Abstract

   Polo-like kinase 1 (PLK1) is a multi-functional protein and its
   aberrant expression is a driver of cancerous transformation and
   progression. To increase our understanding of the clinical value and
   potential molecular mechanism of PLK1 in gastric cancer (GC), we
   performed this comprehensive investigation. A total of 25 datasets and
   12 publications were finally incorporated. Additional
   immunohistochemistry was conducted to validate the expression pattern
   of PLK1 in GC. The pooled standard mean deviation (SMD) indicated that
   PLK1 mRNA was up-regulated in GC (SMD=1.21, 95% CI: 0.65-1.77, P<
   0.001). Similarly, the pooled odds ratio (OR) revealed that PLK1
   protein was overexpressed in GC compared with normal gastric tissue
   (OR=12.12, 95% CI: 5.41-27.16, P<0.001). The area under the curve (AUC)
   of the summary receiver operating characteristic (SROC) curve was 0.86.
   Furthermore, our results demonstrated that GC patients with PLK1
   overexpression were significantly associated with unfavorable overall
   survival (HR =1.54, 95% CI: 1.30–1.83, P<0.001), lymph node metastasis
   (OR = 1.78, 95% CI: 1.13–2.80, P=0.013) and advanced TNM stage
   (OR=1.48, 95% CI: 1.02-2.15, P=0.038). Altogether, 100 similar genes
   were identified by Gene Expression Profiling Interactive Analysis
   (GEPIA) and further with gene-set enrichment analysis. These genes were
   related to gene ontology (GO) terms and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathways relevant to the cell cycle. Gene set enrichment
   analysis (GSEA) indicated that PLK1 is associated with various
   cancer-related pathways. Collectively, this study suggests that PLK1
   overexpression could play vital roles in the carcinogenesis and
   deterioration of GC via regulating tumor-related pathways.

   Keywords: PLK1, gastric cancer, prognosis, bioinformatics analysis,
   immunohistochemistry

INTRODUCTION

   Gastric cancer (GC) is the fourth leading human cancer, and it ranks as
   the second most common cause of tumor-related mortality all over the
   world, seriously threatening human health [[38]1]. It is estimated that
   approximately 28,000 newly diagnosed cases and 10,960 deaths will occur
   in the United States in 2017 [[39]2]. Although diagnostic and
   therapeutic techniques of GC have made advances over the past decades,
   the mortality rate is still fairly high due to its aggressive behavior
   [[40]3–[41]6]. Thus, a number of researchers have focused on probing
   several molecular biomarkers to modify clinical management strategies
   and better understand the molecular mechanism of GC [[42]7–[43]9].
   Because clinically applicable biomarkers are fairly meager, exploring
   novel, effective molecular biomarkers to elucidate effective
   therapeutic targets for GC patients is still imperative.

   Polo-like kinase 1 (PLK1), also known as serine/threonine-protein
   kinase 13(STPK13), is a multi-faceted regulator of the cell cycle
   [[44]10, [45]11]. Due to PLK1's broad biological functionality, it is
   widely deemed as an oncogene and implicated in a broad range of
   malignant human tumors, including breast [[46]12], liver [[47]13], and
   colorectal cancers [[48]14]. Prior to our study, increasing evidence
   has also suggested that dysregulated expressions of PLK1 exerted
   indispensable functions in GC progression. For example, Otsu H
   suggested that GC patients with high expression of PLK1 and DNA
   aneuploidy had inferior survival outcome [[49]15]. Elevated PLK1
   promotes GC cell metastasis rates and epithelial-mesenchymal transition
   by regulating the activation of the protein kinase B pathway [[50]16].
   Despite several independent studies providing various valuable
   perspectives of PLK1 in GC, low numbers of studies have led to a
   limited ability to uncover the complexity of GC and no meta-analysis to
   clarify the reliability and extent of its clinical value in GC has been
   performed.

   Here, we collected all available datasets presenting the PLK1
   expression pattern and the clinicopathological significance in GC from
   the Gene Expression Omnibus (GEO), Oncomine, The Cancer Genome Atlas
   (TCGA) and the literatures. Simultaneously, immunohistochemistry (IHC)
   staining was performed using clinical specimens from in-house GC
   patients to validate the expression pattern of PLK1. Subsequently,
   all-inclusive information related to PLK1 was achieved, and we
   proceeded with a systematic investigation using a meta-analysis to draw
   a comprehensive conclusion. Going further still, potential regulation
   mechanisms of PLK1 were analyzed using bioinformatics methods. Through
   these means, we unveil a key role for PLK1 as a GC promoter.

