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Article

Gene Co-Expression Network Analysis Associated with Endometrial Cancer Tumorigenesis and Survival Outcomes

by
Alexis J. Clark
1,
Rajesh Singh
1,
Regina L. Leonis
2,
Eric A. Stahlberg
3,
Zachary S. Clark
4 and
James W. Lillard, Jr.
1,*
1
Department of Microbiology, Biochemistry and Immunology, Morehouse School of Medicine, Atlanta, GA 30310, USA
2
Department of Obstetrics & Gynecology, Morehouse School of Medicine, Atlanta, GA 30310, USA
3
Cancer Data Science Initiatives, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
4
Department of Information Technology, Clayton State University, Morrow, GA 30260, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(22), 12356; https://doi.org/10.3390/ijms252212356
Submission received: 14 October 2024 / Revised: 12 November 2024 / Accepted: 15 November 2024 / Published: 18 November 2024
(This article belongs to the Special Issue Exploring Markers and Molecular Pathogenesis of Cancer: Genitourinary and Gynecological Tumors)
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Abstract

:
Endometrial cancer (EC) presents a substantial health challenge, with increasing incidence and mortality rates. Despite advances in diagnosis and treatment, understanding the molecular underpinnings of EC progression remains unknown. In this study, we conducted a comprehensive investigation utilizing The Cancer Genome Atlas (TCGA-UCEC n = 588) data to analyze gene co-expression patterns, elucidate biological process pathways, and identify potential prognostic and diagnostic biomarkers for EC, using weighted gene co-expression network analysis (WGCNA), differential gene expression, survival analysis, and functional analysis, respectively. We determined that the Green module (M5) was significantly correlated with patient survival. Functional analysis of the genes in module M5 indicates involvement in cell cycle regulation, mitotic spindle assembly, and intercellular signaling. TPX2, BUB1, and ESPL1 were among the top differentially expressed genes in the Green module, suggesting their involvement in critical pathways that contribute to disease progression and patient survival outcomes. The biological and clinical assessments of our findings provide an understanding of the molecular landscape of EC and identified several potential prognostic markers for patient risk stratification and treatment selection.

Graphical Abstract

1. Introduction

Endometrial cancer (EC) represents a significant health burden worldwide, with rising incidence and mortality rates observed in recent years [1]. This increase is particularly alarming due to the health disparities in EC incidence and mortality rates across different racial and ethnic groups. Statistics have shown that from 2009 to 2018, non-Hispanic White women had a lower incidence rate (~1.2% increase) compared to the 3.5% increased incidence in African American women [2,3]. Disturbingly, African American women also experience double the mortality rate from EC [4]. Several factors contribute to the increased incidence of EC, including obesity and decreased fertility [5]. Despite advances in diagnosis and treatment, a comprehensive understanding of the molecular mechanisms driving EC progression remains elusive. Identifying biomarkers associated with disease development and prognosis is crucial for improving patient outcomes through early detection, personalized treatment strategies, and accurate prognostic assessment.
The Cancer Genome Atlas (TCGA) project has played a pivotal role by generating large-scale genomic, transcriptomic, and clinical data for several cancer types, including EC. The TCGA project employed whole-genome sequencing, exome sequencing, MSI assay, copy number analysis, and proteomics to classify ECs into four genomic marker subtypes: ultramutated/polymerase (POLE-mutated), hypermutated/MSI(MSI-H), copy number-low (microsatellite stable), and copy number-high [6]. This molecular characterization has paved the way for the development of targeted therapies, which have shown promise in improving patient outcomes. For example, EC with ultramutated or MSI harbor a high prevalence of somatic mutations, making them candidates for immunotherapy [7]. Immunotherapy was first incorporated into the treatment of recurrent endometrial cancers with microsatellite instability [8], and is now approved for both recurrent and front-line settings of advanced stage EC, regardless of biomarker status [8,9,10].
In addition to immunotherapy, other targeted therapies have emerged based on specific molecular alterations in EC. HER2 inhibitors, like trastuzumab, are used in combination with chemotherapy for aggressive EC that overexpresses HER2 [11]. Poly ADP-ribose polymerase (PARP) inhibitors, like Olaparib and rucaparib, block the enzymes involved in repairing damaged DNA, making them effective in cancers with dysfunctional DNA repair pathways and BRCA mutations. mTOR inhibitors, like Everolimus, target mTOR, a protein that regulates cell growth, division, and survival. The identification of biomarkers and significant biological pathways is crucial in driving clinical drug development and targeted therapies.
EC is a heterogeneous disease with distinct molecular profiles that may contribute to challenging diagnostic and treatment strategies. Identifying prognostic genes can improve personalized therapies and patient outcomes. The goal of this study is to identify gene co-expression modules associated with survival outcomes in EC. We aim to use computational approaches on TCGA data to investigate the molecular landscape of EC to support prognostic biomarker discovery for the development of future targeted therapies. TCGA data are important for our study because of their clinical annotations, which allow for survival analysis and WGCNA clinical associations.
In this study, we employed weighted gene co-expression network analysis (WGCNA) to identify genes associated with EC patient vital status. WGCNA allows for the identification of gene modules in association with expression patterns. These modules are frequently functionally related, which provides insight into regulatory networks and key biological processes that drive the disease. This method allowed us to prioritize genes that may serve as biomarkers due to their role in the co-expressed networks. Additionally, we conducted differential gene expression analysis to reveal the expression patterns of the hub genes in tumors versus normal tissue. We also conducted a survival analysis to test the significance of these DEGs in patient survival outcomes. Through this comprehensive analysis, we identified novel biomarkers that reflect tumor biology and have prognostic potential. These findings enhance our understanding of the molecular mechanisms driving EC progression and may contribute to improved patient outcomes by providing new targets for personalized therapeutic strategies.

