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

1. Introduction

   Worldwide, breast cancer remains the most commonly seen malignancy in
   women, with a staggering incidence of 2 million cases annually [[36]1].
   Molecular markers of breast cancer, including estrogen and progesterone
   receptors and human epidermal growth factor 2, are crucial to the
   classification, treatment, and prognosis of individuals with this
   disease [[37]2]. While early-stage, non-metastatic breast cancer is
   considered treatable, such curative therapies do not yet exist for
   patients in the more advanced stages with distant organ metastases
   [[38]3]. Combinations of novel biomarkers play a critical role in the
   highly complex diagnostic and treatment algorithms of breast cancer
   [[39]4]. Therefore, efforts to identify additional, robust biomarkers
   that could stratify patients and improve clinical outcomes are wholly
   justified.

   In many instances, cancer arises from disruptions of cell signaling
   pathways, which modulate various cellular processes including survival,
   reproduction, and motility [[40]5]. Among their components, G proteins,
   also known as guanine nucleotide-binding proteins, remain one of the
   most significant and are involved in the transduction of extracellular
   signals into the cell’s interior [[41]6]. These intracellular molecular
   switches are triggered by the membrane-bound G protein-coupled
   receptors, and the ensuing signaling events eventually lead to changes
   in cell function. This process is further regulated by another group of
   proteins, G protein-signaling modulators (GPSM), via their interaction
   with subunits of G proteins. GPSMs are a class of receptor-independent
   activators of G protein signaling, whose family members include GPSM1,
   GPSM2, GPSM3, and GPSM4. In particular, GPSM2 is widely known to
   modulate mitotic spindle orientation [[42]7], and its significance in
   breast cancer has been reported by earlier studies. Fukukawa et al.
   showed that GPSM2 is overexpressed in breast cancer tissues [[43]8],
   while Deng et al. stated that its nuclear expression is an unfavorable
   prognostic indicator [[44]9]. Furthermore, bioinformatics analysis and
   in vivo verification have suggested that GPSM2 negatively influenced
   patient response to paclitaxel, one of the most effective and
   well-tolerated chemotherapy drugs for breast cancer [[45]10].
   Meanwhile, members of the GPSM family are known to be associated with
   other neoplasms, namely lung [[46]11], prostate [[47]12], and
   pancreatic cancer [[48]13].

   A few reports to date have illustrated the expression profiles, as well
   as therapeutic and prognostic role of GPSM class in breast cancer.
   Thanks to an increasing number of publicly available databases and web
   tools for cancer-related genomics studies, researchers are able to
   leverage these resources to discover novel targets and biomarkers
   [[49]14]. In this study, we aim to better understand the significance
   of GPSM family members as potential molecular therapeutic targets and
   biomarkers in breast cancer, through performing an integrated
   bioinformatics analysis. Our analytic workflow follows the standard
   bioinformatics studies [[50]15,[51]16] and it is briefly outlined in
   [52]Figure 1. First, we determine whether GPSM family genes could serve
   as potential biomarkers by comparing their mRNA expression levels in
   breast cancer and normal samples, using Oncomine and UALCAN databases.
   After confirming that GPSMs were differentially expressed in breast
   cancer, their protein expressions in immunohistochemically stained
   breast cancer tissues were visualized by querying the Human Protein
   Atlas platform. Next, GPSMs’ connection with prognosis of patients with
   breast cancer was plotted using the Kaplan–Meier plotter. Univariate
   and multivariate Cox analyses were also performed to assess the
   proportional hazards model. Finally, we identify the GPSMs-related
   signaling pathways by overlapping datasets from METABRIC and TCGA, and
   explore the relationship between GPSMs and immune infiltration levels
   in breast cancer.

Figure 1.

   [53]Figure 1
   [54]Open in a new tab

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

2. Materials and Methods

2.1. Oncomine

   Oncomine ([55]http://www.oncomine.org accessed on 15 June 2021) is an
   online DNA microarray database that allows cancer transcriptome
   analysis, and currently houses 715 datasets and 86,733 samples,
   covering the majority of cancer types [[56]17]. It was used to compare
   the transcriptional levels of GPSM family members between tumor and
   normal samples in 20 types of cancer. Statistics were performed by
   Student’s t-test, and thresholds were set as follows: p value < 0.05,
   fold change >2, gene rank top 10%.