RESULTS

   The present study contained several procedures sequentially (Figure
   [51]1). After screening and inspection, a total of 25 datasets and 12
   publications [[52]15, [53]17–[54]27] were incorporated into the present
   study. Among them, 19 datasets and 2 articles offered the expression
   value of PLK1 mRNA in GC and control groups were included (Table
   [55]1). Additionally, a total of 10 datasets and 11 publications that
   were used for investigating the prognostic value and
   clinicopathological significance of PLK1 were included and summarized
   in Table [56]2.

Figure 1. Flow chart of the study design in this investigation.

   [57]Figure 1
   [58]Open in a new tab

Table 1. Characteristics of microarray and RNA-seq datasets included in the
study.

   First author (publication year) Country Data source Test
   method/Platform Cancer group Normal controls Mean1±SD1 Mean0±SD0 AUC
   TCGA (2017) USA TCGA NR 415 35 11.77±1.13 9.11±1.71 0.915
   Hippo Y et al. (2005) Japan GEO: [59]GSE2685 Affymetrix [60]GPL80 22 8
   7.32±0.43 7.40±0.46 0.426
   Wang G et al. (2013) USA GEO: [61]GSE29272 Affymetrix [62]GPL96 134 134
   6.74±0.53 6.54±0.44 0.594
   Holbrook JD et al. (2012) Singapore GEO: [63]GSE29998 Illumina
   [64]GPL6947 50 49 6.18±0.66 5.76±0.72 0.648
   Cheng L et al. (2012) China GEO: [65]GSE33335 Affymetrix [66]GPL5175 25
   25 5.37±0.70 4.05±0.48 0.918
   Wu YH et al. (2012) Singapore GEO: [67]GSE37023 Affymetrix [68]GPL96
   112 39 5.83±0.55 6.24±0.59 0.286
   Kang M et al. (2014) South Korea GEO: [69]GSE51575 Agilent [70]GPL13607
   26 26 7.88±0.98 7.08±0.69 0.743
   Lin J et al. (2013) USA GEO: [71]GSE52138 Affymetrix [72]GPL96 13 7
   6.70±0.60 6.13±0.301 0.769
   Liu B et al. (2017) China GEO: [73]GSE54129 Affymetrix [74]GPL570 111
   21 6.21±0.69 4.63±0.28 0.988
   Wang J et al. (2014) China GEO: [75]GSE56807 Affymetrix [76]GPL5175 5 5
   7.11±0.92 5.11±1.01 0.960
   Zhang X et al. (2014) China GEO: [77]GSE63089 Affymetrix [78]GPL5175 45
   45 6.81±0.90 5.21±0.60 0.934
   Yoshizawa JM et al. (2015) USA GEO: [79]GSE64951 Affymetrix [80]GPL570
   63 31 7.93±1.71 8.00±1.88 0.485
   Hao L et al. (2015) China GEO: [81]GSE65801 Agilent [82]GPL14550 32 32
   6.18±1.69 7.01±2.02 0.378
   Shao Q et al. (2016) China GEO: [83]GSE79973 Affymetrix [84]GPL570 10
   10 6.11±1.12 5.11±0.89 0.800
   Wang B et al. (2016) China GEO: [85]GSE84787 Agilent [86]GPL17077 10 10
   6.40±3.54 5.91±2.99 0.570
   Cui J et al. (2013) USA Oncomine Affymetrix [87]GPL5175 80 80 5.73±1.54
   4.45±1.55 0.732
   D'Errico M et al. (2010) Italy Oncomine Affymetrix [88]GPL570 38 31
   9.18±0.71 8.26±0.69 0.818
   Wang Q et al. (2010) China Oncomine Affymetrix [89]GPL570 12 15
   8.07±1.09 7.39±1.23 0.656
   Cho JY et al. (2011) USA Oncomine Illumina [90]GPL6884 71 19 7.24±0.71
   7.46±0.80 0.353
   Sun YH et al (2014) China CNKI RT-PCR 60 60 0.38±0.07 0.05±0.03 NR
   Tang M et al. (2014) China CNKI RT-PCR 60 60 0.19±0.05 0.06±0.01 NR
   [91]Open in a new tab

   AUC: area under curve

   Note: Mean1±SD1: gastric cancer tissues; Mean0±SD0: non-tumor tissues.

