Browsing by Author "Zan L"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Bioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancer
    (Elsevier B.V. on behalf of King Saud University, 2022-05-01) Chengcheng L; Raza SHA; Shengchen Y; Mohammedsaleh ZM; Shater AF; Saleh FM; Alamoudi MO; Aloufi BH; Mohajja Alshammari A; Schreurs NM; Zan L
    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.
  • Thumbnail Image
    Item
    Identification of genetic variants the CCKAR gene and based on body measurement and carcass quality characteristics in Qinchuan beef cattle (Bos taurus)
    (Elsevier B.V., 2021-05) Nurgulsim K; Raza SHA; Khan R; Shah MA; Jahejo AR; Batool U; Hongbao W; Zhigerbayevich KN; Schreurs N; Zan L
    Background: This study aimed to explore genetic polymorphisms of the CCKAR gene and their relationship with the growth and development of Qinchuan cattle which could be used as molecular markers for the improvement of the breeding of Qinchuan cattle. Results: Here, we have identified seven single nucleotide polymorphisms (SNPs) at loci g. 1463 C>G; g. 1532 T>A; g. 1570 G>A; g. 1594 C>A; g. 1640 T>C; g. 1677 G>C; and g. 1735 C>T in the coding region of the bovine CCKAR gene. The frequencies identified on allelic and genotypic characteristics have shown that all seven SNPs diverged from the Hardy-Weinberg-Equilibrium. The SNP2, SNP3, SNP6 and SNP7 had the lowest polymorphism information content values, and remaining SNPs were found to be moderate (0.25 < PIC < 0.50). The genotype CG in SNP1 at loci g.1463 C>G had the greatest association with WH, HW, CD and CCF, while the genotype TA at the very same loci was associated with BFT, ULA and IMF content in Qinchuan cattle. The CCKAR gene expression level in adipose tissue, small intestine, liver and skeleton muscle was found to be higher, whereas, the expression level of mRNA in organs of other digestive system including reticulum, abomasum and omasum was moderate. Some expression of CCKAR mRNA was found in the large intestine, kidney and rumen. Conclusion: In summary, our finding suggested that the CCKAR gene could be used as a potential candidate for the improvement of carcass quality and body measurements of Qinchuan cattle.
  • Thumbnail Image
    Item
    Screening and Identification of Muscle-Specific Candidate Genes via Mouse Microarray Data Analysis.
    (Frontiers Media S.A., 2021-12-13) Raza SHA; Liang C; Guohua W; Pant SD; Mohammedsaleh ZM; Shater AF; Alotaibi MA; Khan R; Schreurs N; Cheng G; Mei C; Zan L; Ibelli AMG
    Muscle tissue is involved with every stage of life activities and has roles in biological processes. For example, the blood circulation system needs the heart muscle to transport blood to all parts, and the movement cannot be separated from the participation of skeletal muscle. However, the process of muscle development and the regulatory mechanisms of muscle development are not clear at present. In this study, we used bioinformatics techniques to identify differentially expressed genes specifically expressed in multiple muscle tissues of mice as potential candidate genes for studying the regulatory mechanisms of muscle development. Mouse tissue microarray data from 18 tissue samples was selected from the GEO database for analysis. Muscle tissue as the treatment group, and the other 17 tissues as the control group. Genes expressed in the muscle tissue were different to those in the other 17 tissues and identified 272 differential genes with highly specific expression in muscle tissue, including 260 up-regulated genes and 12 down regulated genes. is the genes were associated with the myofibril, contractile fibers, and sarcomere, cytoskeletal protein binding, and actin binding. KEGG pathway analysis showed that the differentially expressed genes in muscle tissue were mainly concentrated in pathways for AMPK signaling, cGMP PKG signaling calcium signaling, glycolysis, and, arginine and proline metabolism. A PPI protein interaction network was constructed for the selected differential genes, and the MCODE module used for modular analysis. Five modules with Score > 3.0 are selected. Then the Cytoscape software was used to analyze the tissue specificity of differential genes, and the genes with high degree scores collected, and some common genes selected for quantitative PCR verification. The conclusion is that we have screened the differentially expressed gene set specific to mouse muscle to provide potential candidate genes for the study of the important mechanisms of muscle development.