ArticleBMC genomics2017
Robust transcriptional signatures for low-input RNA samples based on relative expression orderings.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Advancements in single-cell sequencing for cervical cancer research.Molecular and cellular biochemistry · 2026Review
- Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer research.Scientific reports · 2025Article
- Qualitative Transcriptional Signature for Predicting the Pathological Response of Colorectal Cancer to FOLFIRI Therapy.International journal of molecular sciences · 2024Article
- Article
- Identifying individualized prognostic signature and unraveling the molecular mechanism of recurrence in early-onset colorectal cancer.European journal of medical research · 2023Article
- Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes.Annals of medicine · 2022Article
- TheiScience · 2022Article
- Hierarchical identification of a transcriptional panel for the histological diagnosis of lung neuroendocrine tumors.Frontiers in genetics · 2022Article
- Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers.Frontiers in medicine · 2022Article
- Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning.iScience · 2021Article
- Two novel qualitative transcriptional signatures robustly applicable to non-research-oriented colorectal cancer samples with low-quality RNA.Journal of cellular and molecular medicine · 2021Article
- A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients.BMC genomics · 2020Article
- A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy.The Prostate · 2020Article
- CLEAR: coverage-based limiting-cell experiment analysis for RNA-seq.Journal of translational medicine · 2020Article
- Article
- Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer.Frontiers in molecular biosciences · 2020Article
- Qualitative transcriptional signature for predicting pathological response of colorectal cancer to FOLFOX therapy.Cancer science · 2020Article
- A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy.Frontiers in molecular biosciences · 2020Article
- A Qualitative Transcriptional Signature for Predicting CpG Island Methylator Phenotype Status of the Right-Sided Colon Cancer.Frontiers in genetics · 2020Article
- A Qualitative Transcriptional Signature for the Risk Assessment of Precancerous Colorectal Lesions.Frontiers in genetics · 2020Article
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12 authors.
Funding
Abstract
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.
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