Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis

Abstract

G-protein signaling modulators (GPSMs) are a class of proteins involved in the regulation of G protein-coupled receptors, the most abundant family of cell-surface receptors that are crucial in the development of various tumors, including breast cancer. This study aims to identify the potential therapeutic and prognostic roles of GPSMs in breast cancer. Oncomine and UALCAN databases were queried to determine GPSM expression levels in breast cancer tissues compared to normal samples. Survival analysis was conducted to reveal the prognostic significance of GPSMs in individuals with breast cancer. Functional enrichment analysis was performed using cBioPortal and MetaCore platforms. Finally, the association between GPSMs and immune infiltration cells in breast cancer was identified using the TIMER server. The experimental results then showed that all GPSM family members were significantly differentially expressed in breast cancer according to Oncomine and UALCAN data. Their expression levels were also associated with advanced tumor stages, and GPSM2 was found to be related to worse distant metastasis-free survival in patients with breast cancer. Functional enrichment analysis indicated that GPSMs were largely involved in cell division and cell cycle pathways. Finally, GPSM3 expression was correlated with the infiltration of several immune cells. Members of the GPSM class were differentially expressed in breast cancer. In conclusion, expression of GPSM2 was linked with worse distant metastasis-free outcomes, and hence could potentially serve as a prognostic biomarker. Furthermore, GPSM3 has potential to be a possible target for immunotherapy for breast cancer.

Keywords: G-protein signaling modulator; biomarker; breast cancer; functional enrichment analysis; gene expression; immunotherapy; survival analysis.

Figure 1

Study flowchart. METABRIC, Molecular Taxonomy of Breast Cancer International Consortium; KEGG, Kyoto Encyclopedia of Genes and Genomes; TIMER, Tumor Immune Estimation Resource.

Figure 2

Expression of GPSMs across various cancer tissues. (A) mRNA expression levels of GPSMs in 20 cancer types. Numbers in red and blue cells represent dataset numbers in which levels of GPSMs are statistically increased or decreased, respectively (p < 0.05, fold change >2, gene rank top 10%, Oncomine). (B) Heatmap plots showing GPSMs expression status in breast cancer cell lines (CCLE), with colored columns on the right side displaying the molecular subtype of each cell line. “Inconsistent” denotes cell lines that are inconsistently annotated regarding the status of markers. “Others” include two cell lines that were not breast cancer (HMEL, engineered breast and HS274T, breast fibroblast). TNBC, triple negative breast cancer.

Figure 3

GPSMs expression in subgroups of people with breast cancer (UALCAN). Boxplots showing GPSMs transcript levels (A) in healthy controls versus individuals with breast cancer, (B) based on breast cancer stages, and (C) based on breast cancer subclasses. (* p < 0.05; *** p < 0.001).

Figure 4

Protein expression profiles of GPSMs in breast cancer samples (Human Protein Atlas). (A) Representative strongly stained IHC images and (B) bar charts showing IHC staining intensity of GPSM1 and GPSM2 in breast cancer tissues. Corresponding data of GPSM3 and GPSM4 were not found. Ab, antibody; IHC, immunohistochemistry.

Figure 5

Distant metastasis-free survival (DMFS) analysis of GPSM2 in breast cancer (Kaplan–Meier plot). Red and black curves represent survival analysis for higher and lower GPSMs mRNA expression levels, respectively. Red and black titles indicate statistically and non-statistically significant survival, respectively. ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor; +, positive; −, negative.

Figure 6

Gene–gene interaction network of GPSMs in breast cancer (GeneMANIA). Nodes represent genes, nodal sizes indicate interaction strengths, and line colors represent types of interactions.

Figure 7

Functional and pathway enrichment analyses of GPSMs. (GO, KEGG). (A) Dot plot of Gene Ontology (GO) analysis. Shown are the most significant enriched categories for Biological process, Cellular component, and Molecular function. (B) Dot plot of Kyoto Encyclopedia of Genes and Genomes (KEGG) enriched analysis. (C) Dot plot of Disease Ontology analysis.

Figure 8

Enrichment pathway analysis of GPSM2 co-expressed genes in breast cancer database (MetaCore). (A) Pathway analysis. Potential gene networks and pathways affected by co-expressed genes (right column) and respective log p value (left column). (B) Biological process analysis. Symbols represent proteins. Arrows depict protein interactions (green, activation; red, inhibition). Thermometer-like histograms indicate microarray gene expression (blue, down-regulation; red, up-regulation).

Figure 9

Relationships between GPSMs expression and immune infiltration level in breast cancer (TIMER). Horizontal axis, expression levels of GPSMs (values represented as log2 RSEM); vertical axis, tumor infiltrating immune cell markers (purity, B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, dendritic cell) TPM, transcript count per million; cor, correlation coefficient.

Figure 10

Relationship between GPSM3 expression and immune infiltration level in breast cancer (TIMER2). Correlation coefficients were calculated from seven cell type quantification algorithms (xCell, CIBERSORT, CIBERSORT abs. mode, EPIC, MCP-counter, TIMER, quanTIseq) and indicated as colors (blue, negatively correlated; red, positively correlated). * p < 0.05; ** p < 0.01; *** p < 0.001. Column: Subtypes of breast cancer, Row: Immune infiltrate levels.