Multiple sequence alignment-based RNA language model and its application to structural inference.

Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

EVLncRNAs 3.0: an updated comprehensive database for manually curated functional long non-coding RNAs validated by low-throughput experiments.

Long noncoding RNAs (lncRNAs) have emerged as crucial regulators across diverse biological processes and diseases. While high-throughput sequencing has enabled lncRNA discovery, functional characterization remains limited. The EVLncRNAs database is the first and exclusive repository for all experimentally validated functional lncRNAs from various species. After previous releases in 2018 and 2021, this update marks a major expansion through exhaustive manual curation of nearly 25 000 publications from 15 May 2020, to 15 May 2023. It incorporates substantial growth across all categories: a 154% increase in functional lncRNAs, 160% in associated diseases, 186% in lncRNA-disease associations, 235% in interactions, 138% in structures, 234% in circular RNAs, 235% in resistant lncRNAs and 4724% in exosomal lncRNAs. More importantly, it incorporated additional information include functional classifications, detailed interaction pathways, homologous lncRNAs, lncRNA locations, COVID-19, phase-separation and organoid-related lncRNAs. The web interface was substantially improved for browsing, visualization, and searching. ChatGPT was tested for information extraction and functional overview with its limitation noted. EVLncRNAs 3.0 represents the most extensive curated resource of experimentally validated functional lncRNAs and will serve as an indispensable platform for unravelling emerging lncRNA functions. The updated database is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs3/.

SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.

Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional neural networks, transformers, and graph neural networks (GNN). However, the conventional approach of constructing K-nearest-neighbors (KNN) graph for GNN has limited the utilization of edge information, which plays a critical role in network performance. Here we introduced SPIN-CGNN based on protein contact maps for nearest neighbors. Together with auxiliary edge updates and selective kernels, we found that SPIN-CGNN provided a comparable performance in refolding ability by AlphaFold2 to the current state-of-the-art techniques but a significant improvement over them in term of sequence recovery, perplexity, deviation from amino-acid compositions of native sequences, conservation of hydrophobic positions, and low complexity regions, according to the test by unseen structures, "hallucinated" structures and diffusion models. Results suggest that low complexity regions in the sequences designed by deep learning, for generated structures in particular, remain to be improved, when compared to the native sequences.

RNA tertiary structure modeling with BRiQ potential in CASP15.

We describe the modeling method for RNA tertiary structures employed by team AIchemy_RNA2 in the 15th Critical Assessment of Structure Prediction (CASP15). The method consists of the following steps. Firstly, secondary structure information was derived from various manually-verified sources. With this information, the full length RNA was fragmented into structural modules. The structures of each module were predicted and then assembled into the full structure. To reduce the searching conformational space, an RNA structure was organized into an optimal base folding tree. And to further improve the sampling efficiency, the energy surface was smoothed at high temperatures during the Monte Carlo sampling to make it easier to move across the energy barrier. The statistical potential energy function BRiQ was employed during Monte Carlo energy optimization.

Glycosylated, Lipid-Binding, CDR-Like Domains of SARS-CoV-2 ORF8 Indicate Unique Sites of Immune Regulation.