2. Results

2.1. WGCNA

We performed weighted gene co-expression network analysis (WGCNA) on 14,802 genes among 588 patient samples to investigate the co-expression patterns and their association with clinical traits. The patient-matched clinical samples were obtained from TCGA. Our WGCNA analysis identified 25 module eigengenes (MEs), which are the first principal component of each module summarizing the gene expression patterns in each module. Once the modules were assigned, gene expression relatedness was calculated by their correlation and visualized in the hierarchical clustering dendrogram in Figure 1. Each branch of the dendrogram represents an ME. Modules that are closer on the tree have more similar eigengene co-expression patterns. The height at which the branches merge indicates the level of similarity. Modules with high similarity have lower heights (shorter branches), are grouped together, and may share similar biological functions or be co-regulated.
Conversely, modules at larger heights are less similar, have more distinct expression profiles, and may be functionally different. The initial module assignment contained several modules. We merged modules based on a 0.25 cut-off threshold, resulting in module genes that are at least 75% similar. This reduces redundancies and ensures the most significant modules are assessed. In addition, larger modules are more stable and reproducible across datasets.
The modules were numbered from the largest to smallest number of genes M1-M25, as shown in Table 1. The dendrogram shows that the Green module is closely related to the Dark-Green and White modules.
In the adjacency matrix, we visualized the pairwise relationships between genes within a network. This displays the strength of the co-expression relationships between the genes and allows for an inspection of the gene relationships and identification of clusters of highly expressed genes. The heatmap in Figure 2 is a graphical representation of the adjacency matrix. The adjacencies are computed using the Pearson correlation method and transformed into adjacency values using the soft-threshold power. This method emphasizes strong correlations and reduces weak correlations. Overall, the adjacency matrix aids in the identification of biologically meaningful patterns and functional gene groups.
Module–trait relationship correlation was assessed using the Pearson coefficient (R) and student t-test p-values to quantify the strength and significance of associations between MEs and clinical traits. Figure 3 depicts the statistical significance of the correlations. Among the identified modules, MEgreen and MEdarkgreen exhibited a moderate positive correlation with vital status (r = 0.15 and r = 0.08), suggesting that the genes within these modules are associated with patient survival. Conversely, the genes in the MElightyellow and MEred are negatively correlated with the patient’s vital status (r = − 0.15 and − 0.1). For subsequent analysis, we will focus on the most significant modules, Green and Light Yellow.
Intramodular connectivity, also known as module membership, was assessed using kME (k within module eigengene) values. This measure is used to quantify how connected or central a gene is within a module and is represented by this equation.
k M E i = c o r ( x i ,   M E M )
where x i represents the gene expression of gene I and ME is the module eigengene of module M. This method is crucial for identifying hub genes that may play a significant role in the module’s network. Our intramodular connectivity assessment identified the top 40 genes (Figure 4) in the Green and Light-Yellow modules that may be key drivers of patient vital status. Subsequent analysis of these hub genes allowed for a more in-depth investigation of biological pathways and networks. This selection increased the statistical power of downstream analyses, increasing the likelihood of identifying significant biological trends, and permitted the evaluation of both high-impact and moderately impactful genes, providing a more robust and reliable analysis of the molecular mechanisms driving EC. In addition, the genes with a high kME may have similar functions or are involved in the same biological pathways. The Green module was found to be positively correlated with patient vital status. The hub genes for the Green module include NCAPH, MCM10, TPX2, DLGAP5, BUB1, DEPD1, MELK, KIF2C, NCAPG, CCNA2, KIF11, TTK, CKAP2L, KIF15, CCNB1, CDCA8, AURKA, CENPA, CCNB2, and KIF4A. These genes are essential for the progression through the cell cycle, mitotic spindle formation, and chromosome segregation. The overexpression of these genes may contribute to uncontrolled proliferation and poor prognosis. Additionally, the genes in the Light-Yellow module include SPDEF, ELAPOR1, C9orf152, PLPP2, and SCGB2A1 and may be involved in several biological processes including tumor immunity and epithelial cell differentiation.

2.2. STRING

WGCNA identified a set of hub genes from the modules that were significantly correlated with patient vital status. Subsequently, Search Tool for the Retrieval of Interacting Genes/proteins (STRING) analysis was conducted to investigate the protein–protein interaction (PPI) of the top 40 hub genes from these modules with a PPI enrichment p-value < 1.0 × 10−16. Genes positively correlated with deceased EC patients (green nodes) and genes negatively associated with patient vital status (yellow nodes) were mapped to visualize and assess possible functional relationships and regulatory pathways.
This network revealed distinct clustering of the Green hub genes (Figure 5). These genes formed a highly interconnected network, with key central nodes such as AURKB, CEP55, CCNB2, and MCM10, suggesting their involvement in common pathways potentially driving aggressive tumor behavior and contributing to poor patient prognosis.
In contrast, the Yellow hub genes depicted less interconnectivity and were associated with various biological processes. FOXA2, PIGR, and SPDEF were shown to be key nodes involved in the immune response and cellular differentiation pathways These results suggest that immune surveillance and cellular differentiation may contribute to improved survival outcomes [12,13,14].
TRPM4 and STX18 are Light-Yellow hub genes connected to the Green hub genes, suggesting a functional crosstalk between cell cycle regulation and processes like immune modulation, vesicular trafficking, and cell differentiation. These genes may mediate the integration of signals from the tumor growth mechanism (Green module) and modulate the environmental response. This response may include immune cell activity or epithelial differentiation (Yellow module). This crosstalk could be crucial for tumor survival, immune evasion, or tumor immunity.

2.3. Differential Gene Expression

We performed differential gene expression analysis to identify differentially expressed genes between tumor and normal tissue samples, as shown by 957 genes in the Green module and 146 genes in the Light-Yellow module (Figure 6). The DEGs were further sorted based on statistical significance, considering both the p-value and log fold change. This allowed us to focus on genes that exhibit robust and biologically meaningful differences between the two sample groups. We hypothesize that the top hub genes identified in WGCNA will also rank among the most significantly differentially expressed genes.
In the Green module, several genes exhibit significant upregulation. ASF1B has the highest log2 fold change and significant p-value (<0.01). Other genes such as BUB1, NCAPH, CDC20, KIF1A, and CCNB1 also show significant upregulation, suggesting their potential involvement in critical pathways that may contribute to disease progression and poor patient survival outcomes. The negative correlation to the WGCNA patient vital status in the Light-Yellow module suggests that a higher expression of these genes is associated with better patient outcomes, potentially indicating protective roles or involvement in pathways that improve survival rates. Genes such as TMPRSS13, AQP5, TFF3, PIGR, and MUC5B were differentially expressed in the Light-Yellow module.
Our differential gene expression analysis provides insights into the molecular alterations underlying endometrial tumorigenesis, highlighting potential biomarkers and therapeutic targets for further investigation.

2.4. Gene Ontology

Gene ontology (GO) analysis provides insights into the functional significance of the differentially expressed genes, highlighting key biological processes, cellular components, molecular functions, and pathways dysregulated in a given disease. To gain insight into the biological processes underlying the differentially expressed genes (DEGs), we conducted a GO analysis, which revealed several key biological processes. The top biological processes among DEGs for the Green module were chromosome segregation and mitotic nuclear division. This suggests that the dysregulation of DNA replication processes may play a crucial role in endometrial cancer tumorigenesis, potentially influencing cellular proliferation, differentiation, and migration within the tumor microenvironment. Lastly, the Light-Yellow module is involved in glycosylation processes. These processes are crucial for cell division and proliferation, and their enrichment among the DEGs highlights their potential roles in driving EC progression.

