Aim: Pancreatic cancer is one of the worst malignant tumors in prognosis. Therefore, to reduce the mortality rate of pancreatic cancer, early diagnosis and prompt treatment are particularly important. Results: We put forward a new feature-selection method that was used to find clinical markers for pancreatic cancer by combination of Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE) algorithms. As a result, seven differentially expressed genes were predicted as specific biomarkers for pancreatic cancer because of their highest accuracy of classification on cancer and normal samples. Conclusion: Three (MMP7, FOS and A2M) out of the seven predicted gene markers were found to encode proteins secreted into urine, providing potential diagnostic evidences for pancreatic cancer.
[1]Jilin Univ, Coll Comp Sci & Technol, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Jilin, Peoples R China.
[2]Jilin Univ, China Japan Union Hosp, Canc Syst Biol Ctr, Changchun 130033, Jilin, Peoples R China.
[3]Ohio State Univ, Coll Med, Dept Biomed Informat, Columbus, OH 43210 USA.
[4]Jilin Univ, Basic Med Sch, Changchun 130021, Jilin, Peoples R China.
[5]Jilin Univ, Sch Artificial Intelligence, Changchun 130021, Jilin, Peoples R China.
[6]Harbin Med Univ, Affiliated Hosp 2, Dept Hepatopancreatobiliary Surg, Harbin 150086, Heilongjiang, Peoples R China.
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