The outbreak of the novel coronavirus SARS-CoV-2 has posed a significant threat to human health and the global economy since the end of 2019. Unfortunately, due to the virus's rapid evolution, preventingand controlling the epidemic remains challenging. The ORF8 protein is a unique accessory protein in SARS-CoV-2 that plays a crucial role in immune regulation, but its molecular details are still largely unknown. In this study, we successfully expressed SARS-CoV-2 ORF8 in mammalian cells and determined its structure using X-ray crystallography at a resolution of 2.3 Å. Our findings reveal several novel features of ORF8. We found that four pairs of disulfide bonds and glycosylation at residue N78 are essential for stabilizing ORF8's protein structure. Additionally, we identified a lipid-binding pocket and three functional loops that tend to form CDR-like domains that may interact with immune-related proteins to regulate the host immune system. On cellular experiments also demonstrated that glycosylation at N78 regulats of ORF8's ability to bind to monocytes cells. These novel features of ORF8 provide structural insights to into its immune-related function and may serve as new targets for developing ORF8-mediated immune regulation inhibitors. IMPORTANCE COVID-19, caused by the novel coronavirus SARS-CoV-2 virus, has triggered a global outbreak. The virus's continuous mutation increases its infectivity and may be directly related to the immune escape response of viral proteins. In this study, we used X-ray crystallography to determine the structure of SARS-CoV-2 ORF8 protein, a unique accessory protein expressed in mammalian cells, at a resolution of 2.3 Å. Our novel structure reveals important structure details that shed light on ORF8's involvement in immune regulation, including conservation disulfide bonds, a glycosylation site at N78, a lipid-binding pocket, and three functional loops that tend to form CDR-like domains that may interact with immune-related proteins to modulate the host immune system. We also conducted preliminary validation experiments on immune cells. These new insights into ORF8's structure and function provide potential targets for developing inhibitors to block the ORF8-mediated immune regulation between viral protein and host, ultimately contributing to the development of novel therapeutics for COVID-19.

MAOA suppresses the growth of gastric cancer by interacting with NDRG1 and regulating the Warburg effect through the PI3K/AKT/mTOR pathway.

OBJECTIVE: Previous studies have indicated that neurotransmitters play important roles in the occurrence and development of gastric cancer. MAOA is an important catecholamine neurotransmitter-degrading enzyme involved in the degradation of norepinephrine, epinephrine and serotonin. To find a potential therapeutic target for the treatment of gastric cancer, the biological functions of MAOA and the underlying mechanism in gastric cancer need to be explored. METHODS: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets, Kaplan‒Meier (KM) plotter were used to identify the differentially expressed genes, which mainly involved the degradation and synthesis enzymes of neurotransmitters in gastric cancer. We also investigated the expression pattern of MAOA in human and mouse tissues and cell lines by immunohistochemistry and Western blotting analysis. Western blotting, quantitative real-time PCR, enzyme-linked immunosorbent assay (ELISA) and a Seahorse experiment were used to identify the molecular mechanism of cancer cell glycolysis. MAOA expression and patient survival were analysed in the Ren Ji cohort, and univariate and multivariate analyses were performed based on the clinicopathological characteristics of the above samples. RESULTS: MAOA expression was significantly downregulated in gastric cancer tissue and associated with poor patient prognosis. Moreover, the expression level of MAOA in gastric cancer tissue had a close negative correlation with the SUXmax value of PET-CT in patients. MAOA suppressed tumour growth and glycolysis and promoted cancer cell apoptosis. We also reported that MAOA can interact with NDRG1 and regulate glycolysis through suppression of the PI3K/Akt/mTOR pathway. MAOA expression may serve as an independent prognostic factor in gastric cancer patients. CONCLUSIONS: MAOA attenuated glycolysis and inhibited the progression of gastric cancer through the PI3K/Akt/mTOR pathway. Loss of function or downregulation of MAOA can facilitate gastric cancer progression. Overexpression of MAOA and inhibition of the PI3K/Akt/mTOR pathway may provide a potential method for gastric cancer treatment in clinical therapeutic regimens.

Nucleolar HEAT Repeat Containing 1 Up-regulated by the Mechanistic Target of Rapamycin Complex 1 Signaling Promotes Hepatocellular Carcinoma Growth by Dominating Ribosome Biogenesis and Proteome Homeostasis.

