ArticleCancer science2019
A qualitative transcriptional signature for the early diagnosis of colorectal cancer.
Article in Cancer science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 21 citations in OpenAlex.
- Role of UGP2 as a Biomarker in Colorectal Cancer: Implications for Tumor Progression, Diagnosis, and Prognosis.Current issues in molecular biology · 2025Article
- Predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer with two step feature selection and ensemble learning.Scientific reports · 2025Article
- Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis.Breast cancer research and treatment · 2024Article
- Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.Scientific reports · 2024Article
- Identification of an Individualized Prognostic Biomarker for Serous Ovarian Cancer: A Qualitative Model.Diagnostics (Basel, Switzerland) · 2022Article
- Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes.Annals of medicine · 2022Article
- Identification of the origin of brain metastases based on the relative methylation orderings of CpG sites.Epigenetics · 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
- GINS complex subunit 4, a prognostic biomarker and reversely mediated by Krüppel-like factor 4, promotes the growth of colorectal cancer.Cancer science · 2020Article
- A Qualitative Transcriptional Signature for the Risk Assessment of Precancerous Colorectal Lesions.Frontiers in genetics · 2020Article
- Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method.Frontiers in bioengineering and biotechnology · 2020Article
- Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer.Frontiers in molecular biosciences · 2020Article
- Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method.Frontiers in cell and developmental biology · 2020Article
- Identification of Tamoxifen-Resistant Breast Cancer Cell Lines and Drug Response Signature.Frontiers in molecular biosciences · 2020Article
- A qualitative transcriptional signature for the early diagnosis of colorectal cancer.Cancer science · 2019Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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