2.2. CCLE (Cancer Cell Line Encyclopedia)

   The Broad Institute CCLE ([57]http://www.broadinstitute.org/ccle
   accessed on 15 June 2021) is a dataset containing more than 1100 cell
   lines for 37 types of cancer [[58]18]. It allows computational genomic
   analysis and visualization, and was used in our study to demonstrate
   the expression levels of GPSMs in breast cancer.

2.3. UALCAN

   UALCAN ([59]http://ualcan.path.uab.edu accessed on 15 June 2021) is a
   web portal that allows online analysis of transcriptome sequencing data
   from The Cancer Genome Atlas (TCGA) project [[60]19]. We used UALCAN to
   validate the relative expressions of GPSMs in normal and breast cancer
   samples, as well as those in different tumor stages and subclasses.
   This measurement was represented as transcript per million reads, and a
   comparison was carried out using Student’s t-test.

2.4. Human Protein Atlas

   The Human Protein Atlas ([61]https://www.proteinatlas.org/ accessed on
   15 June 2021) contains mRNA and protein expression data of normal and
   cancerous human tissues [[62]20]. mRNA and protein expression profiles
   of GPSMs in normal and breast cancer tissues were analyzed using
   RNA-sequencing data and immunohistochemical images.

2.5. Survival Analysis

   The Kaplan–Meier plotter ([63]https://kmplot.com/analysis accessed on
   15 June 2021) is an online tool that assesses survival data of 54,000
   genes in 21 cancer types, including breast, ovarian, lung, and gastric
   cancer [[64]21]. The effect of GPSMs mRNA expression on distant
   metastasis-free survival (DFMS) of patients with breast cancer were
   identified by sorting patients into high and low median expression
   groups.

   Next, univariate and multivariate Cox analyses were performed, using
   the survival package of R language
   ([65]https://cran.r-project.org/web/packages/survival/index.html
   accessed on 15 June 2021), to assess the proportional hazards model.
   Variables including age, gender, tumor stage (TNM), and treatment.

2.6. Functional Enrichment Analysis

   cBioPortal for Cancer Genomics ([66]https://www.cbioportal.org/
   accessed on 15 June 2021) is a portal for analyzing multidimensional
   gene-level data of cancer tissues and cell lines [[67]22]. Two datasets
   from cBioPortal, namely METABRIC (Molecular Taxonomy of Breast Cancer
   International Consortium) and TCGA, were accessed to determine
   functional enrichment analysis [[68]23]. In particular, we first
   identify the gene–gene interaction network using the Gene Multiple
   Association Network Integration Algorithm (GeneMANIA,
   [69]https://genemania.org accessed on 15 June 2021) [[70]24]. The Venny
   tool ([71]https://bioinfogp.cnb.csic.es/tools/venny accessed on 15 June
   2021) was then employed to find the overlap from lists of co-expressed
   genes from METABRIC and TCGA.

   Next, MetaCore ([72]https://portal.genego.com accessed on 15 June
   2021), an integrated functional analysis software, was curated to
   determine the biological processes, biomarker networks, and the breast
   cancer cell–cell signaling pathway.

   Finally, functional annotation was performed using GO and KEGG
   analyses. Gene ontology (GO) is the standardized representation of gene
   and gene products, covering three domains: Cellular component,
   molecular function, and biological process [[73]25]. The Kyoto
   Encyclopedia of Genes and Genomes (KEGG) is the knowledge base for gene
   functions systemic analysis [[74]26]. Results were visualized by R,
   package DOSE (Disease Ontology Semantic and Enrichment, version 3.13)
   [[75]27].

2.7. TIMER (Tumor IMmune Estimation Resource)

   TIMER ([76]http://timer.cistrome.org/ accessed on 15 June 2021) is a
   web server for systematic analysis of immune infiltrates across various
   cancer types [[77]28]. Its algorithm estimates the correlations between
   gene expression and an abundance of six immune infiltrates cells (B
   cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and
   dendritic cells). All Spearman’s correlation coefficients were tumor
   purity-adjusted.

