Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer.

BACKGROUND: The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health. METHODS: In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116). RESULTS: According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29). CONCLUSIONS: Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Distinct neurological phenotypes associated with biallelic loss of NOTCH3 function: evidence for recessive inheritance.

BACKGROUND: NOTCH3 variants are known to be linked to cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). However, some null NOTCH3 variants with homozygous inheritance cause neurological symptoms distinct from CADASIL. The aim of this study was to expand the clinical spectrum of this distinct condition and provide further evidence of its autosomal recessive inheritance. METHODS AND RESULTS: Whole exome sequencing (WES) was performed on a proband who exhibited livedo racemosa, ataxia, cognitive decline, seizures, and MRI white matter abnormalities without anterior temporal pole lesions. Segregation analysis was conducted with Sanger sequencing. WES of the proband identified a novel homozygous NOTCH3 null variant (c.2984delC). The consanguineous parents were confirmed as heterozygous variant carriers. In addition, three heterozygous NOTCH3 null variants were reported as incidental findings in three unrelated cases analyzed in our center. CONCLUSION: The findings of this study suggest an autosomal recessive inheritance pattern in this early-onset leukoencephalopathy, in contrast to CADASIL's dominant gain-of-function mechanism; which is a clear example of genotype-phenotype correlation. Comprehensive genetic analysis provides valuable insights into disease mechanisms and facilitates diagnosis and family planning for NOTCH3-associated neurological disorders.

Immunogenicity of different nanoparticle adjuvants containing recombinant RBD coronavirus antigen in animal model.

Ongoing mutations of SARS-CoV-2 present challenges for vaccine development, promising renewed global efforts to create more effective vaccines against coronavirus disease (COVID-19). One approach is to target highly immunogenic viral proteins, such as the spike receptor binding domain (RBD), which can stimulate the production of potent neutralizing antibodies. This study aimed to design and test a subunit vaccine candidate based on the RBD. Bioinformatics analysis identified antigenic regions of the RBD for recombinant protein design. In silico analysis identified the RBD region as a feasible target for designing a recombinant vaccine. Bioinformatics tools predicted the stability and antigenicity of epitopes, and a 3D model of the RBD-angiotensin-converting enzyme 2 complex was constructed using molecular docking and codon optimization. The resulting construct was cloned into the pET-28a (+) vector and successfully expressed in Escherichia coli BL21DE3. As evidenced by sodium dodecyl-polyacrylamide gel electrophoresis and Western blotting analyses, the affinity purification of RBD antigens produced high-quality products. Mice were immunized with the RBD antigen alone or combined with aluminum hydroxide (AlOH), calcium phosphate (CaP), or zinc oxide (ZnO) nanoparticles (NPs) as adjuvants. Enzyme-linked immunosorbent assay assays were used to evaluate immune responses in mice. In-silico analysis confirmed the stability and antigenicity of the designed protein structure. RBD with CaP NPs generated the highest immunoglobulin G titer compared to AlOH and ZnO after three doses, indicating its effectiveness as a vaccine platform. In conclusion, the recombinant RBD antigen administered with CaP adjuvant NPs induces potent humoral immunity in mice, supporting further vaccine development. These results contribute to ongoing efforts to develop more effective COVID-19 vaccines.

Evaluation of the Effect of Cichorium intybus L. on the Liver Enzymes in Burn Patients: A Randomized Double-Blind Clinical Trial.