BACKGROUND & AIMS: Hyperactivation of ribosome biogenesis leads to hepatocyte transformation and plays pivotal roles in hepatocellular carcinoma (HCC) development. We aimed to identify critical ribosome biogenesis proteins that are overexpressed and crucial in HCC progression. METHODS: HEAT repeat containing 1 (HEATR1) expression and clinical correlations were analyzed using The Cancer Genome Atlas and Gene Expression Omnibus databases and further evaluated by immunohistochemical analysis of an HCC tissue microarray. Gene expression was knocked down by small interfering RNA. HEATR1-knockdown cells were subjected to viability, cell cycle, and apoptosis assays and used to establish subcutaneous and orthotopic tumor models. Chromatin immunoprecipitation and quantitative polymerase chain reaction were performed to detect the association of candidate proteins with specific DNA sequences. Endogenous coimmunoprecipitation combined with mass spectrometry was used to identify protein interactions. We performed immunoblot and immunofluorescence assays to detect and localize proteins in cells. The nucleolus ultrastructure was detected by transmission electron microscopy. Click-iT (Thermo Fisher Scientific) RNA imaging and puromycin incorporation assays were used to measure nascent ribosomal RNA and protein synthesis, respectively. Proteasome activity, 20S proteasome foci formation, and protein stability were evaluated in HEATR1-knockdown HCC cells. RESULTS: HEATR1 was the most up-regulated gene in a set of ribosome biogenesis mediators in HCC samples. High expression of HEATR1 was associated with poor survival and malignant clinicopathologic features in patients with HCC and contributed to HCC growth in vitro and in vivo. HEATR1 expression was regulated by the transcription factor specificity protein 1, which can be activated by insulin-like growth factor 1-mammalian target of rapamycin complex 1 signaling in HCC cells. HEATR1 localized predominantly in the nucleolus, bound to ribosomal DNA, and was associated with RNA polymerase I transcription/processing factors. Knockdown of HEATR1 disrupted ribosomal RNA biogenesis and impaired nascent protein synthesis, leading to reduced cytoplasmic proteasome activity and inhibitory-κB/nuclear factor-κB signaling. Moreover, HEATR1 knockdown induced nucleolar stress with increased nuclear proteasome activity and inactivation of the nucleophosmin 1-MYC axis. CONCLUSIONS: Our study revealed that HEATR1 is up-regulated by insulin-like growth factor 1-mammalian target of rapamycin complex 1-specificity protein 1 signaling in HCC and functions as a crucial regulator of ribosome biogenesis and proteome homeostasis to promote HCC development.

Fast and accurate protein intrinsic disorder prediction by using a pretrained language model.

Determining intrinsically disordered regions of proteins is essential for elucidating protein biological functions and the mechanisms of their associated diseases. As the gap between the number of experimentally determined protein structures and the number of protein sequences continues to grow exponentially, there is a need for developing an accurate and computationally efficient disorder predictor. However, current single-sequence-based methods are of low accuracy, while evolutionary profile-based methods are computationally intensive. Here, we proposed a fast and accurate protein disorder predictor LMDisorder that employed embedding generated by unsupervised pretrained language models as features. We showed that LMDisorder performs best in all single-sequence-based methods and is comparable or better than another language-model-based technique in four independent test sets, respectively. Furthermore, LMDisorder showed equivalent or even better performance than the state-of-the-art profile-based technique SPOT-Disorder2. In addition, the high computation efficiency of LMDisorder enabled proteome-scale analysis of human, showing that proteins with high predicted disorder content were associated with specific biological functions. The datasets, the source codes, and the trained model are available at https://github.com/biomed-AI/LMDisorder.

The SLITRK4-CNPY3 axis promotes liver metastasis of gastric cancer by enhancing the endocytosis and recycling of TrkB in tumour cells.

