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Background & Aims: Primary tumors of colorectal carcinoma (CRC) with liver metastasis might gain some liver-specific characteristics to adapt the liver micro-environment. This study aims to reveal potential liver-like transcriptional characteristics associated with the liver metastasis in primary colorectal carcinoma.Methods: Among the genes up-regulated in normal liver tissues versus normal colorectal tissues, we identified “liver-specific” genes whose expression levels ranked among the bottom 10% (“unexpressed”) of all measured genes in both normal colorectal tissues and primary colorectal tumors without metastasis. These liver-specific genes were investigated for their expressions in both the primary tumors and the corresponding liver metastases of seven primary CRC patients with liver metastasis using microdissected samples.Results: Among the 3958 genes detected to be up-regulated in normal liver tissues versus normal colorectal tissues, we identified 12 liver-specific genes and found two of them, ANGPTL3 and CFHR5, were unexpressed in microdissected primary colorectal tumors without metastasis but expressed in both microdissected liver metastases and corresponding primary colorectal tumors (Fisher's exact test, P < 0.05). Genes co-expressed with ANGPTL3 and CFHR5 were significantly enriched in metabolism pathways characterizing liver tissues, including “starch and sucrose metabolism” and “drug metabolism-cytochrome P450”.Conclusions: For primary CRC with liver metastasis, both the liver metastases and corresponding primary colorectal tumors may express some liver-specific genes which may help the tumor cells adapt the liver micro-environment.

Blood is a promising surrogate for solid tissue to investigate disease-associated molecular biomarkers. However, proportion changes of the constituent cells in the often-used peripheral whole blood (PWB) or peripheral blood mononuclear cell (PBMC) samples may influence the detection of cell-specific alterations under disease states. We propose a simple method, Ref-REO, to detect molecular alterations in leukocytes using the mixed-cell blood samples. The method is based on the predetermined within-sample relative expression orderings (REOs) of genes in purified leukocytes of healthy people. Both the simulated and real mixed-cell blood gene expression profiles were used to evaluate the method. Approximately 99% of the differentially expressed genes (DEGs) detected by Ref-REO in the simulated mixed-cell data are owing to the transcriptional alterations in leukocytes rather than the proportion changes of leukocytes. For the real mixed-cell data, the DEGs detected by Ref-REO in the PBMCs expression data for systemic lupus erythematosus (SLE) patients overlap significantly with the DEGs detected in the expression data of SLE CD4 + T cells and B cells and they are mainly enriched with mRNA editing and interferon-associated genes. The detected DEGs in the PWB data for lung carcinoma patients are significantly enriched with coagulation-associated functional categories that are closely associated with cancer progression. In conclusion, the proposed method is capable of detecting the disease-associated leukocyte-specific molecular alterations, using mixed-cell blood samples, which provides simple, transferable and easy-to-use candidates for disease biomarkers.

Motivation For some specific tissues, such as the heart and brain, normal controls are difficult to obtain. Thus, studies with only a particular type of disease samples (one-phenotype) cannot be analyzed using common methods such as significance analysis of microarrays, edgeR, and limma. The RankComp algorithm, which was mainly developed to identify individual-level differentially expressed genes (DEGs), can be applied to identify population-level DEGs for the one-phenotype data, but cannot identify the dysregulation directions of DEGs. Results Here, we optimized the RankComp algorithm, termed PhenoComp. Compared with RankComp, PhenoComp provided the dysregulation directions of DEGs, and had more robust detection power in both simulated and real one-phenotype data. Moreover, using the DEGs detected by common methods as the “gold standard,” the results showed that the DEGs detected by PhenoComp using only one-phenotype data were comparable to those identified by common methods using case-control samples, independent of the measurement platform. PhenoComp also exhibited good performance for weakly differential expression signal data. Availability The PhenoComp algorithm is available on the web at https://github.com/XJJ-student/PhenoComp. Supplementary information Supplementary data are available at Bioinformatics online.

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