A Multiclassifier Model to Identify Mitochondrial Respiratory Gene Signatures in Human Cancer Whether alteration of mitochondrial gene expression can serve as an effective molecular signature for cancer classification currently remains controversy. To tackle this challenge, here we present a multiclassifier model to identify mitochondrial aberrant gene signatures and then assess the effectiveness on cancer classification. Specifically, we first applied a supervised learning model, Empirical Bayes statistics, to detect differentially expressed genes from a liver cancer mitochondrial gene expression dataset (GEO accession: GSE64505). Next, we applied two wellknown classifiers, Prediction Analysis of Microarrays (PAM) and Random Forest (RF), with several folds of crossvalidation, to find molecular signatures that can classify liver cancer samples from normal samples. We obtained a mitochondrial molecular signature comprising 25 genes. Classification accuracy was 87.5% by PAM classifier (5 fold crossvalidation with 30 repeats), while it was 87.5% and 75.0% by Random Forest with 5 and 2fold crossvalidations (30 repeats), respectively. We further performed literature mining and Gene Set Enrichment Analysis (GSEA) to evaluate the biological significance and novelty of genes in this gene signature.

MultiClassification of Cancer Samples Based on CoExpression Analyses Cancer staging, grading and subtyping all represent important problems for precision diagnosis, treatment and mechanistic studies of cancer. The majority of the existing computational methods solve this problem via multiclassification of differential geneexpressions of cancer samples of specific classes (Stages, Grades and subtypes) vs. controls. However, the performance of such classification techniques is generally not satisfactory since the discerning power of differential expression patterns in such classifications is limited. We present here a multiclassification technique, based on coexpression patterns specific to individual subclasses in provided training data as coexpression patterns tend to be more conserved than differential expressions within each subclass. A challenge in implementing this strategy lies in how to effectively derive coexpression patterns in individual samples, which is solved through comparing coexpression patterns within a subclass and those in the subclass plus a new sample. Compared with the stateoftheart gene expressionbased classification methods, our method outperforms them in cancer staging, grading and subtyping of cancer samples from TCGA in almost all the measures used. In addition, the coexpressed genes computationally selected for classifications are biologically meaningful, which will prove important for diagnostic biomarker design, treatment plan selection and possibly mechanistic studies of cancer.