PURPOSE: Gastric cancer (GC) is a malignant tumour with high mortality, and liver metastasis is one of the main causes of poor prognosis. SLIT- and NTRK-like family member 4 (SLITRK4) plays an important role in the nervous system, such as synapse formation. Our study aimed to explore the functional role of SLITRK4 in GC and liver metastasis. METHODS: The mRNA level of SLITRK4 was evaluated using publicly available transcriptome GEO datasets and Renji cohort. The protein level of SLITRK4 in the tissue microarray of GC was observed using immunohistochemistry. Cell Counting Kit-8, colony formation, transwell migration assays in vitro and mouse model of liver metastasis in vivo was performed to investigate the functional roles of SLITRK4 in GC. Bioinformatics predictions and Co-IP experiments were applied to screen and identify SLITRK4-binding proteins. Western blot was performed to detect Tyrosine Kinase receptor B (TrkB)-related signaling molecules. RESULTS: By comparing primary and liver metastases from GC, SLITRK4 was found to be upregulated in tissues of GC with liver metastasis and to be closely related to poor clinical prognosis. SLITRK4 knockdown significantly abrogated the growth, invasion, and metastasis of GC in vitro and in vivo. Further study revealed that SLITRK4 could interact with Canopy FGF Signalling Regulator 3 (CNPY3), thus enhancing TrkB- related signaling by promoting the endocytosis and recycling of the TrkB receptor. CONCLUSION: In conclusion, the CNPY3-SLITRK4 axis contributes to liver metastasis of GC according to the TrkB-related signaling pathway. which may be a therapeutic target for the treatment of GC with liver metastasis.

Inhibiting the transcription and replication of Ebola viruses by disrupting the nucleoprotein and VP30 protein interaction with small molecules.

Ebola virus (EBOV) causes hemorrhagic fever in humans with high morbidity and fatality. Although over 45 years have passed since the first EBOV outbreak, small molecule drugs are not yet available. Ebola viral protein VP30 is a unique RNA synthesis cofactor, and the VP30/NP interaction plays a critical role in initiating the transcription and propagation of EBOV. Here, we designed a high-throughput screening technique based on a competitive binding assay to bind VP30 between an NP-derived peptide and a chemical compound. By screening a library of 8004 compounds, we obtained two lead compounds, Embelin and Kobe2602. The binding of these compounds to the VP30-NP interface was validated by dose-dependent competitive binding assay, surface plasmon resonance, and thermal shift assay. Moreover, the compounds were confirmed to inhibit the transcription and replication of the Ebola genome by a minigenome assay. Similar results were obtained for their two respective analogs (8-gingerol and Kobe0065). Interestingly, these two structurally different molecules exhibit synergistic binding to the VP30/NP interface. The antiviral efficacy (EC50) increased from 1 µM by Kobe0065 alone to 351 nM when Kobe0065 and Embelin were combined in a 4:1 ratio. The synergistic anti-EBOV effect provides a strong incentive for further developing these lead compounds in future studies.

CCBE1 promotes mitochondrial fusion by inhibiting the TGFß-DRP1 axis to prevent the progression of hepatocellular carcinoma.

The extracellular matrix (ECM), as an important component of the tumor microenvironment, exerts various roles in tumor formation. Mitochondrial dynamic disorder is closely implicated in tumorigenesis, including hyperfission in HCC. We aimed to determine the influence of the ECM-related protein CCBE1 on mitochondrial dynamics in HCC. Here, we found that CCBE1 was capable of promoting mitochondrial fusion in HCC. Initially, CCBE1 expression was found to be significantly downregulated in tumors compared with nontumor tissues, which resulted from hypermethylation of the CCBE1 promoter in HCC. Furthermore, CCBE1 overexpression or treatment with recombinant CCBE1 protein dramatically inhibited HCC cell proliferation, migration, and invasion in vitro and in vivo. Mechanistically, CCBE1 functioned as an inhibitor of mitochondrial fission by preventing the location of DRP1 on mitochondria through inhibiting its phosphorylation at Ser616 by directly binding with TGFßR2 to inhibit TGFß signaling activity. In addition, a higher percentage of specimens with higher DRP1 phosphorylation was present in patients with lower CCBE1 expression than in patients with higher CCBE1 expression, which further confirmed the inhibitory effect of CCBE1 on DRP1 phosphorylation at Ser616. Collectively, our study highlights the crucial roles of CCBE1 in mitochondrial homeostasis, suggesting strong evidence for this process as a potential therapeutic strategy for HCC.

EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning.