2.5. Survival Analysis

Survival analysis was conducted to evaluate the impact of DEGs on patient survival outcomes. The Kaplan–Meier survival analysis model was employed using Survminer (V0.49) and Survival (V3.57) in R to assess the association between gene expression levels and overall survival times, considering vital status (Dead) as the event of interest. The differentially expressed hub genes positively correlated with patient vital status from the Green and Light-Yellow modules (p < 0.05 kME > 5.0, log fold change (LFC) ≥ 1.5). Patients were stratified into high- and low-expressing groups based upon the median values and compared for statistical significance (p < 0.05).
Among the top upregulated DEGs, TPX2, ESPL1, and BUB1 in the Green module and PLPP2, TMGM62, and TRPM4 in the Light-Yellow module exhibited a statistically significant impact on patient survival (Figure 7). Of these genes, PLPP2 also had the greatest prognostic value due to its presence in the top DEGs and hub genes.
These findings in Table 2 highlight the prognostic significance of specific DEGs in endometrial cancer, providing insight into potential biomarkers for patient risk stratification and therapeutic targeting. Six key genes were identified from two significant WGCNA modules.
Within the Green module, TPX2, ESPL1, and BUB1 demonstrate associations with patient outcomes, with p-values indicating statistical significance and AUC values ranging from 0.62 to 0.65. These results suggest that these genes may contribute to patient survival classification. Similarly, the Light-Yellow module genes TRPM4, TMEM62, and PLPP2 show moderate AUC values supporting their possible role in predicting patient survival outcomes. Further validation studies are needed to confirm the clinical utility of these genes in predicting patient outcomes and guiding personalized treatment strategies.
Our validation analysis identified 206 overlapping hub genes between the Red (GEO) and Green (TCGA-UCEC) modules that were previously associated with Grade 3 tumors and patient vital status. This significant overlap suggests that there is a potential link between high-grade tumors and survival outcomes in UCEC. As shown in Figure 8, the TCGA-WGCNA correlation matrix highlights a significant correlation between the Green module and Grade 3 tumors. Moreover, the GEO module–trait correlation matrix indicated that the Red module was the most significantly positively correlated with Grade 3 tumors. The Venn diagram visualizes the overlap. This suggests that shared genes are potentially relevant to both tumor progression and prognosis.
To statistically validate the overlap, we conducted a hypergeometric test (p = 9.90 × 10−159). These results indicate the overlap was highly unlikely to have occurred by chance. We supported this analysis with the Fisher’s exact test, with p = 2.2 × 10−16 and an odds ratio greater than 23. This indicates that there is a strong association between the genes in these modules. Together, these findings suggest that the overlapping genes are functionally relevant to UCEC. Additionally, GO analysis was conducted to investigate biological processes associated with the common genes. Chromosome segregation, organelle fission, and nuclear division were some of the enriched terms, as shown in Figure 9.
The combined statistical validation and GO enrichment analysis illustrates the statistical strength of these overlapping genes and biological relevance in UCEC.

