ArticleOncotarget2016
An individualized prognostic signature and multi‑omics distinction for early stage hepatocellular carcinoma patients with surgical resection.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.Scientific reports · 2024Article
- A novel qualitative signature based on lncRNA pairs for prognosis prediction in hepatocellular carcinoma.Cancer cell international · 2022Article
- Identification of the origin of brain metastases based on the relative methylation orderings of CpG sites.Epigenetics · 2021Article
- Genomic Landscape of HCC.Current hepatology reports · 2020Article
- 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
- The Effects of Age, Cigarette Smoking, Sex, and Race on the Qualitative Characteristics of Lung Transcriptome.BioMed research international · 2020Article
- Qualitative transcriptional signature for predicting pathological response of colorectal cancer to FOLFOX therapy.Cancer science · 2020Article
- Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method.Frontiers in bioengineering and biotechnology · 2020Article
- The Prediction of Survival in Hepatocellular Carcinoma Based on A Four Long Non-coding RNAs Expression Signature.Journal of Cancer · 2020Article
- A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients.BioMed research international · 2020Article
- Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.Frontiers in oncology · 2020Review
- A qualitative transcriptional signature for predicting microsatellite instability status of right-sided Colon Cancer.BMC genomics · 2019Article
- A qualitative transcriptional signature for the early diagnosis of colorectal cancer.Cancer science · 2019Article
- A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection.Frontiers in oncology · 2019Article
- A qualitative signature for early diagnosis of hepatocellular carcinoma based on relative expression orderings.Liver international : official journal of the International Association for the Study of the Liver · 2018Article
- Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer.BMC genomics · 2018Article
- Significant predictors of overall survival in patients with hepatocellular carcinoma after surgical resection.PloS one · 2018Article
- Development Of A Three-Gene Prognostic Signature For Hepatitis B Virus Associated Hepatocellular Carcinoma Based On Integrated Transcriptomic Analysis.Journal of Cancer · 2018Article
- Robust transcriptional signatures for low-input RNA samples based on relative expression orderings.BMC genomics · 2017Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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