Research Paper on "Gene Expression Analysis in Cancer"

Research Paper 12 pages (4084 words) Sources: 7 Style: APA

[EXCERPT] . . . .

Moreover, recurrence is a serious problem for breast cancer patients. The goal of their research was to examine how a set of genes, which they refer to as a gene signature, could be used to help predict the prognosis of breast cancer patients (Martin et al., 2008). They used a 3D cell culture model of non-malignant human mammary epithelial cell morphogeneis as the baseline of the study (Martin et al., 2008). When focusing on single-gene links to cancer, individual groups can dramatically impact the efficacy of the genetic predictive power (Martin et al., 2008). However, when studies are expanded to include gene sets, not just specific genes, the predictive power grows and the power appears to be more fully translatable across populations (Martin et al., 2008). In fact, the 3D signature was able to accurately predict prognosis in different groups, even with the natural variance suggested by different estrogen receptor (ER) status. ER positive and ER negative groups could both receive prognostic information from the 3D model, they just had to focus on different gene sets. In ER positive patients, the prognostic gene set was a combination of AURKA, CEP55, RRM2, EPHA2, FGFBPI, and VRK1 (Martin et al., 2008). In contrast, in ER negative patients, the prognostic gene set was a combination of ACTB, FOXM1, and SERPINE2 (Martin et al., 2008). Therefore, knowing of the genetic makeup of the cancer cells can be helpful in determining how to analyze the remainder of a patient's genetic profile in order to determine treatment plans that are specific to that subtype.

Mutarelli et al. also examined breast cancer cells, specifically hormone-responsive human breast cancer cells (2008). They employed the microarray experimental process because of its ability to provide simultaneous measurements of the transcipts present in cells at various stages (Mutarelli et al., 2008). This measurement can provide a snapshot of cellular activity, not just as a whole, but at each individual stage of development, which could provide insight into the eventual treatment of the disease. They used the Illumina Bead Array platform to examine gene expression in 30-40 replicates on the chip surface (Mutarelli et al., 2008). Using that platform, they identified the genes that were most likely to impact hormonal treatment. While their research had significant detailed applications, the overarching conclusion was that estrogen impacts genetic expression in ER positive breast cancer cells. The role that a hormone can play in cancer is important to know, because hormone therapy may be a more appropriate line of treatment for some patients with cancer variants that are resistant to radiation and to chemotherapy.

Liu et al. also examined chemosensitivity in cancer cells (2006). They focused on brain tumors in both adult and pediatric patients, and built upon emerging research that demonstrated that CD133 was a marker for some leukemia and glioblastoma cancer stem cells. First, they began their research by confirming a high prevalence of CD133 marker in the glioblastoma cells in their study, verifying earlier research that suggested that CD133 was a marker for certain subtypes of glioblastoma (Liu et al., 2006). Next, they examined whether CD133 positive cells exhibited greater chemoresistance than other cells. They did find that CD133 positive cells were more resistant than CD133 negative cells to a number of identified agents used in chemotherapy, including temozolomide, carboplatin, paclitaxel, and etoposide (Liu et al., 2006). In addition, when the cancer cells came from patients with verified recurrences of the underlying glioblastoma cancer, the cells were far more likely to contain CD133 positive cells, suggesting a link between CD133 expression and cancer recurrence (Liu et al., 2006). However, even more significant than the chemoresistant properties of the CD133 positive cells was the overlapping relationship between CD133 positive status and other genetic factors. They found that CD133 positive cells also have higher levels of expression for other factors that have previously been linked to cancer, including BCRP1, MGMT mRNA, and genes inhibiting apoptosis (Liu et al., 2006). To the researchers, these conclusions not only provided laboratory findings, but suggested varying course of treatment for some glioblastoma patients, focusing on eradicating the CD133 positive cancer stem cells (Liu et al., 2006).