Long non-coding RNAs (lncRNAs) played essential roles in nearly every biological process and disease. Many algorithms were developed to distinguish lncRNAs from mRNAs in transcriptomic data and facilitated discoveries of more than 600 000 of lncRNAs. However, only a tiny fraction (<1%) of lncRNA transcripts (~4000) were further validated by low-throughput experiments (EVlncRNAs). Given the cost and labor-intensive nature of experimental validations, it is necessary to develop computational tools to prioritize those potentially functional lncRNAs because many lncRNAs from high-throughput sequencing (HTlncRNAs) could be resulted from transcriptional noises. Here, we employed deep learning algorithms to separate EVlncRNAs from HTlncRNAs and mRNAs. For overcoming the challenge of small datasets, we employed a three-layer deep-learning neural network (DNN) with a K-mer feature as the input and a small convolutional neural network (CNN) with one-hot encoding as the input. Three separate models were trained for human (h), mouse (m) and plant (p), respectively. The final concatenated models (EVlncRNA-Dpred (h), EVlncRNA-Dpred (m) and EVlncRNA-Dpred (p)) provided substantial improvement over a previous model based on support-vector-machines (EVlncRNA-pred). For example, EVlncRNA-Dpred (h) achieved 0.896 for the area under receiver-operating characteristic curve, compared with 0.582 given by sequence-based EVlncRNA-pred model. The models developed here should be useful for screening lncRNA transcripts for experimental validations. EVlncRNA-Dpred is available as a web server at https://www.sdklab-biophysics-dzu.net/EVlncRNA-Dpred/index.html, and the data and source code can be freely available along with the web server.

Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions.

The International Society of RNA Nanotechnology and Nanomedicine (ISRNN) serves to further the development of a wide variety of functional nucleic acids and other related nanotechnology platforms. To aid in the dissemination of the most recent advancements, a biennial discussion focused on biomotors, viral assembly, and RNA nanobiotechnology has been established where international experts in interdisciplinary fields such as structural biology, biophysical chemistry, nanotechnology, cell and cancer biology, and pharmacology share their latest accomplishments and future perspectives. The results summarized here highlight advancements in our understanding of viral biology and the structure-function relationship of frame-shifting elements in genomic viral RNA, improvements in the predictions of SHAPE analysis of 3D RNA structures, and the understanding of dynamic RNA structures through a variety of experimental and computational means. Additionally, recent advances in the drug delivery, vaccine design, nanopore technologies, biomotor and biomachine development, DNA packaging, RNA nanotechnology, and drug delivery are included in this critical review. We emphasize some of the novel accomplishments, major discussion topics, and present current challenges and perspectives of these emerging fields.

GJB3 promotes pancreatic cancer liver metastasis by enhancing the polarization and survival of neutrophil.

Connexins are membrane expressed proteins, which could assemble into hexamers to transfer metabolites and secondary messengers. However, its roles in pancreatic cancer metastasis remains unknown. In this study, by comparing the gene expression patterns in primary pancreatic cancer patients primary and liver metastasis specimens, we found that Gap Junction Protein Beta 3 (GJB3) significantly increased in Pancreatic ductal adenocarcinoma (PDAC) liver metastasis. Animal experiments verified that GJB3 depletion suppressed the hepatic metastasis of PDAC cancer cells. Further, GJB3 over expression increased the neutrophil infiltration. Mechanistic study revealed that GJB3 form channels between PDAC tumor cells and accumulated neutrophil, which transfer cyclic adenosine monophosphate (cAMP) from cancer to neutrophil cells, which supports the survival and polarization. Taken together, our data suggesting that GJB3 could act as a potential therapeutic target of PDAC liver metastasis.

High-throughput mapping of RNA solvent accessibility at the single-nucleotide resolution by RtcB ligation between a fixed 5'-OH-end linker and unique 3'-P-end fragments from hydroxyl radical cleavage.

