Patients with occult lymph node metastasis in endometrioid-type endometrial cancer (EC) are prone to the development of recurrences and have worse outcomes compared with patients without lymph node metastasis. In the current study, the aim was to identify molecular parameters associated with lymph node metastasis in EC clinically early-stage disease. A univariate analysis of differentially expressed genes, proteins and clinicopathological parameters (including myometrial invasion and tumor grade) was performed, comparing EC patients with and without lymph node metastasis (n=262 patients from The Cancer Genome Atlas). Significant parameters were introduced in a multivariate model and a gene expression pathway analysis. Lymph node metastasis was associated with expression of 268 unique genes (P<0.001), 19 unique proteins (P<0.05), tumor grade and myometrial invasion in univariate analysis. Multivariate analysis demonstrated 10 genes independently associated with lymph node metastasis and 4 independently associated proteins. Myometrial invasion was the only independent clinicopathological parameter associated with lymph node status. The enrichment pathway analysis demonstrated that expression of epidermal growth factor receptor, Bcl2 antagonist of cell death and phosphatase and tensin homolog pathways were significantly involved in lymph node metastasis (P≤0.001). A gene expression signature to predict lymph node status in EC was created for future validation. Few studies have focused on the association between EC's molecular characteristics and nodal metastasis. Defining molecular risk factors for EC lymphatic nodal metastasis may help to individualize treatment and improve patient outcomes.
Endometrial cancer (EC) is the most common gynecological malignancy, with nearly 50,000 new cases diagnosed each year in the USA (
Selective surgical staging allows the identification of patients with lymph node metastasis and avoids the morbidity of routine lymphadenectomy. Although a number of preoperative and intraoperative risk factors for nodal metastasis have been identified, they have a low positive predictive value in clinical practice. Preoperative radiological testing appears to identify patients with a low rather than high risk of nodal metastasis. Furthermore, ultrasound (
The Cancer Genome Atlas (TCGA) database of the National Cancer Institute (Bethesda, MD, USA) was used for the current study (
Analysis of gene expression was performed using Biometric Research Branch (BRB) ArrayTools (Version 2.13.2 for ×64 systems), an integrated package for visualization and statistical analysis that utilizes Excel (Microsoft, Redmond, WA) as front-end, and tools developed in the R statistical system. BRB-ArrayTools were developed by Dr Richard Simon and the BRB-ArrayTools development team (
Two strategies were used to design a predictor for lymph node metastasis in EC patients: A gene expression signature to predict lymph node status and significantly independent molecular, clinical and pathological variables predicting lymph node status.
To construct a gene signature profile that would classify patients into lymph node positive and lymph node negative groups, the Class Prediction Tool of BRB-ArrayTools was used. This tool optimizes the significance level threshold used for gene selection. Genes that were differentially expressed between the classes at a univariate significance level of <0.001 were included in the predictor. A number of methods are included in the tool to evaluate the predictor or gene signature. To assess how accurately the groups are predicted by this multivariate class predictor (internal validation), a cross-validated misclassification rate is computed, usually in the form of the leave-one-out cross-validation method (
A univariate two-tailed
Subsequently, independent molecular markers were uploaded to GeneGo MetaCore™ for pathway analysis (
Using publicly available data for EC collected in TCGA database, 262 patients with lymph node status and clinical information were identified. Of these, 203 patients had available information on genomic expression data and lymph node status and 165 patients had available information on protein data and lymph node status. The 203 patients with available genomic expression data were divided according to their lymph node status, with 165 patients having negative lymph nodes and 38 having positive lymph nodes. These groups were similar with regard to age, menopausal status, peritoneal washing status and body mass index (
The class prediction tool identified a gene expression signature composed of 295 genes (
Independent variables predicting lymph node status. The univariate analysis test using lymph node status and EC gene expression and protein expression data identified 268 unique genes (P≤0.001;
Based on these findings, the unique genes, proteins and clinicopathological parameters determined to be significantly associated with lymph node metastasis were subjected to a multivariable analysis. This identified 10 genes [arylsulfatase I (
The precise molecular events that occur during the development, invasion and formation of metastasis in EC are largely uncharacterized and remain poorly understood (
The objective of TCGA is the collection and processing of biospecimens that may be used for cancer diagnosis and analysis. The biospecimens collected from these cancers meet a stringent set of quality criteria, enabling extracted DNA and RNA to be used for advanced genomic analysis and sequencing technologies. The present analysis included a highly stringent level of statistical testing for associations between clinicopathological parameters, gene and protein expression levels and lymph node metastasis in EC. This stringent statistical methodological approach accounts for a potential bias in genomic datasets and ensures that generated P-values may be interpreted as significant at a relative, as well as an absolute, level.
