ArticleOncology letters2019
Identification of prognostic genes in colorectal cancer through transcription profiling of multi-stage carcinogenesis.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Bioinformatics Analysis of RNA-seq Data Reveals Genes Related to Cancer Stem Cells in Colorectal Cancerogenesis.International journal of molecular sciences · 2022Article
- Molecular Mechanisms of Colon Cancer Progression and Metastasis: Recent Insights and Advancements.International journal of molecular sciences · 2020Review
- Circulating lncRNA DANCR as a potential auxillary biomarker for the diagnosis and prognostic prediction of colorectal cancer.Bioscience reports · 2020Article
- Identification of novel biomarkers and small molecule drugs in human colorectal cancer by microarray and bioinformatics analysis.Molecular genetics & genomic medicine · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer is a complex multistage process following the adenoma-carcinoma sequence. Additional research on the basis of molecular dysregulations, particularly in the precancerous stage, may provide insight into the realization of potential biomarkers and therapeutic targets for the disease. In the present study, the expression profile of human multistage colorectal mucosa tissues, including healthy, adenoma and adenocarcinoma samples, was downloaded. Genes that were consistently differentially expressed in precancerous tissues and cancer samples were collected. Based on a merged biological network, the biggest connected component composed of these identified genes and their one-step neighbors were retrieved to conduct random walk with restart algorithm, in order to identify genes significantly affected during carcinogenesis. Therefore, 35 genes significantly affected by carcinogenic dysregulation were successfully identified. Survival and Cox analysis indicated that the expression of these genes was an independent prognostic factor confirmed by six cohorts. In summary, based on the transcription profile of multi-stage carcinogenesis and bioinformatics analysis, 35 genes significantly associated with patient survival were successfully identified, which may serve as promising therapeutic targets for the disease.
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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.