Explore the vast range of titles available.

Gene expression profiling is an effective method for identifying predictive and prognostic biomarkers. However, measurements are prone to uncertainty and errors due to various pre-analytical variables. Systematic evaluating effects of these variables on gene expression measurements and relative expression orderings (REOs) of gene pairs, is necessary. A total of 18 datasets were collected, comprising over 800 paired samples. These paired samples were utilized to assess the impact of pre-analytical variables on gene expression measurements and REOs, including sampling methods, tumor sample heterogeneity, fixed time delays, preservation conditions, degradation levels, library preparation kits, amplification kits, RNA quantity, measuring platforms, and laboratory sites at single and multi-variable level. Low-quality samples served as the case group, while paired high-quality samples constituted the control group. In both single and multiple variable analyses, comparing each case sample to paired control sample revealed thousands of genes exhibited a twofold change in expression values. In contrast, on average, 82% and 76% of gene pairs keep consistent REO pattern between paired samples in single-variable and multi-variable analyses, respectively. Notably, the rate steadily increased after excluding gene pairs with the closest expression levels. Statistical analyses shown a higher proportion of differentially expressed genes (DEGs) than that of reversed gene pairs between case and control groups in both single-variable and multi-variable analyses. Furthermore, the proportion of reversal gene pairs among all gene pairs involving DEGs remained below 20% in the majority of comparisons. Our research demonstrates that REOs exhibit higher robustness under the influence of pre-analytical variables. These findings indicate the potential of the REOs-based approach in transcriptomics research and its applicability for biomarker studies.

Background Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals. Results We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh’s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl . Conclusions The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity. Key points RankCompV3 is a method for identifying differentially expressed genes (DEGs) in either bulk or single-cell RNA transcriptomics. It is based on the counts of relative expression orderings (REOs) of gene pairs in the two groups. The contingency tables are tested using McCullagh’s method. RankCompV3 has comparable or better performance than that of other conventional methods. It has been shown to be effective in identifying DEGs in both single-cell and pseudo-bulk profiles. Pseudo-bulk method is implemented in RankCompV3, which allows the method to achieve higher computational efficiency and improves the concordance with the bulk ground-truth. RankCompV3 is effective in identifying functionally relevant DEGs in weak-signal datasets. The method is not biased towards highly expressed genes.

The gold standard cell-of-origin (COO) classification based on the gene expression profiling (GEP) is robust in diffuse large B cell lymphoma (DLBCL). However, its clinical application remains limited. The qualitative transcriptional characteristic on account of the within-sample relative expression orderings (REO) of genes, which is highly stable against batch effects and variations of specimen quality, presents great potential for clinical use. In this study, we developed a qualitative signature to distinguish the germinal center B cells (GCB) subtype of DLBCL from non-GCB subtypes. The signature consisting of 19 gene pairs (19-GPS), was identified from gene pairs with reversal REO patterns between the GCB and non-GCB subtypes. It was validated across four independent datasets with accuracies of 89.37%, 84.34%, 88.29%, and 92.72%, respectively. Patients classified as the GCB subtype by the 19-GPS had significantly higher overall survival (OS) and progression-free survival (PFS) rates compared to the non-GCB patients. In the validation datasets, the 19-GPS based COO classification demonstrated enhanced performance in prognostic stratification compared to the gold standard COO classification. Three marker genes in 19-GPS, namely HMCES , ZFAND4 , and NLRP4 , were identified with prognostic value for DLBCL. The expression of NLRP4 in B cells may be related to the transformation of follicular lymphoma (FL), a lymphoma derived from GCB, into DLBCL. All three aforementioned genes may be associated with the responsiveness of malignant human B-cell Lines to R-CHOP therapeutic agents. These results demonstrated that 19-GPS signature was effective and might potentially assist in guiding the prognosis and treatment options for DLBCL.

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. Conclusions Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms.

Background It 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. Results We 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. Conclusions REOs-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.

Currently, using biopsy specimens for the early diagnosis of colorectal cancer (CRC) is not entirely reliable due to insufficient sampling amount and inaccurate sampling location. Thus, it is necessary to develop a signature that can accurately identify patients with CRC under these clinical scenarios. Based on the relative expression orderings of genes within individual samples, we developed a qualitative transcriptional signature to discriminate CRC tissues, including CRC adjacent normal tissues from non‐CRC individuals. The signature was validated using multiple microarray and RNA sequencing data from different sources. In the training data, a signature consisting of 7 gene pairs was identified. It was well validated in both biopsy and surgical resection specimens from multiple datasets measured by different platforms. For biopsy specimens, 97.6% of 42 CRC tissues and 94.5% of 163 non‐CRC (normal or inflammatory bowel disease) tissues were correctly classified. For surgically resected specimens, 99.5% of 854 CRC tissues and 96.3% of 81 CRC adjacent normal tissues were correctly identified as CRC. Notably, we additionally measured 33 CRC biopsy specimens by the Affymetrix platform and 13 CRC surgical resection specimens, with different proportions of tumor epithelial cells, ranging from 40% to 100%, by the RNA sequencing platform, and all these samples were correctly identified as CRC. The signature can be used for the early diagnosis of CRC, which is also suitable for minimum biopsy specimens and inaccurately sampled specimens, and thus has potential value for clinical application. Using biopsy and surgically resected specimens from public data and our laboratory, we showed that our relative expression ordering‐based signature is suitable for the early diagnosis of colorectal cancer, even when the specimen was sampled inaccurately. Moreover, we showed that the signature is robust to clinicopathological variations and varied proportions of tumor epithelial cells in specimens sampled from different tumor locations of the same patients.

