Factors Affecting Sexual Function and Body Image of Early-Stage Breast Cancer Survivors in Poland: A Short-Term Observation.

Neuroblastoma (NB) is a commonly occurring cancer among infants and young children. Recently, long non-coding RNAs (lncRNAs) have been using as prognostic biomarkers for therapeutics and interventions in various cancers. Considering the poor survival of NB, the lncRNA-based therapeutic strategies must be improved. This work proposes an overall survival time estimator called SVR-NB to identify the lncRNA signature that is associated with the overall survival of patients with NB. SVR-NB is an optimized support vector regression (SVR)-based method that uses an inheritable bi-objective combinatorial genetic algorithm for feature selection. The dataset of 231 NB patients that contains overall survival information and expression profiles of 783 lncRNAs was used to design and evaluate SVR-NB from the database of gene expression omnibus accession GSE62564. SVR-NB identified a signature of 35 lncRNAs and achieved a mean squared correlation coefficient of 0.85 and a mean absolute error of 0.56 year between the actual and estimated overall survival time using 10-fold cross-validation. Further, we ranked and characterized the 35 lncRNAs according to their contribution towards the estimation accuracy. Functional annotations and co-expression gene analysis of LOC440896, LINC00632, and IGF2-AS revealed the association of co-expressed genes in Kyoto Encyclopedia of Genes and Genomes pathways.

/pdf/identification-and-characterization-of-the-lncrna-signature-4uae4f5dp8.pdf

Identification and characterization of the lncRNA signature associated with overall survival in patients with neuroblastoma.

Epithelial-mesenchymal transition (EMT), a reversible cellular program, is critically important in tumor progression and is regulated by a family of transcription factors, induction factors, and an array of signaling pathway genes. The prognostic role and biological functions of EMT-related lncRNAs in ccRCC are largely unknown. In the present study, we analyzed the gene expression data and clinical information retrieved from The Cancer Genome Atlas (TCGA) database (N=512) and International Cancer Genome Consortium (ICGC) database (N=90) which served as training and external validation dataset, respectively. Then, we constructed an EMT-related lncRNA risk signature based on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information. The Kaplan-Meier curve analysis revealed that patients in the low-risk and high-risk groups exhibited significant divergence in the overall survival (OS) and disease-free survival (DFS) of ccRCC, as was confirmed in the validation dataset. The Cox regression analysis of the clinical factors and risk signature in the OS and DFS demonstrated that the risk signature can be utilized as an independent prognostic predictor. Moreover, we developed an individualized prognosis prediction model relying on the nomogram and receive operator curve (ROC) analysis based on the independent factors. The Gene Set Enrichment Analysis (GSEA) indicated that patients in the low-risk group were associated with adherens junction, focal adhesion, MAPK signaling pathway, pathways in cancer, and renal cell carcinoma pathway. In addition, we identified three robust subtypes (named C1, C2 and C3) of ccRCC with distinct clinical characteristics and prognostic role in the TCGA dataset and ICGC dataset. Among them, C1 was associated with a better survival outcome, whereas C2 and C3 was associated with a worse survival outcome and have more advanced-stage patients. Moreover, C2 was more likely to respond to immunotherapy and was sensitive to chemo drugs, this may provide insights to clinicians to develop an individualized treatment. Collectively, this work developed a reliable EMT-related lncRNA risk signature that can independently predict the OS and DFS of ccRCC. Besides, we identified three stable molecular subtypes based on the EMT-related lncRNA expression, which may comprehensively be vital in elucidating the underlying molecular mechanism of ccRCC.

/pdf/identification-of-epithelial-mesenchymal-transition-related-4d74u912tn.pdf

Identification of Epithelial-Mesenchymal Transition-Related lncRNA With Prognosis and Molecular Subtypes in Clear Cell Renal Cell Carcinoma.

DRAIC (also known as LOC145837 and RP11-279F6.1), is a long non-coding RNA associated with several types of cancer including prostate cancer, lung cancer, and breast cancer. Its expression is elevated in tumor tissues compared to adjacent benign tissues in breast cancer patients and is regulated by estrogen treatment in breast cancer cells. In addition, expression analysis of DRAIC in more than 100 cell lines showed that DRAIC expression is high in luminal and basal subtypes compared to claudin low subtype, suggesting a prognostic value of DRAIC expression in breast cancer. In the present study, we analyzed DRAIC expression in 828 invasive breast carcinomas and 105 normal samples of RNA sequencing datasets from The Cancer Genome Atlas (TCGA) and found that DRAIC expression was correlated with estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status, and is increased in cancerous tissues. Additionally, higher DRAIC expression was associated with poorer survival of patients, especially in ER positive breast cancer. DRAIC was also investigated in the Oncomine database and we found that DRAIC expression predicted patients' response to paclitaxel and FEC as well as lapatinib, which are commonly used therapy options for breast cancer. Finally, DRAIC expression in breast cancer was negatively correlated with immune cell infiltration. These results reinforce the importance of DRAIC in breast cancer.

https://www.mdpi.com/2311-553X/4/4/39/pdf

Upregulation of Long Non-Coding RNA DRAIC Correlates with Adverse Features of Breast Cancer.