Burn injuries are considered an important public health problem in the world. Burns are considered the fourth most common kind of trauma in the world, after traffic accidents, falls, and interpersonal violence. Various biochemical agents are involved in the burn healing process such as cytokines (such as IL-6 and TNF-α), antioxidants, and liver and kidney damage biomarkers. Cichorium intybus L. and milk thistle extracts showed a wide range of pharmacological activities such as significant antimicrobial effect and antioxidant activity, as well as anti-inflammatory, antidiabetic, antiproliferative, antiprotozoal, and hepatoprotective effect. Also, these two herbs possess blood-cleansing, detoxifying, laxative, and invigorating activities. Some research confirmed that the preparations of the extract are very suitable for the treatment of nonalcoholic fatty liver disease. This is a double-blind randomized controlled clinical trial. Patients with 2nd and 3rd degree burns have been selected to participate in the study according to the inclusion criteria. A total of 60 patients were selected and divided into intervention and control groups (30 patients in each group). Patients in the intervention group received chicory seed syrup 10 cc three times a day and 1 placebo capsule, and those in the control group received placebo syrup (10 cc three times a day) and one Livergol (140 mg of silymarin in each capsule) capsule. Lab data such as liver function tests, albumin, creatinine, BUN, and hemoglobin were checked every 3 days and 1 week after discharge. The treatment lasted for 4 weeks. According to the results of the study, although the average of liver enzymes at the end of the study does not show a significant difference between the two groups, the level of liver enzymes in each group decreased on the 15th day of the study compared to the first day. This trial is registered with IRCT20180609040016N1.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review.

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

BACKGROUND: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. RESULTS: We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). CONCLUSIONS: Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

Exploring Co-occurrence patterns and microbial diversity in the lung microbiome of patients with non-small cell lung cancer.

BACKGROUND: It has been demonstrated in the literature that a dysbiotic microbiome could have a negative impact on the host immune system and promote disease onset or exacerbation. Co-occurrence networks have been widely adopted to identify biomarkers and keystone taxa in the pathogenesis of microbiome-related diseases. Despite the promising results that network-driven approaches have led to in various human diseases, there is a dearth of research pertaining to key taxa that contribute to the pathogenesis of lung cancer. Therefore, our primary goal in this study is to explore co-existing relationships among members of the lung microbial community and any potential gained or lost interactions in lung cancer. RESULTS: Using integrative and network-based approaches, we integrated four studies assessing the microbiome of lung biopsies of cancer patients. Differential abundance analyses showed that several bacterial taxa are different between tumor and tumor-adjacent normal tissues (FDR adjusted p-value < 0.05). Four, fifteen, and twelve significantly different associations were found at phylum, family, and genus levels. Diversity analyses suggested reduced alpha diversity in the tumor microbiome. However, beta diversity analysis did not show any discernible pattern between groups. In addition, four distinct modules of bacterial families were detected by the DBSCAN clustering method. Finally, in the co-occurrence network context, Actinobacteria, Firmicutes, Bacteroidetes, and Chloroflexi at the phylum level and Bifidobacterium, Massilia, Sphingobacterium, and Ochrobactrum at the genus level showed the highest degree of rewiring. CONCLUSIONS: Despite the absence of statistically significant differences in the relative abundance of certain taxa between groups, it is imperative not to overlook them for further exploration. This is because they may hold pivotal central roles in the broader network of bacterial taxa (e.g., Bifidobacterium and Massilia). These findings emphasize the importance of a network analysis approach for studying the lung microbiome since it could facilitate identifying key microbial taxa in lung cancer pathogenesis. Relying exclusively on differentially abundant taxa may not be enough to fully grasp the complex interplay between lung cancer and the microbiome. Therefore, a network-based approach can offer deeper insights and a more comprehensive understanding of the underlying mechanisms.

Instabilities in a growing system of active particles: scalar and vectorial systems.

The physics of micron-scale biological colonies usually benefits from different out-of-equilibrium sources. In bacterial colonies and cellular tissues, the growth process is among the important active sources that determine the dynamics. In this article, we study the generic dynamical instabilities associated with the growth phenomena that may arise in both scalar and vectorial systems. In vectorial systems, where the rotational degrees of particles play a role, a phenomenological growth-mediated torque can affect the rotational dynamics of individual particles. We show that such a growth-mediated torque can result in active traveling waves in the bulk of a growing system. In addition to the bulk properties, we analyze the instabilities in the shape of growing interfaces in both scalar and vectorial systems.

An investigation into the relationship between nutritional status, dietary intake, symptoms and health-related quality of life in children and young people with juvenile idiopathic arthritis: a systematic review and meta-analysis.

