ArticleOncotarget2016
A rank-based transcriptional signature for predicting relapse risk of stage II colorectal cancer identified with proper data sources.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Discovery and validation of a 10-gene predictive signature for response to adjuvant chemotherapy in stage II and III colon cancer.Cell reports. Medicine · 2024Article
- Capecitabine inhibits epithelial-to-mesenchymal transition and proliferation of colorectal cancer cells by mediating the RANK/RANKL pathway.Oncology letters · 2022Article
- Article
- 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
- A Qualitative Transcriptional Signature for Predicting CpG Island Methylator Phenotype Status of the Right-Sided Colon Cancer.Frontiers in genetics · 2020Article
- Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method.Frontiers in bioengineering and biotechnology · 2020Article
- Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method.Frontiers in cell and developmental biology · 2020Article
- A qualitative transcriptional signature for predicting microsatellite instability status of right-sided Colon Cancer.BMC genomics · 2019Article
- A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection.Frontiers in oncology · 2019Article
- Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer.BMC genomics · 2018Article
- Robust transcriptional signatures for low-input RNA samples based on relative expression orderings.BMC genomics · 2017Article
- Circumvent the uncertainty in the applications of transcriptional signatures to tumor tissues sampled from different tumor sites.Oncotarget · 2017Article
- Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples.Oncotarget · 2017Article
- An individualised signature for predicting response with concordant survival benefit for lung adenocarcinoma patients receiving platinum-based chemotherapy.British journal of cancer · 2016Article
- Multi-omics landscapes of colorectal cancer subtypes discriminated by an individualized prognostic signature for 5-fluorouracil-based chemotherapy.Oncogenesis · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
The irreproducibility problem seriously hinders the studies on transcriptional signatures for predicting relapse risk of early stage colorectal cancer (CRC) patients. Through reviewing recently published 34 literatures for the development of CRC prognostic signatures based on gene expression profiles, we revealed a surprising phenomenon that 33 of these studies analyzed CRC samples with and without adjuvant chemotherapy together in the training and/or validation datasets. This data misuse problem could be partially attributed to the unclear and incomplete data annotation in public data sources. Furthermore, all the signatures proposed by these studies were based on risk scores summarized from gene expression levels, which are sensitive to experimental batch effects and risk compositions of the samples analyzed together. To avoid the above-mentioned problems, we carefully selected three qualified large datasets to develop and validate a signature consisting of three pairs of genes. The within-sample relative expression orderings of these gene pairs could robustly predict relapse risk of stage II CRC samples assessed in different laboratories. The transcriptional and functional analyses provided clear evidence that the high risk patients predicted by the proposed signature represent patients with micro-metastases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.