Liver cancer is a complex disease responsible for a high number of deaths across the globe each year, making automated solutions for liver cancer classification urgent. The most common form of liver cancer is hepatocellular carcinoma (HCC), accounting for over 90 % of liver cancer cases. There is a distinct lack of publicly available HCC datasets utilizing genomic data, which is necessary for training artificial intelligence (AI) models for automated HCC classification. This study proposes constructing a multi-stage HCC dataset using XGBoost and Semi-Supervised learning on three separate datasets of genomic biomarkers, utilizing their existing labels in the Semi-Supervised learning process to label the proposed dataset. The proposed dataset consists of 770 patient samples in total, categorized into five classes that represent normal tissue alongside different stages of HCC. Each sample in the dataset consists of$\mathbf{1 1, 1 5 0}$different gene expression levels. The XGBoost model demonstrated a final classification accuracy of 96.5 % during the Semi-Supervised learning process.

MOTIVATION: Co-regulated genes are not identified in traditional microarray analyses, but may theoretically be closely functionally linked [guilt-by-association (GBA), guilt-by-profiling]. Thus, bioinformatics procedures for guilt-by-profiling/association analysis have yet to be applied to large-scale cancer biology. We analyzed 2158 full cancer transcriptomes from 163 diverse cancer entities in regard of their similarity of gene expression, using Pearson's correlation coefficient (CC). Subsequently, 428 highly co-regulated genes (|CC| ≥ 0.8) were clustered unsupervised to obtain small co-regulated networks. A major subnetwork containing 61 closely co-regulated genes showed highly significant enrichment of cancer bio-functions. All genes except kinesin family member 18B (KIF18B) and cell division cycle associated 3 (CDCA3) were of confirmed relevance for tumor biology. Therefore, we independently analyzed their differential regulation in multiple tumors and found severe deregulation in liver, breast, lung, ovarian and kidney cancers, thus proving our GBA hypothesis. Overexpression of KIF18B and CDCA3 in hepatoma cells and subsequent microarray analysis revealed significant deregulation of central cell cycle regulatory genes. Consistently, RT-PCR and proliferation assay confirmed the role of both genes in cell cycle progression. Finally, the prognostic significance of the identified KIF18B- and CDCA3-dependent predictors (P = 0.01, P = 0.04) was demonstrated in three independent HCC cohorts and several other tumors. In summary, we proved the efficacy of large-scale guilt-by-profiling/association strategies in oncology. We identified two novel oncogenes and functionally characterized them. The strong prognostic importance of downstream predictors for HCC and many other tumors indicates the clinical relevance of our findings. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Next-generation sequencing technology enables an entirely new perspective for clinical research and will speed up personalized medicine. In contrast to microarray-based approaches, RNA-Seq analysis provides a much more comprehensive and unbiased view of gene expression. Although the perspective is clear and the long-term success of this new technology obvious, bioinformatics resources making these data easily available especially to the biomedical research community are still evolving. RESULTS: We have generated RNA-Seq Atlas, a web-based repository of RNA-Seq gene expression profiles and query tools. The website offers open and easy access to RNA-Seq gene expression profiles and tools to both compare tissues and find genes with specific expression patterns. To enlarge the scope of the RNA-Seq Atlas, the data were linked to common functional and genetic databases, in particular offering information on the respective gene, signaling pathway analysis and evaluation of biological functions by means of gene ontologies. Additionally, data were linked to several microarray gene profiles, including BioGPS normal tissue profiles and NCI60 cancer cell line expression data. Our data search interface allows an integrative detailed comparison between our RNA-Seq data and the microarray information. This is the first database providing data mining tools and open access to large scale RNA-Seq expression profiles. Its applications will be versatile, as it will be beneficial in identifying tissue specific genes and expression profiles, comparison of gene expression profiles among diverse tissues, but also systems biology approaches linking tissue function to gene expression changes. AVAILABILITY AND IMPLEMENTATION: http://medicalgenomics.org/rna_seq_atlas.