BACKGROUND: The association between diet, symptoms and health related quality of life in children and young people with Juvenile idiopathic arthritis (JIA) is not clearly understood. The objectives of this systematic review and meta-analysis were to explore the evidence for a relationship between nutritional status, dietary intake, arthritis symptoms, disease activity and health-related quality of life in children and young people with JIA considering both observational and interventional studies separately. METHOD: The databases PubMed, CINAHL, PsycINFO, Web of Science and Cochrane were searched in October 2019, updated in September 2020 and October 2021. Searches were restricted to English language, human and age (2-18 years old). Studies were included if they measured the effect of dietary supplements, vitamins or minerals, or diet in general, on quality of life and/ or arthritis symptom management. Two researchers independently screened titles and abstracts. Full texts were sourced for relevant articles. PRISMA guidelines were used for extracting data. For variables (vitamin D and disease activity), a random-effects meta-analysis model was performed. Two authors using a standardized data extraction form, extracted data independently. RESULTS: 11,793 papers were identified through database searching, 26 studies met our inclusion criteria with 1621 participants. Overall studies quality were fair to good. Results from controlled trial and case control studies with total 146 JIA patients, found that Ɯ-3 PUFA improved the mean active joint count (p < 0.001), Juvenile Arthritis Disease Activity Score (JADAS-27) (p < 0.001) and immune system (≤ 0.05). Furthermore, n-3 and n-6 PUFAs have a negative correlation with CRP (C-reactive protein) and ESR (erythrocyte sedimentation rate) (p < 0.05). Improvement in JIA symptoms were observed in one case, one pilot and one exploratory study with overall 9 JIA patients after receiving Exclusive Enteral Nutrition (EEN) which contains protein and what is required for a complete nutrition, A clinical trial study found Kre-Celazine nutrition (composed of a proprietary alkali buffered, creatine monohydrate and fatty acids mixture) in 16 JIA patients improved symptoms of JIA. No association was found between vitamin D and disease activity from three studies. Height and weight values in relation to healthy controls varied across studies (p = 0.029). CONCLUSIONS: We were only able to include small studies, of lower design hierarchy, mainly pilot studies. We found some evidence of lower height and weight across studies in JIA, but were unable to confirm an association between diet, symptoms and health-related quality of life in children and young people with JIA. Well-designed, carefully measured and controlled interventional studies of dietary patterns in combination with important contributing factors such as medication and lifestyle behaviours, including physical activity, are required to determine the impact of diet in improving symptoms and growth patterns in children and young people with JIA, with an aim to improve the quality of their life. TRIAL REGISTRATION: PROSPERO [CRD42019145587].

Comparative proteomic analysis of mustard lung as a complicated disease using systems biology approach.

During Iraq-Iran conflict, chemical weapons, particularly SM gas, were used numerous times, whose aftereffects are still present. This study aimed to compare serum proteome in the chronic ML (n = 10) and HC (n = 10). TMT label-based quantitative proteomics was used to examine serums from two groups. Among total significant proteins, 14 proteins were upregulated (log2 ≥ FC 0.5, p 0.05), and 6 proteins were downregulated (log2 ≤ FC - 0.5, p 0.05). By helping PPI network, and EA, 11 main pathways connected to significantly different protein expression levels were discovered, including inflammatory and cell adhesion signaling pathways. It may be deduced that the wounded organs of exposed individuals experience poor repair cycles of cell degeneration and regeneration because certain repair signals were elevated while other structural and adhesion molecules were downregulated. The systems biology approach can help enhance our basic knowledge of biological processes, and contribute to a deeper understanding of pathophysiological mechanisms, as well as the identification of potential biomarkers of disease.

In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.

Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico methods have been developed to improve the enrichment processes rate. However, the majority of these methods did not show any effort in designing novel aptamers. Moreover, some target proteins may have not any binding RNA candidates in nature and a reductive mechanism is needed to generate novel aptamer pools among enormous possible combinations of nucleotide acids to be examined in vitro. We have applied a genetic algorithm (GA) with an embedded binding predictor fitness function to in silico design of RNA aptamers. As a case study of this research, all steps were accomplished to generate an aptamer pool against aminopeptidase N (CD13) biomarker. First, the model was developed based on sequential and structural features of known RNA-protein complexes. Then, utilizing RNA sequences involved in complexes with positive prediction results, as the first-generation, novel aptamers were designed and top-ranked sequences were selected. A 76-mer aptamer was identified with the highest fitness value with a 3 to 6 time higher score than parent oligonucleotides. The reliability of obtained sequences was confirmed utilizing docking and molecular dynamic simulation. The proposed method provides an important simplified contribution to the oligonucleotide-aptamer design process. Also, it can be an underlying ground to design novel aptamers against a wide range of biomarkers.

