IDENTIFYING BIOMARKER FOR LUNG CANCER USING GENE EXPRESSION ANALYSIS
Cancer biomarkers are proteins which found highly elevated in blood plasma and it act as phenotypic parameter that characterizes an organism’s state of health or disease, or a response to a particular therapeutic intervention. Examining biomarkers can be useful in diagnosing various kinds of cancer and lung cancer is the second most frequently diagnosed cancer which is the leading cause of cancer-related mortality in both women and men because of failure in early diagnosis. Especially, cancer biomarker discovery is eminent in lung cancer due to its anticipated critical role in early diagnosis, therapy guidance, and prognosis monitoring of cancers. Till now, DNA biomarkers such as hypermethylations of the promoters and mutations in K-ras, p53, and protein biomarkers; carcinoembryonic antigen (CEA), CYFRA21-1, plasma kallikrein B1 (KLKB1), Neuron-specific enolase, etc. have been disclosed as lung cancer biomarkers. Despite extensive studies thus far, few are turned out to be useful clinically. Even those used in clinic do not show enough sensitivity, specificity and reproducibility for general use. So there is a need of comprehensive approach which has three components:
- First, check usefulness of available biomarkers in a large set of clinical samples including other cancer types and inflammatory diseases.
- Second, analyze the more specific and less abundant lung cancer biomarkers focused on specific subtypes of lung cancers.
- Third, one specific biomarker may not be enough to predict or monitor lung cancers and thus several good biomarkers need to be combined, with quantitative information to act efficiently.
The methodology part contains three segments :
- Initially based on the available type of raw data and format types the statistical analysis has to be carried out. After preliminary analysis specifically expressed genes for each cancer subtypes has to be predicted by combining available statistical packages. The number of specific genes identified from each dataset will be differing. To find good biomarkers the identified genes has to be linked.
- In the second part, the cancer network has to be build to locate identified genes position correlation with the other genes. It can be done by using gene ontology / text mining based cancer network building with machine learning methods.
- Final segment contains validation of the biomarker using functional genomic methods.