Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32957968", "kind": "science-record", "revision": 7, "content_hash": "sha256:2fe197122b8e9dac746706dff717b997cd5d530832bb1984d98a5f2f593d3e7c", "created_at": "2026-11-01T15:41:11Z", "updated_at": "2026-11-01T22:02:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice.", "doi": "10.1186/s12920-020-00775-0", "pmid": "32957968", "authors": ["Jiannan Liu", "Chuanpeng Dong", "Guanglong Jiang", "Xiaoyu Lu", "Yunlong Liu", "Huanmei Wu"], "journal": "BMC medical genomics", "year": 2020, "abstract": "BACKGROUND: Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary.\nMETHODS: We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients.\nRESULTS: A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis.\nCONCLUSIONS: Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective.", "claims": [{"id": "claim-method", "statement": "The log-rank test [21] was conducted to test the significance of the difference between subgroups since the log-rank test is a very robust statistical method to test important differences between two groups and is widely used in clinical trial experiments.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32957968", "locator": "sections[5] \"Details on the variable selection and survival analysis methods\" paragraph 4", "quote": "The log-rank test [21] was conducted to test the significance of the difference between subgroups since the log-rank test is a very robust statistical method to test important differences between two groups and is widely used in clinical trial experiments."}], "state": "complete"}, {"id": "claim-data-access", "statement": "TCGA COAD dataset can be downloaded from http://xena.ucsc.edu, GEO datasets used in this study can be downloaded from GEO database by using access numbers: GSE39582, GSE17536, GSE37892, GSE17537.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32957968", "locator": "sections[21] \"notes\" paragraph 1", "quote": "TCGA COAD dataset can be downloaded from http://xena.ucsc.edu, GEO datasets used in this study can be downloaded from GEO database by using access numbers: GSE39582, GSE17536, GSE37892, GSE17537."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:32957968"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE17536"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE17536"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32957968", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-020-00775-0", "sha256": 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