Given the challenges for the experimental determination of RNA tertiary structures, probing solvent accessibility has become increasingly important to gain functional insights. Among various chemical probes developed, backbone-cleaving hydroxyl radical is the only one that can provide unbiased detection of all accessible nucleotides. However, the readouts have been based on reverse transcription (RT) stop at the cleaving sites, which are prone to false positives due to PCR amplification bias, early drop-off of reverse transcriptase, and the use of random primers in RT reaction. Here, we introduced a fixed-primer method called RL-Seq by performing RtcB Ligation (RL) between a fixed 5'-OH-end linker and unique 3'-P-end fragments from hydroxyl radical cleavage prior to high-throughput sequencing. The application of this method to E. coli ribosomes confirmed its ability to accurately probe solvent accessibility with high sensitivity (low required sequencing depth) and accuracy (strong correlation to structure-derived values) at the single-nucleotide resolution. Moreover, a near-perfect correlation was found between the experiments with and without using unique molecular identifiers, indicating negligible PCR biases in RL-Seq. Further improvement of RL-Seq and its potential transcriptome-wide applications are discussed.

Disulfiram inhibits liver fibrosis in rats by suppressing hepatic stellate cell activation and viability.

BACKGROUND: Liver fibrosis is a wound-healing response to chronic injury, featuring with excess accumulation of extracellular matrix secreted by the activated hepatic stellate cells (HSC). Disulfiram (DSF), also known as Antabuse, has been used for the treatment of alcohol addiction and substance abuse. Recently, overwhelming studies had revealed anti-cancer effects of DSF in multiple cancers, including liver cancer. But the actual effects of DSF on liver fibrosis and liver function remain unknown. METHODS: In this study, we evaluated the effects of low-dose DSF in CCl4- and Bile Duct Ligation (BDL)-induced hepatic fibrosis rat models. Cell proliferation was detected by using the Cell-Light™ EdU Apollo®567 Cell Tracking Kit. Cell apoptosis was analyzed using a TdT-mediated dUTP nick end labeling (TUNEL) kit, viability was measured with Cell Counting Kit-8(CCK8). Relative mRNA expression of pro-fibrogenic was assessed using quantitative RT-PCR. The degree of liver fibrosis, activated HSCs, were separately evaluated through Sirius Red-staining, immunohistochemistry and immunofluorescence. Serum alanine aminotransferase (ALT) and asparagine aminotransferase (AST) activities were detected with ALT and AST detecting kits using an automated analyzer. RESULTS: Liver fibrosis was distinctly attenuated while liver functions were moderately ameliorated in the DSF-treated group. Activation and proliferation of primary rat HSCs isolated from rat livers were significantly suppressed by low-dose DSF. DSF also inhibited the viability of in vitro cultured rat or human HSC cells dose-dependently but had no repressive role on human immortalized hepatocyte THLE-2 cells. Interestingly, upon DSF treatment, the viability of LX-2 cells co-cultured with THLE-2 was significantly inhibited, while that of THLE-2 co-cultured with LX-2 was increased. Further study indicated that HSCs apoptosis was increased in DSF/CCl4-treated liver samples. These data indicated that DSF has potent anti-fibrosis effects and protective effects toward hepatocytes and could possibly be repurposed as an anti-fibrosis drug in the clinic. CONCLUSIONS: DSF attenuated ECM remodeling through suppressing the transformation of quiet HSCs into proliferative, fibrogenic myofibroblasts in hepatic fibrosis rat models. DSF provides a novel approach for the treatment of liver fibrosis.

Spatiotemporal quantification of metastatic tumour cell growth and distribution in lymph nodes by whole-mount tissue 3D imaging.

