Background Liver cancer is among top deadly cancers worldwide with a very poor prognosis, and the liver is a vulnerable site for metastases of other cancers. Early diagnosis is crucial for treatment of the predominant liver cancers, namely hepatocellular carcinoma (HCC). Here we developed a novel computational framework for the stage-specific analysis of HCC. Methods Using publicly available clinical and RNA-Seq data of cancer samples and controls and the AJCC staging system, we performed a linear modelling analysis of gene expression across all stages and found significant genome-wide changes in the log fold-change of gene expression in cancer samples relative to control. To identify genes that were stage-specific controlling for confounding differential expression in other stages, we developed a set of six pairwise contrasts between the stages and enforced a p -value threshold (< 0.05) for each such contrast. Genes were specific for a stage if they passed all the significance filters for that stage. The monotonicity of gene expression with cancer progression was analyzed with a linear model using the cancer stage as a numeric variable. Results Our analysis yielded two stage-I specific genes (CA9, WNT7B), two stage-II specific genes (APOBEC3B, FAM186A), ten stage-III specific genes including DLG5, PARI, NCAPG2, GNMT and XRCC2, and 35 stage-IV specific genes including GABRD, PGAM2, PECAM1 and CXCR2P1. Overexpression of DLG5 was found to be tumor-promoting contrary to the cancer literature on this gene. Further, GABRD was found to be signifincantly monotonically upregulated across stages. Our work has revealed 1977 genes with significant monotonic patterns of expression across cancer stages. NDUFA4L2, CRHBP and PIGU were top genes with monotonic changes of expression across cancer stages that could represent promising targets for therapy. Comparison with gene signatures from the BCLC staging system identified two genes, HSP90AB1 and ARHGAP42. Gene set enrichment analysis indicated overrepresented pathways specific to each stage, notably viral infection pathways in HCC initiation. Conclusions Our study identified novel significant stage-specific differentially expressed genes which could enhance our understanding of the molecular determinants of hepatocellular carcinoma progression. Our findings could serve as biomarkers that potentially underpin diagnosis as well as pinpoint therapeutic targets.

Colorectal cancer remains an increasingly common disease with uncommon burden of disease, heterogeneity in manifestation, and no definitive treatment. Against this backdrop, renewed efforts to unravel the genetic drivers of colorectal cancer progression are paramount. Early-stage detection of cancer increases success of treatment as well as prognosis. Here, we have executed a comprehensive computational workflow aimed at uncovering the discrete stagewise genetic drivers of colorectal cancer progression. Using the TCGA COADREAD expression data and clinical metadata, we constructed stage-specific linear models and contrasts to identify stage-specific differentially expressed genes. Stage-specific differentially expressed genes with a significant monotone trend of expression across the stages were identified as progression-significant biomarkers. Among the biomarkers identified are: CRLF1, CALB2 (stage-I specific), GREM2, ADCY5, PLAC2, DMRT3 (stage-II specific), PIGR, SLC26A9 (stage-III specific), GABRD, DLX3, CST6, HOTAIR (stage-IV specific), and CDH3, KRT80, AADACL2, OTOP2, FAM135B, HSP90AB1 (top linear model genes). In particular the study yielded 19 genes that are progression-significant such as CCDC19, SERPINE1, HOXC11, SOX10. The existing literature for many of these biomarkers are discussed and analyzed for encouraging evidence of translational utility that would still need clinical validation. The study yielded many classes of biomarkers, which could serve in signature panels for early-stage colorectal cancer diagnosis as well as establish strategies for therapy. Our work is a concrete step in the direction of establishing the molecular signatures of the discrete progressive stages of colorectal cancer, with the future goal of securing more effective treatment for the condition.

Liver cancer is among the top deadly cancers worldwide with a very poor prognosis, and the liver is a particularly vulnerable site for metastasis of other cancers. In this study, we developed a novel computational framework for the stage-specific analysis of hepatocellular carcinoma initiation and progression. Using publicly available clinical and RNA-Seq data of cancer samples and controls, we annotated the gene expression matrix with sample stages. We performed a linear modelling analysis of gene expression across all stages and found significant genome-wide changes in gene expression in cancer samples relative to control. Using a contrast against the control, we were able to identify differentially expressed genes (log fold change >2) that were significant at an adjusted pvalue < 10E-3. In order to identify genes that were specific to each stage without confounding differential expression in other stages, we developed a full set of pairwise stage contrasts and enforced a p-value threshold (<0.05) for each such contrast. Genes were specific for a stage if they passed all the significance filters for that stage. Our analysis yielded two stage