Currently, due to the low quality of RNA caused by degradation or low abundance, the accuracy of gene expression measurements by transcriptome sequencing (RNA‐seq) is very challenging for non‐research‐oriented clinical samples, majority of which are preserved in hospitals or tissue banks worldwide with complete pathological information and follow‐up data. Molecular signatures consisting of several genes are rarely applied to such samples. To utilize these resources effectively, 45 stage II non‐research‐oriented samples which were formalin‐fixed paraffin‐embedded (FFPE) colorectal carcinoma samples (CRC) using RNA‐seq have been analysed. Our results showed that although gene expression measurements were significantly affected, most cancer features, based on the relative expression orderings (REOs) of gene pairs, were well preserved. We then developed two REO‐based signatures, which consisted of 136 gene pairs for early diagnosis of CRC, and 4500 gene pairs for predicting post‐surgery relapse risk of stage II and III CRC. The performance of our signatures, which included hundreds or thousands of gene pairs, was more robust for non‐research‐oriented clinical samples, compared to that of two published concise REO‐based signatures. In conclusion, REO‐based signatures with relatively more gene pairs could be robustly applied to non‐research‐oriented CRC samples.

Purpose Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment. Methods We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls. Results We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC. Conclusion Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.

Background It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. RankComp, an algorithm, could analyze the highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue that are widely reversed in the cancer condition, thereby detecting DEGs for individual disease samples measured by a particular platform. Methods In the present study, Gene Expression Omnibus (GEO) Series (GSE) GSE75540, GSE138206 were downloaded from GEO, by analyzing DEGs in oral squamous cell carcinoma based on online datasets using the RankComp algorithm, using the Kaplan-Meier survival analysis and Cox regression analysis to survival analysis, Gene Set Enrichment Analysis (GSEA) to explore the potential molecular mechanisms underlying. Results We identified 6 reverse gene pairs with stable REOs. All the 12 genes in these 6 reverse gene pairs have been reported to be associated with cancers. Notably, lower Interferon Induced Protein 44 Like (IFI44L) expression was associated with poorer overall survival (OS) and Disease-free survival (DFS) in oral squamous cell carcinoma patients, and IFI44L expression showed satisfactory predictive efficiency by receiver operating characteristic (ROC) curve. Moreover, low IFI44L expression was identified as risk factors for oral squamous cell carcinoma patients’ OS. IFI44L downregulation would lead to the activation of the FRS-mediated FGFR1, FGFR3, and downstream signaling pathways, and might play a role in the PI3K-FGFR cascades. Conclusions Collectively, we identified 6 reverse gene pairs with stable REOs in oral squamous cell carcinoma, which might serve as gene signatures playing a role in the diagnosis in oral squamous cell carcinoma. Moreover, high expression of IFI44L, one of the DEGs in the 6 reverse gene pairs, might be associated with favorable prognosis in oral squamous cell carcinoma patients and serve as a tumor suppressor by acting on the FRS-mediated FGFR signaling.

Hepatocellular carcinoma (HCC) patients, featured by markedly heterogeneous tumor microenvironment (TME), meet diverse clinical outcome and neoadjuvant response. Yet the comprehensive influences of aberrant glycosylation on the TME of HCC remain elusive. In this study, by integrated transcriptome profiling, we systemically analyzed the considerable value of glycosylation-regulating signature in diagnosis and prognosis of HCC. A diagnostic model for HCC based on glycosylation-regulating REOs (relative expression orderings) was constructed. A robust glycoscore system was developed to evaluate distinct glycosylation patterns of patients in both the discovery and independent validation cohorts. Mechanisms for prognostic discrepancies between these patterns were dissected in tumor immunoediting, metabolic reprogramming, somatic mutations, and copy number variation (CNV). An individual survival prediction webserver based on a nomogram model (https://survpredict.shinyapps.io/DynNom/) was also established, which facilitates the translational and clinical application of glycoscore. The glycoscore could also effectively predict therapeutic response to sorafenib, Transhepatic Arterial Chemotherapy and Embolization (TACE), and anti-PD-1 therapies in patients with divergent glycosylation patterns, which was validated by a machine learning model. In summary, our study provided a unique insight into the HCC diagnosis and prognostic stratification based on integrated glycosylation-regulating signature. The robust glycosylation scoring system could comprehensively evaluate TME traits, predict prognosis and clinical benefits from neoadjuvant therapies, which may hold promise for promoting personalized clinical decision-making. •Glycosylation-regulating relative expression orderings (REOs) are helpful for HCC diagnosis.•Prognostic stratification by glycosylation pattern was validated in cross-platform datasets for HCC patients.•Glycoscore for predicting tumor patients' therapeutic benefits in neoadjuvant treatments.•Individual survival prediction webserver for HCC patients facilitates personalized medicine.