Table 2. General characteristics of outcome-related published studies and
microarray datasets.

   Author Data source Year Country Patients (n) Test method/Platform Cut
   off values HR estimation survival analysis HR (95% CI)
   Kanaji S et al. PMID:16645325 2006 Japan 160 IHC NR Multivariate
   analysis OS 2.02 (1.10–3.72)
   Weichert W et al. PMID: 16630118 2006 Germany 135 IHC IRS> 6 Survival
   curve OS 2.09 (1.29-3.41)
   Otsu H et al. PMID: 27245623 2016 Japan 207 IHC IRS≥6 Survival curve
   OSRFS 1.77 (0.93-3.36)1.38 (0.95-2.02)
   Jang YJ et al. PMID: 16865274 2006 Korea 280 IHC NR Survival curve OS
   0.40 (0.17-0.91)
   Chen ZW et al CNKI 2009 China 80 IHC Score≥2 NR NR NR
   Sun YH et al. CNKI 2014 China 60 IHC IOD≥994 Survival curve OS 2.25
   (1.21-4.16)
   Zou CG et al CNKI 2009 China 49 IHC Score≥2 NR NR NR
   Lan B et al. PMID: 17253180 2007 China 89 IHC Score>6 Survival curve OS
   2.77 (1.09-4.73)
   Zhang Q et al. CNKI 2005 China 54 IHC Positive cells≥6% NR NR NR
   Xia D et al. CNKI 2014 China 80 IHC SCORE>1 NR NR NR
   Yao HL et al. PMID: 21051833 2010 China 59 IHC Positive cells≥6% NR NR
   NR
   TCGA TCGA 2017 USA 415 NR ≥11.95 Multivariate analysis OSRFS 1.25
   (0.87-1.78)1.27 (0.93-1.75)
   Kim HK et al. [92]GSE14208 2011 USA 123 [93]GPL571 ≥6.67 Univariate
   analysis OSPFS 1.41 (0.97-2.06)1.47 (1.01-2.14)
   Ooi CH et al. [94]GSE15459 2014 Switzerland 192 [95]GPL570 ≥8.38
   Multivariate analysis OS 0.95 (0.61-1.50)
   Paik S et al. [96]GSE26253 2014 South Korea 432 [97]GPL8432 ≥9.07
   Univariate analysis RFS 0.68 (0.51-0.91)
   Lee J et al (2). [98]GSE26901 2016 USA 109 [99]GPL6947 ≥7.18 NR NR NR
   Lee J et al (1). [100]GSE28541 2012 USA 40 [101]GPL13376 ≥6.81 NR NR NR
   Lei Z et al. [102]GSE34942 2014 Singapore 56 [103]GPL570 ≥5.52
   Multivariate analysis OS 1.96 (0.73-5.28)
   Zhang X et al. [104]GSE63089 2014 China 45 [105]GPL5175 ≥6.79 NR NR NR
   Cui J et al. Oncomine 2011 USA 80 [106]GPL5175 ≥7.06 NR NR NR
   Cho JY et al. Oncomine 2011 USA 65 [107]GPL6884 ≥7.07 NR NR NR
   [108]Open in a new tab

   NR: not reported; OS: overall survival; IHC: immunohistochemistry; RFS:
   recurrence-free survival; IRS: immunoreactivity scoring system; IOD:
   integrated optical density; PFS: progression-free survival.

The expression level of PLK1 in GC via various databases

   First, by mining online databases (TCGA, GEO, and Oncomine databases),
   19 datasets were obtained, which provided PLK1 mRNA expression values
   in GC tissues and adjacent non-tumor tissues. We investigated the
   expression pattern of PLK1 in gastric cancer based on each independent
   dataset. In Figure [109]2, [110]3, [111]4 and [112]5, the PLK1
   expression pattern of each data set was displayed in the form of
   scatter plots and receiver operating characteristic (ROC) diagrams.
   Then, we detected the expression of PLK1 protein in 43 pairs of GC
   tissues by IHC staining. Among these GC samples, the immunoreactivity
   score (IRS) indicated that PLK1 protein expression was significantly
   higher in the 43 GC samples (9.42±3.06) than in adjacent non-tumor
   tissues (7.02±3.17, P=0.001, Figure [113]6).

Figure 2. Different expression levels of PLK1 between gastric cancer and
non-tumor gastric tissues based on 10 datasets.

