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Statistical Tests and Support Vector Machine with Recursive Feature Elimination:
Complementary Approaches to Gene Expression Analysis of Breast Tumor
Progression
Renato Campanini1, Giovanni d’Ario1, Giuseppe Palermo1, Alessandro Riccardi1,
Manuela Vecchi2, Stefano Confalonieri2, Fabrizio Bianchi2, Pietro Mariani2, Pier Paolo
Di Fiore2
1
Dipartimento di Fisica, Università di Bologna, Viale B.Pichat 6/2, 40127, Bologna, Italy
2
IFOM Istituto FIRC di Oncologia Molecolare Via Adamello 16, 20134, Milan, Italy
ABSTRACT
Metastases to regional lymph nodes, detected at diagnosis and surgery in approximately
one-third of breast cancer patients, provides significant information for staging,
prognosis, and designing of therapeutic regimens. However, the molecular mechanisms
that control the spread of cancer to proximal lymph nodes are still poorly understood.
Here, we systematically analyzed the molecular expression profiles of 26 breast primary
tumors and synchronous lymph node metastases using the Affymetrix gene chip
technology. An unsupervised hierarchical analysis demonstrated that approximately 80%
of the primary tumors clustered together with their synchronous metastases, suggesting
that they shared similar overall gene expression patterns.
To identify genes that could discriminate primary tumors from synchronous lymph node
metastases we integrated approaches from areas of statistical analysis and machine
learning. Statistical Tests and Support Vector Machine with Recursive Feature
Elimination algorithm demonstrated to be complementary approaches in order to identify
a list of significantly differentially regulated genes, which were further confirmed by
quantitative PCR analysis.
Furthermore, we took advantage of the in situ hybridization technique on dedicated tissue
microarrays of paraffin-embedded breast tumors to evaluate the expression levels, the
cell-type origin, and the biological significance of the identified regulated genes.