3. Results

   The basic characteristics, including their approved symbols, IDs,
   alternative names, and chromosomal locations, of all members of the
   GPSM family are shown in [78]Table 1. It is worth noting that GPSM4 is
   more commonly known as PCP2 (Purkinje cell protein 2).

Table 1.

   Basic characteristics of GPSM family genes.
   Approved Symbol HGNC ID Gene ID Aliases Location on Chromosome
   GPSM1 17858 26086 AGS3
   DKFZP727I051 9q34.3
   GPSM2 29501 29899 LGN
   Pins 1p13.3
   GPSM3 13945 63940 NG1
   G18
   G18.1a
   G18.1b
   G18.2
   AGS4 6p21.32
   GPSM4 30209 126006 PCP2
   MGC41903 19p13.2
   [79]Open in a new tab

3.1. Differential Expression of GPSM Family Genes in Breast Cancer

   Analysis from Oncomine revealed that all four GPSMs were differentially
   expressed in breast cancer ([80]Figure 2A). In particular, GPSM1 and
   GPSM4 were downregulated while GPSM2 and GPSM3 were over-expressed.
   Detailed overexpression profiles of GPSM2 between cancer and normal
   tissues are provided in [81]Supplementary Table S1.

Figure 2.

   [82]Figure 2
   [83]Open in a new tab

   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.

   Next, expression patterns of the GPSM family in various cell lines were
   analyzed from the CCLE database. The molecular subtypes of cell lines
   were also shown [[84]29]. GPSM1, GPSM2, and GPSM3 were most highly
   expressed in triple negative breast cancer cell lines, while GPSM4
   showed high expression in multiple luminal A cell lines ([85]Figure
   2B).

3.2. GPSMs Expression in Subgroups of Individuals with Breast Cancer

   Regarding the comparison between healthy and breast cancer samples,
   UALCAN analysis illustrated higher transcription levels of all four
   GPSM genes in breast cancer patients ([86]Figure 3A). For GPSM1, this
   result was not consistent with that from Oncomine and the Human Protein
   Atlas, which indicated that GPSM1 was under-expressed and had low
   protein expression status in individuals with breast cancer.

Figure 3.

   [87]Figure 3
   [88]Open in a new tab

   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).

   In terms of stratification by cancer stages, all GPSMs expression
   levels in healthy controls were significantly lower than those at stage
   1, 2, and 3 ([89]Figure 3B). GPSM3 and GPSM4 also showed similar
   patterns for stage 4 breast cancer. With respect to breast cancer
   subclasses, all GPSMs’ expression statuses were lower in normal
   individuals compared to those with luminal or triple negative breast
   cancer ([90]Figure 3C). Detailed statistics of the comparison are
   provided in [91]Supplementary Table S2.

3.3. GPSMs Expression Profiles on mRNA and Protein Level

   Expression levels of GPSMs in non-cancer tissues were derived from the
   GTEx (Genotype-Tissue Expression) dataset in the Human Protein Atlas
   ([92]Supplementary Figure S1). Among all members of the GPSM family,
   GPSM3 and GPSM2 were most and least abundant in normal breast tissues
   (pTPM of 16.8 and 5.9, respectively). Furthermore, analysis revealed
   that GPSM1 had weak protein expression, in contrast to the moderate
   expression of GPSM2 in clinical breast cancer specimens ([93]Figure 4).
   Corresponding data of GPSM3 and GPSM4 were not found in the database.

Figure 4.

   [94]Figure 4
   [95]Open in a new tab

   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.

   To summarize, in the GPSM family genes, GPSM2 expression was
   consistently higher in people with breast cancer than in normal
   controls at both mRNA and protein levels.

3.4. Prognostic Roles of GPSMs in Breast Cancer

   The Kaplan–Meier plot was used to identify prognostic significance of
   the GPSM family gene in individuals with breast cancer. Among the four
   members, only GPSM2 is consistently associated with poor distant
   metastasis-free survival (DMFS) outcomes ([96]Figure 5). In particular,
   higher expression levels of GPSM2 indicated worse DMFS time overall and
   in the following subtypes of breast cancer: ER or PR positive, HER2
   positive or negative, luminal A or B, and with or without chemotherapy.

