Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing.

Circulating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. We developed four tree-based models and we trained and tested them on a dataset consisting of Smart-Seq2 sequenced data from primary tumor sections of breast cancer patients and PBMCs and on a public dataset with manually annotated CTC expression profiles from 34 metastatic breast patients, including triple-negative breast cancer. Our best models achieved about 95% balanced accuracy on the CTC test set on per cell basis, correctly detecting 133 out of 138 CTCs and CTC-PBMC clusters. Considering the non-invasive character of the liquid biopsy examination and our accurate results, we can conclude that our work has potential application value.

Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures.

Background: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays. Methods: We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15). Results: Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time. Conclusions: Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.

Screening of predicted synergistic multi-target therapies in glioblastoma identifies new treatment strategies.

Background: IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients. Methods: We performed the largest drug combination screen to date in GBM, using a high-throughput effort where we selected 90 drug combinations for their activity onto 25 patient-derived GBM cultures. 43 drug combinations were selected for interaction analysis based on their monotherapy efficacy and were tested in a short-term (3 days) as well as long-term (18 days) assay. Synergy was assessed using dose-equivalence and multiplicative survival metrics. Results: We observed a consistent synergistic interaction for 15 out of 43 drug combinations on patient-derived GBM cultures. From these combinations, 11 out of 15 drug combinations showed a longitudinal synergistic effect on GBM cultures. The highest synergies were observed in the drug combinations Lapatinib with Thapsigargin and Lapatinib with Obatoclax Mesylate, both targeting epidermal growth factor receptor and affecting the apoptosis pathway. To further elaborate on the apoptosis cascade, we investigated other, more clinically relevant, apoptosis inducers and observed a strong synergistic effect while combining Venetoclax (BCL targeting) and AZD5991 (MCL1 targeting). Conclusions: Overall, we have identified via a high-throughput drug screening several new treatment strategies for GBM. Moreover, an exceptionally strong synergistic interaction was discovered between kinase targeting and apoptosis induction which is suitable for further clinical evaluation as multi-targeted combination therapy.

Platelet-Based Liquid Biopsies through the Lens of Machine Learning.

Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics.

Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study.

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.

Pathway-level mutation analysis in primary high-grade serous ovarian cancer and matched brain metastases.

Brain metastases (BMs) in ovarian cancer (OC) are a rare event. BMs occur most frequently in high-grade serous (HGS) OC. The molecular features of BMs in HGSOC are poorly understood. We performed a whole-exome sequencing analysis of ten matched pairs of formalin-fixed paraffin-embedded samples from primary HGSOC and corresponding BMs. Enrichment significance (p value; false discovery rate) was computed using the Reactome, the Kyoto Encyclopedia of Genes and Genomes pathway collections, and the Gene Ontology Biological Processes. Germline DNA damage repair variants were found in seven cases (70%) and involved the BRCA1, BRCA2, ATM, RAD50, ERCC4, RPA1, MLHI, and ATR genes. Somatic mutations of TP53 were found in nine cases (90%) and were the only stable mutations between the primary tumor and BMs. Disturbed pathways in BMs versus primary HGSOC constituted a complex network and included the cell cycle, the degradation of the extracellular matrix, cell junction organization, nucleotide metabolism, lipid metabolism, the immune system, G-protein-coupled receptors, intracellular vesicular transport, and reaction to chemical stimuli (Golgi vesicle transport and olfactory signaling). Pathway analysis approaches allow for a more intuitive interpretation of the data as compared to considering single-gene aberrations and provide an opportunity to identify clinically informative alterations in HGSOC BM.

Diagnostic Accuracy of Liquid Biopsy in Endometrial Cancer.

