Identification of gene expression signatures for molecular classification in human leukemia cells.
Although the methods by which leukemia is classified have been improved for effective therapies, leukemia patients occasionally exhibit diverse, sometimes unpredictable, responses to treatment. Consequently, these patients also evidence individually different clinical courses when administered with anti-leukemia drugs. In order to find new, more precise molecular markers for leukemia classification, we have analyzed the gene expression profiles from 65 diagnostic bone marrow specimens of adult patients with AML, ALL, CML or CLL by using high-throughput DNA microarrays harboring approximately 8,300 unique human genes or expression sequence tags. In the present study, we identified a group of leukemia-specific genes, which manifest gene expression profiles distinctly representative of normal bone marrow samples, as determined by a significance analysis of microarray (SAM) and GeneSpring 6.1 programs. We also determined the minimal number of genes showing a difference between acute and chronic leukemia patient groups. Furthermore, the unsupervised cluster analysis revealed a gene subset which can be used to distinguish between AML, ALL, CML and CLL patient groups, based on expression signatures. The expression levels of differentially regulated genes were verified via the principle component analysis (PCA). Our results may provide a novel set of molecular criteria for the classification of leukemia patients, and may also facilitate effects to discovery new targets, allowing for more effective treatment of leukemia patients.
Song
Ju Han
JH
College of Pharmacy, Chonnam National University, Gwangju, Republic of Korea.
Kim
Hyeoung-Joon
HJ
Lee
Chang Hyun
CH
Kim
Seung Jun
SJ
Hwang
Seung Yong
SY
Kim
Tae Sung
TS
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015703:Antigens, CD / Q000235:genetics / Q000378:metabolism; D000945:Antigens, Differentiation, T-Lymphocyte / Q000235:genetics / Q000378:metabolism; D014408:Biomarkers, Tumor / Q000032:analysis* / Q000235:genetics; D001706:Biopsy; D001854:Bone Marrow Cells / Q000737:chemistry / Q000378:metabolism*; D058534:Class I Phosphatidylinositol 3-Kinases; D016000:Cluster Analysis; D005260:Female; D020869:Gene Expression Profiling; D015973:Gene Expression Regulation, Leukemic; D005819:Genetic Markers; D018398:Homeodomain Proteins / Q000235:genetics / Q000378:metabolism; D006801:Humans; D037181:Lectins, C-Type; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000145:classification / Q000175:diagnosis* / Q000235:genetics; D015470:Leukemia, Myeloid, Acute / Q000145:classification / Q000175:diagnosis* / Q000235:genetics; D008297:Male; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D019869:Phosphatidylinositol 3-Kinases / Q000235:genetics / Q000378:metabolism; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000145:classification / Q000175:diagnosis* / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism; D000099192:CD69 Antigens
D015703:Antigens, CD; D000945:Antigens, Differentiation, T-Lymphocyte; D014408:Biomarkers, Tumor; D058534:Class I Phosphatidylinositol 3-Kinases; D005819:Genetic Markers; D018398:Homeodomain Proteins; D037181:Lectins, C-Type; D019869:Phosphatidylinositol 3-Kinases; D014157:Transcription Factors; D000099192:CD69 Antigens; C078492:HHEX protein, human; C484760:PIK3CA protein, human