Figure 5.

   [97]Figure 5
   [98]Open in a new tab

   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.

   Meanwhile, survival results of the remaining members of GPSM family
   were more inconsistent ([99]Supplementary Figure S2). Specifically,
   while a higher level of GPSM1 mRNA showed significant improvement in
   outcome for patients with HER2+/− and luminal A and B subtypes of
   breast cancer, it was linked to poor prognosis in patients with ER+/−,
   PR +/−, and chemotherapy-included subtypes (S2-1). Furthermore, GPSM3
   and GPSM4 with low expression levels were considerably associated with
   better survival outcomes (S2-2, S2-3).

   As the Kaplan–Meier curve showed that higher GPSM2 expression and poor
   DMFS were consistently and significantly correlated, we next
   investigated whether GPSM2, along with variables such as age, gender,
   and tumor stage, were risk factors for survival in breast cancer
   patients. Univariate Cox analysis revealed that advanced age, TNM stage
   III/IV, and metastasis, but not GPSM2 expression, were independent
   high-risk factors for breast cancer survival. Subsequent multivariate
   Cox analysis showed that these three risk factors remained to be
   associated with decreased overall survival ([100]Table 2).

Table 2.

   Univariate and multivariate Cox proportional hazards regression
   analysis of factors affecting overall survival. Variables with
   significant association with overall survival in univariate analysis
   were then put into the multivariate analysis. HR, hazard ratio; CI,
   confidence interval; *: significant risk factor, p < 0.05.
   Number of Patients Univariate Multivariate
   Variables HR (95% CI) p Value HR (95% CI) p Value
   Age (y)
   <60 366 reference reference
   ≥60 287 2.032 (1.277–3.235) 0.003 * 2.361 (1.436–3.881) 0.0004 *
   Gender
   Male 10 reference
   Female 643 0.887 (0.122–6.417) 0.906
   Tumor stage
   Stage I/II 471 reference reference
   Stage III/IV 176 2.687 (1.657–4.356) 6.15 × 10^−05 * 2.517 (1.516–4.18)
   0.0002 *
   M stage
   M0 530 reference reference
   M1 9 3.965 (1.828–8.59) 0.0004 * 2.28 (1.014–5.128) 0.04 *
   MX 114 1.268 (0.622–2.584) 0.513 0.726 (0.306–1.723) 0.49
   T stage
   T1/T2 532 reference
   T3/T4 118 1.55 (0.920–2.609) 0.099
   GPSM2 expression
   Low 336 reference
   High 317 0.888 (0.56–1.41) 0.617
   [101]Open in a new tab

3.5. Biological Functions and Pathway Enrichment Analysis

   [102]Figure 6 illustrates the gene interaction networks of GPSMs and
   their neighboring genes. The relationship between co-localization,
   shared protein domains, co-expression, prediction, and pathways of
   GPSMs is shown. Notably, we found that GPSM members were highly
   correlated with metastatic markers, such as ATG3 (autophagy-related
   gene 3) [[103]30], DTX3 (Deltex E3 Ubiquitin Ligase 3) [[104]31], and
   PALM [[105]32]. Next, GO and KEGG resources were used to explore the
   functions of GPSM and their neighboring genes ([106]Figure 7). We found
   that GPSMS were mostly involved in cell division from their “Biological
   Process” results: Nuclear division, organelle fission, chromosome
   segregation, mitotic nuclear division, and nuclear chromosome
   segregation. Subcellular locations of GPSMs were mostly around the
   chromosomal region, spindle, and chromosome and centromeric region.
   Molecular functions of these genes were mostly related to ATPase
   activity, catalytic activity (acting on DNA), and tubulin binding. KEGG
   pathways that were involved in breast cancer pathogenesis included cell
   cycle, oocyte meiosis, and cellular senescence. Finally, Disease
   Ontology analysis revealed that GPSMs played roles in autosomal
   dominant disease, non-small cell lung carcinoma, and hereditary breast
   ovarian cancer.

