Development of a risk scoring system for patients with papillary thyroid cancer

As the importance of personalized therapeutics in aggressive papillary thyroid cancer (PTC) increases, accurate risk stratification is required. To develop a novel prognostic scoring system for patients with PTC (n = 455), we used mRNA expression and clinical data from The Cancer Genome Atlas. We performed variable selection using Network‐Regularized high‐dimensional Cox‐regression with gene network from pathway databases. The risk score was calculated using a linear combination of regression coefficients and mRNA expressions. The risk score and clinical variables were assessed by several survival analyses. The risk score showed high discriminatory power for the prediction of event‐free survival as well as the presence of metastasis. In multivariate analysis, the risk score and presence of metastasis were significant risk factors among the clinical variables that were examined together. In the current study, we developed a risk scoring system that will help to identify suitable therapeutic options for PTC.     

miR‐487b and TRAK2 that form an axis to regulate the aggressiveness of osteosarcoma,are potential therapeutic targets and prognostic biomarkers

To investigate the effect of microRNA‐487b (miR‐487b) as well as the underlying mechanism in osteosarcoma (OS). Data downloaded from the Gene Expression Omnibus (GEO) database were used to analyze the expression and prognostic value of miR‐487b/TRAK2. Cell counting kit‐8, colony formation, and transwell assays were performed to investigate the biological functions of miR‐487b and TRAK2. Luciferase reporter assay was applied to confirm the interactions between miR‐487b and TRAK2. miR‐487b was overexpressed in OS tissues and was inversely associated with the prognosis of OS patients. We discovered that miR‐487b could contribute to the proliferative, clonogenic, invasive, and migratory capabilities of OS cells. Through target prediction using miRWalk and differential expression analysis based on the GEO data set, trafficking kinesin protein 2 (TRAK2) was recognized as a potential target of miR‐487b, which was further verified by luciferase reporter assay. The expression of TRAK2 was decreased in OS tissues compared with normal tissues and was positively correlated with the prognosis of OS patients. A negative relevance was presented between the expression of miR‐487b and TRAK2 in OS cells. Of note, further mechanistic analyses indicated that TRAK2 was implicated in the regulatory effect of miR‐487b on the cell malignant behaviors in OS. To sum up, these results demonstrated that miR‐487b played an oncogenic role in OS progression via directly targeting TRAK2, which could advance the development of cancer treatment.     

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.     

Bioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancer

Lung cancer is the most talked about cancer in the world. It is also one of the cancers that currently has a high mortality rate. The aim of our research is to find more effective therapeutic targets and prognostic markers for human lung cancer. First, we download gene expression data from the GEO database. We performed weighted co-expression network analysis on the selected genes, we then constructed scale-free networks and topological overlap matrices, and performed correlation modular analysis with the cancer group. We screened the 200 genes with the highest correlation in the cyan module for functional enrichment analysis and protein interaction network construction, found that most of them focused on cell division, tumor necrosis factor-mediated signaling pathways, cellular redox homeostasis, reactive oxygen species biosynthesis, and other processes, and were related to the cell cycle, apoptosis, HIF-1 signaling pathway, p53 signaling pathway, NF-κB signaling pathway, and several cancer disease pathways are involved. Finally, we used the GEPIA website data to perform survival analysis on some of the genes with GS > 0.6 in the cyan module. CBX3, AHCY, MRPL12, TPGB, TUBG1, KIF11, LRRC59, MRPL17, TMEM106B, ZWINT, TRIP13, and HMMR was identified as an important prognostic factor for lung cancer patients. In summary, we identified 12 mRNAs associated with lung cancer prognosis. Our study contributes to a deeper understanding of the molecular mechanisms of lung cancer and provides new insights into drug use and prognosis.     

