Integrative Genomic Analysis In Cancer Essay

I have worked on DNA methylation, RNA expression, clinical patient data based on the Pancreatic Adenocarcinoma dataset through the cancer genome atlas (TCGA). The study

The researched statistic would show how quantitative evidences support the ideas of unhealthy food and sporadic cancer. It would be helpful to provide information contrasting hereditary cancer and sporadic cancer, as well as narrowing down the topic to specific

One of the main reasons I chose to research this gene is because of its association with

Cancer is a leading cause of death and devastating experiences for patients’ family members and close friends. The disease undermines the emotional wellbeing of the survivors during and after treatment. It often affects the physical appearance and exposes the family members to financial constraints thereby leading to depression, reduced productivity at workplaces and many other social problems. However, the controversies over the causes of cancer continue to impact the awareness campaigns and the general population attitude towards treatment options. According to Thun, Linet, Cerhan, Haiman, and Schottenfeld (2017), many people believe that gene composition is the leading risk factor for cancer. In contrast, etiological studies have confirmed

Cancer is associated with mutated genes, and the analysis of tumour-linked genetic alterations is commonly used for diagnostic, prognostic and treatment purposes. Currently, the use of tissue biopsies to obtain the genetic profile of solid tumours for cancer diagnosis is common and widespread. Tumour biopsy tissue, generally from the primary tumour, is used to determine molecular targets at a single point in time, (information acquired from a single biopsy provides a limited snap-shot of a tumour, which might fail to reflect its true heterogeneity because it only provides the information on a portion of the tumour, at a certain point in time) before treatment begins. In the United States, for example, it is estimated that 1.6 million breast biopsies and 1 million prostate biopsies are performed each year. Regardless of whether such procedures are performed as highly invasive surgery, endoscopic examinations, or needle punctures, each tissue biopsy is an invasion of the patient’s body. All invasive approaches carry the risk of infection or other complications for the patient, not to mention they are painful, costly, and the process takes a significant amount of time. Also, given the complexities of tumour heterogeneity, both within a tumour and between a primary tumour and metastases, a tissue sample may not be an accurate representation of the

Komen’s Emerging Areas in Chemotherapy page have wrote, “These tests allow researchers to study hundreds of tumor genes at one time.

Figure 1. Collisson, E. A. et al. (2012) What are we learning from the cancer genome? Nat. Rev. Clin. Oncol. doi:10.1038/nrclinonc.2012.159

LA exploits the fact that genes have a tendency to be inherited together because they are located close to each other. After identification of a linkage, all genes in the linked region need to be identified and in this manner candidate genes of interest for a disease can be established. LA has been successful in localizing chromosomal regions containing highly penetrant genetic variants. However, in a common disease, such as OA, the success of LA is very limited for a number of reasons, including the low power of identification of genes with modest effects and the large impact of environmental influences (Risch et al., 1996).

Raw data were processed and normalized by the Robust Multichip Averaging (RMA) method (Irizarry et al., 2003) using affy packages of R(v. 3.1.3) (Team, 2004) .The linear regression model package limma (Smyth, 2004) was used to classify chips into each group. The Bayes method (Benjamini and Hochberg, 1995) was used to correct for multiple testing. |logFC| > 2 and P-value < 0.05 were used as cutoff to identify genes which are differentially expressed in

Genomic profiling has furthered research and understanding of this complex disease process by identifying further tumor subtypes and molecular alterations of the disease. (Stopeck, 2009). Based upon the specific gene

The test uses gene sequencing technology to spot traces of DNA related to different cancers in screen blood samples. To be precise, it looks at 96 genetic markers associated with breast, ovarian, lung, colon and melanoma cancers - ones that have specific treatment regimens and are well known. Many cancer

three specific databases. These organisms have been used as model organisms through multiple clinical researches to inform us about biology. They are ideal organisms due to characteristics such as how roundworms and fruit flies grow to adulthood quickly. In addition, they share principles such as the cell cycle in yeast that is regulated by homologous proteins that are similar in humans; hence they will be able to enlighten us on the genetic profiling in humans. The five most used organisms are yeast, fruit fly, bacteria, mouse, and roundworm. Out of those five most common organisms we used roundworm, yeast, and fruit fly to conduct our analysis. To analyze the organisms we had to use databases specifically for each organism (i.e. wormbase.org, flybase.org, yeastgenome.org). We also used the ApE software to analyze gene sequences whose information was not readying available in the databases. From these databases we were given the information needed to analyze each organism.

Genome wide association study has been very useful in the recent years. It has helped researchers to detect genes that are not detected that easily. With the detection, it helps the researchers to prevent and treat the particular disease. Breast cancer has been one of the most common cancer in the world and the most common in women. Even though, breast cancer is detected easily with mammograms or self examination there are times where it can be hard to detect. Genome wide association studies help to identify the genes that are in risk for breast cancer. Every population has different lifestyle, genetic variants and other factors. Sometimes not every SNP (single nucleotide polymorphisms) are present in every population. Genome- wide

Researchers screened over 3,000 genes and selected 19 random genomic regions for their comparisons, which was much more than previously cited studies. The regions that were chosen for comparison were attached to a core SNP in the coding region of a gene. This was further qualified using only fully known genomic sequence up to 160 kb in either direction of the core SNP and having a frequency of at least 35% in all ethnic groups sampled. The choice of high-frequency SNPs was explained by known factors that affect the rate. Mainly, that they tend to be found in all populations, when LD are around rare alleles the are expected to extend further because these alleles are younger and have had less recombination, and when around common alleles they tend to be more closely associated with diseases. The researchers examined 251,310 bp and found a total of 272 high-frequency SNP’s of which they chose the 19 that they examined and analyzed for this

Bootstrap sampling in cluster analysis is a valuable tool that can be used in bioinformatics as well as in other areas of research. In bioinformatics, clustering can be used in genetics studies to find clusters of subjects according to their gene expression levels. We can then see if subjects with the same disease state or treatment have the same gene profiles, which can give us more information about diseases or treatments and their relations to genetics.