3. Discussion

The comprehensive analysis of gene expression patterns in endometrial cancer provides insights into the molecular mechanisms underlying tumorigenesis and patient outcomes. In this study, we used a multi-faceted approach, integrating differential gene expression analysis, gene ontology enrichment, and survival analysis to elucidate the biological significance of dysregulated genes and their potential implications for disease prognosis and treatment. We highlight the central role of cell cycle regulation, mitotic spindle assembly, and intercellular signaling in endometrial cancer progression. Our findings corroborate previous studies that have implicated TPX2, BUB1, and ESPL1 in the development and progression of endometrial cancer [15,16,17,18,19,20,21,22]. Our analysis supports that these genes are associated with poor outcomes in EC, consistent with previous multi-omics studies [23]. Our study builds upon the applications of WGCNA in endometrial cancer where Chou et al. and Zhu et al. demonstrated the utility of gene co-expression network analysis in endometrial cancer [24,25]. While these studies investigated cancer-related modules, our work provides deeper insights into the molecular mechanisms by integrating STRING network analysis. This additional layer of network analysis further supports direct or indirect interactions and adds functional relevance to the co-expressed genes. Unlike studies that identified specific prognostic markers (e.g., ANO1, TICRR, and SKA3), our study highlights clinically significant gene modules that correlate with survival and clinical traits [26]. This approach emphasizes enriched biological processes that provide a new perspective on potential therapeutic targets.
Weighted gene co-expression analysis is a useful tool in identifying co-expressed and functionally related genes associated with clinical traits. Previous studies have used WGCNA to investigate gene co-expression in association with phenotypic characteristics of EC such as type, stage, and grade [22,25]. The 2014 study revealed that TP53, BUB1, AURKB, and CENPA among other hub genes were potentially prognostic genes in EC [24]. Additional co-expression analysis studies have shown that AKT1, TICRR, PPIF, ANO1, and PTGDS are possible regulators of endometrial cancer tumorigenesis [27,28,29]. Our WGCNA analysis identified 957 genes in the Green module significantly positively associated with patient vital status. These genes are most associated with regulatory networks impacting patient vitality. Of those genes, the top 40 hub genes were assessed. These genes are primarily involved in cell cycle regulation and mitosis. Specifically, cyclin A2 (CCNA2) binds to CDK2 and cyclin-dependent kinase 1 (CDK1), forming a regulatory complex for the S phase and the G2/M transition of the cell cycle [30,31]. The overexpression of CCNA2 can lead to uncontrolled cell division [32]. Cyclin B1 (CCNB1) also forms a complex with CDK1, initiating mitosis [32]. Overexpression can lead to the premature entry of mitosis, leading to genomic instability, as shown in the promotion of breast cancer [33]. Similarly, Minichomosome Maintenance 10 (MCM10) is required for the initiation of DNA replication before mitosis [34,35,36]. MCM10 may also work to load and stabilize the replication fork [36,37]. During mitosis, targeting protein for Xklp2 (TPX2) is critical for spindle assembly and the organization of microtubules into a bipolar spindle [38,39]. For example, it binds and activates AURKA and localizes it to the spindle [38,39,40]. Kinesin family genes KIF11 and KIF15 work together for spindle formation and chromosome alignment [41]. It traverses down microtubules, separating the spindle poles [41,42]. KIF2C is a microtubule depolymerase that regulates microtubules and is required for spindle function and chromosome segregation [42,43,44].
In non-cancerous cells, there is a functional spindle assembly checkpoint. Budding Uninhibited by Benzimidaloes 1 (BUB1) is a crucial component of the spindle assembly checkpoint, ensuring that chromosomes are properly attached to the spindle microtubules before the cell proceeds to anaphase [45,46]. Similarly, Threonine Tyrosine Kinase (TTK) plays a key role in the spindle checkpoint by preventing aneuploidy by assessing chromosomal attachment to the spindle [47,48,49]. Non-SMC Condensin 1 Complex subunits H and G (NCAPH and NCAPG) are part of the condensing complex essential for chromosome condensation during mitosis [50,51]. In addition, Cytoskeleton-Associated Protein 2 Like (CKAP2L) is involved in spindle assembly and chromosome stability, ensuring chromosomes are correctly partitioned to daughter cells [51]. DEP Domain Containing 1 (DEPDC1) and Maternal Embryonic Leucine Zipper Kinase (MELK) are directly associated with oncogenesis and tumor progression [52,53,54,55]. DEPDC1 is involved in cell cycle progression and promotes cancer cell survival by inhibiting apoptosis [53]. MELK regulates the G2/M transition of the cell cycle and promotes tumor proliferation and survival [54,55].
The WGCNA methods of using binarized clinical data provide a nuanced understanding of the gene expression profiles associated with patient outcomes. The positive correlation between the Green module with deceased patients suggests that upregulated genes within this module may be linked to adverse clinical outcomes. Specifically, the strong upregulation of cell cycle-related genes in the Green module could indicate that dysregulated cell proliferation contributes to disease progression and poorer prognosis. On the other hand, the Light-Yellow module, which is negatively correlated with deceased patients, highlights genes that might play a protective role. These findings show the importance of module-specific gene expression in predicting patient outcomes and identifying potential therapeutic targets. The positively correlated module (Green) may include genes that could serve as biomarkers for poor prognosis, while the negatively correlated Light-Yellow module points to genes that might be leveraged to improve survival rates. Table 3 highlights the key genes identified in this study.
The genes in the Green module likely contain key genes that drive tumor progression. The upregulation of TEOC2, CDC20, and CCNB1 in the Green module suggests that these genes may play roles in promoting unchecked cells. The genes in the Green module likely contain key genes that drive tumor progression, The upregulation of TEOC2, CDC20, and CCNB1 in the Green module suggests that these genes may play roles in promoting unchecked cell proliferation, genomic instability, and evasion of apoptosis, all of which are hallmarks of cancer [56,57]. For instance, CDC20, a known regulator of the cell cycle, may facilitate rapid tumor cell division, while CCNB1’s involvement in the G2/M transition could enhance the proliferative capacity of tumor cells. The sustained upregulation of these genes in tumor tissues, as revealed by the differential gene expression analysis, further supports their contribution to aggressive tumor behavior and poor clinical outcomes (Figure 10).
In addition to promoting tumor cell proliferation, the genes in the Green module might also influence the tumor microenvironment (TME). For example, the upregulation of immune-modulatory genes could lead to immune evasion, allowing the tumor to escape immune surveillance. This could result in a more permissive environment for tumor growth and metastasis. On the other hand, the PLPP2 identified in the Light-Yellow module may have a protective role in EC by reducing proliferation, angiogenesis, and modulating cellular immunity [58,59].
The validation analysis demonstrated a significant overlap in high-grade endometrioid tumors, and those linked to patient survival. The GEO clinical data lacked comprehensive clinical annotations compared to the TCGA dataset. Specifically, this dataset was missing patient vital status information, which is essential for conducting direct survival analysis. The literature revealed that the FIGO stage and tumor grade could be positive indicators of patient outcomes [60,61,62]. In total, 206 genes were found to be present in both the TCGA-UCEC Green module and the Red module from the GEO dataset. Statistical validation through hypergeometric and Fisher’s exact test adds robustness to our findings. The observed overlap likely reflects underlying biological processes that drive EC tumor progression and patient outcomes. We contextualized the genes using GO enrichment. This analysis showed that the common genes in both datasets were involved in critical processes for cell cycle regulation and genomic stability. This is relevant because high-grade tumors or tumor progression that contributes to patient death may have disrupted chromosomal segregation and nuclear division, which could lead to increased mutation rates and genomic instability. The enrichment of these processes aligns with their potential role in promoting aggressive tumor behavior and poorer patient outcomes.
Our analysis identified two critical Light-Yellow hub genes, TRPM4 and STX18, that are connected to the Green module. This intermodular connection suggests a functional crosstalk between cellular proliferation and processes related to immune modulation and vesicular transport. TRPM4 is known for its role in ion transport and immune response regulation [63]. It may act as a mediator between the rapidly proliferating cancer cells and the surrounding tumor microenvironment [63,64,65]. Its connection to the Green module genes implies that ion flux and immune modulation could play a key role in regulating tumor proliferation. STX18 is involved in vesicular trafficking and the facilitation of protein and signal transport [66,67,68]. This function may influence tumor progression and cell– cell communication in the TME. The connection between STX18 and the Green module genes suggests that vesicular transport may regulate the malignant phenotype by mediating communication between proliferating tumor cells and surrounding tissues. The interplay between these modules may present new therapeutic opportunities, specifically targeting immune-regulatory and vesicular trafficking pathways.
The integration of WGCNA and differential gene expression analyses provides an overview of genes that are not only differentially expressed between normal and tumor tissues but are also critically associated with patient outcomes. This dual approach allows for the identification of key molecular drivers in tumor development that directly influence survival rates. The genes identified in the Green module likely represent oncogenic drivers, while those in the Light-Yellow module may serve as tumor suppressors or protective factors.
Differential gene co-expression analysis originally revealed that TPX2 and BUB1 may be associated with the development of EC [69]. GO enrichment analysis revealed the biological processes and pathways associated with the DEGs. Survival analysis revealed the prognostic significance of specific genes in endometrial cancer. Genes such as BUB1, TPX2, and ESPL1 demonstrated significant associations with patient survival outcomes and tumor progression, suggesting their potential utility as prognostic biomarkers for risk stratification and treatment selection. Yang et al. provided evidence that the overexpression of TPX2 in functional assays promoted cancer cell progression, invasion, and migration [15]. Additionally, TPX2 was shown to activate the CX3CR1/CXCL10 chemokine and PI3K/Akt pathways and SP1 was shown to negatively regulate TPX2 expression [15]. Others have shown that KIF4A interacts with TPX2 to enhance its stability via the inhibition of ubiquitination for the genomic stability of EC cells during mitosis [70], which validates or confirms our findings for the important role of TPX2. Moreover, previous studies validate the TPX2 protein as a prognostic biomarker of EC [71]. This study illustrated the utility of bioinformatics in identifying biomarkers with significant impacts on tumor progression and prognosis. Cicirò et al. illustrated that BUB1 was significantly overexpressed in TCGA gynecological tumors (UCS, CESC, UCEC, and OV) [18]. Their Kaplan–Meier analysis revealed that the higher expression of BUB1 led to worse overall survival. Furthermore, they showed that an elevated BUB1 level may influence drug resistance [18]. Indeed, Kaplan–Meier curve analysis revealed TPX2, BUB1, and four other genes were significantly negatively related to the 5-year overall survival in EC patients [72]. ESPL1 was revealed to be associated with poor prognosis in a 2024 study where overexpression promoted proliferation in an EC cell line [20]. Their results support the oncogenic role of ESPL1 in EC.
Inhibitors for the differentially expressed genes, BUB1, CDC20, CDC6, MYBL2, KIF14, SPAG5, and ORC6, are currently in pre-clinical development [40,73,74,75]. The MELK inhibitor OTSSP167 has shown potential in pre-clinical models for several cancer types, including endometrial cancer [73,76]. Some potential investigational targets include the SKA1/3 kinetochore–microtubule interface, ASF1B inhibitors targeting chromatin regulation, SGO1 for mitotic regulation, the NCAPH condensing complex, and PKMYT1 cell cycle regulation. Additionally, TPX2, targeted by the CDK4/6 inhibitor Ademaciclib, has shown advancements in regulating cell cycle progression primarily in HR-positive breast cancer [77]. Alisertib targets AURKA by disrupting mitotic spindle formation and is being investigated for treating peripheral T-cell lymphoma [78,79]. Moreover, therapies targeting S1P1R, such as Fingolimod and Siponimod, are under investigation for modulating immune responses in cancer immunotherapy [80]. PLPP22/LPR2, involved in the PI3K/AKT pathway, may serve as a target for therapies like LY294002, which are being investigated for breast and prostate cancers [81,82]. The targets in Table 4 highlight avenues for intervention in cancer progression and patient survival.