Automated system for gene annotation and metabolic pathway reconstruction using general sequence databases

Despite the growing number of genomes published or currently being sequenced, there is a relative paucity of software for functional classification of newly discovered genes and their assignment to metabolic pathways. Available software for such analyses has a very steep learning curve and requires the installation, configuration, and maintenance of large amounts of complex infrastructure, including complementary software and databases. Many such tools are restricted to one or a few data sources and classification schemes. In this work, we report an automated system for gene annotation and metabolic pathway reconstruction (ASGARD), which was designed to be powerful and generalizable, yet simple for the biologist to install and run on centralized, commonly available computers. It avoids the requirement for complex resources such as relational databases and web servers, as well as the need for administrator access to the operating system. Our methodology contributes to a more rapid investigation of the potential biochemical capabilities of genes and genomes by the biological researcher, and is useful in biochemical as well as comparative and evolutionary studies of pathways and networks.     

Quercetin augments apoptosis of canine osteosarcoma cells by disrupting mitochondria membrane potential and regulating PKB and MAPK signal transduction

Osteosarcoma is a mesenchymal malignant bone tumor accompanied by a high rate of lung metastasis and short survival in dogs. Although various therapies have been reported, the etiological mechanism of osteosarcoma remains undetermined and the development of novel therapeutic agents is warranted. In this study, we have reported the diverse functions of quercetin, one of the well-known flavonoid, in D-17 and DSN (canine osteosarcoma) cell lines. Current results indicate that quercetin decreases proliferative properties and increases programmed cell death, in addition to altering the cell cycle, mitochondrial depolarization, level of reactive oxygen species, and concentration of cytoplasmic calcium in both cells. Furthermore, it was observed that quercetin suppresses phosphorylation of AKT, P70S6K, and S6 proteins and upregulates phosphorylation of ERK1 or 2, P38, c-Jun N-terminal kinase, and P90RSK proteins in both cell lines. Collectively, we suggest that quercetin can be used as a pharmacological agent for suppressing the proliferation and inducing the apoptosis of canine osteosarcoma cells.     

Of patterns and pathways: microarray technologies for the analysis of filamentous fungi

During recent years, microarrays have been firmly established as valuable tools for the discovery of novel biological phenomena. Especially in combination with whole genome sequences, microarray data can help unravel the dynamics of the expressed genome. For filamentous fungi, microarray studies have already been performed with more than 20 different species; these investigations have explored a variety of different aspects of fungal biology. In this review, I will give an overview of some of the key questions that have been addressed using microarray hybridizations with filamentous fungi, with particular focus on the analysis of co-regulated pathways and physically clustered genes, as well as on the use of microarray data to determine a molecular phenotype. Additionally, a number of useful, freely available software tools for the analysis of fungal microarray data will be discussed.     

氯雷他定的生物信息学分析及其对COVID-19的潜在治疗意义

为探讨氯雷他定对新型冠状病毒肺炎的潜在治疗作用.从基因表达谱数据库(Gene expression omnibus,GEO)获取氯雷他定数据,分别进行差异分析、基因本体学(GO)富集分析、京都基因与基因组百科全书(KEGG)富集分析、蛋白质相互作用(Protein-protein interaction,PPI)网络分...     

Identification of immune-associated signatures and potential therapeutic targets for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) comprises a heterogeneous group of diseases with diverse aetiologies. It is characterized by increased pulmonary arterial pressure and right ventricular (RV) failure without specific drugs for treatment. Emerging evidence suggests that inflammation and autoimmune disorders are common features across all PAH phenotypes. This provides a novel idea to explore the characteristics of immunological disorders in PAH and identify immune-related genes or biomarkers for specific anti-remodelling regimens. In this study, we integrated three gene expression profiles and performed Gene Ontology (GO) and KEGG pathway analysis. CIBERSORT was utilized to estimate the abundance of tissue-infiltrating immune cells in PAH. The PPI network and machine learning were constructed to identify immune-related hub genes and then evaluate the relationship between hub genes and differential immune cells using ImmucellAI. Additionally, we implemented molecular docking to screen potential small-molecule compounds based on the obtained genes. Our findings demonstrated the density and distribution of infiltrating CD4 T cells in PAH and identified four immune-related genes (ROCK2, ATHL1, HSP90AA1 and ACTR2) as potential targets. We also listed 20 promising molecules, including TDI01953, pemetrexed acid and radotinib, for PAH treatment. These results provide a promising avenue for further research into immunological disorders in PAH and potential novel therapeutic targets.     