Figure 6.

   [107]Figure 6
   [108]Open in a new tab

   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.

   [109]Figure 7
   [110]Open in a new tab

   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.

   Next, we illustrate the functional pathway enrichment analysis from
   MetaCore, which involves determining gene networks and biological
   processes of each GPSM family member co-expressed genes. Again, among
   all GPSMs, GPSM2 is most relevant to breast cancer tumor biology
   ([111]Figure 8). In particular, GPSM2 co-expressed genes play essential
   roles in the cell cycle and DNA damage pathways in breast cancer
   growth. The five pathways that showed the most significant p values are
   “cell cycle_role of APC (Anaphase-promoting complex) in cell cycle
   regulation”, “cell cycle_metaphase checkpoint”, “cell cycle_spindle
   assembly and chromosome separation”, “DNA damage_intra S-phase
   checkpoint”, and “cell cycle_start of DNA replication in early S
   phase”.

Figure 8.

   [112]Figure 8
   [113]Open in a new tab

   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).

   Similar analyses of GPSM1, GPSM3, and GPSM4 showed that their related
   biological pathways are only minimally linked to breast cancer
   development ([114]Supplementary Figure S3). For example, “prolactin/ERK
   (extracellular signal-regulated kinase) signaling in breast cancer” was
   ranked ninth among GPSM1 statistically significant pathway analysis,
   and “mitogenic action of ESR1 (Estrogen Receptor 1) (membrane) in
   breast cancer” was ranked 15th for GPSM4. Biological process analyses
   revealed that GPSM1, GPSM3, and GPSM4 showed biological significance in
   colon cells, adipose tissue, and neuronal cell, respectively.

3.6. Correlation between GPSMs and Tumor Purity and Immune Infiltrate in
Breast Cancer

   Knowledge regarding the host immune system is crucial to developing
   cancer immunotherapy [[115]28], and high levels of tumor-infiltrating
   lymphocytes are associated with better prognosis [[116]33]. TIMER
   analysis revealed that GPSM1, GPSM2, and GPSM4 were only slightly
   correlated with tumor purity and infiltrating lymphocytes (correlation
   coefficients ranging from approximately −0.3 to 0.2) ([117]Figure 9).
   On the other hand, GPSM3 showed significant negative correlation with
   tumor purity (r = −0.48, p = 2 × 10^−58), and moderate to strong
   positive association with six types of immune infiltrate cells
   (dendritic cell, r = 0.65; CD4+ T cell, 0.64; neutrophil, 0.56; B cell,
   0.44; CD8+ T cell, 0.36; and macrophage, 0.29; all p values < 10^−20).
   We further clarify the association between GPSM3 expression and immune
   infiltrates in different subtypes of breast cancer ([118]Figure 10).
   The results demonstrated that GPSM3 expression was most strongly
   correlated with levels of CD8+ T cells, B cells, macrophage, and
   myeloid dendritic cells in subtypes basal and HER2 of breast cancer.
   Taken together, it is suggested that GPSM3 expression was related to
   immune infiltration levels in breast cancer.

Figure 9.

   [119]Figure 9
   [120]Open in a new tab

   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.

   [121]Figure 10
   [122]Open in a new tab

   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.

4. Discussion

   Breast cancer is the most prevalent malignancy in women globally
   [[123]3]. It is widely acknowledged as a highly heterogeneous disease
   at multiple levels, from clinical and histopathologic classification to
   the expression of predictive and prognostic biomarkers, all of which
   must be taken into account to design targeted and personalized
   treatment plans for patients [[124]34]. Considerable efforts are being
   directed at detecting accurate biomarkers as leads to anti-cancer
   drugs, or to predict treatment outcome and survival in breast cancer
   [[125]35,[126]36,[127]37,[128]38]. Among them, accumulating evidence
   suggests that signal transduction pathways, the process in which
   extracellular molecules interact with receptors to bring about changes
   in and around the cells, are highly attractive targets in breast cancer
   treatment and prevention, specifically by modulating G protein-coupled
   receptors [[129]39,[130]40,[131]41]. A recent review by Usman et al.
   highlighted several ongoing anti-GPCR drugs for individuals with cancer
   [[132]42]. Thus, further investigation of the roles of GPCRs and their
   modulators, including GPSMs, are warranted. To our knowledge, this is
   the first study that evaluates the potential therapeutic and prognostic
   value of all members of the GPSM family gene together, and their roles
   in breast cancer specifically. Prior studies have confirmed the
   contributions of each gene individually in various types of cancer,
   with GPSM2 so far being the most relevant to breast cancer.