4. Materials and Methods

4.1. Data Acquisition and Preprocessing

All analyses were conducted in R version 4.3 in the Jupyter environment. We obtained n = 553 transcriptomic Uterine Corpus Endometrial Carcinoma (UCEC) primary tumor samples and n = 35 matched normal endometrial tissue samples from the Cancer Genome Atlas (TCGA) using the TCGAbiolinks R package (Version 2.30.0) [83], as shown in Supplemental Figure S1. The GDCquery function was used to access the dataset using the following specified parameters: project = “TCGA-UCEC”, data. category = “Transcriptome Profiling”, and workflow. type = “STAR-counts”, sample. type = c (“Primary Tumor”, “Solid Tissue Normal”). After the data were retrieved, the GDCprepare function was employed to process and organize the data. Then, the SummarizedExperiment function was used to extract the initial gene expression matrix.

4.2. Preprocessing and Normalization

Subsequently, we conducted quality control on our gene expression matrix (n = 60,660 reads). We filtered out genes with less than 15 counts in at least 75% of samples (n = 441). After filtering, 14,802 genes remained. Principal components analysis (PCA) was performed to assess and visualize potential batch effects. No significant clustering was observed, indicating minimal batch effects in the dataset. We then applied variance stabilization using the VarianceStabilizingTransformation (VST) function with the blind = FALSE setting from the DESeq2 (Version 1.44.0) package [84]. VST is a necessary step because it removes technical noise and improves the compatibility of the data. This is important because it allows for an observance of differences that most likely reflect co-expression patterns and biological processes instead of sequencing artifacts. Following VST, the counts matrix was normalized using the estimateSizeFactors function to adjust for library size differences and creating a DESeqDataSet object with uniform expression levels. The normalized gene expression matrix was then transposed to align the data structure with the requirements of WGCNA analysis where the samples are the row names, and the gene ids are the columns. This transformation is required for calculating the module–trait correlations and network analysis because it allows for each gene to be its own variable in relation to the sample traits.

4.3. Gene Clustering and Network Analysis

Clinical Trait Binarization

The clinical data were accessed through the TCGABiolinks gdc_query function. The clinical data were matched with the expression data based on the submitter_id. Clinical traits, such as vital status and MSI marker status, were binarized for statistical association in the WGCNA. For example, Vital status “1” = “Dead” and “0” = “Alive”, as shown in Table 5.
Weighted gene co-expression network analysis (WGCNA) is a robust bioinformatics tool used to identify and interpret gene expression patterns in high-dimension genomic data [85]. It calculates the eigengene, Pearson coefficient, and p-values for each module and correlates the first principal component of each module to the clinical traits. WGCNA (Version 1.72.5) was installed from the Comprehensive R Archive Network (CRAN). Using the pickSoftThreshold function, we tested powers from 1 to 50 to determine a power with the ability to achieve a scale-free topology fit index above 0.8. Based on our data, we selected the power of 9 to meet this threshold. This approach aligns with established practices in WGCNA, as demonstrated in previous studies [86,87]. By using this threshold, it ensures our results are consistent with the methodological rigor in previous WGCNA studies and aids the biological interpretability of our subsequent analysis.
The network was constructed using the WGCNA blockwiseModules function with the following parameters: WGCNA dynamic tree-cutting algorithm, cuttreeDynamic, power = 9, dendro = geneTree, dist-M = dissTOM, deepsplit = 2, minModuleSize = 30, mergeCutHeight = 0.25, corType = “p”, networkType = “signed”, and verbose = “5”.

4.4. STRING Network Analysis

WGCNA hub gene interactions were analyzed using STRING Version 11.5, https://string-db.org/ (accessed on 20 September 2024). The list of hub genes was uploaded to STRING, with the species set to Homo sapiens. The interaction score was set to a minimum confidence threshold of 0.7 (high confidence). K-means clustering was applied to identify clusters of interacting genes (PPI enrichment p-value < 1.0 × 10−16).

4.5. Differential Gene Expression Analysis

Differential gene expression (DEG) analysis between endometrial cancer samples (n = 553) and normal endometrial tissue samples (n = 35) was performed using DESeq2 with 14,802 features [88]. DESeq2 (Version 1.42.1) is an R package used for analyzing count data from high-throughput sequencing assays, such as RNA-Seq. DESeq2 was employed to identify genes that were significantly upregulated or downregulated in endometrial cancer compared to normal tissue, with a predefined threshold of an adjusted p-value < 0.01 and |log2 fold change| > 1.5. We set our statistical significance threshold to 0.01 and applied the Benjamin–Hochberg correction to account for multiple comparisons. This approach ensures that the differentially expressed genes are rigorously assessed while maintaining discovery and statistical reliability. The gene modules significantly correlated with patient survival from WGCNA were used to filter the DEG analysis and visualized with the EnhancedVolcano (Version 1.20.0) R package.

4.6. Gene Ontology Analysis

The differentially expressed genes identified from the previous step were subjected to gene ontology analysis to elucidate the biological processes associated with endometrial cancer progression. This analysis was performed using the ClusterProfiler R (Version 4.10.1) package, with a significant threshold of an adjusted p-value < 0.05.

4.7. Survival Analysis

Survival analysis was conducted using Survminer (Version 0.49) and Survival (Version 3.57) to assess the association between the expression levels of DEGs and patient survival outcomes. Patients were stratified into high-expression and low-expression groups based on the median expression levels of the individual genes, and Kaplan–Meier survival curves were generated. The Log-Rank test was used to evaluate the significance of differences in survival between the two groups, with a p-value < 0.05 considered statistically significant.

4.8. ROC Analysis

The top differentially expressed genes positively and negatively correlated with EC patient vital status (Green and Light Yellow) were analyzed using a Receiver–Operator Curve (ROC) from the pROC (Version 1.18.5) R package. p-values (<0.05) were calculated using 95% confidence intervals (DeLong) [89]. This analysis enables an evaluation of the predictive ability of the differentially expressed genes as an indicator of EC vital status.

4.9. Validation Analysis

We conducted a validation study using the Gene Expression Omnibus (GEO) GSE17025 dataset using the GEOquery R package (Version 2.70.0). We acquired expression data from 12 normal endometrial samples and 91 samples of stage 1 endometrial cancers. The gene expression data were acquired and preprocessed using the same steps as the TCGA-UCEC data. The top 14,802 most variable genes based on variance across samples were used for subsequent analysis. For the WGCNA module–trait association, we created binary columns representing each histological grade level (Grade 1, Grade 2, and Grade 3). This enabled the examination of the association of these clinical traits and gene modules. The identified module contained Probe IDs that were mapped to gene symbols using hgu133plus2.db in R. The identified validation module genes were compared with the TCGA-UCEC WGCNA vital status module to identify a cohort of overlapping genes.
To statistically validate this association between the TCGA-UCEC and GEO datasets, we performed a hypergeometric test to assess the probability of overlap given the gene set sizes. Additionally, we conducted a Fisher’s exact test to confirm the association. Lastly, we conducted gene ontology enrichment analysis using the ClusterProfiler (Version 4.10.1) R package to identify significant biological processes of the shared genes [90].

