Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools

Abstract

Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (TPX2) was the most promising independent prognostic biomarker in stage I EC. TPX2 expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, TPX2 mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1-2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of TPX2.

The Perspectives of Fertility Preservation in Women with Endometrial Cancer

“…These genes were associated with worse overall survival in endometrial cancer. Validated on tissue samples, TPX2 was shown to be relevant for risk assessment especially in grade 1 and 2 endometrial cancer [ 73 ]. Further bioinformatic research with archival sample validation also proposed, that UBE2C could serve as a marker of worse prognosis [ 74 ].…”

“…Figure 3 depicts the candidate genes adding to the pool of risk stratification markers. The depiction of different markers in Figure 2 and Figure 3 shows, that there is still much unknown about the interactions of different molecular signaling pathways [ 68 , 73 , 74 , 75 ]. The major pathways involved in endometrial cancer seem to be PI3K/Akt/mTOR, MAPK, WNT, and FBXW7 signaling.…”

“… Functional grouping of candidate genes for potential risk stratification in early endometrial cancer [ 68 , 73 , 74 , 75 ]. …”

Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

“…In the past decades, a lot of data mining analysis of mRNA, microRNA, long non-coding RNA, and DNA methylation have been performed on human cancers, including colon cancer [16][17][18][19]. As the biomarkers identified by the above techniques are of diagnostic and prognostic values in cancers and the revolution of sequencing technologies and bioinformatics tools facilitates the identification of more potential biomarkers related to disease progression [20][21][22][23], the more potential biomarkers identified, the more recognition and options for the diagnosis and treatment of colon cancer.…”

Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression Data

“…The ongoing clinical trials rely on this new TCGA classification [13,14], but the gene expression data and results published by TCGA also carry in themselves the opportunity for the development of a procedure that could identify high-risk patients and enable better therapeutic planning. By using gene expression measurements, as primary data, certain research groups were able to estimate the prognosis of the disease [15] or they found promising biomarker candidates that were associated with recurrent early-stage endometrial carcinoma [16]. Others who used only clinical features failed to produce a highly accurate model [17].…”