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. 2025 Apr 30:13:e19360.
doi: 10.7717/peerj.19360. eCollection 2025.

Integrative bioinformatics analysis and experimental validation of key biomarkers driving the progression of cirrhotic portal hypertension

Meilin Li #  1 , Lilin Jiang #  2 , Yunrui Ru  3 , Zhonghua Lu  4 , Peng Gu  5
Affiliations

Integrative bioinformatics analysis and experimental validation of key biomarkers driving the progression of cirrhotic portal hypertension

Meilin Li et al. PeerJ. .

Abstract

Background: Portal hypertension is a driving factor of cirrhosis complications, but the specific molecular mechanism of portal hypertension in cirrhosis remains unclear. The aim of this study was to identify hub genes for predicting persistent progression of portal hypertension in patients with liver cirrhosis.

Methods: Related microarray datasets were obtained from the Gene Expression Omnibus database. Weighted gene co-expression network analysis and differential expression genes analysis were used to identify the correlation sets of genes. In addition, protein-protein interaction networks and machine learning algorithms were conducted to screen center of candidate genes. To validate the diagnostic effect of hub genes, receiver operating characteristic curves were utilized in another dataset that is publicly accessible. Furthermore, the CIBERSORT algorithm was employed to investigate the immune infiltration levels of 22 immune cells and their connection to hub gene markers. Immunohistochemistry and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were conducted to validate novel hub genes in clinical specimens.

Results: We obtained 671 differentially expressed genes and 11 module genes related to cirrhotic portal hypertension. Two candidate genes namely oncoprotein-induced transcript 3 protein (OIT3) and lysyl oxidase like protein 1 (LOXL1) were identified as biomarkers. RT-qPCR and immunohistochemistry (IHC) verified the expression of LOXL1 and OIT3 at mRNA and protein levels in liver tissue.

Conclusions: OIT3 and LOXL1 were identified as potential novel targets for the diagnosis and treatment of cirrhotic portal hypertension (CPH).

Keywords: Bioinformatic analysis; Cirrhosis; Lysyl Oxidase Like Protein 1; Machine learning; Oncoprotein-induced Transcript 3 Protein; Portal hypertension; Weighted gene co-expression network analysis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. The flowchart depicting the overall data screening strategy.
Figure 2
Figure 2. Identification of CPH-related DEGs.
(A) The volcano plot of DEGs in HC vs CPH group. (B) Heatmap displayed the top 30 genes that show significant differences.
Figure 3
Figure 3. Construction of WGCNA modules.
(A) Hierarchical clustering dendrogram of module eigengenes. (B) The “soft” threshold was chosen based on the combined analysis of scale independence and average connectivity. (C) The cluster dendrogram of co-expression network modules from WGCNA depending on a dissimilarity measure. (D) The CPH condition was characterized by 11 gene co-expression modules. Each cell within these modules displays the correlation coefficient and the corresponding p-value. (E) Shared genes identified by overlapping DEGs and WGCNA.
Figure 4
Figure 4. Functional enrichment analysis and visual representation of the PPI networks.
(A) The enriched terms in GO analysis. (B) The KEGG enrichment analysis bubble plot displays the signaling pathways most closely related to 173 shared genes. (C) PPI network of interacted genes. (D) Gene clustering based on the MCODE algorithm.
Figure 5
Figure 5. Application of machine learning for screening hub genes.
(A, B) LASSO regression identified seven genes with minimal deviance. (C, D) RF algorithm selected 25 genes. (E, F) SVM-RFE algorithm highlighted two genes achieving peak diagnostic accuracy. (G) The intersection of three machine learning algorithms.
Figure 6
Figure 6. Exploring the expression levels and predictive value of hub genes.
(A) Expression levels of two hub genes in the GSE139602: CPH group vs. HC group. (B) Expression levels of two hub genes in the GSE77627: CPH group vs. HC group. (C) ROC curve in the GSE139602. (D) ROC curve in the GSE77627. ****p < 0.0001.
Figure 7
Figure 7. Single-Gene GSEA of hub genes.
(A, B) GSEA analysis of LOXL1. (C, D) GSEA analysis of OIT3.
Figure 8
Figure 8. Results of immune cell analysis.
(A) The stacked plot displaying the relative proportion of 21 immune cells in different samples. (B) The correlated heatmap between two hub genes and immune cells. (C) The box plot comparing the expression of immune cells between HC and CPH groups. *p < 0.05; **p < 0.01; ***p < 0.001.
Figure 9
Figure 9. Verification of the two hub genes.
(A) Immunohistochemical staining of LOXL1 in liver tissue. (B) Immunohistochemical staining of OIT3 in liver tissue. (C) The expression patterns of LOXL1. (D) The expression patterns of OIT3. (E) Relative mRNA levels of LOX1 in control and CPH group. (F) Relative mRNA levels of OIT3 in control and CPH group. Scale bar, 100 um. *p < 0.05; ***p < 0.001.
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