5. Conclusions

In our study, we utilized a comprehensive multi-omics approach to investigate the complex molecular landscape of EC. Through the analysis of TCGA-UCEC data, we identified significant gene co-expression patterns, biological processes, and pathways that contribute to our understanding of EC progression. WGCNA revealed 957 genes within the Green module that are significantly associated with patient vital status. Our findings highlight the central role of critical cellular functions such as cell cycle regulation, mitotic spindle assembly, and intercellular signaling. The differential expression analysis further highlighted several key genes including TPX2, BUB1, and ESPL1 as key drivers of EC progression. These genes are implicated in essential oncogenic processes such as chromosomal segregation, DNA replication, and mitotic regulation, highlighting their potential role in tumorigenesis and poor patient prognosis. Gene ontology enrichment analysis revealed crucial biological processes and pathways associated with EC, such as chromosome segregation, mitotic nuclear division, and microtubule motor activity. Our survival analysis identified several differentially expressed genes with significant associations with patient survival outcomes, suggesting their utility as prognostic markers for risk stratification and treatment selection. Although not all of the genes in the WGCNA vital_status cohort (Green n = 957 and Light Yellow n = 146 modules) have prognostic value in this study, some may be possible predictive markers for targeted combination or adjuvant therapies. For example, the intermodular crosstalk between Light-Yellow genes, TRPM4 and STX18, point to interactions between tumor proliferation and immune responses/vesicular trafficking. While TRPM4 was found to be significant in our survival analysis, both of these genes may be important for modulating the TME through immune regulation and protein transport. Targeting these intermodular connections may offer a novel therapeutic strategy.

5.1. Limitations

The strengths in our study lie in its large sample size and the comprehensive representation of EC tumor phenotypes, providing a robust framework for identifying key molecular drivers of EC. However, we had several limitations. First, our analysis utilizes TCGA-UCEC data, which have missing clinical annotations. The independent validation using GEO (GSE17025) also had missing clinical annotations such as patient survival data, which are necessary for direct survival analysis. In addition, this study focuses on the UCEC subtype, and biomarkers may not be the same for rare and underrepresented subtypes. Furthermore, our analysis identified prognostic markers using bioinformatic methods. Functional validation experiments are needed to confirm the biological relevance and clinical utility in EC. Future studies should be aimed at addressing these limitations and build on our findings to advance precision medicine in endometrial cancer.

5.2. Future Directions

This study provides a computational approach to identify prognostic biomarkers. Future research directions should focus on validating these biomarkers in independent cohorts and exploring their functional roles in EC pathophysiology. For instance, in vitro cell line studies or patient-derived xenografts could be used to validate the biological relevance of these biomarkers in endometrial cancer. Since this study mainly focuses on transcriptomic data, multi-omic approaches like those mentioned by Boroń et al. could be used in future studies to gain a more in-depth picture of the biological processes contributing to tumor progression [23]. As discussed in the introduction, there are cancer health disparities among endometrial cancer patients. Like many cancer datasets, the TCGA-UCEC data had a limited racial diversity patient distribution, as shown in Supplemental Table S1. Future studies that investigate differences in gene expression between racial groups may provide insight into gene expression patterns that contribute to the health disparities. Additionally, investigating potential therapeutic targets among the identified differentially expressed genes could pave the way for novel treatment strategies, ultimately improving patient outcomes and advancing precision oncology in endometrial cancer.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms252212356/s1.

Author Contributions

Conceptualization, A.J.C. and J.W.L.J.; methodology, A.J.C.; software, A.J.C.; validation, A.J.C.; formal analysis, A.J.C.; investigation, A.J.C.; resources, A.J.C.; data curation, A.J.C.; writing—original draft preparation, A.J.C.; writing—review and editing, A.J.C., J.W.L.J., E.A.S., R.L.L., R.S., and Z.S.C.; visualization, A.J.C. and Z.S.C.; supervision, J.W.L.J.; project administration, A.J.C. and J.W.L.J.; funding acquisition, J.W.L.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by MSM/TU/UABCCC Partnership NCI award #U54CA118638, National Institute of General Medical Sciences award #R25GM058268, and the Chan Zuckerberg Initiative Accelerate Precision Health Program award #CZIF2002-007045.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The Uterine Corpus Endometrial Carcinoma (UCEC) data analyzed in this study were obtained from The Cancer Genome Atlas (TCGA) through the TCGABiolinks R Package (Version 2.30.0). The TCGA UCEC dataset is publicly available from the Genomic Data Commons (GDC) Data Portal at https://portal.gdc.cancer.gov/ (accessed on 27 August 2024), in accordance with TCGA’s data usage policies.