   [114]Figure 2
   [115]Open in a new tab

   (A) TCGA; (B) [116]GSE2685; (C) [117]GSE29272; (D) [118]GSE29998; (E)
   [119]GSE33335; (F) [120]GSE37023; (G) [121]GSE51575; (H) [122]GSE52138;
   (I) [123]GSE54129; (J) [124]GSE56807.

Figure 3. Different expression levels of PLK1 between gastric cancer and
non-tumor gastric tissues based on another 9 datasets.

   [125]Figure 3
   [126]Open in a new tab

   (A) [127]GSE63089; (B) [128]GSE64951; (C) [129]GSE65801; (D)
   [130]GSE79973; (E) [131]GSE84787; (F) Cui Gastric; (G) D’errico
   Gastric; (H) Wang Gastric; (I) Cho Gastric.

Figure 4. Receiver Operating Characteristic curves of PLK1 expression for the
differentiation of gastric cancer from non-tumor tissues based on 10
datasets.

   [132]Figure 4
   [133]Open in a new tab

   (A) TCGA; (B) [134]GSE2685; (C) [135]GSE29272; (D) [136]GSE29998; (E)
   [137]GSE33335; (F) [138]GSE37023; (G) [139]GSE51575; (H) [140]GSE52138;
   (I) [141]GSE54129; (J) [142]GSE56807.

Figure 5. Receiver Operating Characteristic curves of PLK1 expression for the
differentiation of gastric cancer from non-tumor tissues based on another 9
datasets.

   [143]Figure 5
   [144]Open in a new tab

   (A) [145]GSE63089; (B) [146]GSE64951; (C) [147]GSE65801; (D)
   [148]GSE79973; (E) [149]GSE84787; (F) Cui Gastric; (G) D’errico
   Gastric; (H) Wang Gastric; (I) Cho Gastric.

Figure 6. PLK1 protein expression in gastric cancer and non-tumor tissues
assessed by immunohistochemistry.

   [150]Figure 6
   [151]Open in a new tab

   (A, B) A normal gastric tissue showing low PLK1 expression. (C, D) A
   poorly differentiated tumor tissue showing high PLK1 expression. (E, F)
   A well differentiated tumor tissue showing high PLK1 expression.
   Magnification: ×100 (A, C, E) or ×400 (B, D, F).

Verification of PLK1 up-regulation in GC via a meta-analysis

   As some individual studies were too small to yield a valid conclusion,
   we integrated all of the data. A random-effects model was selected, as
   apparent heterogeneity existed among the 21 studies, which were listed
   in Table [152]1 (I^2 = 96.1%, P<0.001; Figure [153]7). The pooled
   standard mean deviation (SMD) of PLK1 mRNA was 1.21 (95% CI: 0.65-1.77,
   P< 0.001; Figure [154]7), which suggested that PLK1 mRNA was remarkably
   up-regulated in GC. Additionally, the pooled odds ratio (OR) calculated
   based on 7 articles and our IHC staining results revealed that PLK1
   protein expression was also up-regulated in GC compared to normal
   gastric tissue (OR=12.12, 95% CI: 5.41-27.16, P<0.001, random effect
   model; Figure [155]8) for heterogeneity (I^2 =67%, P=0.002).

Figure 7. Forest plot for evaluating PLK1 mRNA expression between gastric
cancer and non-tumor tissues.

   [156]Figure 7
   [157]Open in a new tab

Figure 8. Forest plot for the different PLK1 protein expression levels
between gastric cancer and non-tumor tissues.

   [158]Figure 8
   [159]Open in a new tab

   Sensitivity analysis suggested that the pooled SMD was stable (Figure
   [160]9A). Furthermore, Begg's funnel plot and Egger's test were carried
   out to visualize the publication bias. Begg's regression plot showed no
   potential publication bias (P= 0.139, Figure [161]9B). Equally, Egger's
   test indicated that no publication bias was found for the PLK1
   overexpression in GC (P= 0.134). In summary, these current results
   confirmed the overexpression of PLK1 in GC.

Figure 9. Results of the sensitivity analysis and Begg's funnel plot of PLK1
expression.

   [162]Figure 9
   [163]Open in a new tab

   (A) Sensitivity analysis of standard mean deviation (random effects
   model) based on stepwise omitting one study at a time. (B) Analysis of
   the detection of publication bias in the meta-analysis of studies
   assessing the expression pattern of PLK1 in gastric cancer.