BACKGROUND: Liquid biopsy is a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, the potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. METHODS: TEPs from 295 subjects (53 EC patients, 38 patients with benign gynecologic conditions, and 204 healthy women) were RNA-sequenced. DNA sequencing data were obtained for 519 primary tumor tissues and 16 plasma samples. Artificial intelligence was applied to sample classification. RESULTS: Platelet-dedicated classifier yielded AUC of 97.5% in the test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was more challenging, with AUC of 84.1%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 96% and ctDNA blood samples with AUC of 69.8%. CONCLUSIONS: Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work involving more cases is warranted.

Transcriptomic landscape of blood platelets in healthy donors.

Blood platelet RNA-sequencing is increasingly used among the scientific community. Aberrant platelet transcriptome is common in cancer or cardiovascular disease, but reference data on platelet RNA content in healthy individuals are scarce and merit complex investigation. We sought to explore the dynamics of platelet transcriptome. Datasets from 204 healthy donors were used for the analysis of splice variants, particularly with regard to age, sex, blood storage time, unit of collection or library size. Genes B2M, PPBP, TMSB4X, ACTB, FTL, CLU, PF4, F13A1, GNAS, SPARC, PTMA, TAGLN2, OAZ1 and OST4 demonstrated the highest expression in the analysed cohort, remaining substantial transcription consistency. CSF3R gene was found upregulated in males (fold change 2.10, FDR q < 0.05). Cohort dichotomisation according to the median age, showed upregulated KSR1 in the older donors (fold change 2.11, FDR q < 0.05). Unsupervised hierarchical clustering revealed two clusters which were irrespective of age, sex, storage time, collecting unit or library size. However, when donors are analysed globally (as vectors), sex, storage time, library size, the unit of blood collection as well as age impose a certain degree of between- and/or within-group variability. Healthy donor platelet transcriptome retains general consistency, with very few splice variants deviating from the landscape. Although multidimensional analysis reveals statistically significant variability between and within the analysed groups, biologically, these changes are minor and irrelevant while considering disease classification. Our work provides a reference for studies working both on healthy platelets and pathological conditions affecting platelet transcriptome.

Bortezomib induces methylation changes in neuroblastoma cells that appear to play a significant role in resistance development to this compound.

The anticancer activity of bortezomib (BTZ) has been increasingly studied in a number of indications and promising results for the use of this treatment have been shown in neuroblastoma. As BTZ treatment is usually administered in cycles, the development of resistance and side effects in patients undergoing therapy with BTZ remains a major challenge for the clinical usage of this compound. Common resistance development also means that certain cells are able to survive BTZ treatment and bypass molecular mechanisms that render BTZ anticancer activity. We studied the methylome of neuroblastoma cells that survived BTZ treatment. Our results indicate that BTZ induces pronounced genome wide methylation changes in cells which recovered from the treatment. Functional analyses of identified methylation changes demonstrated they were involved in key cancer pathology pathways. These changes may allow the cells to bypass the primary anticancer activity of BTZ and develop a treatment resistant and proliferative phenotype. To study whether cells surviving BTZ treatment acquire a proliferative phenotype, we repeatedly treated cells which recovered from the first round of BTZ treatment. The repetitive treatment led to induction of the extraordinary proliferative potential of the cells, that increased with subsequent treatments. As we did not observe similar effects in cells that survived treatment with lenalidomide, and non-treated cells cultured under the same experimental conditions, this phenomenon seems to be BTZ specific. Overall, our results indicate that methylation changes may play major role in the development of BTZ resistance.

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics.

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available.

Combining measures of immune infiltration shows additive effect on survival prediction in high-grade serous ovarian carcinoma.