   GPSM2 is also known as LGN (Leu-Gly-Asn repeat-enriched protein), due
   to its N-terminal half containing a ten Leucine-Glycine-Asparagine
   repeats within to eight tetratricopeptide motifs [[133]43]. This
   structure binds to NuMA, the nuclear-mitotic apparatus protein, and
   therefore establishes the important role of GPSM2 in modulating G
   protein signaling to mitotic spindle positioning during oriented cell
   division [[134]44]. Furthermore, GPSM2 also plays a vital role in cell
   division symmetry control and in the G2/M phase of the cell cycle
   [[135]45,[136]46]. Cancer-related research has revealed that GPSM2
   could potentially serve as a drug target and prognostic biomarker in
   hepatocellular carcinoma [[137]47], lung adenocarcinoma [[138]48], and
   pancreatic cancer [[139]13]. GPSM2 was first reported to be highly
   over-expressed in breast cancer and play a crucial role in cytokinesis
   by Fukukawa et al. [[140]8]. Subsequent studies confirmed this finding
   and also found that its nuclear expression is significantly linked to
   worse prognosis [[141]11]. Interestingly, Zhang et al. demonstrated
   that GPSM2 is down-regulated in paclitaxel-resistant samples of breast
   cancer patients and could be a drug-resistance gene in breast cancer
   [[142]10]. Consistent with previous research, we also found that the
   expression level of GPSM2 in breast cancer tissues increased
   significantly when compared to normal samples from Oncomine and UALCAN
   analysis. Survival analysis then showed breast cancer patients with
   high GPSM2 expression to have consistently worse distant
   metastasis-free survival. TIMER analysis, however, did not suggest a
   relationship between GPSM2 and immune infiltration patterns. Further
   experiments are required to validate these results. Taken together, we
   propose that GPSM2 is a potential prognostic marker and therapeutic
   target for breast cancer.

   GPSM1 is also known as AGS3, as it encodes the activator of G protein
   signaling 3. GPSM1 gene has been found to be involved in cell division,
   reproduction, and differentiation [[143]49,[144]50,[145]51]. Recent
   studies have identified GPSM1 as a predisposition gene of premature
   ovarian insufficiency [[146]52], a plausible gene involved in
   regulation of skeletal muscle in diabetes mellitus [[147]53], and to be
   overexpressed in prostate adenocarcinoma [[148]12]. In our study,
   analyses from Oncomine and UALCAN suggested different results: GPSM1
   was found to be under-expressed in breast cancer in the former
   database, while the latter indicated the reverse. This inconsistency
   could be due to the heterogeneity of datasets and nature of in silico
   analysis as a guide to further experimentally driven experiments, in
   vitro or in vivo [[149]54]. Subsequent analysis did not show clear
   relationships between GPSM1 and survival time or immune infiltration
   levels in breast cancer.

