Multiple sclerosis (MS) is a chronic autoimmune, inflammatory neurological disease that is widely associated with Grey and white matter degradation due to the demyelination of axons. Thus exposing the underlying causes of this condition can lead to a novel treatment approach for Multiple Sclerosis. The total RNA microarray processed data from GEO for Multiple sclerotic patients was comprehensively analyzed to find out underlying differences between Grey Matter lesions (GML), Normal appearing Grey Matter (NAGM), and Control Grey matter at the transcriptomics level. Thus, in the current study, we performed various bioinformatics analyses on transcriptional profiles of 184 samples including 105 NAGM, 37 GML, and 42 Controls obtained from the NCBI-Bio project (PRJNA543111). First, exploratory data analysis based on gene expression data using principal component analysis (PCA) depicted distinct patterns between GML and CG samples. Subsequently, the Welch T-test differential gene expression analysis identified 15,525 significantly differentially expressed genes (p.adj value <0.05, Fold change(>=+/-1.5) between these conditions. This study reveals the genes like CREB3L2, KIF5B, WIPI1, EP300, NDUFA1, ATG101, AND TAF4 as the key features that may substantially contribute to loss of cognitive functions in Multiple sclerosis and several other neurodegenerative disorders. Further, this study also proposes genes associated with Huntington disease in Multiple sclerotic patients. Eventually, the results presented here reveal new insights into MS and how it affects the development of male primary sexual characteristics.

Glioblastoma multiforme(GBM) is a group of aggressive tumors of the central nervous system. Despite advancements in the treatment of GBM, patients diagnosed with these tumors typically have a poor prognosis and poor quality of life as the disease develops. The single-cell RNA high-throughput sequencing processed data for Glioma cancer stem cells were taken from GEO and analyzed to find out the underlying expression differences at the gene level between glioma neural stem cells(GSCs) and Normal neural stem cells(NSCs). In the current study, we have performed an RNA-sequencing analysis between GSCs and NSCs to better understand the origin of GBM. We have performed bioinformatics analysis on the transcriptional profile of 134 samples which consisted of 75 GSCs and 59 NSCs obtained from the NCBI bio project(PRJNA546254). First, an exploratory analysis was performed which showed significant variation patterns between GSCs and NSCs. Subsequently, Deseq2 differential gene expression analysis identified 1436 differentially expressed genes be-tween GSCs and NSCs[(padj. value <0.05, log2 fold change (>=+/-1.5)]. This study reveals genes like MAOA, MAOB, GATM, GLDC, AMT, and SHMT1 as the key features contributing to the disturbed processes of Glycine, threonine, and serine amino acid metabolism, axonal cone growth curve, and cell migration in Glioma. Conclusively, our study also depicts gene expression changes in amyloid beta-binding protein in between GSCs and NSCs which plays an important role in tumor microenvironment formation. Besides, the results presented here reveal new insight into the progression of GBM and the identification of novel genes involved in gliomagenesis.

Glioblastoma multiforme(GBM) is a group of fatal and aggressive tumors of the central nervous system. Despite advancements in the treatment of GBM, patients diagnosed with these tumors typically have a poor prognosis and poor quality of life as the disease develops. The single-cell RNA high-throughput sequencing processed data for Glioma cancer stem cells were taken from GEO and analyzed to find out the underlying expression differences at the gene level between glioma neural stem cells(GSCs) and Normal neural stem cells(NSCs). In the current study, we have performed an RNA-sequencing analysis between GSCs and NSCs to better understand the origin of GBM. We have performed bioinformatics analysis on the transcriptional profile of 134 samples which consisted of 75 GSCs and 59 NSCs obtained from the NCBI bio project(PRJNA546254). First, an exploratory analysis was performed which showed significant variation patterns between GSCs and NSCs. Subsequently, Deseq2 differential gene expression analysis identified 1436 differentially expressed genes between GSCs and NSCs[(padj. value <0.05, log2 fold change (>=+/-1.5)]. This study reveals genes like MAOA, MAOB, GATM, GLDC, AMT, and SHMT1 as the key features contributing to the disturbed processes of Glycine, threonine, and serine amino acid metabolism, axonal cone growth curve, and cell migration in Glioma. Conclusively, our study also depicts gene expression changes in amyloid beta-binding protein in between GSCs and NSCs which plays an important role in tumor microenvironment formation. Besides, the results presented here reveal new insight into the progression of GBM and the identification of novel genes involved in gliomagenesis.

Background: Every year, more than 12 million people are diagnosed with colorectal cancer (CRC), and more than 600,000 people die from it, making it second most deadly form of cancer. This work analyzes differential gene expression across CRC and other glandular tumour samples to identify expression changes potentially contributing to the development of CRC tumorogenesis. Methods: This work defines 13 gene signatures representing four CRC tumour and 10 other glandular tumours that are colonic by origin. Gene Set Enrichment Analysis (GSEA) is used to define positive and negative CRC gene panels from GSEA-identified leading-edge genes using two CRC signatures. GSEA then is used to verify enrichment and leading-edge gene membership of CRC panels in two independent CRC gene signatures. Analysis is then extended to four individual and 10 glandular tumour signatures. Genes most associated with CRC tumorogenesis are predicted by intersecting membership of GSEA-identified leading-edges across signatures. Results: Significant enrichment is observed between CRC gene identification signatures, from which the positive (55 genes) and negative (77 genes) CRC panels are defined. Non-random significant enrichment is observed between CRC gene panels and verification signatures, from which 54 over- and 72 under-expressed genes are shared across leading-edges. Considering other glandular tumour samples individually and in combination with CRC, significant non-random enrichment is observed across these signatures. Eight solute carrier family genes such as (SLC25A32, SLC22A3, SLC25A20, SLC36A1, SLC26A3,SLC9A2, SLC4A4 and SLC26A2) from the CRC panel were shared commonly across all the gene signatures leading-edges, regardless of the colonic tumour type. Conclusion: This meta-analysis identifies gene expression changes associated with the process of CRC tumorogenesis. These changes may contribute to developing therapeutic treatments available for CRC patients. Keywords: Glandular tumours, CRC, GSEA, Meta-analysis, Gene Expression.