decodeRNA-predicting non-coding RNA functions using guilt-by-association

Although the long non-coding RNA (lncRNA) landscape is expanding rapidly, only a small number of lncRNAs have been functionally annotated. Here, we present decodeRNA (http://www.decoderna.org), a database providing functional contexts for both human lncRNAs and microRNAs in 29 cancer and 12 normal tissue types. With state-of-the-art data mining and visualization options, easy access to results and a straightforward user interface, decodeRNA aims to be a powerful tool for researchers in the ncRNA field

The Challenges and Opportunities of lncRNAs in Ovarian Cancer Research and Clinical Use

[Abstract] Ovarian cancer is one of the most lethal gynecological malignancies worldwide because it tends to be detected late, when the disease has already spread, and prognosis is poor. In this review we aim to highlight the importance of long non-coding RNAs (lncRNAs) in diagnosis, prognosis and treatment choice, to make progress towards increasingly personalized medicine in this malignancy. We review the effects of lncRNAs associated with ovarian cancer in the context of cancer hallmarks. We also discuss the molecular mechanisms by which lncRNAs become involved in cellular physiology; the onset, development and progression of ovarian cancer; and lncRNAs’ regulatory mechanisms at the transcriptional, post-transcriptional and post-translational stages of gene expression. Finally, we compile a series of online resources useful for the study of lncRNAs, especially in the context of ovarian cancer. Future work required in the field is also discussed along with some concluding remarks.This work was funded by Plan Estatal I + D + I by the Instituto de Salud Carlos III (ISCIII, Spain) under grant agreement AES number PI18/01714, cofounded by Fondo Europeo de Desarrollo Regional-FEDER (The European Regional Development Fund-ERDF) “A way of Making Europe,” and by Xunta de Galicia (Consolidación Grupos Referencia Competitiva contract number ED431C 2016-012). M.S.M. was funded by a predoctoral fellowship from FPU-2018 (Spain)Xunta de Galicia; ED431C 2016-01

A variant in HMMR/HMMR-AS1 is associated with serum alanine aminotransferase levels in the Ryukyu population

The Ryukyu archipelago is located southwest of the Japanese islands, and people originally from this region, the Ryukyu population, have a unique genetic background distinct from that of other populations, including people from mainland Japan. However, few genetic studies have focused on the Ryukyu population. In this study, we performed genome-wide association studies (GWAS) on the serum levels of alanine aminotransferase (ALT, n = 15,224), aspartate aminotransferase (AST, n = 15,203), and gamma-glutamyl transferase (GGT, n = 14,496) in the Ryukyu population. We found 13 loci with a genome-wide significant association (P < 5 × 10⁻⁸), three for ALT, four for AST, and six for GGT, including one novel locus associated with ALT: rs117595134-A in HMMR/HMMR-AS1, ß =  − 0.131, standard error = 0.024, P = 4.90 × 10⁻⁸. Rs117595134-A is common in the Japanese population but is not observed in other ethnic populations in the 1000 genomes database. Additionally, 77 of 80 loci derived from Korean GWAS and 541 of 716 loci from European GWAS showed the same directions of effect (P = 1.41 × 10⁻¹⁹, P = 2.50 × 10⁻⁴⁴, binomial test), indicating that most of susceptibility loci are shared between the Ryukyu population and other ethnic populations.http://purl.org/coar/resource_type/c_650

Natural Antisense Transcripts: Molecular Mechanisms and Implications in Breast Cancers.