   To further identify the capability of PLK1 in discriminating cancer
   from non-cancerous gastric tissues, we generated a summary receiver
   operating characteristic (SROC) curve and then calculated the area
   under the curve (AUC). A total of 28 groups of data obtained from TCGA,
   Oncomine, GEO, publications and IHC staining were summarized. The
   overall AUC of PLK1 in GC was 0.86 (95% CI: 0.82-0.88), with a
   sensitivity and specificity of 0.82 (95% CI: 0.72-0.89) and 0.75 (95%
   CI: 0.62-0.84), respectively (Figure [164]10).

Figure 10. Summary receiver operating characteristic curve of the
distinguishing capability of PLK1 for cancer from non-cancerous tissues.

   [165]Figure 10
   [166]Open in a new tab

PLK1 overexpression and clinicopathological features in GC patients

   For clinical parameters, we performed integrative analyses and revealed
   that PLK1 overexpression in GC patients was associated with several
   clinicopathological parameters. As shown in Table [167]3, the increased
   expression of PLK1 was positively associated with lymph node metastasis
   (OR: 1.78, 95% CI: 1.13–2.80, P=0.013, random effect model) and
   advanced TNM stage (OR: 1.48, 95% CI: 1.02–2.15, P=0.038, random effect
   model). No significant relationships were observed between PLK1
   expression and age (OR=1.19, 95% CI: 0.77-1.84, P=0.437, random effect
   model), gender (OR=0.87, 95% CI: 0.71-1.07, P=0.191, fixed effect
   model), histology grade (OR=1.07, 95% CI: 0.66-1.73, P=0.783, random
   effect model), depth of tumor invasion (OR=1.70, 95% CI: 0.97-2.98,
   P=0.061, random effect model), or metastasis (OR=1.37, 95% CI:
   0.79-2.37, P=0.209, fixed effect model).

Table 3. The relationships between PLK1 overexpression and
clinicopathological significance in gastric cancer patients.

   Clinicopathological features Studies (n) Patients (n) Meta-analysis
   model OR (95% CI) P Heterogeneity
   I^2(%) p
   Lymph node metastasis 12 1395 random effects model 1.78(1.13–2.80)
   0.013 64.2 0.001
   TNM stage 15 1937 random effects model 1.48(1.02-2.15) 0.038 63.6
   <0.001
   Age 8 1006 random effects model 1.19(0.77-1.84) 0.437 53.2 0.037
   Gender 15 1915 fixed effects model 0.87(0.71-1.07) 0.191 23.2 0.197
   Tumor histology grade 13 1447 random effects model 1.07(0.66-1.73)
   0.783 64.9 0.001
   Depth of tumor invasion 12 1405 random effects model 1.70(0.97-2.98)
   0.061 78.6 <0.001
   Metastasis 5 667 fixed effects model 1.37(0.79-2.37) 0.209 31.9 0.209
   [168]Open in a new tab

   TNM: tumor node metastasis.

Evaluation of the prognostic value of PLK1 in GC

   After screening, ten studies assessed the correlation between
   overexpressed PLK1 and overall survival (OS), with three studies
   containing information regarding the association between up-regulated
   PLK1 and recurrence free survival (RFS). Additionally, one study had
   sufficient information to calculate the relationship between high PLK1
   expression and progression-free survival (PFS). It indicated that PLK1
   overexpression was markedly correlated with worse OS (hazard ratio
   (HR): 1.51, 95% CI: 1.14–1.99, P=0.004; Figure [169]11A) with
   significant heterogeneity (I^2 =59.3%, P=0.009) when employing the
   random-effect model. Furthermore, a sensitivity analysis was performed,
   and the result implied the pooled HR was stable (Figure [170]12A). As
   shown in Figure [171]12B, Begg's regression plot revealed no evidence
   of potential publication bias (P = 0.641). Additionally, Egger's test
   indicated no statistically significant publication bias was found for
   the positive PLK1 expression on OS (P = 0.721). To minimize
   heterogeneity, we omitted the article written by Jang YJ, as the
   overexpression of PLK1 was significantly correlated with a shorter OS
   (HR =1.54, 95% CI: 1.30–1.83, P<0.001; Figure [172]11B) with no
   heterogeneity (I^2= 36.2%, P =0.129). The overexpression of PLK1
   clearly led to an inferior PFS (HR: 1.47, 95% CI: 1.01-2.14). However,
   the result had low credibility as only 1 study provided data.
   Additionally, the RFS between high and low expression levels of PLK1
   did not differ significantly (HR: 1.05, 95% CI: 0.67–1.66, P=0.834).