Associastion of In-hospital Use of Statins, Aspirin, and Renin-Angiotensin-Aldosterone Inhibitors with Mortality and ICU Admission Due to COVID-19.

The exaggerated host response to Sars-CoV-2 plays an important role in COVID-19 pathology but provides a therapeutic opportunity until definitive virus targeted therapies and vaccines become available. Given a central role of endothelial dysfunction and systemic inflammation, repurposing ACE inhibitors (ACEIs), angiotensin receptor blockers (ARBs), statins, and aspirin has been of interest. In this retrospective, single-center study, we evaluated the primary outcomes of mortality and ICU admission in 587 hospitalized patients with documented COVID-19 with or without ACEIs, ARBs, statins, and aspirin. Atorvastatin was associated with reduced mortality, which persisted after adjusting for age, lockdown status, and other medications (OR: 0.18. 95% CI: 0.06-0.49, P = 0.001). ACEIs were also associated with reduced mortality in the crude model (OR: 0.20, CI: 0.06-0.66, P = 0.008), as ACEIs and ARBs were combined as a single group (OR: 0.35, CI: 0.16-0.75, P = 0.007), although ARBs alone did not reach statistical significance. There was no association between any medications and risk of ICU admission. Aspirin only achieved a significant association of reduced mortality in a subgroup of patients with diabetes in the crude model (OR: 0.17, CI: 0.04-0.80, P = 0.02). The reduced mortality observed with atorvastatin is consistent with other literature, and consideration should be given to atorvastatin as a COVID-19 treatment. While there was suggested benefit of ACEIs and ARBs in the present study, other studies are varied and further studies are warranted to recommend employing these medications as a treatment strategy. Nevertheless, this study combined with others continues to give credibility that ACEIs and ARBs are safe to continue in the setting of COVID-19.

An efficient hybrid feature selection method to identify potential biomarkers in common chronic lung inflammatory diseases.

Asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious lung inflammatory diseases. The understanding of the pathogenesis mechanism and the identification of potential prognostic biomarkers of these diseases can provide the patients with more efficient treatments. In this study, an efficient hybrid feature selection method was introduced in order to extract informative genes. We implemented an ontology-based ranking approach on differentially expressed genes following a wrapper method. The examination of the different gene ontologies and their combinations motivated us to propose a biological functional-based method to improve the performance of further wrapper methods. The results identified: TOM1L1, SRSF1, and GIT2 in asthma; CHCHD4, PAIP2, CRLF3, UBQLN4, TRAK1, PRELID1, VAMP4, CCM2, and APBB1IP in COPD; and TUFT1, GAB2, B4GALNT1, TNFRSF17, PRDM8, and SETDB2 in IPF as the potential biomarkers. The proposed method can be used to identify hub genes in other high-throughput datasets.

RPINBASE: An online toolbox to extract features for predicting RNA-protein interactions.

Feature extraction is one of the most important preprocessing steps in predicting the interactions between RNAs and proteins by applying machine learning approaches. Despite many efforts in this area, still, no suitable structural feature extraction tool has been designed. Therefore, an online toolbox, named RPINBASE which can be applied to different scopes of biological applications, is introduced in this paper. This toolbox employs efficient nested queries that enhance the speed of the requests and produces desired features in the form of positive and negative samples. To show the capabilities of the proposed toolbox, the developed toolbox was investigated in the aptamer design problem, and the obtained results are discussed. RPINBASE is an online toolbox and is accessible at http://rpinbase.com.

Oxidant/antioxidant status in Type-2 diabetes mellitus patients with metabolic syndrome.