Acknowledgments

We extend our gratitude to all of our research team members for their invaluable contributions and dedication to this study. We would like to acknowledge the support from The Chan Zuckerberg Initiative, MSM/TU/UABCCC Partnership, and the Frederick National Laboratory for Cancer Research, Frederick, MD, USA.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Cluster dendrogram and module–trait relatedness of the expressed genes based upon the topological overlap with the corresponding module colors. The top panel represents the gene dendrogram obtained by hierarchical clustering the gene expression similarity based on consensus topological overlap, with the corresponding module colors indicated by the color row. Each leaf (end) of the dendrogram represents a gene; the branches connect genes that are more similar in expression. The height of the branches represents the dissimilarity between clusters of genes. The higher the branch point, the more dissimilar the gene clusters are. The dynamic tree cut color row represents the initial module assignments based on the dynamic tree cut method. The second color bar shows the module assignments after merging similar modules based on their eigengenes. The difference in the number of modules (colors) indicates that several smaller modules were merged, making more statistically and biologically robust results. The module–trait relatedness clustering visualizes how related each module is to one another.
Figure 1. Cluster dendrogram and module–trait relatedness of the expressed genes based upon the topological overlap with the corresponding module colors. The top panel represents the gene dendrogram obtained by hierarchical clustering the gene expression similarity based on consensus topological overlap, with the corresponding module colors indicated by the color row. Each leaf (end) of the dendrogram represents a gene; the branches connect genes that are more similar in expression. The height of the branches represents the dissimilarity between clusters of genes. The higher the branch point, the more dissimilar the gene clusters are. The dynamic tree cut color row represents the initial module assignments based on the dynamic tree cut method. The second color bar shows the module assignments after merging similar modules based on their eigengenes. The difference in the number of modules (colors) indicates that several smaller modules were merged, making more statistically and biologically robust results. The module–trait relatedness clustering visualizes how related each module is to one another.
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Figure 2. Adjacency heatmap. Each row and column of the heatmap represents a gene in the network. The genes are ordered to group highly co-expressed genes according to their hierarchical clustering. The color of each cell in the heatmap corresponds to the adjacency value between a pair of genes. Blue indicates low adjacency or weak co-expression. Red has high adjacency and strong co-expression.
Figure 2. Adjacency heatmap. Each row and column of the heatmap represents a gene in the network. The genes are ordered to group highly co-expressed genes according to their hierarchical clustering. The color of each cell in the heatmap corresponds to the adjacency value between a pair of genes. Blue indicates low adjacency or weak co-expression. Red has high adjacency and strong co-expression.
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Figure 3. WGCNA Module–trait relationship map. This heatmap shows the relationship between clinical traits and unique modules. The color intensity represents the strength of the correlation, with red indicating a positive correlation and blue a negative correlation. Statistical significance levels are indicated by * p < 0.05, ** p < 0.01, ***, and p < 0.001. In vital_status, MEgreen (M5) has an R = 0.15. This indicates a moderate positive correlation with vital status and strong association with survival outcomes. MEdarkgreen also displays a statistically significant positive association (R = 0.08). MElightyellow (R = − 0.15) and MEred (R = − 0.1) have a statistically significant negative correlation to vital status. Additionally, the matrix includes microsatellite instability (MS) high, low, and stable (H, L, S), providing a comprehensive overview of how these gene modules interact with tumor characteristics and patient outcomes.
Figure 3. WGCNA Module–trait relationship map. This heatmap shows the relationship between clinical traits and unique modules. The color intensity represents the strength of the correlation, with red indicating a positive correlation and blue a negative correlation. Statistical significance levels are indicated by * p < 0.05, ** p < 0.01, ***, and p < 0.001. In vital_status, MEgreen (M5) has an R = 0.15. This indicates a moderate positive correlation with vital status and strong association with survival outcomes. MEdarkgreen also displays a statistically significant positive association (R = 0.08). MElightyellow (R = − 0.15) and MEred (R = − 0.1) have a statistically significant negative correlation to vital status. Additionally, the matrix includes microsatellite instability (MS) high, low, and stable (H, L, S), providing a comprehensive overview of how these gene modules interact with tumor characteristics and patient outcomes.
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Figure 4. Endometrial cancer patient vital status hub genes identified by WGCNA. Visualization showing the top 40 positively or negatively correlated genes in significant modules ranked by their intramodular connectivity (kME). (a) The genes highlighted by the green color depict the top positively correlated genes in the Green module associated with vital status. These genes are involved in cell cycle regulation and mitosis, which include NCAPH, MCM10, TPX2, and DLGAP5; (b) the Light-Yellow module, depicted by the black bars, is negatively associated with patient vital status. The genes in this module include SPDEF, ELAPOR1, and C9orf152. They are involved in mucosal immunity and epithelial cell differentiation.
Figure 4. Endometrial cancer patient vital status hub genes identified by WGCNA. Visualization showing the top 40 positively or negatively correlated genes in significant modules ranked by their intramodular connectivity (kME). (a) The genes highlighted by the green color depict the top positively correlated genes in the Green module associated with vital status. These genes are involved in cell cycle regulation and mitosis, which include NCAPH, MCM10, TPX2, and DLGAP5; (b) the Light-Yellow module, depicted by the black bars, is negatively associated with patient vital status. The genes in this module include SPDEF, ELAPOR1, and C9orf152. They are involved in mucosal immunity and epithelial cell differentiation.
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Figure 5. STRING network analysis of hub genes. The Green module hub genes are represented by the green dots and are highly interconnected. This suggests that they may be involved in common pathways. Conversely, the Yellow hub genes are not as interconnected as compared to the Green module genes. Thicker edges represent stronger interactions, whereas thinner edges are weak interactions. This may indicate that these genes are involved in a variety of functions such as the immune response and tumor suppression.
Figure 5. STRING network analysis of hub genes. The Green module hub genes are represented by the green dots and are highly interconnected. This suggests that they may be involved in common pathways. Conversely, the Yellow hub genes are not as interconnected as compared to the Green module genes. Thicker edges represent stronger interactions, whereas thinner edges are weak interactions. This may indicate that these genes are involved in a variety of functions such as the immune response and tumor suppression.
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Figure 6. Volcano plot of module gene expression identified in WGCNA. This figure illustrates the differential expression in tumor vs. normal tissue for all genes in the Green and Light-Yellow modules. The green points reflect the genes in the Green module, whereas the black points represent the Light-Yellow module genes. The labeled genes represent the most significant log2 fold change and the − log adjusted p-value.
Figure 6. Volcano plot of module gene expression identified in WGCNA. This figure illustrates the differential expression in tumor vs. normal tissue for all genes in the Green and Light-Yellow modules. The green points reflect the genes in the Green module, whereas the black points represent the Light-Yellow module genes. The labeled genes represent the most significant log2 fold change and the − log adjusted p-value.
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Figure 7. Kaplan–Meier plots and ROC curves. The top 40 WGCNA hub genes underwent survival analysis using the Kaplan–Meier analysis and were evaluated using ROC-AUC. Through this analysis, we were able to identify the top 3 genes that possessed moderate predictive value (p < 0.05). The Green module indicated that TPX2, BUB1, and ESPL1 could serve as possible prognostic genes for both tumor development and poor patient outcomes. The Light-Yellow module suggests that TMEM62, PLPP2, and TRPM4 could be involved in protective mechanisms and better patient outcomes.