Ten genes were determined to be independently associated with lymph node metastasis in EC:
The multivariate analysis identified 4 proteins independently associated with lymph node metastasis in EC: EF2K, EGFR, PDK1, and YB. EGFR is the prototypic member of the ErbB/HER receptor tyrosine kinase family and binds to multiple ligands, including epidermal growth factor, transforming growth factor α and amphiregulin. EGFR is crucial in cellular functions implicated in cancer development (
PDK1 is a key regulator of the AGC protein kinase family, which includes the proto-oncogene AKT/protein kinase B implicated in a number of malignancies, including breast cancer. YB-1 appears to play a critical role in cell proliferation and growth, DNA replication, cell cycle and drug resistance, as well as malignancy. Furthermore, YB-1 is overexpressed in cisplatin-resistant cancer cell lines (
The current data demonstrated an association between lymph node metastasis and a number of gene expression pathways in EC:
The clinical heterogeneity of EC is likely a reflection of an underlying molecular heterogeneity. As such, the mechanisms by which EC cells metastasize are likely to be equally diverse. In the present study, a genome-wide strategy was adopted to characterize some of the molecular signaling pathways and cellular processes that are associated with lymph node metastasis in EC. Such findings may have substantial implications for future clinical treatment of patients with this disease. Empirical treatment based on one-size-fits-all could be replaced with a more tailored therapy that matches the right patient with the right treatment plan based on their own molecular fingerprint. The next phase of this study will be to evaluate and validate the ability of a signature containing independent molecular and clinicopathological parameters to predict lymph node metastasis in patients with EC.
The authors would like to thank the Jacquie Liggett Hearing the Ovarian Cancer Whisper for their fellowship grant support and Rasa Hamilton (Moffitt Cancer Center) for editorial assistance.
Gene expression signature as predictor of lymph node (LN) status. (A) Heatmap of the signature predictive of lymph node status. (B) Performance of the model in the training dataset measured by the receiver operating characteristic (ROC) curve. (C) Contingency table of level of agreement between TCGA data and the predictive model: kappa coefficient of 0.2. Patients classified correctly (Neg-Neg and Pos-Pos) are on blue background. Neg, patients with negative lymph node status; Pos, patients with positive lymph node status.
Volcano plot of significant genes from univariate analysis. LN, lymph node. Black points represent significant genes; white points represent nonsignificant genes.
Characteristics of patients with available genetic expression data (n=203).
| Characteristics | Lymph node positive (n=38) | Lymph node negative (n=165) | P-value |
|---|---|---|---|
| Age, years (mean) | 62.6 | 62.7 | 0.98 |
| Menopausal status, % | 0.73 | ||
| Premenopausal | 8.8 | 7.3 | |
| Postmenopausal | 91.2 | 92.7 | |
| Surgical approach, % | 0.08 | ||
| Laparotomy | 81.6 | 67.3 | |
| Minimally invasive | 18.4 | 32.7 | |
| Body mass index (mean) | 33.6 | 32.1 | 0.29 |
| Depth of invasion, % (mean) | 65.0 | 37.6 | <0.01 |
| Grade, % | <0.01 | ||
| 1 | 5.3 | 27.9 | |
| 2 | 7.9 | 33.9 | |
| 3 | 86.8 | 38.2 | |
| Peritoneal washings, % | 1.00 | ||
| Positive | 12.5 | 12.8 | |
| Negative | 87.5 | 87.2 |
Characteristics of patients with available protein expression (n=165).
