The highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue are widely reversed in the cancer condition. Based on this finding, we have recently proposed an algorithm named RankComp to detect differentially expressed genes (DEGs) for individual disease samples measured by a particular platform. In this paper, with 461 normal lung tissue samples separately measured by four commonly used platforms, we demonstrated that tens of millions of gene pairs with significantly stable REOs in normal lung tissue can be consistently detected in samples measured by different platforms. However, about 20% of stable REOs commonly detected by two different platforms (e.g., Affymetrix and Illumina platforms) showed inconsistent REO patterns due to the differences in probe design principles. Based on the significantly stable REOs (FDR<0.01) for normal lung tissue consistently detected by the four platforms, which tended to have large rank differences, RankComp detected averagely 1184, 1335 and 1116 DEGs per sample with averagely 96.51%, 95.95% and 94.78% precisions in three evaluation datasets with 25, 57 and 58 paired lung cancer and normal samples, respectively. Individualized pathway analysis revealed some common and subtype-specific functional mechanisms of lung cancer. Similar results were observed for colorectal cancer. In conclusion, based on the cross-platform significantly stable REOs for a particular normal tissue, differentially expressed genes and pathways in any disease sample measured by any of the platforms can be readily and accurately detected, which could be further exploited for dissecting the heterogeneity of cancer.

The expression measurements of thousands of genes are correlated with the proportions of tumor epithelial cell (PTEC) in clinical samples. Thus, for a tumor diagnostic or prognostic signature based on a summarization of expression levels of the signature genes, the risk score for a patient may dependent on the tumor tissues sampled from different tumor sites with diverse PTEC for the same patient. Here, we proposed that the within-samples relative expression orderings (REOs) based gene pairs signatures should be insensitive to PTEC variations. Firstly, by analysis of paired tumor epithelial cell and stromal cell microdissected samples from 27 cancer patients, we showed that above 80% of gene pairs had consistent REOs between the two cells, indicating these REOs would be independent of PTEC in cancer tissues. Then, by simulating tumor tissues with different PTEC using each of the 27 paired samples, we showed that about 90% REOs of gene pairs in tumor epithelial cells were maintained in tumor samples even when PTEC decreased to 30%. Especially, the REOs of gene pairs with larger expression differences in tumor epithelial cells tend to be more robust against PTEC variations. Finally, as a case study, we developed a gene pair signature which could robustly distinguish colorectal cancer tissues with various PTEC from normal tissues. We concluded that the REOs-based signatures were robust against PTEC variations.

BackgroundIt is often difficult to obtain sufficient quantity of RNA molecules for gene expression profiling under many practical situations. Amplification from low-input samples may induce artificial signals.ResultsWe compared the expression measurements of low-input mRNA samples, from 25 pg to 1000 pg mRNA, which were amplified and profiled by Smart-seq, DP-seq and CEL-seq techniques using the Illumina HiSeq 2000 platform, with those of the paired high-input (50 ng) mRNA samples. Even with 1000 pg mRNA input, we found that thousands of genes had at least 2 folds-change of expression levels in the low-input samples compared with the corresponding paired high-input samples. Consequently, a transcriptional signature based on quantitative expression values and determined from high-input RNA samples cannot be applied to low-input samples, and vice versa. In contrast, the within-sample relative expression orderings (REOs) of approximately 90% of all the gene pairs in the high-input samples were maintained in the paired low-input samples with 1000 pg input mRNA molecules. Similar results were observed in the low-input total RNA samples amplified and profiled by the Whole-Genome DASL technique using the Illumina HumanRef-8 v3.0 platform. As a proof of principle, we developed REOs-based signatures from high-input RNA samples for discriminating cancer tissues and showed that they can be robustly applied to low-input RNA samples.ConclusionsREOs-based signatures determined from the high-input RNA samples can be robustly applied to samples profiled with the low-input RNA samples, as low as the 1000 pg and 250 pg input samples but no longer stable in samples with less than 250 pg RNA input to a certain degree.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4280-7) contains supplementary material, which is available to authorized users.

Background: Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. Results: Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization.