BACKGROUND: The concurrence of metabolic syndrome (MS) and diabetes mellitus (DM) is increasing worldwide. The long-term complications of these chronic diseases are a threat to patients' well-being. Oxidative stress is involved in the pathogenesis of several diseases. To understand the basic pathophysiological mechanisms of Type-2 DM (T2DM) and its related complications, we aimed to investigate the oxidant/antioxidant status and Na+-K+ ATPase activity in T2DM with MS. MATERIALS AND METHODS: A population of ninety individuals including fifty patients diagnosed with T2DM and MS, but without overt diabetes complications, and forty individuals without T2DM or MS as control group participated in this study. Plasma malondialdehyde (MDA), catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx) activities, total antioxidant capacity (TAC), and Na+-K+ ATPase activity were assessed by standard laboratory methods. RESULTS: Plasma MDA in patients group was statistically significantly higher than that of controls (P ≤ 0.05). Whereas, Na+-K+ ATPase activity was statistically significantly lower in patient group (P ≤ 0.05). TAC, CAT, SOD, and GPx enzyme activities were not statistically significantly different between two groups (P > 0.05). Results from the patient group showed positive correlations between CAT activity and triglyceride and positive correlations between GPx activity and weight, body mass index (BMI), and waist circumference. In addition, there was a positive correlation between MDA results with high-density lipoprotein-cholesterol (HDL-C) and total cholesterol and a negative correlation with TAC, BMI, and weight (P ≤ 0.05) in controls. CONCLUSION: Because T2DM patients were without any vascular complications, antioxidant defense results may reflect the lack of progression of diabetes complications in these patients. These results emphasize the need for initial and continued assessment of cardiovascular disease risks in diabetic individuals. Implementation of timely interventions may improve the management of diabetes and prevent the progression of diabetes complications.

Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis.

BACKGROUND: asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious pulmonary diseases that contain common and unique characteristics. Therefore, the identification of biomarkers that differentiate these diseases is of importance for preventing misdiagnosis. In this regard, the present study aimed to identify the disorders at the early stages, based on lung transcriptomics data and drug-target interactions. METHODS: To this end, the differentially expressed genes were found in each disease. Then, WGCNA was utilized to find specific and consensus gene modules among the three diseases. Finally, the disease-disease similarity was analyzed, followed by determining candidate drug-target interactions. RESULTS: The results confirmed that the asthma lung transcriptome was more similar to COPD than IPF. In addition, the biomarkers were found in each disease and thus were proposed for further clinical validations. These genes included RBM42, STX5, and TRIM41 in asthma, CYP27A1, GM2A, LGALS9, SPI1, and NLRC4 in COPD, ATF3, PPP1R15A, ZFP36, SOCS3, NAMPT, and GADD45B in IPF, LRRC48 and CETN2 in asthma-COPD, COL15A1, GIMAP6, and JAM2 in asthma-IPF and LMO7, TSPAN13, LAMA3, and ANXA3 in COPD-IPF. Finally, analyzing drug-target networks suggested anti-inflammatory candidate drugs for treating the above mentioned diseases. CONCLUSION: In general, the results revealed the unique and common biomarkers among three chronic lung diseases. Eventually, some drugs were suggested for treatment purposes.

LncRNA and mRNA integration network reconstruction reveals novel key regulators in esophageal squamous-cell carcinoma.

Many experimental and computational studies have identified key protein coding genes in initiation and progression of esophageal squamous cell carcinoma (ESCC). However, the number of researches that tried to reveal the role of long non-coding RNAs (lncRNAs) in ESCC has been limited. LncRNAs are one of the important regulators of cancers which are transcribed dominantly in the genome and in various conditions. The main goal of this study was to use a systems biology approach to predict novel lncRNAs as well as protein coding genes associated with ESCC and assess their prognostic values. By using microarray expression data for mRNAs and lncRNAs from a large number of ESCC patients, we utilized "Weighted Gene Co-expression Network Analysis" (WGCNA) method to make a big coding-non-coding gene co-expression network, and discovered important functional modules. Gene set enrichment and pathway analysis revealed major biological processes and pathways involved in these modules. After selecting some protein coding genes involved in biological processes and pathways related to cancer, we used "LncTar", a computational tool to predict potential interactions between these genes and lncRNAs. By combining interaction results with Pearson correlations, we introduced some novel lncRNAs with putative key regulatory roles in the network. Survival analysis with Kaplan-Meier estimator and Log-rank test statistic confirmed that most of the introduced genes are associated with poor prognosis in ESCC. Overall, our study reveals novel protein coding genes and lncRNAs associated with ESCC, along with their predicted interactions. Based on the promising results of survival analysis, these genes can be used as good estimators of patients' survival, or even can be analyzed further as new potential signatures or targets for the therapy of ESCC disease.

