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.

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

PURPOSE Thyroid cancer (TC) is the most common malignancy of the endocrine system and its incidence is gradually rising. Research has demonstrated a close link between autophagy and thyroid cancer. We constructed a prognostic model of autophagy-related long non-coding RNA (lncRNA) in thyroid cancer and explored its prognostic value. METHODS The data used in this study were all obtained from The Cancer Genome Atlas (TCGA) database and the Human Autophagy Database (HADb). We construct a co-expression network by autophagy-related genes and lncRNA to obtain autophagy-related lncRNAs. After univariate Cox regression analysis and multivariate Cox regression analysis, autophagy-related lncRNAs significantly associated with prognosis were identified. Based on the risk score of lncRNA, thyroid cancer patients are divided into high-risk group and low-risk group. RESULTS A total of 14,142 lncRNAs and 212 autophagy-related genes (ATGs) were obtained from the TCGA database and the HADb, respectively. We performed lncRNA-ATGs correlation analysis and finally obtained 1,166 autophagy-associated lncRNAs. Subsequently, we conducted univariate Cox regression analysis and multivariate Cox regression analysis, nine autophagy-related lncRNAs (AC092279.1, AC096677.1, DOCK9-DT, LINC02454, AL136366.1, AC008063.1, AC004918.3, LINC02471 and AL162231.2) significantly associated with prognosis were identified. Based on these autophagy-related lncRNAs, a risk model was constructed. The area under the curve (AUC) of the risk score was 0.905, proving that the accuracy of risk signature was superior. In addition, multiple regression analysis showed that risk score was a significant independent prognostic risk factor for thyroid cancer. CONCLUSION In this study, nine autophagy-related lncRNAs in thyroid cancer were established to predict the prognosis of thyroid cancer patients.

/pdf/an-autophagy-related-lncrna-prognostic-risk-model-for-48twsn622m.pdf

An autophagy-related lncRNA prognostic risk model for thyroid cancer.

Glioma is a common brain malignancy, and the purpose of this study is to investigate the function of LINC02308 in glioma. The differentially expressed lncRNAs were screened by microarray. The expression of LINC02308 in glioma tissues and cells was evaluated. The interaction among LINC02308, miR-30e-3p, and TM4SF1 was determined. Cell proliferation and apoptosis were evaluated. The expression of mTOR/AKT-signaling and apoptosis-related markers was detected by Western blot. A xenograft tumor mouse model was constructed to investigate the roles of LINC02308. LINC02308 was significantly overexpressed in glioma, and a high LINC02308 level was correlated with a poor prognosis. LINC02308 silencing markedly inhibited proliferation and reduced apoptosis of glioma cells and also suppressed tumor growth in the xenograft tumor mouse model. Finally, we demonstrated that LINC02308 played its oncogenic role through binding to miR-30e-3p so as to relieve miR-30e-3p-induced suppression of TM4SF1. LINC02308 promoted glioma tumorigenesis as a sponge of miR-30e-3p to upregulate TM4SF1 and activate AKT/mTOR pathway.

LINC02308 promotes the progression of glioma through activating mTOR/AKT-signaling pathway by targeting miR-30e-3p/TM4SF1 axis.

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