Application Of Computational Approaches To Target Genes In Breast Cancer

As one of the most prevalent diseases worldwide,cancer impacts human life and development severely because of its high incident and death rate.Among many types of cancer,breast cancer is the most common type seen in females,resulting in more than 40,000 death per year.Furthermore,it has been predicted that by 2030,breast cancer will be the most prevalent type of cancer,and will rank as the 5th most deadly cancer type.China has a very high occurrence of breast cancer,accounting for 12.2%of the world's new diagnoses and 9.6%of related deaths.By 2021,there will be an estimated 2,500,000 breast cancer patients in China.With the continuous accumulation of biological data,bioinformatics allows scientists to use computational approaches to organize large-scale biological data,summarize novel knowledge from those data and deeply understand the mechanism of breast cancer.This will provide new opportunities and challenges for discovering biomarkers which are closely related to diagnoisi or treatment of breast cancer.First,identification of effective target genes of regulatories associated with breast cancer prognosis.Regulatory modulates gene transcription and plays an important role in many critical biological processes.Identifying the effective target genes of regulatories is a key factor for breast cancer target therapy.Second,assessing breast cancer patients' activities of the homologous recombination(HR)pathway.The HR pathway is a core pathway in repairing double strand DNA breakage during gene replication.Gene mutations or loss of functions associated with the HR pathway will lead to a change of the pathway activity.Moreover,the HR pathway is a very important pathway that is widely studied in the field of targeting cancer associated drug discovery.Therefore,mathematically and quantitatively evaluating the activity of the patients' HR pathway is critical in personalized breast cancer diagnostic and treatment.Thirdly,in sporadic breast cancer patients,some have very similar genetic profile to those hereditary patients carrying BRCA1 or BRCA2 mutations.Such sporadic patients are thus defined as "BRCAness".BRACness is a measurement to the activity of the HR pathway.In conclusion,using computational method to discover the BRACness biomarkers for breast cancer patients is of great importance for breast cancer personalized treatment.Based on these three prerequisite and challenge,this paper has accomplished the following goals in recognizing target genes for transcription factors,evaluating the activity of patients' HR pathway and assessing the BRCAness score for breast cancer patients,by using computational methods and bioinformatics tools.First of all,we developed an algorithm to identify real target genes of regulators in the context of breast cancer.Because the current knowledge of target genes of the regulators is not accurate,the downstream analysis accuracy is hugged affected.In order to solve this problem,this paper came up with the algorithm to recognize target genes for the regulators,based on the actual data from breast cancer patients and target genes of the regulator.By using the activity of the regulator and real gene expression data of the target genes,our algorithm generates a set of genes that are targeted by the regulator.Our method highly reduced the number of target genes of the regulator,is able to recognize effective target genes,and won't change the function and activity of the regulator,thus increases the accuracy of downstream analysis.Moreover,our algorithm decreases the time and cost in conducting breast cancer target gene researches.Secondly,we came up with an algorithm to calculate the similarity of different HR pathway target genes in response to RNA interfering experiments.To solve the problems with current method of using a small subset of genes,this paper used the entire genome gene expression data of HR pathway gene knockout experiments as the basis to come up with a mathematical model to calculate the similarity of HR pathway target genes in response to RNA interference.Using such calculation,we could evaluate the activity of the HR pathway in each of the patients.This method is effective in breast cancer patients'prognosis,predicting the patient's response to novel facilitated chemotherapy,and predicting the instability of the genome as well.Compared with the gene integration method,our model is better at grasping the overall effect of changes in HR pathway and allows for a more accurate downstream analysis.Lastly,we developed a new algorithm to calculate the similarity of shared gene expression pattern in hereditary and sporadic breast cancer samples.Targeting to improve the copy number variation in current method,this paper used the whole genome gene expression data to overcome the influence of tumor diversity.By comparing the inherited and sporadic breast cancer patients' gene expression data,we re-calculated the BRCAness score based on their traits weight.What's more,we used the gene expression data and the similarity to the weighed BRCAness to get a BRCAness score for the patients.Our algorithm is very effective in distinguishing between inherited and sporadic patients,predicting the patients' prognosis,assessing the instability of the genome as well as predicting their response to new facilitated chemotherapy.In summary,this paper used computational models to analyze the gene expression data of the breast cancer patients to face the challenges and opportunities in discovering new breast cancer biomarkers.We came ups with three new algorithm in total,the algorithm to recognize effective target gents,the algorithm to calculate the similarity of the HR pathway targeted genes in response to RNA interference and the algorithm to calculate the similarity of the shared gene expression pattern in different breast cancer samples.The result from this paper could be used as the biomarkers for effective breast cancer prognosis and treatment.This not only provides a more profound understanding of the molecular mechanism of breast cancer,but also could offer additional help to treat breast cancer besides current clinical pathology methods.