Survival analysis and functional annotation of long non-coding RNAs in lung adenocarcinoma.

Long non-coding RNAs (lncRNAs) are a subclass of non-protein coding transcripts that are involved in several regulatory processes and are considered as potential biomarkers for almost all cancer types. This study aims to investigate the prognostic value of lncRNAs for lung adenocarcinoma (LUAD), the most prevalent subtype of lung cancer. To this end, the processed data of The Cancer Genome Atlas LUAD were retrieved from GEPIA and circlncRNAnet databases, matched with each other and integrated with the analysis results of a non-small cell lung cancer plasma RNA-Seq study. Then, the data were filtered in order to separate the differentially expressed lncRNAs that have a prognostic value for LUAD. Finally, the selected lncRNAs were functionally annotated using a bioinformatic and systems biology approach. Accordingly, we identified 19 lncRNAs as the novel LUAD prognostic lncRNAs. Also, based on our results, all 19 lncRNAs might be involved in lung cancer-related biological processes. Overall, we suggested several novel biomarkers and drug targets which could help early diagnosis, prognosis and treatment of LUAD patients.

Chemotaxis mediated interactions can stabilize the hydrodynamic instabilities in active suspensions.

Ordered phases in active suspensions of polar swimmers are under long-wavelength hydrodynamic mediated instabilities. In this article, we show that chemical molecules dissolved in aqueous suspensions, as an unavoidable part of most wet active systems, can mediate long-range interactions and subsequently stabilize the polar phase. Chemoattractants in living suspensions and dissolved molecules in synthesized Janus suspensions are reminiscent of such chemical molecules. Communication between swimmers through the gradients of such chemicals is the foundation of this stabilization mechanism. To classify the stable states of such active systems, we investigate the detailed phase diagrams for two classes of systems with momentum conserving and non-conserving dynamics. Our linear stability analysis shows that the proposed stabilization mechanism can work for swimmers with different dynamical properties, e.g., pushers or pullers and with various static characteristics, e.g., spherical, oblate or prolate geometries.

Dynamics near planar walls for various model self-phoretic particles.

For chemically active particles suspended in a liquid solution and moving by self-phoresis, the dynamics near chemically inert, planar walls is studied theoretically by employing various choices for the activity function, i.e., the spatial distribution of the sites where various chemical reactions take place. We focus on the case of solutions composed of electrically neutral species. This analysis extends previous studies of the case that the chemical activity can be modeled effectively as the release of a "product" molecular species from parts of the surface of the particle by accounting for annihilation of the product molecules by chemical reactions, either on the rest of the surface of the particle or in the volume of the surrounding solution. We show that, for the models considered here, the emergence of "sliding" and "hovering" wall-bound states is a generic, robust feature. However, the details of these states, such as the range of parameters within which they occur, depend on the specific model for the activity function. Additionally, in certain cases there is a reversal of the direction of the motion compared to the one observed if the particle is far away from the wall. We have also studied the changes of the dynamics induced by a direct interaction between the particle and the wall by including a short-ranged repulsive component to the interaction in addition to the steric one (a procedure often employed in numerical simulations of active colloids). Upon increasing the strength of this additional component, while keeping its range fixed, significant qualitative changes occur in the phase portraits of the dynamics near the wall: for sufficiently strong short-ranged repulsion, the sliding steady states of the dynamics are transformed into hovering states. Furthermore, our studies provide evidence for an additional "oscillatory" wall-bound steady state of motion for chemically active particles due to a strong, short-ranged, and direct repulsion. This kind of particle translates along the wall at a distance from it which oscillates between a minimum and a maximum.