Searching for potential drug targets in two-component and phosphorelay signal-transduction systems using three-dimensional cluster analysis

Two-component and phosphorelay signal transduction systems are central components in the virulence and antimicrobial resistance responses of a number of bacterial and fungal pathogens; in some cases, these systems are essential for bacterial growth and viability. Herein, we analyze in detail the conserved surface residue clusters in the phosphotransferase domain of histidine kinases and the regulatory domain of response regulators by using complex structure-based three-dimensional cluster analysis. We also investigate the protein-protein interactions that these residue clusters participate in. The Spo0B-Spo0F complex structure was used as the reference structure, and the multiple aligned sequences of phosphotransferases and response regulators were paired correspondingly. The results show that a contiguous conserved residue cluster is formed around the active site, which crosses the interface of histidine kinases and response regulators. The conserved residue clusters of phosphotransferase and the regulatory domains are directly involved in the functional implementation of two-component signal transduction systems and are good targets for the development of novel antimicrobial agents.     

Proteomic search for potential diagnostic markers and therapeutic targets for ovarian clear cell adenocarcinoma

Clear cell adenocarcinoma (CCA) has a highly malignant potential in human epithelial ovarian cancer. The serum CA-125 is widely used as a marker for ovarian cancer, but the level is relatively low in CCA. Therefore, new sensitive biomarkers are required. In this report, we describe a promising proteomic analysis that is differentially expressed in CCA when compared to mucinous adenocarcinoma, using the ovarian cultured cell lines OVISE, OVTOKO, and MCAS. The disease-associated proteins were identified by 2-D differential gel electrophoresis (2-D DIGE) and MS. In this analysis, 18 up-regulated and 31 down-regulated spots were observed that had at least two-fold differences in the two CCA cell lines than in MCAS as control cells. Some of the proteins differentially expressed in CCA were previously observed as alternative expression levels in ovarian and/or other cancers in clinical samples. In a subsequent preliminary differential study using surgical specimens from patients with CCA, it was demonstrated that the identified proteins were expressed differentially in actual tissues, as well as in the CCA culture cells. The results from this investigation show the potentiality of a proteomic approach for identifying disease-associated proteins, which may eventually serve as diagnostic markers or therapeutic targets in CCA.     

Gene amplification and vector engineering to achieve rapid and high-level therapeutic protein production using the Dhfr-based CHO cell selection system

Demand is increasing for therapeutic biopharmaceuticals such as monoclonal antibodies. Achieving maximum production of these recombinant proteins under developmental time constraints has been a recent focus of study. The majority of these drugs are currently produced in altered Chinese hamster ovary (CHO) cells due to the high viability and the high densities achieved by these cells in suspension cultures. However, shortening the process of developing and isolating high-producing cell lines remains a challenge. This article focuses on current expression systems used to produce biopharmaceuticals in CHO cells and current methods being investigated to produce biopharmaceuticals more efficiently. The methods discussed include modified gene amplification methods, modifying vectors to improve expression of the therapeutic gene and improving the method of selecting for high-producing cells. Recent developments that use gene targeting as a method for increasing production are discussed.     

Discrimination and scoring using small sets of genes for two-sample microarray data

Comparison of gene expression for two groups of individuals form an important subclass of microarray experiments. We study multivariate procedures, in particular use of Hotelling's T2 for discrimination between the groups with a special emphasis on methods based on few genes only. We apply the methods to data from an experiment with a group of atopic dermatitis patients compared with a control group. We also compare our methodology to other recently proposed methods on publicly available datasets. It is found that (i) use of several genes gives a much improved discrimination of the groups as compared to one gene only, (ii) the genes that play the most important role in the multivariate analysis are not necessarily those that rank first in univariate comparisons of the groups, (iii) Linear Discriminant Analysis carried out with sets of 2-5 genes selected according to their Hotelling T2 give results comparable to state-of-the-art methods using many more genes, a feature of our method which might be crucial in clinical applications. Finding groups of genes that together give optimal multivariate discrimination (given the size of the group) can identify crucial pathways and networks of genes responsible for a disease. The computer code that we developed to make computations is available as an R package.     

