Identification CXCL9 is a Potential Prognostic Biomarker in Ovarian and Gastric Cancer and is Correlated with Immune Infiltrates

Background: CXCL9 also known as an interferon gamma-inducible chemokine that belonging to the CXC chemokine family. It plays a role in promoting chemotaxis, inducing leukocyte differentiation and multiplication, and triggering tissue extravasation. Methods: The TIMER (Tumor Immune Estimation Resource) and cancer microarray database Oncomine were used to dig at CXCL9 expression. The clinic prognostic level of CXCL9 was evaluated via Kaplan-Meier plotter. Then, Using TIMER and GEPIA, we investigated whether CXCL9 expression impacted cancer immune infiltrates. Results: CXCL9 expression has been found to be significantly lower in ovarian and gastric cancers relative to normal tissues. In patients with ovarian cancer (OS HR = 0.78, P = 0.0017; PFS HR = 0.85, R = 0.015) and gastric cancer (OS HR = 0.55, P = 1.1e-08; PFS HR = 0.58, R = 7.6e-07), low CXCL9 expression was correlation to PFS (progression-free survival) and OS (poor overall survival). Furthermore, in OV and GC, CXCL9 was shown to have a close interaction with tumor-infiltrating immunity cells (B cells, CD4+ and CD8+ T cells, macrophages, neutrophils, and dendritic cells). CXCL9 expression, on the other hand, was shown to be closely related to several immune markers.Conclusion: In OV and GC, CXCL9 mRNA level is strongly associated with prognosis and levels of penetration tumor-infiltrating immunity cell. The CXCL9 expression may also play a role in controlling TAMs (tumor-associated macrophages), DCs (Dendritic cells), CTLs (cytotoxic lymphocytes), and NK (natural killer) cells in OV and GC. CXCL9 may be seen as an independent marker that assesses the prognosis in OV and GC patients. Besides, CXCL9 expression level also can assess the immune cell subtypes of tumor microenvironment in OV and GC.

Identification CXCL9 is a Potential Prognostic Biomarker in Ovarian and Gastric Cancer and is Correlated with Immune Infiltrates

Background CXCL9 also known as an interferon gamma-inducible chemokine that belonging to the CXC chemokine family. It plays a role in promoting chemotaxis, inducing leukocyte differentiation and multiplication, and triggering tissue extravasation. Methods The TIMER (Tumor Immune Estimation Resource) and cancer microarray database Oncomine were used to dig at CXCL9 expression. The clinic prognostic level of CXCL9 was evaluated via Kaplan-Meier plotter. Then, Using TIMER and GEPIA, we investigated whether CXCL9 expression impacted cancer immune infiltrates. Results CXCL9 expression has been found to be significantly lower in ovarian and gastric cancers relative to normal tissues. In patients with ovarian cancer (OS HR = 0.78, P = 0.0017; PFS HR = 0.85, R = 0.015) and gastric cancer (OS HR = 0.55, P = 1.1e-08; PFS HR = 0.58, R = 7.6e-07), low CXCL9 expression was correlation to PFS (progression-free survival) and OS (poor overall survival). Furthermore, in OV and GC, CXCL9 was shown to have a close interaction with tumor-infiltrating immunity cells (B cells, CD4 + and CD8 + T cells, macrophages, neutrophils, and dendritic cells). CXCL9 expression, on the other hand, was shown to be closely related to several immune markers. Conclusion In OV and GC, CXCL9 mRNA level is strongly associated with prognosis and levels of penetration tumor-infiltrating immunity cell. The CXCL9 expression may also play a role in controlling TAMs (tumor-associated macrophages), DCs (Dendritic cells), CTLs (cytotoxic lymphocytes), and NK (natural killer) cells in OV and GC. CXCL9 may be seen as an independent marker that assesses the prognosis in OV and GC patients. Besides, CXCL9 expression level also can assess the immune cell subtypes of tumor microenvironment in OV and GC.