Dopaminergic (DA) neurons derived from human pluripotent stem cells (hPSCs) represent a renewable and available source of cells useful for understanding development, developing disease models, and stem-cell therapies for Parkinson's disease (PD). To assess the utility of stem cell cultures as an in vitro model system of human DA neurogenesis, we performed high-throughput transcriptional profiling of ~20,000 ventral midbrain (VM)-patterned stem cells at different stages of maturation using droplet-based single-cell RNA sequencing (scRNAseq). Using this dataset, we defined the cellular composition of human VM cultures at different timepoints and found high purity DA progenitor formation at an early stage of differentiation. DA neurons sharing similar molecular identities to those found in authentic DA neurons derived from human fetal VM were the major cell type after two months in culture. We also developed a bioinformatic pipeline that provided a comprehensive long noncoding RNA landscape based on temporal and cell-type specificity, which may contribute to unraveling the intricate regulatory network of coding and noncoding genes in DA neuron differentiation. Our findings serve as a valuable resource to elucidate the molecular steps of development, maturation, and function of human DA neurons, and to identify novel candidate coding and noncoding genes driving specification of progenitors into functionally mature DA neurons.

/pdf/single-cell-profiling-of-coding-and-noncoding-genes-in-human-4hihn579py.pdf

Single-Cell Profiling of Coding and Noncoding Genes in Human Dopamine Neuron Differentiation

Introduction: Breast cancer is one of the most common malignant tumors in the United States. However, the molecular mechanism involved in the progression of breast cancer has remained unclear. Long non-coding RNAs (lncRNA) have been reported as key regulators in the progression and metastasis of cancer. Material and methods: In this study, we identified significantly differentially expressed mRNAs and lncRNAs in breast cancer using the GSE70947 dataset. Gene ontology (GO) and KEGG pathway was used to explore the key roles of differentially expressed lncRNAs in breast cancer. The dysregulated lncRNAs and mRNAs expression profiles in HER2-positive and ER-negative breast cancer were also analyzed in this study. Results: Our results showed that PVT1, LOC145837, FLJ40504 and FLJ45983 were significantly decreased in HER2-positive and ER-negative breast cancers. We also constructed the PVT1, LOC145837, FLJ40504 and FLJ45983 mediated cRNA networks in HER2-positive and ER-negative breast cancers. Moreover, using the Betastasis dataset, we found that high PVT1 expression levels were associated with a lower survival rate in breast cancer patients. Conclusions: These results elucidate the functions of lncRNAs and provide useful information for exploring therapeutic candidate targets and new molecular biomarkers for ER-negative and HER-2 enriched subtype breast cancer.

/pdf/identification-of-differentially-expressed-profiles-of-4cxqmoifra.pdf

Identification of differentially expressed profiles of lncRNAs and mRNAs in ER-negative and HER-2 positive breast cancer

BACKGROUND Thyroid carcinoma is a malignancy with high morbidity and mortality. Genetic alterations play pivot roles in the pathogenesis of thyroid carcinoma, where long noncoding RNA (lncRNA) have been identified to be crucial. This study sought to investigate the biological functions of lncRNA expression profiles in thyroid carcinoma. MATERIAL AND METHODS The lncRNAs expression profiles were acquired from The Cancer Genome Atlas (TCGA) database according to 510 thyroid cancer tissues and 58 normal thyroid tissues. By using R package edgeR, differentially expressed RNAs were obtained. Also, an overall survival model was established based on Cox regression and clinical data then testified by Kaplan-Meier plot, receiver operating characteristic (ROC)-curve and C-index analysis. We investigated the co-expressed genes with lncRNAs involved in the prognostic model, as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted R package clusterProfile. RESULTS A total of 352 lncRNAs were identified as differentially expressed in thyroid carcinoma, and an overall survival model consisting of 8 signature lncRNAs was proposed (ROC=0.862, C-index=0.893, P<0.05), 3 of which (DOCK9-DT, FAM111A-DT, and LINC01736) represent co-expressed mRNAs. However, as an oncogene, only FAM111A-DT increased the prognostic risk in thyroid carcinoma. Furthermore, we found differential genes LINC01016, LHX1-DT, IGF2-AS, ND MIR1-1HG-AS1, significantly related to lymph node metastasis (P<0.05). CONCLUSIONS In this study, we clarified the differential lncRNA expression profiles which were related to the tumorigenesis and prognosis in thyroid carcinoma. Our results provide new rationale and understandings to the pathogenesis and regulatory mechanisms of thyroid carcinoma.

Identification of Long Non-Coding RNA Expression Profiles and Co-Expression Genes in Thyroid Carcinoma Based on The Cancer Genome Atlas (TCGA) Database.

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Identification of differentially expressed profiles of lncRNAs and mRNAs in ER-negative and HER-2 positive breast cancer

Identification of Long Non-Coding RNA Expression Profiles and Co-Expression Genes in Thyroid Carcinoma Based on The Cancer Genome Atlas (TCGA) Database.