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. 2024 May 28:15:1351908.
doi: 10.3389/fimmu.2024.1351908. eCollection 2024.

Integrated bioinformatics combined with machine learning to analyze shared biomarkers and pathways in psoriasis and cervical squamous cell carcinoma

Luyu Liu #  1   2 , Pan Yin #  1   2 , Ruida Yang #  1   2 , Guanfei Zhang  1 , Cong Wu  1   2 , Yan Zheng  1 , Shaobo Wu  1   2 , Meng Liu  1
Affiliations

Integrated bioinformatics combined with machine learning to analyze shared biomarkers and pathways in psoriasis and cervical squamous cell carcinoma

Luyu Liu et al. Front Immunol. .

Abstract

Background: Psoriasis extends beyond its dermatological inflammatory manifestations, encompassing systemic inflammation. Existing studies have indicated a potential risk of cervical cancer among patients with psoriasis, suggesting a potential mechanism of co-morbidity. This study aims to explore the key genes, pathways, and immune cells that may link psoriasis and cervical squamous cell carcinoma (CESC).

Methods: The cervical squamous cell carcinoma dataset (GSE63514) was downloaded from the Gene Expression Omnibus (GEO). Two psoriasis-related datasets (GSE13355 and GSE14905) were merged into one comprehensive dataset after removing batch effects. Differentially expressed genes were identified using Limma and co-expression network analysis (WGCNA), and machine learning random forest algorithm (RF) was used to screen the hub genes. We analyzed relevant gene enrichment pathways using GO and KEGG, and immune cell infiltration in psoriasis and CESC samples using CIBERSORT. The miRNA-mRNA and TFs-mRNA regulatory networks were then constructed using Cytoscape, and the biomarkers for psoriasis and CESC were determined. Potential drug targets were obtained from the cMAP database, and biomarker expression levels in hela and psoriatic cell models were quantified by RT-qPCR.

Results: In this study, we identified 27 key genes associated with psoriasis and cervical squamous cell carcinoma. NCAPH, UHRF1, CDCA2, CENPN and MELK were identified as hub genes using the Random Forest machine learning algorithm. Chromosome mitotic region segregation, nucleotide binding and DNA methylation are the major enrichment pathways for common DEGs in the mitotic cell cycle. Then we analyzed immune cell infiltration in psoriasis and cervical squamous cell carcinoma samples using CIBERSORT. Meanwhile, we used the cMAP database to identify ten small molecule compounds that interact with the central gene as drug candidates for treatment. By analyzing miRNA-mRNA and TFs-mRNA regulatory networks, we identified three miRNAs and nine transcription factors closely associated with five key genes and validated their expression in external validation datasets and clinical samples. Finally, we examined the diagnostic effects with ROC curves, and performed experimental validation in hela and psoriatic cell models.

Conclusions: We identified five biomarkers, NCAPH, UHRF1, CDCA2, CENPN, and MELK, which may play important roles in the common pathogenesis of psoriasis and cervical squamous cell carcinoma, furthermore predict potential therapeutic agents. These findings open up new perspectives for the diagnosis and treatment of psoriasis and squamous cell carcinoma of the cervix.

Keywords: biomarkers; cervical squamous cell carcinoma (CESC); immune cell infiltration; machine learning; psoriasis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of analytical steps in this study.
Figure 2
Figure 2
Identification and analysis of key module of psoriasis and cervical squamous cell carcinoma by WGCNA. (A) Principal component analysis of the two original Psoriasis datasets before batch effect correction. (B) Principal component analysis of the corrected Psoriasis dataset. (C, D) Scale independence and average connectivity plots of psoriasis. (E, F) Scale independence and average connectivity plots of cervical cancer. (G, H) Gene dendrogram and heatmap of the modular signature gene network. (I, J) Identification of weighted gene co-expression network modules associated with psoriasis and cervical cancer, and module characterized genes in relation to psoriasis and cervical cancer status.
Figure 3
Figure 3
Screening of hub-genes by machine learning algorithm. (A, B) Volcano plot demonstrating an overview of the differential expression of all genes in CESC and Psoriasis. (C) DEGs in cervical cancer and psoriasis samples were intersected with key genes in WGCNA taken to obtain the Wayne plots of 27 genes. (D) PPI network of 27 genes. (E) Major PPI network analysis of the top 10 hub genes by CytoHubba software. (F) RF algorithm screened out 16 characterized genes in psoriasis samples. (G) The RF algorithm screened 8 characterized genes in cervical cancer samples. (H) Wayne diagram of 5 key genes identified. The threshold in the volcano plot was -log10 (adjusted P-value) > 2 and |log2 (fold change)| > 0.5; red dots indicate significant differential expressed genes. FDR was used for P value adjustment.
Figure 4
Figure 4
Significant gene module and enrichment analysis of the modular genes. (A-C) Results of GO analysis of 27 genes, biological process (BP), cellular component (CC) and molecular function (MF) of the genes. (D) Results of KEGG analysis of 27 genes.
Figure 5
Figure 5
Immune cell infiltration analysis. (A) Heat map of the relative proportions of 22 types of infiltrating immune cells in patients with psoriasis and cervical cancer. (B) Violin plot of the abundance of each type of immune cell infiltration in the psoriasis and cervical cancer group. (C) Correlation graph representing the association of immune cells with five central genes.
Figure 6
Figure 6
Screening of the potential small-molecular compounds for the treatment of psoriasis and CESC via cMAP analysis. (A) Intersection Wayne plots of DEGs genes up-regulated in psoriasis and cervical cancer with hub genes taken from the WCGNA module. (B) Heatmap of the top 10 compounds with the highest enrichment in 10 cell lines based on cMAP analysis. (C) Top 10 compounds information and targeting pathways. (D) Chemical structures of the 10 compounds.
Figure 7
Figure 7
Validation of hub-genes in external datasets and experiments databases. (A) Validation of the center gene of cervical cancer in TCGA-CESC database. (B) Validation of center gene in the psoriasis dataset GSE35182. (C) RT-qPCR results of 5 key genes in psoriasis cell samples. (D) RT-qPCR results of 5 key genes in cervical cancer cell samples. (*p< 0.05, **p < 0.01, ***p < 0.001).
Figure 8
Figure 8
The diagnostic value evaluation in the validation cohort and enrichment analysis. (A) ROC plot of each key gene (NCAPH, UHRF1, CDCA2, CENPN, and MELK) based on the AUC. (B) The bubble plot demonstrates the results of GO enrichment analysis of hub gene-related differential genes in psoriasis. (C) The results of the KEGG enrichment analysis of hub gene-related differential genes in psoriasis are demonstrated by a lollipop plot. AUC, area under the curve.
Figure 9
Figure 9
Screening of potential miRNAs and TF-mRNA network of 5 targeting hub-gene. (A) An Interaction network of five hub genes and potential miRNAs-targeted. (B) TF-mRNA network of 5 hub genes. The pink squares represent the top TFs associated with the hub genes.
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