Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma.

Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.

Synthesis of Silver Nano Particles Using Myricetin and the In-Vitro Assessment of Anti-Colorectal Cancer Activity: In-Silico Integration.

The creation of novel anticancer treatments for a variety of human illnesses, including different malignancies and dangerous microbes, also potentially depends on nanoparticles including silver. Recently, it has been successful to biologically synthesize metal nanoparticles using plant extracts. The natural flavonoid 3,3', 4', 5,5', and 7 hexahydroxyflavon (myricetin) has anticancer properties. There is not much known about the regulatory effects of myricetin on the possible cell fate-determination mechanisms (such as apoptosis/proliferation) in colorectal cancer. Because the majority of investigations related to the anticancer activity of myricetin have dominantly focused on the enhancement of tumor cell uncontrolled growth (i.e., apoptosis). Thus, we have decided to explore the potential myricetin interactors and the associated biological functions by using an in-silico approach. Then, we focused on the main goal of the work which involved the synthesis of silver nanoparticles and the labeling of myricetin with it. The synthesized silver nanoparticles were examined using UV-visible spectroscopy, dynamic light scattering spectroscopy, Fourier transform infrared spectroscopy, and scanning electron microscopy. In this study, we have investigated the effects of myricetin on colorectal cancer where numerous techniques were used to show myricetin's effect on colon cancer cells. Transmission Electron Microscopy was employed to monitor morphological changes. Furthermore, we have combined the results of the colorectal cancer gene expression dataset with those of the myricetin interactors and pathways. Based on the results, we conclude that myricetin is able to efficiently kill human colorectal cancer cell lines. Since, it shares important biological roles and possible route components and this myricetin may be a promising herbal treatment for colorectal cancer as per an in-silico analysis of the TCGA dataset.

In-Silico Study of Immune System Associated Genes in Case of Type-2 Diabetes With Insulin Action and Resistance, and/or Obesity.

Type-2 diabetes and obesity are among the leading human diseases and highly complex in terms of diagnostic and therapeutic approaches and are among the most frequent and highly complex and heterogeneous in nature. Based on epidemiological evidence, it is known that the patients suffering from obesity are considered to be at a significantly higher risk of type-2 diabetes. There are several pieces of evidence that support the hypothesis that these diseases interlinked and obesity may aggravate the risk(s) of type-2 diabetes. Multi-level unwanted alterations such as (epi-) genetic alterations, changes at the transcriptional level, and altered signaling pathways (receptor, cytoplasmic, and nuclear level) are the major sources that promote several complex diseases, and such a heterogeneous level of complexity is considered as a major barrier in the development of therapeutics. With so many known challenges, it is critical to understand the relationships and the shared causes between type-2 diabetes and obesity, and these are difficult to unravel and understand. For this purpose, we have selected publicly available datasets of gene expression for obesity and type-2 diabetes, have unraveled the genes and the pathways associated with the immune system, and have also focused on the T-cell signaling pathway and its components. We have applied a simplified computational approach to understanding differential gene expression and patterns and the enriched pathways for obesity and type-2 diabetes. Furthermore, we have also analyzed genes by using network-level understanding. In the analysis, we observe that there are fewer genes that are commonly differentially expressed while a comparatively higher number of pathways are shared between them. There are only 4 pathways that are associated with the immune system in case of obesity and 10 immune-associated pathways in case of type-2 diabetes, and, among them, only 2 pathways are commonly altered. Furthermore, we have presented SPNS1, PTPN6, CD247, FOS, and PIK3R5 as the overexpressed genes, which are the direct components of TCR signaling.