Figure 11. Forest plot for the association between PLK1 expression and
overall survival (OS).

   [173]Figure 11
   [174]Open in a new tab

   (A) Correlation between OS and PLK1 expression based on all studies
   included. (B) Correlation between OS and PLK1 expression after the
   study of Jang et al. was removed.

Figure 12. Results of the sensitivity analysis and publication bias.

   [175]Figure 12
   [176]Open in a new tab

   (A) Sensitivity analysis of the hazard ratio (random effects model),
   calculated by sequentially each study removal. (B) The funnel plot
   detecting the potential publication bias among the included records.

Relevant genes of PLK1 and gene-annotation enrichment analysis

   The 100 genes most relevant to PLK1 in GC were obtained using Gene
   Expression Profiling Interactive Analysis (GEPIA) as described.
   Determining the biological value of these genes could provide important
   information on the mechanism of PLK1 in GC. These 100 relevant genes
   were input into DAVID to perform GO and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathway analysis. As shown in Figure [177]13, GO
   analyses were implemented in three categories, including biological
   process (BP), cellular component (CC) and molecular function (MF). For
   BP, the most notably enriched functional terms were cell cycle, M
   phase, and cell cycle phase (P<0.001). Regarding CC, genes markedly
   assembled at condensed chromosome, chromosome and microtubule
   cytoskeleton (P<0.001). On the basis of MF, genes prominently
   accumulated in ribonucleotide binding, purine ribonucleotide binding
   and purine nucleotide binding (P<0.001). Additionally, these genes in
   the KEGG enrichment analysis were shown to be particularly related to
   the cell cycle (P<0.001, Figure [178]14).

Figure 13. Gene Ontology analysis of the genes relevant to PLK1 in GC.

   [179]Figure 13
   [180]Open in a new tab

   (A) Biological process; (B) Cellular component; (C) Molecular function.

Figure 14. The significantly enriched annotation of the Kyoto Encyclopedia of
Genes and Genomes pathway analysis of the genes relevant to PLK1 in GC.

   [181]Figure 14
   [182]Open in a new tab

Protein–protein interactions (PPI) network construction and the hub genes
identification

   To further insight into the molecular mechanism of PLK1, we used a
   functional protein network database STRING to construct the PPI
   network. Through the constructed PPI network, nine hub genes of PLK1
   were obtained (Figure [183]15A). These genes were MAD2L1, CHEK1, CDC45,
   CDC20, CCNB2, CCNB1, CCNA2, BUB1B and BUB1. Then, we also provided
   expression matrix plots of these hub genes (Figure [184]15B).
   Furthermore, the correlations between PLK1 and hub genes were
   calculated (Figure [185]15C-15K). These nine genes were positively
   correlated with PLK1.

Figure 15. Protein-protein interaction (PPI) network of several related genes
enriched in cell cycle pathway.

   [186]Figure 15
   [187]Open in a new tab

   (A) PPI network was drawn using STRING online tool. (B) Plots of nine
   hub genes drawn by GEPIA online tool. The density of color in each
   block represented the median expression value of a gene. (C-K) Scatter
   plot showed the correlation between the PLK1 expression and the nine
   hub genes signature.

Identification of PLK1-mediated molecular functions in GC by GSEA

   To identify molecular functions of PLK1 in GC in-depth, we performed
   Gene Set Enrichment Analysis (GSEA) using TCGA data. Among all of the
   predefined hallmark gene sets, a total of 9 items were most commonly
   associated with PLK1 overexpression in the TCGA cohort (FDR
   q-value<0.01), including MTORC1 signaling, E2F targets and the G2M
   checkpoint, etc. (Figure [188]16), suggesting that PLK1 may be deeply
   engaged in GC tumorigenesis and progression through several
   cancer-associated signaling pathways.

Figure 16. GSEA plot showing that PLK1 expression positively correlated with
the three most significant enrichment sets.

   [189]Figure 16
   [190]Open in a new tab

   (A) MTORC1 signaling; (B) E2F targets; (C) G2M checkpoints.

