Endometriosis is a clinical condition characterized by the presence of endometrial glands outside the uterine cavity. While its incidence remains mostly uncertain, endometriosis impacts around 180 million women worldwide. Despite the presentation of several epidemiological and clinical explanations, the precise mechanism underlying the disease remains ambiguous. In recent years, researchers have examined the hereditary dimension of the disease. Genetic research has aimed to discover the gene or genes responsible for the disease through association or linkage studies involving candidate genes or DNA mapping techniques.

To identify genetic biomarkers linked to endometriosis by the application of machine learning (ML) approaches.

This case-control study accounted for the open-access transcriptomic data set of endometriosis and the control group. We included data from 22 controls and 16 endometriosis patients for this purpose. We used AdaBoost, XGBoost, Stochasting Gradient Boosting, Bagged Classification and Regression Trees (CART) for classification using five-fold cross validation. We evaluated the performance of the models using the performance measures of accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1 score.

Bagged CART gave the best classification metrics. The metrics obtained from this model are 85.7%, 85.7%, 100%, 75%, 75%, 100% and 85.7% for accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1 score, respectively. Based on the variable importance of modeling, we can use the genes CUX2, CLMP, CEP131, EHD4, CDH24, ILRUN, LINC01709, HOTAIR, SLC30A2 and NKG7 and other transcripts with inaccessible gene names as potential biomarkers for endometriosis.

This study determined possible genomic biomarkers for endometriosis using transcriptomic data from patients with/without endometriosis. The applied ML model successfully classified endometriosis and created a highly accurate diagnostic prediction model. Future genomic studies could explain the underlying pathology of endometriosis, and a non-invasive diagnostic method could replace the invasive ones.

• Endometriosis
• RNA-seq
• Transcriptomics
• Machine learning
• Classification

• clinicopathological characteristics
• endometrial carcinoma
• long non‐coding RNA
• meta‐analysis
• prognosis

• Humans
• RNA, Long Noncoding/genetics
• Endometrial Neoplasms/genetics
• Endometrial Neoplasms/pathology
• Endometrial Neoplasms/mortality
• Endometrial Neoplasms/metabolism
• Female
• Prognosis
• Gene Expression Regulation, Neoplastic
• Biomarkers, Tumor/genetics
• Up-Regulation
• Lymphatic Metastasis
• Neoplasm Staging

• The Heilongjiang Provincial Department of Human Resources and Social Security (LBH-Q20110) and the First Affiliated Hospital of Harbin Medical University (2020M20, 2020M08)

• Endometrial carcinoma
• HOTAIR
• SOX17
• Wnt-β-catenin pathway
• ZBTB7A

• Humans
• RNA, Long Noncoding/genetics
• Female
• Endometrial Neoplasms/genetics
• Endometrial Neoplasms/pathology
• Endometrial Neoplasms/metabolism
• SOXF Transcription Factors/genetics
• SOXF Transcription Factors/metabolism
• ELAV-Like Protein 1/metabolism
• ELAV-Like Protein 1/genetics
• Cell Proliferation
• Neovascularization, Pathologic/genetics
• Neovascularization, Pathologic/pathology
• Transcription Factors/genetics
• Transcription Factors/metabolism
• DNA-Binding Proteins/genetics
• DNA-Binding Proteins/metabolism
• Prognosis
• Cell Line, Tumor
• Gene Expression Regulation, Neoplastic
• Cell Movement/genetics
• Animals
• Mice
• Middle Aged
• Wnt Signaling Pathway/genetics
• Angiogenesis

• carcinogenesis
• endometrial cancer
• lncRNA
• long non-coding RNA

• DNA damage repair
• apoptosis
• cervical cancer
• long noncoding RNA (lncRNA)
• radiosensitive

• Uterine corpus endometrial carcinoma
• bioinformatics
• immune subtypes
• immune-related lncRNAs
• lncRNAs
• paired-lncRNAs

Endometrial carcinoma (EC) is one of the major gynecologic malignancy cancers affecting females with dismal prognosis and high mortality around the world. Numerous studies have proven that an aberrant level of long noncoding RNAs is present in many endometrial cancer patients, while the underlying molecular mechanism remains unclear.