Natural antisense transcripts are RNA sequences that can be transcribed from both DNA strands at the same locus but in the opposite direction from the gene transcript. Because strand-specific high-throughput sequencing of the antisense transcriptome has only been available for less than a decade, many natural antisense transcripts were first described as long non-coding RNAs. Although the precise biological roles of natural antisense transcripts are not known yet, an increasing number of studies report their implication in gene expression regulation. Their expression levels are altered in many physiological and pathological conditions, including breast cancers. Among the potential clinical utilities of the natural antisense transcripts, the non-coding|coding transcript pairs are of high interest for treatment. Indeed, these pairs can be targeted by antisense oligonucleotides to specifically tune the expression of the coding-gene. Here, we describe the current knowledge about natural antisense transcripts, their varying molecular mechanisms as gene expression regulators, and their potential as prognostic or predictive biomarkers in breast cancers

Crosstalk between hypoxia-inducible factor (HIF) and lncRNAs in digestive tumors: from molecular mechanisms to clinical translation

Hypoxia is a characteristic feature of the tumor microenvironment that significantly influences cancer progression and treatment responses. Hypoxia-inducible factor (HIF), a key regulator of hypoxic adaptation, has been demonstrated to modulate hypoxic gene expression profiles and signaling networks, thereby serving as a potential therapeutic target. Long-stranded non-coding RNAs (lncRNAs), defined as non-coding RNAs exceeding 200 nucleotides in length, regulate various cellular processes by modulating gene expression at transcriptional, post-transcriptional, and epigenetic levels. Evidence suggests that lncRNAs can be regulated by HIF at the transcriptional level. Conversely, HIF itself can be modulated by numerous lncRNAs, with alterations in these lncRNAs being associated with tumorigenesis, resulting in a reciprocal regulatory network. Recently, the critical role of lncRNAs in hypoxia-driven cancer progression has been elucidated in digestive tumors, including colorectal, pancreatic, gastric, and hepatocellular carcinomas. An increasing number of studies have revealed the complex interplay between lncRNAs and HIF in regulating various processes such as proliferation, metastasis, apoptosis, and drug resistance. In this paper, we aim to provide a comprehensive summary of recent advances regarding the roles of hypoxia and lncRNAs in digestive system tumors and to illustrate the mechanisms through which lncRNAs interact with hypoxia in tumor cells. This will enhance our understanding of the regulatory roles of lncRNAs in modulating the microenvironment of digestive system tumors, thereby facilitating the development of novel anticancer drugs

Crosstalk between exosomes and tumor-associated macrophages in hepatocellular carcinoma: implication for cancer progression and therapy

Hepatocellular carcinoma (HCC), the most prevalent type of primary liver cancer, represents a significant cause of cancer-related mortality. While our understanding of its pathogenesis is comparatively comprehensive, the influence of the tumor microenvironment (TME) on its progression warrants additional investigation. Tumor-associated macrophages (TAMs) have significant impacts on cancer cell proliferation, migration, invasion, and immune response, facilitating a complex interaction within the TME. Exosomes, which measure between 30 and 150 nanometers in size, are categorized into small extracellular vesicles, secreted by a wide range of eukaryotic cells. They can transfer biological molecules including proteins, non-coding RNAs, and lipids, which mediates the intercellular communication within the TME. Emerging evidence has revealed that exosomes regulate macrophage polarization, thus impacting cancer progression and immune responses within the TME of HCC. Moreover, TAM-derived exosomes also play crucial roles in malignant transformation, which hold immense potential for cancer therapy. In this review, we elaborate on the crosstalk between exosomes and TAMs within TME during HCC development. Moreover, we delve into the feasible treatment approaches for exosomes in cancer therapy and emphasize the limitations and challenges for the translation of exosomes derived from TAMs into clinical courses for cancer therapy, which may provide new perspectives on further ameliorations of therapeutic regimes based on exosomes to advance their clinical applications

Exploring the Potential of Chaihu-Danggui Tang in Breast Cancer Treatment Based on Network Pharmacology, Molecular Docking, and Experimental Validation