UBC and YWHAZ as suitable reference genes for accurate normalisation of gene expression using MCF7, HCT116 and HepG2 cell lines

Relative quantification of in vitro gene expression using real-time PCR requires stably expressed reference gene for normalisation. In this study, total RNA from MCF7, HCT116 and HepG2 cells were extracted and converted to cDNA using commercially available kit, and real-time PCR was then performed to analyse the expression levels of twelve reference genes to select the most ideal reference gene for accurate normalisation in gene expression study. geNorm and NormFinder software were used to analyse the stabilities of the reference genes, which showed a wide range of C_t values. The geNorm analysis showed the following ranking for stability of genes: UBC, YWHAZ > RPLP > TBP > ACTB > HPRT1 > PPIA > GAPDH > GUSB > B2M > TUBB > RRN18S. A similar ranking of reference genes was obtained by NormFinder, and the four most stable reference genes were identical using both approaches. UBC and YWHAZ were proposed to be the two most suitable reference genes based on the above analyses. To further assess the stabilities of the UBC and YWHAZ in a formal experiment, MCF7, HCT116 and HepG2 cell lines were subjected to treatments with 5-aza-dC and TSA. Both UBC and YWHAZ exhibited stable expression levels across control and treatment groups. Therefore, we propose that UBC and YWHAZ are the two most suitable reference genes for our gene expression studies using MCF7, HCT116 and HepG2 cell lines.     

Current and emerging therapeutic strategies for Fanconi anemia

Fanconi Anemia (FA) is a rare disorder with incidence of 1in 350,000 births. It is characterized by progressive bone marrow failure leading to death of many patients in their childhood while development of cancer at later stages of life in some. The treatment of FA is still a medical challenge. Current treatments of FA include androgen administration, hematopoietic growth factors administration and hematopoietic stem cell transplantation (HSCT). Clinical gene therapy trials are still ongoing. The partial success of current therapies has renewed interest in the search for new treatments. Generation of patient-specific induced pluripotent stem (iPS) has shown promising results for cell and gene based therapy. Small molecule interventions have been observed to delay tumor onset in FA. Tumors deficient in FA pathway can be treated by profiling of DNA repair pathway through synthetic lethality mechanism. Targeting toll-like receptor 8 (TLR8) dependent TNFα overexpression is yet another upcoming therapeutic approach to treat FA patients. In conclusion, in the present scenario of treatments available for FA, a proper algorithm of treatment decisions must be followed for better management of FA patients and to ensure their increased survival. Innovative therapeutic approaches that can prevent both anemia and cancer should be developed for more effective treatment of FA.     

Molecular cloning,expression pattern and phylogenetic analysis of myosin light chain 2 gene from Antheraea pernyi: A potential marker for phylogenetic inference

Myosin light chain 2 (MLC-2) gene was isolated and characterized from Antheraea pernyi, a well-known wild silkmoth. The isolated cDNA sequence is 905 bp in length with an open reading frame of 612 bp encoding a polypeptide of 203 amino acids. Semi-quantitative RT-PCR analysis showed that the MLC-2 gene was transcribed during four developmental stages (egg, larva, pupa, and moth), and present in all tissues tested. Alignment analysis revealed that the deduced protein sequence has over 95% identity to myosin light chain 2 of lepidopteran species, and 57–88% identity to other insect species, suggesting that insect MLC-2 proteins are highly conserved throughout evolution. The protein sequence was used to construct phylogenetic trees with other known vertebrate and invertebrate MLC-2 sequences, and the obtained trees demonstrated similar topology with the classical systematics, indicating the potential value of MLC-2 gene in phylogenetic study.