SUMMARY The discovery of long non-coding RNA (lncRNA) has dramatically altered our understanding of cancer. Here, we describe a comprehensive analysis of lncRNA alterations at transcriptional, genomic, and epigenetic levels in 5,037 human tumor specimens across 13 cancer types from the Cancer Genome Atlas (TCGA). Our results suggest that the expression and dysregulation of lncRNAs are highly cancer-type specific compared to protein-coding genes. Using the integrative data generated by this analysis, we present a clinically guided small interfering RNA screening strategy and a co-expression analysis approach to identify cancer driver lncRNAs and predict their functions. This provides a resource for investigating lncRNAs in cancer and lays the groundwork for the development of new diagnostics and treatments.

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

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Exosomes mediate cell-cell crosstalk in cancer progression by transferring their molecular cargos, including long noncoding RNAs (lncRNAs). Metastasis‑associated lung adenocarcinoma transcript 1 (MALAT1) is a well-known lncRNA associated with cancer angiogenesis and metastasis. However, the presence of MALAT1 in exosomes and the roles and clinical values of exosomal MALAT1 in epithelial ovarian cancer (EOC) remain unknown. The present study focused on the crosstalk between EOC cells and endothelial cells mediated by exosomal MALAT1 and aimed to explore the roles of exosomes and exosomal MALAT1 in EOC angiogenesis and to reveal the clinical relevance and prognostic predictive value of serum exosomal MALAT1 in EOC. We observed that MALAT1 was increased in both metastatic EOC cells and their secreted exosomes. Exosomal MALAT1 derived from EOC cells was transferred to recipient human umbilical vein endothelial cells (HUVECs) via exosomes. In vitro and in vivo experiments demonstrated that MALAT1 knockdown impaired the exosome-mediated proangiogenic activity of HUVECs through certain key angiogenesis-related genes. Clinically, elevated serum exosomal MALAT1 was highly correlated with an advanced and metastatic phenotype of EOC and was an independent predictive factor for EOC overall survival (OS). Moreover, a prognostic nomogram model we constructed showed a good prediction of the probability of 3-year OS of EOC patients according to the c-index (0.751, 95% confidence interval [CI]=0.691-0.811) and calibration curve. Collectively, our data provide a novel mechanism by which EOC cells transfer MALAT1 via exosomes to recipient HUVECs and influence HUVECs by stimulating angiogenesis-related gene expression, eventually promoting angiogenesis. Additionally, circulating exosomal MALAT1 can serve as a promising serum-based, noninvasive predictive biomarker for EOC prognosis.