High-throughput bisulfite sequencing is widely used to measure cytosine methylation at single-base resolution in eukaryotes. It permits systems-level analysis of genomic methylation patterns associated with gene expression and chromatin structure. However, methods for large-scale identification of methylation patterns from bisulfite sequencing are lacking. We developed a comprehensive tool, CpG_MPs, for identification and analysis of the methylation patterns of genomic regions from bisulfite sequencing data. CpG_MPs first normalizes bisulfite sequencing reads into methylation level of CpGs. Then it identifies unmethylated and methylated regions using the methylation status of neighboring CpGs by hotspot extension algorithm without knowledge of pre-defined regions. Furthermore, the conservatively and differentially methylated regions across paired or multiple samples (cells or tissues) are identified by combining a combinatorial algorithm with Shannon entropy. CpG_MPs identified large amounts of genomic regions with different methylation patterns across five human bisulfite sequencing data during cellular differentiation. Different sequence features and significantly cell-specific methylation patterns were observed. These potentially functional regions form candidate regions for functional analysis of DNA methylation during cellular differentiation. CpG_MPs is the first user-friendly tool for identifying methylation patterns of genomic regions from bisulfite sequencing data, permitting further investigation of the biological functions of genome-scale methylation patterns.

Previously reported prognostic signatures for predicting the prognoses of postsurgical hepatocellular carcinoma (HCC) patients are commonly based on predefined risk scores, which are hardly applicable to samples measured by different laboratories. To solve this problem, using gene expression profiles of 170 stage I/II HCC samples, we identified a prognostic signature consisting of 20 gene pairs whose within-sample relative expression orderings (REOs) could robustly predict the disease-free survival and overall survival of HCC patients. This REOs-based prognostic signature was validated in two independent datasets. Functional enrichment analysis showed that the patients with high-risk of recurrence were characterized by the activations of pathways related to cell proliferation and tumor microenvironment, whereas the low-risk patients were characterized by the activations of various metabolism pathways. We further investigated the distinct epigenomic and genomic characteristics of the two prognostic groups using The Cancer Genome Atlas samples with multi-omics data. Epigenetic analysis showed that the transcriptional differences between the two prognostic groups were significantly concordant with DNA methylation alternations. The signaling network analysis identified several key genes (e.g. TP53, MYC) with epigenomic or genomic alternations driving poor prognoses of HCC patients. These results help us understand the multi-omics mechanisms determining the outcomes of HCC patients.

Until recently, few molecular signatures of drug resistance identified in drug-induced resistant cancer cell models can be translated into clinical practice. Here, we defined differentially expressed genes (DEGs) between pre-chemotherapy colorectal cancer (CRC) tissue samples of non-responders and responders for 5-fluorouracil and oxaliplatin-based therapy as clinically relevant drug resistance genes (CRG5-FU/L-OHP). Taking CRG5-FU/L-OHP as reference, we evaluated the clinical relevance of several types of genes derived from HCT116 CRC cells with resistance to 5-fluorouracil and oxaliplatin, respectively. The results revealed that DEGs between parental and resistant cells, when both were treated with the corresponding drug for a certain time, were significantly consistent with the CRG5-FU/L-OHP as well as the DEGs between the post-chemotherapy CRC specimens of responders and non-responders. This study suggests a novel strategy to extract clinically relevant drug resistance genes from both drug-induced resistant cell models and post-chemotherapy cancer tissue specimens.

BackgroundDue to the heterogeneity of cancer, identifying differentially methylated (DM) CpG sites between a set of cancer samples and a set of normal samples cannot tell us which patients have methylation aberrations in a particular DM CpG site.MethodsWe firstly showed that the relative methylation-level orderings (RMOs) of CpG sites within individual normal lung tissues are highly stable but widely disrupted in lung adenocarcinoma tissues. This finding provides the basis of using the RankComp algorithm, previously developed for differential gene expression analysis at the individual level, to identify DM CpG sites in each cancer tissue compared with its own normal state. Briefly, through comparing with the highly stable normal RMOs predetermined in a large collection of samples for normal lung tissues, the algorithm finds those CpG sites whose hyper- or hypo-methylations may lead to the disrupted RMOs of CpG site pairs within a disease sample based on Fisher’s exact test.ResultsEvaluated in 59 lung adenocarcinoma tissues with paired adjacent normal tissues, RankComp reached an average precision of 94.26% for individual-level DM CpG sites. Then, after identifying DM CpG sites in each of the 539 lung adenocarcinoma samples from TCGA, we found five and 44 CpG sites hypermethylated and hypomethylated in above 90% of the disease samples, respectively. These findings were validated in 140 publicly available and eight additionally measured paired cancer-normal samples. Gene expression analysis revealed that four of the five genes, HOXA9, TAL1, ATP8A2, ENG and SPARCL1, each harboring one of the five frequently hypermethylated CpG sites within its promoters, were also frequently down-regulated in lung adenocarcinoma.ConclusionsThe common DNA methylation aberrations in lung adenocarcinoma tissues may be important for lung adenocarcinoma diagnosis and therapy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-017-1122-y) contains supplementary material, which is available to authorized users.