DISCUSSION

   Identification of risk factors and accurate prognostic prediction in GC
   patients are critical to selecting appropriate therapies and guiding
   clinical follow-up [[191]28–[192]30]. Unfortunately, accurately
   identifying the risks in individual patients is relatively difficult
   [[193]31–[194]33]. Although accumulating advances have been achieved to
   further understand the clinical value of PLK1 in GC, the conclusions
   have been controversial. Therefore, a comprehensive view of PLK1 in GC
   is needed. Taking advantage of the vast publicly available databases,
   we sought to unravel a crucial function of PLK1 in GC via excavating
   the Oncomine, TCGA, and GEO databases, published literature and IHC
   staining results in our hospital. Here, we first gathered all-inclusive
   data to confirm that PLK1 is markedly up-regulated in GC patients.
   Furthermore, we observed significant differences between PLK1 high and
   low expression levels regarding tumor pathological stage. More
   importantly, GC patients with PLK1 overexpression had inferior survival
   outcome and a significantly increased risk of lymph node metastasis.
   Through the bioinformatics analysis, we firmly verified that PLK1
   played a vital part in the regulation of the cell cycle and contributed
   to cancer-related signaling pathways, thus contributing to the stepwise
   tumorigenesis and progression in GC. Based on the above evidence,
   cautious monitoring of PLK1 could be clinically useful for
   decision-making in GC.

   The first intention of the study was to clarify the expression pattern
   of PLK1 in GC. As expected, a pooled SMD reached 1.21 (95% CI:
   0.65-1.77, P<0.001) presented by the random-effects model and no
   publication bias was observed. Similar to PLK1 mRNA, the pooled OR also
   revealed remarkably increased expression of PLK1 protein in GC, in
   comparison to normal gastric tissue. Furthermore, a robust ability of
   PLK1 to distinguish cancer from non-cancerous gastric tissues was
   observed via ROC (AUC: 0.86; 95% CI: 0.82–0.88, P<0.001). These
   demonstrated that the expression levels of PLK1 in GC tissues were
   significantly overexpressed. As a consequence, PLK1 might act as a
   cancer promoting factor and participate in GC tumorigenesis.

   To further evaluate the clinical relevance and prognostic value of PLK1
   in GC patients, we analyzed, for the first time, the remarkably
   increased expression of PLK1 as an unfavorable prognostic factor for OS
   in GC patients using a meta-analysis. The prognostic value of PLK1 in
   cancers is currently high profile. Zhang et al performed a
   meta-analysis using eleven eligible articles and demonstrated that an
   increased expression of PLK1 indicated a higher risk of worse survival
   in breast cancer patients [[195]12]. Liu et al compared the OS time
   between higher PLK1 and lower PLK1 groups in 25 cancer types based on
   the data provided by TCGA. The study revealed that cases with higher
   PLK1 suffered remarkably worse OS as compared to those with lower PLK1
   in 10 cancer types: adrenocortical carcinoma, bladder urothelial
   carcinoma, breast invasive carcinoma, kidney renal clear cell
   carcinoma, kidney renal papillary cell carcinoma, brain lower-grade
   glioma, lung adenocarcinoma, pancreatic adenocarcinoma, skin cutaneous
   melanoma, and uterine corpus endometrial carcinoma [[196]34]. For GC,
   the results from our study fully proved that PLK1 overexpression may be
   an indispensable biomarker for the accurate assessment of prognosis.
   Additionally, this meta-analysis documented that elevated PLK1 was
   closely linked with advanced TNM stage and lymph node metastasis.
   Metastasis is a major death cause in cancers and complicates treatment
   opportunities [[197]35]. Hence, an in-depth comprehension of the
   molecular mechanism of PLK1 in tumor metastasis and inferior prognosis
   may pave the way toward new prognostic models.