The expression levels of lncRNA OIP5-AS1, miR200c-3p, and PTEN were measured by a quantitative real-time polymerase chain reaction in endometrial cancer tissue and endometrial cancer cells. CCK8 assay, wound-healing assay, and cell colony formation were applied to evaluate cell proliferation, cell migration, and cell colony formation ability. Cell cycle and cell apoptosis were detected by flow cytometry. The interactions between OIP5-AS1, miR200c-3p, and PTEN were explored by luciferase activity.

In the present study, we demonstrated that long noncoding RNA OIP5-AS1 was significantly reduced in EC tissue compared with normal tissue. The lower expression level of OIP5-AS1 was also confirmed in four kinds of EC cell lines compared with the normal endometrial cell line. Gain- and loss-of-function of experiments indicated that upregulation of OIP5-AS1 could inhibit the proliferation, migration, and invasion of EC cells in vitro. Meanwhile, overexpression of OIP5-AS1 could also suppress the growth of tumor in the xenograft model. Moreover, further study revealed that miR-200c-3p could bind to OIP5-AS1, and the loss function of miR-200c-3p could reverse the elevated OIP5-AS1's inhibitory effect on the progression of EC. Furthermore, we found that downregulation of miR-200c-3p was inversely correlated with PTEN expression in EC cells. Reduced OIP5-AS1 could lead to the accumulation of miR-200c-3p, which could induce the upregulation of PTEN indirectly.

Our study demonstrated a novel molecular mechanism that lncRNA OIP5-AS1 could modulate the progression of EC by combining competitively with miR-200c-3p to control the PTEN/AKT pathway in EC cells, which might supply important information for developing novel therapeutic strategies for EC patients.

• Cancer
• EMT
• HOTAIR
• LncRNAs
• Polymorphisms
• miRNAs

• EC
• HOTAIR
• PI3K/Akt signaling pathway
• PTEN
• apoptosis
• proliferation

• epithelial ovarian cancer
• lncRNA HOTAIR
• miR-200c
• snail
• tumorigenicity

• Biomarkers
• Endometrial cancer
• Long non-coding RNAs
• Metastasis
• Tumorigenesis

Several studies suggest that upregulated expression of the long non-coding RNA HOX transcript antisense RNA (HOTAIR) is a negative predictive biomarker for numerous cancers. Herein, we performed a meta-analysis to further investigate the prognostic value of HOTAIR expression in diverse human cancers. To this end, a systematic literature review was conducted in order to select scientific studies relevant to the association between HOTAIR expression and clinical outcomes, including overall survival (OS), recurrence-free survival (RFS)/disease-free survival (DFS), and progression-free survival (PFS)/metastasis-free survival (MFS) of cancer patients. Collectively, 53 eligible studies including a total of 4873 patients were enrolled in the current meta-analysis. Pooled hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs) were calculated to assess the relationship between HOTAIR and cancer patients’ survival. Elevated HOTAIR expression was found to be significantly associated with OS, RFS/DFS and PFS/MFS in diverse types of cancers. These findings were also corroborated by the results of bioinformatics analysis on overall survival. Therefore, based on our findings, HOTAIR could serve as a potential biomarker for the prediction of cancer patient survival in many different types of human cancers.

