Establishment of a circular RNA regulatory stemness-related gene pair signature for predicting prognosis and therapeutic response in colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35958575", "kind": "science-record", "revision": 4, "content_hash": "sha256:aeddb826187161ffc562ca62a7386caf31813443995ade0ded05dd54cca0e1d7", "created_at": "2026-11-01T18:15:04Z", "updated_at": "2026-11-01T22:07:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment of a circular RNA regulatory stemness-related gene pair signature for predicting prognosis and therapeutic response in colorectal cancer.", "doi": "10.3389/fimmu.2022.934124", "pmid": "35958575", "authors": ["Qian Chen", "Peng Tang", "Huishen Huang", "Xiaoqiang Qiu"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Colorectal cancer (CRC) is a common malignant tumor of the digestive tract with a poor prognosis. Cancer stem cells (CSCs) affect disease outcomes and treatment responses in CRC. We developed a circular RNA (circRNA) regulatory stemness-related gene pair (CRSRGP) signature to predict CRC patient prognosis and treatment effects.\nMethods: The circRNA, miRNA, and mRNA expression profiles and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CRSRGPs were established based on stemness-related genes in the competing endogenous RNA (ceRNA) network. A CRSRGP signature was generated using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis of TCGA training set. The prognosis was predicted by generating a nomogram integrating the CRSRGP signature and clinicopathologic features. The model was validated in an external validation set (GSE17536). The antitumor drug sensitivity and immunotherapy responses of CRC patients in the high-risk group (HRG) and low-risk group (LRG) were evaluated by the pRRophetic algorithm and immune checkpoint analysis.\nResults: We established an 18-CRSRGP signature to predict the prognosis and treatment responses of CRC patients. In the training and external validation sets, risk scores were used to categorize CRC patients into the HRG and LRG. The Kaplan-Meier analysis showed a poor prognosis for patients in the HRG and that subgroups with different clinical characteristics had significantly different prognoses. A multivariate Cox analysis revealed that the CRSRGP signature was an independent prognostic factor. The nomogram integrating clinical features and the CRSRGP signature efficiently predicted CRC patient prognosis, outperformed the current TNM staging system, and had improved practical clinical value. Anticancer drug sensitivity predictions revealed that the tumors of patients in the HRG were more sensitive to pazopanib, sunitinib, gemcitabine, lapatinib, and cyclopamine. Analysis of immune checkpoint markers demonstrated that patients in the HRG were more likely to benefit from immunotherapy.\nConclusion: An efficient, reliable tool for evaluating CRC patient prognosis and treatment response was established based on the 18-CRSRGP signature and nomogram.", "claims": [{"id": "claim-method", "statement": "Using the “glmnet” package in R, we established the CRSRGP signature by Lasso Cox regression to estimate CRC outcome (iteration = 1,000).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35958575", "locator": "sections[6] \"Construction and verification of the circular RNA regulatory stemness-related gene pair signature\" paragraph 2", "quote": "Using the “glmnet” package in R, we established the CRSRGP signature by Lasso Cox regression to estimate CRC outcome (iteration = 1,000)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. These data can be found here: https://cancergenome.nih.gov/ and https://www.ncbi.nlm.nih.gov/geo/. The accession number(s) can be found in the article/ supplementary material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35958575", "locator": "sections[22] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. These data can be found here: https://cancergenome.nih.gov/ and https://www.ncbi.nlm.nih.gov/geo/. The accession number(s) can be found in the article/ supplementary material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An 18-CRSRGP signature was established to predict the prognosis and treatment responses of colorectal cancer patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35958575", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We established an 18-CRSRGP signature to predict the prognosis and treatment responses of CRC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An 18-CRSRGP signature was established to predict the prognosis and treatment responses of colorectal cancer patients.", "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:35958575"], "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. 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