BACKGROUND: In colorectal and breast cancer, the density and localisation of immune infiltrates provides strong prognostic information. We asked whether similar automated quantitation and combined analysis of immune infiltrates could refine prognostic information in high-grade serous ovarian carcinoma (HGSOC) and tested associations between patterns of immune response and genomic driver alterations. METHODS: Epithelium and stroma were semi-automatically segmented and the infiltration of CD45RO+, CD8+ and CD68+ cells was automatically quantified from images of 332 HGSOC patient tissue microarray cores. RESULTS: Epithelial CD8 [p = 0.027, hazard ratio (HR) = 0.83], stromal CD68 (p = 3 × 10-4, HR = 0.44) and stromal CD45RO (p = 7 × 10-4, HR = 0.76) were positively associated with survival and remained so when averaged across the tumour and stromal compartments. Using principal component analysis, we identified optimised multiparameter survival models combining information from all immune markers (p = 0.016, HR = 0.88). There was no significant association between PTEN expression, type of TP53 mutation or presence of BRCA1/BRCA2 mutations and immune infiltrate densities or principal components. CONCLUSIONS: Combining measures of immune infiltration provided improved survival modelling and evidence for the multiple effects of different immune factors on survival. The presence of stromal CD68+ and CD45RO+ populations was associated with survival, underscoring the benefits evaluating stromal immune populations may bring for prognostic immunoscores in HGSOC.

The Prognostic Significance of Eukaryotic Translation Initiation Factors (eIFs) in Endometrial Cancer.

Whilst the role of eukaryotic translation initiation factors (eIFs) has already been investigated in several human cancers, their role in endometrial cancer (EC) is relatively unknown. In the present retrospective study, 279 patients with EC (1180 samples) were included (mean age: 63.0 years, mean follow-up: 6.1 years). Samples were analysed for expression of 7 eIFs subunits (eIF2α, eIF3c, eIF3h, eIF4e, eIF4g, eIF5, eIF6) through immunohistochemistry and western blotting. Fifteen samples of healthy endometrium served as controls. Density and intensity were assessed and mean combined scores (CS) calculated for each patient. Upon immunohistochemistry, median eIF5 CS were significantly higher in EC as compared with non-neoplastic tissue (NNT, p < 0.001), whilst median eIF6 CS were significantly lower in EC (p < 0.001). Moreover, eIF5 (p = 0.002), eIF6 (p = 0.032) and eIF4g CS (p = 0.014) were significantly different when comparing NNT with EC grading types. Median eIF4g CS was higher in type II EC (p = 0.034). Upon western blot analysis, eIF4g (p < 0.001), peIF2α (p < 0.001) and eIF3h (p < 0.05) were significantly overexpressed in EC, while expression of eIF3c was significantly reduced in EC as compared with NNT (p < 0.001). The remaining eIFs were non-significant. Besides tumour stage (p < 0.001) and patient's age (p < 0.001), high eIF4g CS-levels were independently associated with poor prognosis (HR: 1.604, 95%CI: 1.037-2.483, p = 0.034). The other eIFs had no prognostic significance. Notably, the independent prognostic significance of eIF4g was lost when adding tumour type. Considering the difficulties in differentiating EC type I and II, eIF4g may serve as a novel prognostic marker indicating patient outcome.

Selection of reference genes for measuring the expression of aiiO in Ochrobactrum quorumnocens A44 using RT-qPCR.

Reverse transcription quantitative PCR (RT-qPCR), a method of choice for quantification of gene expression changes, requires stably expressed reference genes for normalization of data. So far, no reference genes were established for the Alphaproteobacteria of the genus Ochrobactrum. Here, we determined reference genes for gene expression studies in O. quorumnocens A44. Strain A44 was cultured under 10 different conditions and the stability of expression of 11 candidate genes was evaluated using geNorm, NormFinder and BestKeeper. Most stably expressed genes were found to be rho, gyrB and rpoD. Our results can facilitate the choice of reference genes in the related Ochrobactrum strains. O. quorumnocens A44 is able to inactivate a broad spectrum of N-acyl homoserine lactones (AHLs) - the quorum sensing molecules of many Gram-negative bacteria. This activity is attributed to AiiO hydrolase, yet it remains unclear whether AHLs are the primary substrate of this enzyme. Using the established RT-qPCR setup, we found that the expression of the aiiO gene upon exposure to two AHLs, C6-HLS and 3OC12-HSL, does not change above the 1-fold significance threshold. The implications of this finding are discussed in the light of the role of quorum sensing-interfering enzymes in the host strains.