Figure 7. Kaplan–Meier plots and ROC curves. The top 40 WGCNA hub genes underwent survival analysis using the Kaplan–Meier analysis and were evaluated using ROC-AUC. Through this analysis, we were able to identify the top 3 genes that possessed moderate predictive value (p < 0.05). The Green module indicated that TPX2, BUB1, and ESPL1 could serve as possible prognostic genes for both tumor development and poor patient outcomes. The Light-Yellow module suggests that TMEM62, PLPP2, and TRPM4 could be involved in protective mechanisms and better patient outcomes.
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Figure 8. Overlap WGCNA module-trait relationship map of Green module in TCGA dataset and Red module in the GEO dataset. WGCNA analysis indicates Grade 3 tumors are most significantly correlated with the Green module (vital status) in the TCGA-UCEC dataset. Statistical significance levels are indicated by * p < 0.05, ** p < 0.01, ***, and p < 0.001. Using the GSE17025 data, we aimed to validate our Green module genes. We found that the Grade 3 tumors in this dataset corresponded with the Red module. After matching genes between datasets, N/A values were removed, leaving 751 Green module genes (TCGA-UCEC) and 177 (GSE17025) genes. Both WGCNA groups had 206 genes in common. This supports our findings that these genes are also implicated in tumor progression and patient survival.
Figure 8. Overlap WGCNA module-trait relationship map of Green module in TCGA dataset and Red module in the GEO dataset. WGCNA analysis indicates Grade 3 tumors are most significantly correlated with the Green module (vital status) in the TCGA-UCEC dataset. Statistical significance levels are indicated by * p < 0.05, ** p < 0.01, ***, and p < 0.001. Using the GSE17025 data, we aimed to validate our Green module genes. We found that the Grade 3 tumors in this dataset corresponded with the Red module. After matching genes between datasets, N/A values were removed, leaving 751 Green module genes (TCGA-UCEC) and 177 (GSE17025) genes. Both WGCNA groups had 206 genes in common. This supports our findings that these genes are also implicated in tumor progression and patient survival.
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Figure 9. Visualization of the statistical and functional validation of module genes. (A) The Red and Green module genes were subject to the hypergeometric test (p = 9.9 × 10−159) and Fisher’s exact test p = 2.2 × 10−16. These results emphasize the statistical significance of the overlapped genes and the biological function of these genes in endometrial cancer. (B). Depicts the enriched GO terms such as chromosome segregation, organelle fission, and nuclear division. These processes may play a critical role in EC.
Figure 9. Visualization of the statistical and functional validation of module genes. (A) The Red and Green module genes were subject to the hypergeometric test (p = 9.9 × 10−159) and Fisher’s exact test p = 2.2 × 10−16. These results emphasize the statistical significance of the overlapped genes and the biological function of these genes in endometrial cancer. (B). Depicts the enriched GO terms such as chromosome segregation, organelle fission, and nuclear division. These processes may play a critical role in EC.
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Figure 10. Overview of key regulators identified in the Green and Light-Yellow modules from endometrial cancer RNA-Seq data. The Green module is associated with cell cycle regulation, which may contribute to uncontrolled proliferation. The Light-Yellow module includes genes that are involved in cellular proliferation, pro-apoptotic pathways, and tumor microenvironment regulation. Key regulators in the Light-Yellow module are PLPP2 (lipid signaling and metabolism), TRPM4 (cation channel affecting ion transport and immune responses), and TMEM62 (transmembrane protein involved in cell signaling). The selection criteria for the identified genes included WGCNA hub status, differential expression in tumor vs. normal tissue, and significance in Kaplan–Meier analysis.
Figure 10. Overview of key regulators identified in the Green and Light-Yellow modules from endometrial cancer RNA-Seq data. The Green module is associated with cell cycle regulation, which may contribute to uncontrolled proliferation. The Light-Yellow module includes genes that are involved in cellular proliferation, pro-apoptotic pathways, and tumor microenvironment regulation. Key regulators in the Light-Yellow module are PLPP2 (lipid signaling and metabolism), TRPM4 (cation channel affecting ion transport and immune responses), and TMEM62 (transmembrane protein involved in cell signaling). The selection criteria for the identified genes included WGCNA hub status, differential expression in tumor vs. normal tissue, and significance in Kaplan–Meier analysis.
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Table 1. Modules in WGCNA analysis.
Table 1. Modules in WGCNA analysis.
OrderModule Colors# of Genes
M1Turquoise2888
M2Blue2727
M3Brown1719
M4Light Cyan1277
M5Green957
M6Midnight Blue850
M7Red753
M8Black687
M9Magenta658
M10Green Yellow518
M11Tan273
M12Dark Turquoise256
M13Salmon249
M14Dark Green200
M15Light Green148
M16Light Yellow146
M17Dark Gray99
M18Orange93
M19Sky Blue64
M20Steel Blue47
M21Violet46
M22Dark Magenta39
M23Dark Olive Oreen39
M24Sienna335
M25Yellow Green34
Table 2. ROC AUC scores for the top 3 module genes based on their intermodular connectivity.
Table 2. ROC AUC scores for the top 3 module genes based on their intermodular connectivity.
Module NameGenep ValueAUC
GreenTPX20.00396470.6493689
ESPL10.00034820.6262506
BUB10.00041550.6235858
Light YellowTRPM42.68 × 10−50.6462833
TMEM623.74 × 10−60.6381019
PLPP21.52 × 10−50.6324451
The input genes were of the top 40 hub genes and top 40 differentially expressed genes among 957 Green module genes and 146 Light-Yellow module genes.
Table 3. Overview of potential prognostic hub genes.
Table 3. Overview of potential prognostic hub genes.
ModuleGeneHub GeneSignificant AUC ScoreLog FC > 1.5
GreenTPX2✓✓✓
ESPL1✓✓✓
BUB1✓✓✓
YellowTRPM4✓✓✓
TMEM62✓✓✓
PLPP2✓✓✓
Table 4. Clinical and regulatory matrix of targeted therapies.
Table 4. Clinical and regulatory matrix of targeted therapies.
TargetTherapyTypes of Cancer TreatedMechanism of ActionFDA Approval StatusDrug Class/CategoryCombination Therapies
TPX2AdemaciclibBreast cancer (HR-positive, HER2-negative advanced, or metastatic breast cancer)Inhibits CDK4/6, preventing cell cycle progression from G1 to S phaseFDA-approvedCDK4/6 inhibitorOften combined with hormone therapy like Fulvestrant
AURKAAlisertibRelapsed or refractory peripheral T-cell lymphoma (PTCL), neuroblastoma, and other cancersInhibits Aurora kinase A, disrupting mitotic spindle formation, leading to apoptosis in cancer cellsNot FDA-approvedAurora kinase inhibitorStudied with chemotherapy agents like paclitaxel
S1P1RFingolimod, Siponimod, Ozanimod, PonesimodMultiple sclerosis (Fingolimod and Siponimod), studied in cancer immunotherapy for solid tumorsModulates S1P receptor, preventing lymphocyte egress from lymph nodes, reducing immune activityFDA-approved
Under investigation for cancer		S1P receptor modulatorsInvestigated with immune checkpoint inhibitors (e.g., anti-PD1 therapies)
PLPP2/LPR2Orthovanadate, Okadaic Acid, LY294002Experimental studies in breast, prostate, and ovarian cancers (due to involvement in PI3K/AKT pathway)PI3K/AKT pathway inhibitors, block protein phosphatase activity, leading to apoptosis in cancer cellsNot FDA-approvedPhosphatase inhibitors/PI3K inhibitorsStudied in combination with other targeted therapies like PI3K inhibitors
Table 5. Overview of clinical traits in analysis.
Table 5. Overview of clinical traits in analysis.
Clinical TraitsClassification
Vital Status“Alive”, “Dead”
MSI Marker StatusMSI-H, MSS, MSI-L, Indeterminant
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Clark, A.J.; Singh, R.; Leonis, R.L.; Stahlberg, E.A.; Clark, Z.S.; Lillard, J.W., Jr. Gene Co-Expression Network Analysis Associated with Endometrial Cancer Tumorigenesis and Survival Outcomes. Int. J. Mol. Sci. 2024, 25, 12356. https://doi.org/10.3390/ijms252212356

AMA Style

Clark AJ, Singh R, Leonis RL, Stahlberg EA, Clark ZS, Lillard JW Jr. Gene Co-Expression Network Analysis Associated with Endometrial Cancer Tumorigenesis and Survival Outcomes. International Journal of Molecular Sciences. 2024; 25(22):12356. https://doi.org/10.3390/ijms252212356

Chicago/Turabian Style

Clark, Alexis J., Rajesh Singh, Regina L. Leonis, Eric A. Stahlberg, Zachary S. Clark, and James W. Lillard, Jr. 2024. "Gene Co-Expression Network Analysis Associated with Endometrial Cancer Tumorigenesis and Survival Outcomes" International Journal of Molecular Sciences 25, no. 22: 12356. https://doi.org/10.3390/ijms252212356

APA Style

Clark, A. J., Singh, R., Leonis, R. L., Stahlberg, E. A., Clark, Z. S., & Lillard, J. W., Jr. (2024). Gene Co-Expression Network Analysis Associated with Endometrial Cancer Tumorigenesis and Survival Outcomes. International Journal of Molecular Sciences, 25(22), 12356. https://doi.org/10.3390/ijms252212356

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