Yusheng Liu,1,2,&amp;ast; Junfeng Zhang,3,&amp;ast; Yigui Lai,1 Chunying Wu,1 Dongsheng Liu,2 Rongyao Liang,1 Gang Chen,1 Xuefeng Jiang1,2 1Comprehensive Laboratory, Yangjiang People’s Hospital, Yangjiang, 529500, People’s Republic of China; 2Department of Traditional Chinese Medicine, Hainan West Central Hospital, Danzhou, 571700, People’s Republic of China; 3School of Medicine, Anhui University of Science and Technology, Huainan, 232001, People’s Republic of China&amp;ast;These authors contributed equally to this workCorrespondence: Xuefeng Jiang, Email [email protected] Gang Chen, Email [email protected]: Chaihu-Danggui Tang (CHDGT) has a long history in traditional Chinese medicine (TCM) as an adjuvant therapy for breast cancer (BC), but its precise anti-tumor mechanisms remain unknown. In this study, we used network pharmacology, molecular docking, and experimental validation methods to investigate the core components, key targets, and possible mechanisms through which CHDGT may exert therapeutic effects in BC treatment.Methods: The Traditional Chinese Medicine Systems Pharmacology (TCMSP) was employed to obtain the active ingredient and targets of CHDGT. Meanwhile, the GeneCards databases were used to retrieve pertinent targets for BC. The Venn plot was used to obtain intersection targets. Cytoscape software was used to construct an “CHDGT-active ingredients-targets” network and identify core targets. The common targets after STRING processing were imported into the Metascape database for GO and KEGG pathway enrichment analysis. Molecular docking of key ingredients and core targets of drugs was accomplished using Autodock and PyMol software. The cell and animal experiments confirmed CHDGT efficacy and mechanism in treating BC.Results: We screened 5 key effector components, 8 core targets, and multiple signaling pathways of CHDGT in treating BC. In vitro, the results of CCK-8 assay showed that CHDGT can dose-dependently inhibits BC cell growth, and at 100 mg L− 1 after 48 hours, the cell inhibition rate reached approximately 50%. Further analysis showed that CHDGT can promote apoptosis of BC cell, and regulate the expression levels of apoptosis-related genes, such as Caspase3, p53, and Bcl-2. The animal experiments verified that CHDGT can significantly inhibit the progression of BC, the tumor inhibition rate of CHDGT-H groups was as high as 60.06 ± 4.82%. In addition, H&amp;E staining and blood biochemical analysis suggest that CHDGT exhibits favorable safety.Conclusion: This study may provide perspectives for the development of anticancer Chinese herbs for the treatment of BC. Keywords: chaihu-danggui tang, breast cancer, network pharmacology, molecular docking, apoptosi

Antitumor effect of 4MU on glioblastoma cells is mediated by senescence induction and CD44, RHAMM and p-ERK modulation

The extracellular matrix plays a key role in cancer progression. Hyaluronan, the main glycosaminoglycan of the extracellular matrix, has been related to several tumor processes. Hyaluronan acts through the interaction with cell membrane receptors as CD44 and RHAMM and triggers signaling pathways as MEK/ERK. 4-methylumbelliferone (4MU), a well-known hyaluronan synthesis inhibitor, is a promising alternative for cancer therapy. 4MU is a coumarin derivative without adverse effects that has been studied in several tumors. However, little is known about its use in glioblastoma (GBM), the most malignant primary brain tumor in adults. Glioblastoma is characterized by fast growth, migration and tissue invasiveness, and a poor median survival of the patients after treatment. Several reports linked glioblastoma progression with HA levels and even with CD44 and RHAMM expression, as well as MEK/ERK activation. Previously, we showed on a murine GBM cell line that HA enhances GBM migration, while 4MU markedly inhibits it. In this work we showed for the first time, that 4MU decreases cell migration and induces senescence in U251 and LN229 human GBM cell lines. Furthermore, we observed that HA promotes GBM cell migration on both cell lines and that such effects depend on CD44 and RHAMM, as well as MEK/ERK signaling pathway. Interestingly, we observed that the exogenous HA failed to counteract the effects of 4MU, indicating that 4MU effects are independent of HA synthesis inhibition. We found that 4MU decreases total CD44 and RHAMM membrane expression, which could explain the effect of 4MU on cell migration. Furthermore, we observed that 4MU increases the levels of RHAMM inside the cell while decreases the nucleus/cytoplasm relation of p-ERK, associated with 4MU effects on cell proliferation and senescence induction. Overall, 4MU should be considered as a promising therapeutic alternative to improve the outcome of patients with GBM.Fil: Pibuel, Matías Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Inmunología; ArgentinaFil: Poodts, Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Inmunología; ArgentinaFil: Díaz, Mariángeles. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; ArgentinaFil: Molinari, Yamila Azul. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Físico-Química Biológicas "Prof. Alejandro C. Paladini". Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Físico-Química Biológicas; ArgentinaFil: Franco, Paula Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Físico-Química Biológicas "Prof. Alejandro C. Paladini". Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Físico-Química Biológicas; ArgentinaFil: Hajos, Silvia Elvira. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Inmunología; ArgentinaFil: Lompardía, Silvina Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Inmunología; Argentin

SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy. Due to its wide heterogeneity, PDAC acts aggressively and responds poorly to most chemotherapies, causing an urgent need for the development of new therapeutic strategies. Cell lines have been used as the foundation for drug development and disease modeling. CRISPR-Cas9 plays a key role in every step-in drug discovery: from target identification and validation to preclinical cancer cell testing. Using cell-line models and CRISPR-Cas9 technology together make drug target prediction feasible. However, there is still a large gap between predicted results and actionable targets in real tumors. Biological network models provide great modus to mimic genetic interactions in real biological systems, which can benefit gene perturbation studies and potential target identification for treating PDAC. Nevertheless, building a network model that takes cell-line data and CRISPR-Cas9 data as input to accurately predict potential targets that will respond well on real tissue remains unsolved. Methods: We developed a novel algorithm 'Spectral Clustering for Network-based target Ranking' (SCNrank) that systematically integrates three types of data: expression profiles from tumor tissue, normal tissue and cell-line PDAC; protein-protein interaction network (PPI); and CRISPR-Cas9 data to prioritize potential drug targets for PDAC. The whole algorithm can be classified into three steps: 1. using STRING PPI network skeleton, SCNrank constructs tissue-specific networks with PDAC tumor and normal pancreas tissues from expression profiles; 2. With the same network skeleton, SCNrank constructs cell-line-specific networks using the cell-line PDAC expression profiles and CRISPR-Cas 9 data from pancreatic cancer cell-lines; 3. SCNrank applies a novel spectral clustering approach to reduce data dimension and generate gene clusters that carry common features from both networks. Finally, SCNrank applies a scoring scheme called 'Target Influence score' (TI), which estimates a given target's influence towards the cluster it belongs to, for scoring and ranking each drug target. Results: We applied SCNrank to analyze 263 expression profiles, CRPSPR-Cas9 data from 22 different pancreatic cancer cell-lines and the STRING protein-protein interaction (PPI) network. With SCNrank, we successfully constructed an integrated tissue PDAC network and an integrated cell-line PDAC network, both of which contain 4414 selected genes that are overexpressed in tumor tissue samples. After clustering, 4414 genes are distributed into 198 clusters, which include 367 targets of FDA approved drugs. These drug targets are all scored and ranked by their TI scores, which we defined to measure their influence towards the network. We validated top-ranked targets in three aspects: Firstly, mapping them onto the existing clinical drug targets of PDAC to measure the concordance. Secondly, we performed enrichment analysis to these drug targets and the clusters there are within, to reveal functional associations between clusters and PDAC; Thirdly, we performed survival analysis for the top-ranked targets to connect targets with clinical outcomes. Survival analysis reveals that overexpression of three top-ranked genes, PGK1, HMMR and POLE2, significantly increases the risk of death in PDAC patients. SCNrank is an unbiased algorithm that systematically integrates multiple types of omics data to do potential drug target selection and ranking. SCNrank shows great capability in predicting drug targets for PDAC. Pancreatic cancer-associated gene candidates predicted by our SCNrank approach have the potential to guide genetics-based anti-pancreatic drug discovery