   The mechanisms responsible for PLK1 in GC are still not clear. We
   collected 100 genes that had a similar expression pattern to PLK1 in GC
   and conducted GO and KEGG pathway enrichment analysis. GO enrichment
   analysis indicated the possible functions of PLK1 in GC, and the
   results from three GO terms and KEGG pathway analysis determined that
   PLK1 was mainly involved in the cell cycle, which might be the
   mechanism by which PLK1 exerts its versatile and critical biological
   function in GC. Consistent with the results from GO and KEGG analysis,
   PLK1, as a master controller of the cell cycle, has been widely
   recognized [[198]36, [199]37]. Several studies have identified that
   PLK1 could impair the apoptosis, and enhance mitosis, cell growth and
   metastasis potential of GC cells. For example, in one of the GC cell
   lines, SNU-638, Jang YJ [[200]19] found that depletion of PLK1
   inhibited cell proliferation and caused apoptosis. Interestingly, Chen
   XH [[201]38] also confirmed similar results by blockage of PLK1
   expression in GC cells MKN45. To further probe into the molecular
   mechanism of PLK1, we obtained 9 hub genes by using PPI analysis. These
   hub genes were MAD2L1, CHEK1, CDC45, CDC20, CCNB2, CCNB1, CCNA2, BUB1B
   and BUB1, which were all upregulated in GC significantly and were
   positively related to PLK1. Hence, it could be speculated that PLK1
   regulates cell cycle pathways by co-operating with these nine genes,
   and thereby facilitates the carcinogenesis and development of GC. Based
   on these findings, PLK1 is regarded as a key player in orchestrating
   the cell cycle, leading to the progression of GC.

   In addition to GO and KEGG analysis, the results from GSEA suggested
   that MTORC1 signaling was most closely related with elevated PLK1
   expression in GC patients. Today, most studies on PLK1 and MTORC1
   signaling have been separate, and little is known about the crosstalk
   between them. Recently, Ruf et al proposed that PLK1 inhibited MTORC1
   and thereby positively contributed to autophagy in HeLa cells
   [[202]39]. The role of autophagy in cancer is one that has been highly
   researched but is still deeply mysterious. Cai et al [[203]16]
   demonstrated that PLK1 drove gastric tumor epithelial-mesenchymal
   transition (EMT) via targeting AKT signaling. AKT is also a key part of
   the MTORC1 signaling pathway and is closely related to metastasis,
   proliferation and invasion processes of gastric cancer
   [[204]40–[205]42]. All of the findings above lead to the opinion that
   PLK1 may be involved in autophagy in GC.

   Molecularly targeted therapy and prognosis assessment have opened up
   new avenues for clinical cancer treatment. For molecular therapy,
   several preclinical studies with PLK1 inhibitors are underway
   [[206]43–[207]45]. Thus, we speculated that the combinatorial targeting
   of MTORC1 or AKT and PLK1 may be more meaningful and efficient for
   clinicopathological and prognostic estimation. Although some
   preclinical studies have been terminated due to side effects, looking
   for efficient and specific PLK1 molecular inhibitors will be the next
   priority for the clinical treatment of tumors. This truly pleiotropic
   kinase, assuredly, will continue to fascinate in the future.

   However, the results should be interpreted cautiously due to some
   limitations that may have influenced the reliability of our
   conclusions. First, PLK1 expression was uniformly analyzed by various
   detection methods and diverse RNA detection platforms. This may be
   caused by the lack of standardization, which may result in inaccurate
   results. Therefore, a large multicenter study is needed to determine
   the most suitable detection method of PLK1. Consequently, the
   random-effects model was selected to reduce the impact of the
   heterogeneity on our results. Second, as some studies did not provide
   accurate original survival data, some of the survival data were
   indirectly extracted from Kaplan-Meier curves by Engauge Digitizer.
   Accordingly, the corresponding HR and 95% CI may lack credibility.
   Third, specific signaling pathways regulated by PLK1 in GC still must
   be validated using in vitro or in vivo experiments.

   Taken together, the study proposes that PLK1 is a valid prognostic
   marker and will play a significant role in future clinical trials.
   Future, more high-quality and big population studies are desirable to
   update this analysis.

MATERIALS AND METHODS

Data mining

   Microarray or RNA-seq datasets, which were available for PLK1
   expression pattern and relevant clinical information appraisal in GC,
   were downloaded and extracted from TCGA data portal
   ([208]https://cancergenome.nih.gov/), GEO
   ([209]https://www.ncbi.nlm.nih.gov/geo/) and Oncomine
   ([210]https://www.oncomine.org/resource/main.html). In addition, a
   computer-aided systematic literature search was also performed in
   electronic databases PubMed, Embase, MEDLINE, Web of Science, Cochrane
   library, China National Knowledge Infrastructure (CNKI), and Wanfang,
   terminating in June 2017. The following keywords were used: (“polo-like
   kinase 1” OR STPK13 OR PLK1) AND (malignan^* OR cancer OR tumor OR
   tumour OR neoplas^* OR carcinoma) AND (gastric OR stomach). Cited
   references from identified primary studies or reviews were also