The Comprehensive Analysis of Weighted Gene Co-Expression Network Analysis and Machine Learning Revealed Diagnostic Biomarkers for Breast Implant Illness Complicated with Breast Cancer

Abstract

Purpose: An increasing number of breast cancer (BC) patients choose prosthesis implantation after mastectomy, and the occurrence of breast implant illness (BII) has received increasing attention and the underlying molecular mechanisms have not been clearly elucidated. This study aimed to identify the crosstalk genes between BII and BC and explored their clinical value and molecular mechanism initially.

Methods: We retrieved the data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and identified the differentially expressed genes (DEG) as well as module genes using Limma and weighted gene co-expression network analysis (WGCNA). Enrichment analysis, the protein-protein interaction network (PPI), and machine learning algorithms were performed to explore the hub genes. We employed a nomogram and receiver operating characteristic curve to evaluate the diagnostic accuracy. Single-cell analysis disclosed variations in the expression of key genes across distinct cellular populations. The expression levels of the key genes were further confirmed in BC cell lines. Immunohistochemical analysis was utilized to examine protein levels from 25 patients with breast cancer undergoing prosthetic implant surgery. Ultimately, we deployed single-sample Gene Set Enrichment Analysis (ssGSEA) to scrutinize the immunological profiles between the normal and BC cohorts, as well as between the non-BII and BII groups.

Results: WGCNA identified 1137 common genes, whereas DEG analysis found 541 overlapping genes in BII and BC. After constructing the PPI network, 17 key genes were selected, and three potential hub genes include KRT14, KIT, ALB were chosen for nomogram creation and diagnostic assessment through machine learning. The validation of these results was conducted by examining gene expression patterns in the validation dataset, breast cancer cell lines, and BII-BC patients. However, ssGSEA uncovered different immune cell infiltration patterns in BII and BC.

Conclusion: We pinpointed shared three central genes include KRT14, KIT, ALB and molecular pathways common to BII and BC. Shedding light on the complex mechanisms underlying these conditions and suggesting potential targets for diagnostic and therapeutic strategies.

Keywords: breast cancer; breast implant illness; hub genes; weighted gene co-expression network analysis.

Figure 1

Differentially expressed genes (DEGs) between the BC and normal groups. (A) In this figure, red and green colours indicate DEGs with notably higher and lower expression levels in the BC groups, respectively. (B) Heatmap presenting the significantly expressed genes in both the BC and control groups.

Figure 2

Identification of DEGs in BII using Limma and WGCNA module genes. (A) Volcano plot displaying DEGs, with red and green triangles highlighting significant genes. (B) Heatmap displaying the upregulated and downregulated DEGs identified from the BII dataset. (C and D) The soft threshold of b = 9 was selected based on scale Independence and average connectivity. (E) Clustering dendrogram illustrating the separation between BII and non-BII samples. (F) Gene coexpression modules are represented by different colours under the gene tree. (G) Heatmap illustrating eigengene adjacency. (H) Heatmap depicting the correlation between module genes and BII indicating that the magenta module is most strongly associated with BII. For each pair, the top left triangle represents the correlation coefficient, whereas the bottom right triangle indicates the p value. (I) Correlation plot between module membership and the significance of genes within the magenta module.

Figure 3

Functional enrichment analysis of the overlapping genes associated with BII. (A) The overlap between DEGs identified by the Deseq2 and WGCNA module genes consists of 386 genes, as depicted in the Venn diagram. (B) KEGG pathway analysis of these intersecting genes reveals various significant pathways, with different colours indicating distinct pathways and their associated genes. (C–E) The GO analysis encompasses biological processes, cellular components, and molecular functions. The y-axis indicates the GO terms, whereas the x-axis shows the proportion of genes associated with each term. The circle size reflects the number of genes, and their colour denotes the significance level, as indicated by the p value.

Figure 4

Functional enrichment analysis of genes associated with both breast cancer (BC) and breast implant illness (BII). (A) Venn diagram illustrating that 541 genes were identified from the intersection of genes identified in BC using Limma and in BII using WGCNA. (B) KEGG analysis of these 541 common genes. (C–E) The GO analysis revealed the biological process, cellular component, and molecular function of these 541 genes.

Figure 5

Illustration of the construction of a PPI network and the identification of key genes. (A) Identification of the 17 candidate hub genes using four different algorithms: DEGREE (B), EPC (C), MCC (D), and MNC (E).

Figure 6

Identification of key hub genes using LASSO analysis, the RF algorithm, and SVM. (A) LASSO regression analysis. (B) Application of the RF algorithm. (C) Machine learning strategy employing SVM. (D) Venn diagram highlighting the central genes pinpointed by LASSO, SVM-RFE, and RF.

Figure 7

Creation of a nomogram and its diagnostic value assessment. (A) The nomogram designed for diagnosing BII in the context of breast cancer. (B-F) ROC curves for each candidate gene (KRT14, KIT, ALB), the nomogram, and its validation using the GSE31448 dataset.

Figure 8

Overview of single-cell atlases in BC patients. (A) t-SNE clustering plot of 6 samples. (B) t-SNE clustering plot of BC and normal samples. (C) 9 cell types identified based on marker gene expression. T-SNE plot and Dot plot (H) highlighting the expression patterns of KRT14 (D), KIT (E) and ALB (F) for the 9 cell types and different samples (G).

Figure 9

Evaluation of the diagnostic significance of key genes in breast cancer cell lines and paraffin-embedded tissues. Comparison of the transcription levels of KRT14 (A), KIT (B), and ALB (C) mRNAs in breast cancer cell lines to normal human epithelial breast cell line. (D) Immunohistochemical (IHC) staining with Diaminobenzidine (DAB) demonstrates strong positivity for the target antigen, indicating high KRT14 protein expression levels in breast cancer. (E) IHC staining with DAB demonstrates weak positivity for the target antigen, indicating low KRT14 protein expression levels in normal breast tissue. (F) IHC staining of KRT14 expression in breast cancer and normal breast tissue. (G) IHC staining with DAB demonstrates strong positivity for the target antigen, indicating high KRT14 protein expression levels in normal breast tissue with BII. (H) IHC staining with DAB demonstrates weak positivity for the target antigen, indicating low KRT14 protein expression levels in normal breast tissue with Non-BII. (I)IHC staining of KRT14 expression in normal breast tissue stratified by BII and Non-BII. * p<0.05; *** p<0.001; **** p<0.0001.

Figure 10

Investigation of the immune landscape in BII complicating breast cancer (BC) and its link to pivotal genes using single-sample gene set enrichment analysis (ssGSEA). (A and B) Analysis of the levels of immune cell infiltration across 28 immune cell subsets in the BC group versus the normal group and the BII group versus the non-BII group. (C and D) Correlation between distinct immune cell populations in the BC group versus the normal group and the BII group versus the non-BII group. (E and F) Relationships between the infiltration of immune cells and two central genes in patients from both the BC group and the normal group and between the BII group and the non-BII group. * p<0.05; ** p<0.01;*** p<0.001; **** p<0.0001.