; LIHC, liver hepatocellular carcinoma.
See this image and copyright information in PMC

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