Hsu et al. focused specifically on chemosensitivity and tumor metastasis because treatment resistance and the ability to spread throughout the body are both highly linked to the overall danger of a particular type of cancer (2013). Using the NCI-60 panel, a panel of 60 human cancer cells that the National Cancer Institute has developed for cancer research, Hsu et al. sough to examine the relationship between invasiveness (IA) and chemosensitivity (2013). They did so by first identifying invasion-associated genes in the NCI-60 panel, and then wanted to test drugs that had been previously used in chemosensitivity trials. They identified 633 IA genes and, from those genes, narrowed down a subset of eight genes that were correlated with drug-sensitivity profiles. Those gene variants were less likely to relapse after successful treatment for cancer. The eight genes identified were the EGFR, ITGA3, MYLK, RAI14, AHNAK, GLS, IL32, and NNMT genes (Hsu et al., 2013). What was interesting about this research was that the genes they identified were linked, in many ways, to both metastasis and recurrence, as the genes were linked to cell adhesion, tumor growth, tumor progression, migration, and invasiveness (Hsu et al., 2013). Practically, there results showed two distinct genetic signatures that could be linked to cancer cell survival in both lung and breast cancers (Hsu et al., 2013). In a clinical environment, those signatures could be used to help determine which patients were at high risk of metastasis, dictating how aggressive treatment should be, or at risk for recurrence, dictating clinical guidelines for follow up care. For example, for breast cancer patients, this marker might make a difference between a recommendation for a lumpectomy or double mastectomy upon diagnosis with breast cancer.

Not all gene-expression research focuses on individual genes or markers and how they impact cancer patients. Instead, some of the newer gene-expression research actually focuses on analyzing genes. Subramananian et al., examined Gene Set Enrichment Analysis (GSEA) (2005). Rather than focusing on the presence or absence of particular genes or whether certain genes have been activated, GSEA focuses on the fact that genes frequently work in combination, and, therefore, looks at sets of genes that have common traits. What they found is that GSEA analysis can reveal significant information about the genetic links to cancer, which are not revealed, or may seem less significant when concentrating on single-gene analysis (Subramanian et al., 2005). Their results suggest that there are certain biological pathways for specific cancers, and that the genes that share common traits can have a cumulative impact on the course of development, metastasis, treatment, and recurrence of those cancers. This focus on gene sets helps examine how environmental factors impact genetic expression by looking beyond the presence of certain genes and examining biological processes that can be present in an entire gene network, such as transcriptional programs, metabolic pathways, and stress responses, that might have a minimal impact on a single gene (Subramanian et al., 2005). GSEA is a powerful tool because it is flexible; Subramanian et al. created an initial database with 1,325 gene sets, but any number of gene sets could be analyzed using the GSEA approach (2005).

Discussion

The research makes it clear that the presence of a genetic predisposition to cancer cannot, by itself, predict whether a person will develop a cancer, whether that cancer will respond to treatment, whether the cancer will metastasize quickly, or whether the cancer will recur. While knowing that some genes are linked to cancer is helpful, that knowledge is not sufficient for predictive and diagnostic purposes. Instead, the answers to those questions are more complex than simply examining the presence of a single gene in a person's body without considering the surrounding environment and whether the gene will be triggered in a way that leads to the mutations of cancer. However, the fact that genetic analysis cannot provide absolute answers to those questions does not mean that genetic analysis is not an extremely useful tool for helping determine and predict cancer and post-diagnosis prognosis.

One of the first advances in examining gene expression in cancer cells was the notion that the ECM had an impact on gene expression. In the laboratory environment, the ECM can be manipulated by researchers. However, in the body, where cancer develops, the ECM is also subject to significant changes. These changes not only impact tissue and organ structure, but can impact cellular structure. Likewise, the ECM can impact the development and structure of cancer cells as they mutate from non-malignant cells in the body. As cells transition from the 2D monolayer to the 3D environment, they undergo structural and shape changes, and these changes can influence whether or not particular genes are expressed. Therefore, something as basic as surrounding structure may help determine whether a patient with a genetic predisposition… READ MORE

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