   GPSM3, alternatively known as AGS4 or G18, encodes for the activator of
   G-protein signaling 4, and was found to be overexpressed in breast
   cancer in our study. Previous research has indicated that GPSM3 is
   important in inflammation and autoimmune diseases, including rheumatoid
   arthritis and ankylosing spondylitis [[150]55]. Furthermore, Robichaux
   et al. found that GPSM3 played critical role in the chemokine signal
   processing in leukocytes, and GPSM3-null mice exhibited significant
   neutropenia and leukocytosis [[151]56]. These findings could explain
   why expression levels of GPSM3 were moderately to strongly correlated
   with all six types of immune infiltrate cells in breast cancer, using
   TIMER analysis. The immune system is indispensable in fighting cancer,
   and one of the most recent FDA-approved treatments for breast cancer
   includes anti-PD-1, an antibody-based immunotherapy that inhibits
   checkpoint on T cells [[152]57,[153]58]. However, only a subset of
   patients has been reported to see positive clinical response with such
   treatments. It is believed that understanding the cellular proportions,
   heterogeneity, and spatial distribution of the tumor immune
   microenvironment would help better stratify patients for whom
   immunotherapy is most beneficial [[154]59]. While breast cancer was
   previously regarded as relatively non-immunogenic, it is now suggested
   that breast cancer is in fact rich in immune infiltrates, with varied
   functions and prognostic values [[155]60]. In this study, we propose
   that GPSM3 plays a potential role in the diagnosis and inflammation in
   breast cancer.

   GPSM4 is more commonly known as PCP2 (Purkinje cell protein 2), and is
   traditionally known to be highly expressed in cerebellar Purkinje cells
   and retinal bipolar neurons [[156]61]. There are relatively few
   cancer-related reports about GPSM4. Reyes-Gibby et al. Reported that
   GPSM4 could be a novel target gene for neuropathic pain in patients
   with head and neck cancer [[157]62]. In our study, Oncomine analysis
   showed that GPSM4 was differentially expressed in only breast cancer
   (downregulated) and prostate cancer (upregulated), among 20 types of
   cancer. Lower transcription levels of GPSM4 were found to be
   significantly related to worse distant metastasis-free survival
   outcome.

5. Conclusions

   To summarize, our study proposed GPSM2 as a potential progsnotic marker
   and therapeutic target, and GPSM3 as a possible target for
   immunotherapy for breast cancer. Further in-depth experiments are
   warranted to confirm the role of these two GPSM members and their
   effectiveness as treatment for individuals with breast cancer.

Supplementary Materials

   The following are available online at
   [158]https://www.mdpi.com/article/10.3390/biomedicines9091144/s1.
   Supplementary Table S1: GPSM2 over-expression in Cancer vs. Normal
   analyses (Oncomine). Supplementary Table S2: Statistics of GPSMs
   transcript levels between subgroups of individuals with breast cancer
   (UALCAN). Supplementary Figure S1: Distant metastasis-free survival
   (DFMS) analysis of GPSMs in breast cancer (Kaplan-Meier plot).
   Supplementary Figure S2: Enrichment pathway analysis of GPSM family
   coexpressed genes in breast cancer database (MetaCore).
   [159]Click here for additional data file.^ (1.9MB, zip)

Author Contributions

   Conceptualization, H.-H.D., H.D.K.T. and N.Q.K.L.; methodology,
   H.-H.D., H.D.K.T. and G.A.; software, H.D.K.T., G.A. and C.-Y.W.;
   validation, H.D.K.T., G.A., C.-Y.W. and N.Q.K.L.; formal analysis,
   H.-H.D. and H.D.K.T.; investigation, H.-H.D.; resources, H.D.K.T.,
   C.-Y.W. and K.-H.L.; data curation, C.-Y.W. and K.-H.L.;
   writing—original draft preparation, H.-H.D., H.D.K.T. and T.T.T.N.;
   writing—review and editing, H.D.K.T., T.T.T.N. and N.Q.K.L.;
   visualization, H.-H.D., H.D.K.T., T.T.T.N. and N.Q.K.L.; supervision,
   C.-Y.W., K.-H.L. and N.Q.K.L.; funding acquisition, N.Q.K.L. All
   authors have read and agreed to the published version of the
   manuscript.

Funding

   This research was partly supported by the Taiwan Higher Education
   Sprout Project by the Ministry of Education (DP2-110-21121-01-A-06).

Institutional Review Board Statement

   Ethical review and approval were waived for this study because all data
   were derived from the public databases.

Informed Consent Statement

   Patient consent was waived because all data were derived from the
   public TCIA database.

Data Availability Statement

   Not applicable.

Conflicts of Interest

   The authors declare no conflict of interest.

Footnotes

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   claims in published maps and institutional affiliations.

References