| Characteristics | Lymph node positive (n=26) | Lymph node negative (n=139) | P-value |
|---|---|---|---|
| Age, years (mean) | 62.3 | 62.8 | 0.89 |
| Menopausal status, % | 0.67 | ||
| Premenopausal | 8.0 | 6.5 | |
| Postmenopausal | 92.0 | 93.5 | |
| Surgical approach, % | 0.02 | ||
| Laparotomy | 92.4 | 67.4 | |
| Minimally invasive | 7.6 | 32.6 | |
| Body mass index (mean) | 31.9 | 33.1 | 0.45 |
| Depth of invasion, % (mean) | 68.6 | 37.2 | <0.01 |
| Grade, % | <0.01 | ||
| 1 | 7.7 | 32.4 | |
| 2 | 11.5 | 36.7 | |
| 3 | 80.8 | 30.9 | |
| Peritoneal washings, % | 0.73 | ||
| Positive | 15.0 | 13.3 | |
| Negative | 85.0 | 86.7 | |
| Stage, n | <0.01 | ||
| 1 | 0 | 113 | |
| 2 | 0 | 11 | |
| 3 | 22 | 11 | |
| 4 | 4 | 4 | |
| Overall survival time (days) | 764 | 928 | 0.34 |
| Histology, n | <0.01 | ||
| Serous | 9 | 13 | |
| Non-serous | 17 | 126 |
Univariate analysis of protein and clinicopathological data.
| Characteristics | P-value |
|---|---|
| Invasion | <0.01 |
| Grade | <0.01 |
| BCL-2-M-V | 0.03 |
| Dvl3-R-V | <0.01 |
| eEF2K-R-V | 0.04 |
| EGFR-R-C | 0.03 |
| EGFR_pY1068-R-V | 0.01 |
| EGFR_pY1173-R-C | <0.01 |
| FOXO3a_pS318_S321-R-C | <0.01 |
| HER3_pY1298-R-C | 0.02 |
| Notch1-R-V | 0.01 |
| p21-R-C | 0.02 |
| p70S6K_pT389-R-V | 0.02 |
| PDK1_pS241-R-V | 0.01 |
| PR-R-V | 0.04 |
| Shc_pY317-R-NA | <0.01 |
| Smad4-M-V | 0.02 |
| Src_pY527-R-V | 0.03 |
| YB-1-R-V | 0.02 |
Statistically significant results from multivariate analysis of genes, proteins, and clinical variables.
| Characteristics | Standard error | t-value | P-value |
|---|---|---|---|
| Clinicopathological variables | |||
| Invasion | 6.12×10−4 | 5.115 | <0.01 |
| Differentially expressed genes | |||
| |
2.39×10−4 | −2.802 | 0.01 |
| |
2.41×10−4 | −4.644 | <0.01 |
| |
2.37×10−4 | 3.321 | <0.01 |
| |
2.47×10−4 | −2.934 | <0.01 |
| |
2.62×10−4 | −3.171 | <0.01 |
| |
2.84×10−4 | −3.036 | <0.01 |
| |
2.52×10−4 | −2.970 | <0.01 |
| |
2.15×10−4 | −2.846 | 0.01 |
| |
3.30×10−4 | 3.477 | <0.01 |
| |
2.54×10−4 | −2.791 | 0.01 |
| Independently associated proteins | |||
| EF2K | 3.42×10−4 | 3.705 | <0.01 |
| EGFR | 3.38×10−4 | 3.330 | <0.01 |
| PDK1 | 2.93×10−4 | 2.557 | 0.01 |
| YB | 3.51×10−4 | −2.399 | 0.02 |