Identification and fine mapping of qGR6.2, a novel locus controlling rice seed germination under salt stress

Background Rice growth is frequently affected by salinity. When exposed to high salinity, rice seed germination and seedling establishment are significantly inhibited. With the promotion of direct-seeding in Asia, improving rice seed germination under salt stress is crucial for breeding. Results In this study, an indica landrace Wujiaozhan (WJZ) was identified with high germinability under salt stress. A BC1F2 population derived from the crossing WJZ/Nip (japonica, Nipponbare)//Nip, was used to quantitative trait loci (QTL) mapping for the seed germination rate (GR) and germination index (GI) under H2O and 300 mM NaCl conditions. A total of 13 QTLs were identified, i.e. ten QTLs under H2O conditions and nine QTLs under salt conditions. Six QTLs, qGR6.1, qGR8.1, qGR8.2, qGR10.1, qGR10.2 and qGI10.1 were simultaneously identified under two conditions. Under salt conditions, three QTLs, qGR6.2, qGR10.1 and qGR10.2 for GR were identified at different time points during seed germination, which shared the same chromosomal region with qGI6.2, qGI10.1 and qGI10.2 for GI respectively. The qGR6.2 accounted for more than 20% of phenotypic variation under salt stress, as the major effective QTL. Furthermore, qGR6.2 was verified via the BC2F2 population and narrowed to a 65.9-kb region with eleven candidate genes predicted. Based on the microarray database, five candidate genes were found with high transcript abundances at the seed germination stage, of which LOC_Os06g10650 and LOC_Os06g10710 were differentially expressed after seed imbibition. RT-qPCR results showed the expression of LOC_Os06g10650 was significantly up-regulated in two parents with higher levels in WJZ than Nip during seed germination under salt conditions. Taken together, it suggests that LOC_Os06g10650, encoding tyrosine phosphatase family protein, might be the causal candidate gene for qGR6.2. Conclusions In this study, we identified 13 QTLs from a landrace WJZ that confer seed germination traits under H2O and salt conditions. A major salt-tolerance-specific QTL qGR6.2 was fine mapped to a 65.9-kb region. Our results provide information on the genetic basis of improving rice seed germination under salt stress by marker-assisted selection (MAS).

A Genes selected after application modified logistic regression in the microarrays gene expression for breast cancer.

The modified logistic regression (Morais-rodrigues et al., 2020) was used to classify breast cancer subtypes using all microarray database samples. A stabilizing term, that allows the assignment of values to αi∗ parameters, was inserted in this methodology and with that, during the derivation there is the insertion of the identity matrix (positive defined) which added to the other semi-defined part, results in a positive defined matrix, which has auto values > 0 and determinant > 0, square matrix is full rank if it is reversible (determinant > 0) , which results in a single solution. In the results it was observed that some genes were located topologically in the extremities after plotting the parameters αi∗, these parameters are related to the expression of genes with a suppressor or oncogenic profile in breast cancer, and with genes not studied yet. Some of these genes were found in gene regulatory networks from the search of Iglesias-Martinez et al. (2016), and S-score values were associated with these genes, negative value S-score is indicative of tumor suppressing or reduced gene activity and the positive value S-score is indicative of oncogene or increased gene activity (de Souza et al., 2014). In view of the importance of these genes, this article provides a literary review of them.

Novel modelling of clustering for enhanced classification performance on gene expression data

Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expression data. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches.

GlyMDB: Glycan Microarray Database and analysis toolset

Motivation Glycan microarrays are capable of illuminating the interactions of glycan-binding proteins (GBPs) against hundreds of defined glycan structures, and have revolutionized the investigations of protein–carbohydrate interactions underlying numerous critical biological activities. However, it is difficult to interpret microarray data and identify structural determinants promoting glycan binding to glycan-binding proteins due to the ambiguity in microarray fluorescence intensity and complexity in branched glycan structures. To facilitate analysis of glycan microarray data alongside protein structure, we have built the Glycan Microarray Database (GlyMDB), a web-based resource including a searchable database of glycan microarray samples and a toolset for data/structure analysis. Results The current GlyMDB provides data visualization and glycan-binding motif discovery for 5203 glycan microarray samples collected from the Consortium for Functional Glycomics. The unique feature of GlyMDB is to link microarray data to PDB structures. The GlyMDB provides different options for database query, and allows users to upload their microarray data for analysis. After search or upload is complete, users can choose the criterion for binder versus non-binder classification. They can view the signal intensity graph including the binder/non-binder threshold followed by a list of glycan-binding motifs. One can also compare the fluorescence intensity data from two different microarray samples. A protein sequence-based search is performed using BLAST to match microarray data with all available PDB structures containing glycans. The glycan ligand information is displayed, and links are provided for structural visualization and redirection to other modules in GlycanStructure.ORG for further investigation of glycan-binding sites and glycan structures. Availability and implementation http://www.glycanstructure.org/glymdb. Supplementary information Supplementary data are available at Bioinformatics online.