Comparison of three variant callers for human whole genome sequencing.

Testing of patients with genetics-related disorders is in progress of shifting from single gene assays to gene panel sequencing, whole-exome sequencing (WES) and whole-genome sequencing (WGS). Since WGS is unquestionably becoming a new foundation for molecular analyses, we decided to compare three currently used tools for variant calling of human whole genome sequencing data. We tested DeepVariant, a new TensorFlow machine learning-based variant caller, and compared this tool to GATK 4.0 and SpeedSeq, using 30×, 15× and 10× WGS data of the well-known NA12878 DNA reference sample. According to our comparison, the performance on SNV calling was almost similar in 30× data, with all three variant callers reaching F-Scores (i.e. harmonic mean of recall and precision) equal to 0.98. In contrast, DeepVariant was more precise in indel calling than GATK and SpeedSeq, as demonstrated by F-Scores of 0.94, 0.90 and 0.84, respectively. We conclude that the DeepVariant tool has great potential and usefulness for analysis of WGS data in medical genetics.

Enhanced detection of circulating tumor DNA by fragment size analysis.

Existing methods to improve detection of circulating tumor DNA (ctDNA) have focused on genomic alterations but have rarely considered the biological properties of plasma cell-free DNA (cfDNA). We hypothesized that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for noninvasive genomic analysis of cancer. We surveyed ctDNA fragment sizes in 344 plasma samples from 200 patients with cancer using low-pass whole-genome sequencing (0.4×). To establish the size distribution of mutant ctDNA, tumor-guided personalized deep sequencing was performed in 19 patients. We detected enrichment of ctDNA in fragment sizes between 90 and 150 bp and developed methods for in vitro and in silico size selection of these fragments. Selecting fragments between 90 and 150 bp improved detection of tumor DNA, with more than twofold median enrichment in >95% of cases and more than fourfold enrichment in >10% of cases. Analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of plasma samples from patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA, with area under the curve (AUC) >0.99 compared to AUC <0.80 without fragmentation features. Increased identification of cfDNA from patients with glioma, renal, and pancreatic cancer was achieved with AUC > 0.91 compared to AUC < 0.5 without fragmentation features. Fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection and could complement or provide an alternative to deeper sequencing of cfDNA.

Copy number signatures and mutational processes in ovarian carcinoma.

The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.

MiR-192 and miR-662 enhance chemoresistance and invasiveness of squamous cell lung carcinoma.

OBJECTIVES: Overexpression of miR-192, miR-192* and miR-662 was previously found to correlate with poor prognosis of early-stage squamous cell lung cancer (SCC) patients. In this study, we investigated the relevance of these miRNAs to cancer cell biology and chemoresistance. MATERIALS AND METHODS: MiRNA expression profile was analysed in 10 non-small cell lung cancer (NSCLC) cell lines using RT-qPCR. H520 and H1703 cells were transfected with miRNA inhibitors (anti-miR-192, -192* and -662) for functional studies. Chemoresistance to cisplatin and etoposide was evaluated using MTT colorimetric assay. H520 cells were subjected to 3D soft-agar colony formation assay and H1703 cells to wound healing assay. Whole transcriptome analysis was used to assess the effect of miR-192 and miR-662 inhibition on gene expression. RESULTS: SCC cell lines, H520 and H1703, differed in miRNA expression and phenotypic features. MiR-192 and miR-662 inhibition decreased clonogenicity and motility of SCC cells. MiR-192 and miR-662 inhibition sensitized SCC cells to etoposide but not to cisplatin. Whole transcriptome analysis revealed genes regulated by miR-192 and miR-662 in SCC, relevant to maintaining chemoresistance, invasiveness, epithelial-mesenchymal transition (EMT) and immune evasion. CONCLUSIONS: We showed for the first time that miR-192 and miR-662 have functional role in SCC cells. Our findings suggest that targeting these miRNAs may impact both chemoresistance and invasiveness of SCC, and add to the evidence linking these aspects of tumour biology. Overexpression of miR-192 and miR-662 might be useful as a marker of resistance to etoposide.