• HOTAIR
• cancer
• meta-analysis
• prognostic biomarker
• survival

Recent studies have revealed both the promise and challenges of targeting long non-coding RNAs (lncRNAs) to diagnose and treat endometrial cancer (EC). LncRNAs are upregulated or downregulated in ECs compared to normal tissues and their dysregulation has been linked to tumor grade, FIGO stage, the depth of myometrial invasion, lymph node metastasis and patient survival. Tumor suppressive lncRNAs (GAS5, MEG3, FER1L4 and LINC00672) and oncogenic lncRNAs (CCAT2, BANCR, NEAT1, MALAT1, H19 and Linc-RoR) have been identified as upstream modulators or downstream effectors of major signaling pathways influencing EC metastasis, including the PTEN/PI3K/AKT/mTOR, RAS/RAF/MEK/ERK, WNT/β-catenin and p53 signaling pathways. TUG1 and TDRG1 stimulate the VEGF-A pathway. PCGEM1 is implicated in activating the JAK/STAT3 pathway. Here, we present an overview of the expression pattern, prognostic value, biological function of lncRNAs in EC cells and their roles within the tumor microenvironment, focusing on the influence of lncRNAs on established EC-relevant pathways. We also describe the emerging classification of EC subtypes based on their lncRNA signature and discuss the clinical implications of lncRNAs as valuable biomarkers for EC diagnosis and potential targets for EC treatment.

• endometrial cancer
• epigenetics
• long non-coding RNA
• microRNA
• prognostic biomarker
• regulatory mechanism
• therapeutic target
• tumor microenvironment

• breastfeeding
• healthy women
• long noncoding RNAs
• reproductive history
• u-Eleanor, and HOTAIR

• Breast cancer (BC)
• Invasion
• Long non-coding RNAs (lncRNAs)
• Metastasis

• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• Cell Movement/genetics
• Female
• Gene Expression Regulation, Neoplastic
• Gene Regulatory Networks/genetics
• Humans
• Neoplasm Invasiveness/genetics
• Neoplasm Metastasis
• Prognosis
• RNA, Long Noncoding/genetics

This study was implemented to figure out whether lncRNA HOTAIR/miR‐17‐5p/PTEN axis played a role that was opposite to Shenqifuzheng (SQFZ) injection in regulating the chemosensitivity of gastric cancer cells. The gastric cancer tissues were gathered and four gastric cancer cell lines were prepared, including BGC‐823, MGC‐803, SGC‐7901, and MKN28. Moreover, cisplatin, adriamycin, mitomycin, and 5‐fluoroura were managed as the chemo‐therapeutics, and SQFZ was prepared as a Chinese medicine. Striking distinctions of HOTAIR, miR‐17‐5p, and PTEN expressions were observed between gastric cancer tissues and para‐carcinoma normal tissues (P < 0.05). MKN28 was associated with the highest resistance to cisplatin, adriamycin, mitomycin, and 5‐fluoroura among all the cell types, and SQFZ significantly improved the MKN28 cells’ sensitivity to the drugs (P < 0.05). The over‐expressed HOTAIR and miR‐17‐5p, as well as under‐expressed PTEN tended to significantly facilitate the viability, EMT process and proliferation of MKN28 cells that were subject to treatment of chemo‐therapies (P < 0.05). SQFZ could amplify the effects of si‐HOTAIR, miR‐17‐5p inhibitor, and pcDNA‐PTEN on boosting the chemosensitivity of gastric cancer cells (P < 0.05). In addition, HOTAIR was also found to directly target miR‐17‐5p, and PTEN appeared to be subject to the modification of HOTAIR and miR‐17‐5p in its acting on the viability, proliferation, EMT process, and apoptosis of gastric cancer cells. The HOTAIR/miR‐17‐5p/PTEN axis could be regarded as the potential treatment targets for gastric cancer, and adjuvant therapy of SQFZ injection could assist in further improving the treatment efficacy of chemo‐therapies for gastric cancer.

• PTEN
• Shenqifuzheng injection
• chemoresistance
• gastric cancer
• lncRNA HOTAIR
• miR-17-5p

• HOTAIR
• NPM1
• endometrial carcinoma
• miR-646

• GAPLINC
• NONO
• PSF
• colorectal cancer
• long noncoding RNA

• Cancer
• Cancer stem cells
• Differentiation
• Long noncoding RNAs
• Pluripotency
• Stemness