Identification of biomarkers of prostate cancer using regression analysis
Bivariate regression analysis is a quick and simple method for the Identification of the importance of biomarkers for gene expression a
ays with respect to a given question. By regressing the gene expression profile of one sample against another sample of a different type we can measure the similarity and difference in expression of all the genes on the a
ay. This is achieved by conducting a regression analysis and recording the standard residual values for each gene. The higher the standard residual value the more significant the difference in expression. A negative standard residual means down regulation with respect to control and positive regulation. A standard residual of greater than 2 is significant. By ranking the standard residual values, we can identify the most important genes. Here we use this technique for a set of prostate cancer cases with matched control cases.
Aims
1. To utilise regression analysis to analyse gene expression a
ay data.
2. To identify genes associated with prostate cancer.
3. To review the known roles of these genes in the context of cancer.
ASSESSMENT (Overview of Assignment)
ABSTRACT (≤ 300 words)
· Provide a 300 word abstract that summarises the study.
· Include Background, Approach, Results, Conclusions.
· Look at examples of abstracts from microa
ay studies and try and emulate this here.
RESULTS AND ANALYSIS
· Include clear supportive commentary that sets the scene for the analysis performed.
· A clear summary table of data that defines the top 5 overexpressed genes and the top 5 under expressed genes. Where possible provide additional detail to this summary table.
Provide supportive commentary that describes the main findings.
· Make use of pathway analysis tools (http:
www.reactome.org/) using the identified genes mention the pathways that are associated with top 5 overexpressed genes and the top 5 under expressed genes in the form of table. Give supportive commentary under the table.
Below mentioned are the top 5 overexpressed genes and the top 5 under expressed genes
· Make the use of genemania and input each of the top 20 and bottom 20 genes from the microa
ay study. This will produce a gene interaction network. Then select any 2 genes other than the top 5 overexpressed genes and the top 5 under expressed genes that are highly expressed in the gene interaction network. And mention about their role in prostate cancer and explain why you choose that 2 gene. Present the data in an appropriate figure (gene interaction network)
Below mentioned are the top 20 overexpressed genes and the bottom 20 under expressed genes
· Make use of Protein Atlas database. Include an overview table figure.
Input each of the top 5 overexpressed genes and the top 5 under expressed genes from the microa
ay study.
e.g. Headers that you could use include:
TISSUE TYPE: Gene name, Tissue with highest protein expression, Protein expression level in Prostate
CELLULAR: Main cellular location, Expression in prostate cell lines (RNA expression overview)
PATHOLOGY: % expression in patients with prostate cancer.
DISCUSSION (≤ 1000 words)
· Interpret the results by assessing the link between the differentially expressed genes and prostate cancer.
· Association of the target genes with prostate cancer or mechanisms associated with tumor proliferation, survival, metastasis, etc.
· Consider and define how further validation of the differentially expressed genes could be conducted.
· Identify appropriate methods to further investigate how the proteome from control and tumour tissue could differ. How could the results from the cDNA microa
ay experiment be linked to the changes that may occur in the proteome.
· Appropriate use of the literature.
· Review the genes discovered in the context of cancer. Make use of the databases (e.g. Genecards, Ensemble, NCBI, UniProt etc). Also paste into PubMed, Science Direct, Google scholar, etc) to find articles and information that relates to the role of the gene in prostate cancer and pathways linked to cancer.
CONCLUSIONS
· Interpretation.
· Clear points that summarise the findings.
REFERENCES
· Co
ect citation.
· Appropriate Harvard referencing.
Marking grid
Abstract
Clear, relevant & contextualized aims / objectives
The approach is accurate and utilizes relevant information that aligns with the topics under investigation.
Exceptional knowledge and understanding of the area of study that considers
elates all relevant information.
Work is of an exceptional standard and draws on material beyond the prescribed range.
Results & analysis
Exceptional demonstration of analysis skills producing high quality work
Exceptional handling of data analysis.
Accurate / defined regression analysis examples with collated data tables.
Excellent and appropriate presentation of data (e.g. tables figures with comprehensive titles and legends).
Results are described concisely and appropriately and reflect the data presented. Work is of an excellent standard with strong scientific writing evident.
Discussion, Interpretation & Conclusion
Logical and detailed interpretation of the data supported by critical evaluation/ synthesis/ analysis and evidenced with appropriate references.
Work is of an excellent standard with strong scientific writing evident and drawing on material beyond the prescribed range
Resources/ References & Overall Presentation
References are relevant and cu
ent and are cited appropriately. Presentation is of a professional standard.
Performance in all areas deemed beyond expectation of the level and the prescribed range.