Expression analysis and prognostic value of candidate genes using PrognoScan

PrognoScan database and the results represented with expression plots, expression histograms, P-value plots and Kaplan-Meier plots (survival curves). Again, not all genes were found in this database, with the following three genes: SEPT2, GP1BA (see appendix D) and TNFSF4 (See appendix E) and only one gene, SEPT2 (figure 4.6), showed possible prognostic value. The probability of survival with this gene highly expressed is significant in the early stages of the disease, showing possibility as a good prognostic biomarker at that stage. There is differential expression between the low and high expression during this stage for SEPT2. See table 4.3 displaying a significant p-value for this gene.

Figure 4.6: Expression plot, Expression histogram, P-value plot and Kaplan-Meier plots for high and low SEPT2 -expressing groups in prostate cancer

Table 4.3: PrognoScan SEPT2 gene result-­‐Statistically significant gene with corrected p- value= 0.026191

DATA  POSTPROCESSING   None  

PROBE_NAME   DAP1_0256  [6K  DASL]  

PROBE_DESCRIPTION   septin  2  

GENE_SYMBOL   SEPT2  

GENE_DESCRIPTION   septin  2  

DATASET   GSE16560  

CANCER_TYPE   Prostate  cancer  

SUBTYPE      

N   281  

ENDPOINT   Overall  Survival  

PERIOD   Months  

COHORT   Sweden  (1977-­‐1999)  

ARRAY  TYPE   6K  DASL  

CONTRIBUTOR   Sboner  

SAMPLE  PREPARATION   DASL  

CUTPOINT   0.84   MINIMUM  P-­‐VALUE   0.001033   CORRECTED  P-­‐VALUE   0.026191   ln(HRhigh  /  HRlow)   0.59   COX  P-­‐VALUE   0.254584   ln(HR)   0.22   HR  [95%  CI]   1.25  [0.85  -­‐  1.83]   4.9. Conclusion

Despite the introduction of PSA screening, the mortality from prostate cancer has remained relatively high. Although the benefits of PSA screening are widely debated, this serum marker remains one of only a few preoperative parameters of prognostic utility (Henshall et

al., 2003). In silico biomarker validation could be a substantially more cost-effective strategy

for biomarker development, which typically requires costly and lengthy processes. Survival analysis tools and resources, clinically deployed genome-based biomarkers are still scarce, highlighting the unresolved challenges in biomarker development from genomic studies (Chen et al., 2014).

Novel and clinical markers for prostate cancer diagnosis, prognosis, and prediction is essential to the optimal identification and treatment of this disease and to bring potential biomarkers from the laboratory environment into clinical use at the patient bedside requires a comprehensive pursuit and rigorous analysis (Tricoli et al., 2004).

The prognostic gene signatures related to patient outcome such as survival time and tumour stage must be genes that are important in tumour development and progression (Li et al., 2015).

Larger patient cohorts are needed for prostate cancer, as compared to other cancers such as breast cancer, for data outcomes not to be ambiguous (Sutcliffeet al., 2009). This was evident in the comparison of the PROGgene and SurvExpress results of GSE16560 dataset. A limitation is that the cohort was not big enough therefore the genes were difficult to assess for prognosis for prostate cancer patients in general.

Although, the results of the SurvExpress analysis revealed gene expression differences that were not significantly sufficient to be distinguished as strong prognostic biomarkers, one marker, GP1BA did stand out, supporting its prognostic value based on the statistical p-value.

Another gene, from the Prognoscan database SEPT2 shows promise in that has some prognostic value in the early stages of the disease.

This study provides some promising evidence that bioinformatics data mining can be a highly beneficial means to identify novel biomarkers, although combined with clinical biomarker validation by qRT-PCR, using a molecular approach and functional evaluation of candidate genes, it can be considered for a detailed follow-up study on selected candidates in the near future.

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