Interactions between FGFR2 and RSK2-implications for breast cancer prognosis.

We have previously demonstrated that fibroblast growth factor receptor 2 (FGFR2) activates ribosomal s6 kinase 2 (RSK2) in mammary epithelial cells and that this pathway promotes in vitro cell growth and migration. Potential clinical significance of FGFR2 and RSK2 association has never been investigated. Herein, we have undertaken an evaluation of a possible relationship between FGFR2/RSK2 interdependence and disease outcome in breast cancer (BCa) patients. The clinical analysis was complemented by an in vitro investigation of an involvement of RSK2 in the regulation of FGFR2 function. Primary tumour samples from 152 stage I-III BCa patients were examined for FGFR2 and RSK2 gene and protein expression. FGFR2 showed a positive correlation with RSK2 at both protein (p = 0.003) and messenger RNA (mRNA) (p = 0.001) levels. Lack of both FGFR2 and activated RSK (RSK-P) significantly correlated with better disease-free survival (DFS) (p = 0.01). Patients with tumours displaying immunoreactivity for either or both FGFR2 and RSK-P had 4.89-fold higher risk of recurrence when compared to the FGFR2/RSK-P-negative subgroup. FGFR2-RSK2 interactions were verified by co-immunoprecipitation and internalization assays in HB2 mammary epithelial cell line (characterized by high endogenous FGFR2 and RSK2 expression). In vitro analyses revealed that FGFR2 and RSK2 formed an indirect complex and that activated RSK exerted a significant impact on fibroblast growth factor 2 (FGF2)-triggered internalization of FGFR2. Our results suggest that the FGFR2-RSK2 signalling pathway is involved in pathophysiology of BCa and evaluation of FGFR2/RSK-P expression may be useful in disease prognostication.

HOTAIR in relation to epithelial-mesenchymal transition and cancer stem cells in molecular subtypes of endometrial cancer.

BACKGROUND: Endometrial cancer (EC) is a hormone-related disease, showing highly diverse features of ER/PR/HER2 status-based molecular subtypes. Long noncoding RNA (lncRNA) HOX antisense intergenic RNA (HOTAIR) has recently emerged as a key molecule in many cancers, triggering epithelial-mesenchymal transition (EMT)-mediated cancer stem cell (CSC) formation, but little is known about its significance in EC. Thus, we aimed to investigate the clinical significance of HOTAIR itself in different molecular subtypes of EC and possible links between HOTAIR, EMT and CSC-related markers. METHODS: The study group included 156 consecutive, stage I-IV EC patients treated between 2000 and 2010. ER, PR and HER2 protein expression were examined by immunohistochemistry (IHC) on tissue microarrays. RT-qPCR was used to analyze the expression levels of HOTAIR, EMT-related genes - SNAIL and SLUG - and the CSCs marker CD133. RESULTS: Molecular subtypes, defined as ER/PR+HER2+, ER/PR+HER2-, ER-PR-HER2+ and ER-PR-HER2-, occurred in 40.2%, 52.3%, 4.7% and 1.9% of cases, respectively. The expression of HOTAIR did not differ between the subtypes, but high HOTAIR expression correlated with shorter overall survival (p = 0.04) in the entire group. The expression levels of HOTAIR, SNAIL, SLUG and CD133 were similar in defined EC molecular subtypes. CONCLUSIONS: Our data do not confirm the role of HOTAIR in EMT-mediated CSC formation in EC. Neither does the diversity of EC molecular subtypes influence these processes. But HOTAIR expression could serve as an independent prognostic factor in EC. The clinical importance of the above discoveries requires further studies.