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. 2020 Jun 12:13:5541-5550.
doi: 10.2147/OTT.S255300. eCollection 2020.

Identification of Potential Key Genes and Pathways for Inflammatory Breast Cancer Based on GEO and TCGA Databases

Qing Lv #  1 , Yansong Liu #  2 , Hu Huang  1 , Mingjie Zhu  1 , Junqiang Wu  1 , Dong Meng  1
Affiliations

Identification of Potential Key Genes and Pathways for Inflammatory Breast Cancer Based on GEO and TCGA Databases

Qing Lv et al. Onco Targets Ther. .

Abstract

Introduction: Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognosis, and the pathogenesis of this life-threatening disease is yet to be fully elucidated. This study aims to identify key genes of IBC, which could be potential diagnostic or therapeutic targets.

Methods: Four datasets GSE5847, GSE22597, GSE23720, and GSE45581 were downloaded from the Gene Expression Omnibus (GEO) and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential bio-functions of the differentially expressed genes (DEGs). Protein-protein interaction (PPI) network was constructed for functional modules analysis and hub genes identification, and TCGA survival analysis and qRT-PCR of clinical samples were used to further explore and validate the effect of hub genes on IBC.

Results: A total of 114 DEGs were identified from the GEO datasets. GO and KEGG analyses showed that the DEGs were mainly enriched in oncogenesis and cell adhesion. From the PPI network, we screened out five hub genes, including PTPRC, IL6, SELL, CD40, and SPN. Survival analysis and expression validation verified the robustness of the hub genes.

Discussion: The present study provides new insight into the understanding of IBC pathogenesis and the identified hub genes may serve as potential targets for diagnosis and treatment.

Keywords: bioinformatic analysis; hub genes; inflammatory breast cancer; microarray.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Identification of DEGs in four IBC datasets from GEO. Notes: (A–D) Volcano plots of differential expression analysis for GSE5847, GSE22597, GSE23720 and GSE45581. The red dots represent for significantly up-regulated genes, and the blue ones represent for down-regulated genes. The dotted lines indicate thresholds for DEG identification [Adj.P < 0.05 and |log2 fold-change (FC)| > 1]. (E) Upset plot of DEGs overlapped in the four datasets. (F) Expression heatmap of top 10 up- and down-regulated genes. Abbreviations: DEGs, differentially expressed genes; GEO, Genome Expression Omnibus.
Figure 2
Figure 2
GO and KEGG pathway enrichment analyses for DEGs. Notes: (A) Chord plot of GO terms under BP category. (B) Chord plot of GO terms under MF category. (C) Chord plot of GO terms under CC category. (D) Dot plot of KEGG pathway enrichment analyses. Abbreviations: GO, Gene Ontology; BP, biological process; MF, molecular function; CC, cellular component; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Figure 3
Figure 3
PPI network construction and functional module analysis. Notes: (A) The whole PPI network. (B) Network of functional module 1. (C) Network of functional module 2. (D) Network of functional module 3. (E) Network of functional module 4. Abbreviation: PPI, protein–protein network.
Figure 4
Figure 4
Hub genes identification and survival analysis. Notes: (A) PPI sub-network of the five hub genes. (B) Kaplan–Meier survival analysis of SELL. (C) Kaplan–Meier survival analysis of SPN. (D) Survival map of hub genes in multiple malignancies.
Figure 5
Figure 5
Validation of hub genes expression in IBC and non-IBC clinical samples by qRT-PCR. Notes: The relative expression level of the five hub genes PTPRC (A), IL6 (B), SELL (C), CD40 (D) and SPN (E) are shown in violin plot. Abbreviation: qRT-PCR, quantitative reverse transcriptional polymerase chain reaction.
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