Lymph nodes (LNs) are a common site of metastasis in many solid cancers. Tumour cells can migrate to LNs for further metastatic colonization of distant organs, indicating poor prognosis and requiring different clinical interventions. The histopathological diagnostic methods currently used to detect clinical lymph node metastasis (LNM) have limitations, such as incomplete visualization. To obtain a complete picture of metastatic LNs on the spatial and temporal scales, we used ultimate 3D imaging of solvent-cleared organs (uDISCO) and 3D rapid immunostaining. MC38 cells labelled with EGFP were injected into the left footpads of C57BL/6 mice. Draining lymph nodes (DLNs) harvested from these mice were cleared using the uDISCO method. Metastatic colorectal cancer (CRC) cells in various regions of DLNs from mice at different time points were quantified using 3D imaging of whole-mount tissue. Several stages of tumour cell growth and distribution in LNs were identified: 1) invasion of lymphatic vessels (LVs) and blood vessels (BVs); 2) dispersion outside LVs and BVs for proliferation and expansion; and 3) re-entry into BVs and efferent lymphatic vessels (ELVs) for further distant metastasis. Moreover, these data demonstrated that mouse fibroblast cells (MFCs) could not only promote LNM of tumour cells but also metastasize to LNs together with tumour cells, thus providing a "soil" for tumour cell colonization. In conclusion, 3D imaging of whole-mount tissue and spatiotemporal analysis of LNM may collectively constitute an auxiliary method to improve the accuracy of clinical LNM detection.

Liposomal siRNA Delivery in Papillary Thyroid Carcinoma Cells.

The discovery of RNA interference (RNAi) has opened a new strategy in cancer therapy, especially by silencing target genes. Pharmacologically it can be achieved by introducing of small (19-21 base pairs) dsRNA molecules known as small interfering RNA (siRNA) targeting interested genes. siRNA mediated gene has been widely investigated for its utility in treating various diseases including cancer. However, the systemic delivery of interested siRNA via non-viral methods remains a major challenge with large numbers of polymeric and liposomal systems being tested. The most effective methods involving cationic liposomes delivery to cells. Nonetheless, systemic delivery of siRNA via cationic lipid particles is often poor due to rapid uptake by reticuloendothelial organs, resulting in decreased delivery of these particles to the site of interest. Polyethylene glycol (PEG) has been used in siRNA-liposomes formulation to minimize reticuloendothelial uptake. Also, PEGylation permits the accumulation of the liposomes-loaded siRNA at the tumor sites with defective vasculatures such as enhanced permeability and retention phenomena. Thus, a simple method to prepare stable PEGylated siRNA-loaded lipid particles could provide better systemic delivery system in treating various cancers, including papillary thyroid carcinoma. Here we illustrate a simple protocol for the formulation of siRNA-loaded lipid particles by hydration of freeze-dried matrix (HFDM) method for effective delivery of target specific siRNA to papillary thyroid carcinoma cells.

Long Non-Coding RNAs Profiling Using Microarray in Papillary Thyroid Carcinoma.

Long non-coding RNAs (lncRNAs) have been implicated in various cancers, including papillary thyroid carcinomas (PTCs). Genome-wide analysis (GWAS) of lncRNAs expression in PTC samples exhibited up and down regulation of lncRNAs, thus, acting as tumor promoting oncogenes or tumor suppressors in the pathogenesis of PTC by interacting with target genes. For example, lncRNAs such as HOTAIR, NEAT1, MALAT1, FAL1, HOXD-AS1, etc. are overexpressed in PTC in comparison to that of non-cancerous thyroid tissues, which stimulate the pathogenesis of PTC. On the other hand, lncRNAs such as MEG3, CASC2, PANDAR, LINC00271, NAMA, PTCSC3, etc. are down regulated in PTC tissues when compared to that of non-cancerous thyroid samples, suppressing formation of PTC. Also, several lncRNAs such as BANCR acts as oncogenic or tumor suppressor in PTC formation depending on which they are interacting with. In addition, lncRNAs expression in patients with PTC associated with clinicopathological parameters such as distance metastasis, lymph node metastasis, tumor size, pathological stage, and response to therapy. Thus, lncRNAs profiles could have the potential to be used as prognostic or predictive biomarker in patients with PTC. Therefore, we describe the microarray method to examine lncRNAs expression in PTC tissue samples, which could facilitate better management of patients with PTC. Furthermore, this method could be fabricated to examine lncRNAs expression in other biological and/or clinical samples.