Identification and verification of the temozolomide resistance feature gene DACH1 in gliomas.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36959804", "kind": "science-record", "revision": 4, "content_hash": "sha256:e4dfc09a77751780d2693cf277654ab1d371d9d05f69be00dc89bfeb9df975b0", "created_at": "2026-11-01T19:01:36Z", "updated_at": "2026-11-01T22:51:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and verification of the temozolomide resistance feature gene DACH1 in gliomas.", "doi": "10.3389/fonc.2023.1120103", "pmid": "36959804", "authors": ["Qiang Gu", "Lang Li", "Jiahao Yao", "Fa-Yan Dong", "Yifan Gan", "Shuhuai Zhou", "Xinyu Wang", "Xue-Feng Wang"], "journal": "Frontiers in oncology", "year": 2023, "abstract": "Introduction: The most important chemotherapy treatment for glioma patients is temozolomide. However, the development of drug resistance severely restricts the use of temozolomide. Therefore, elucidating the mechanism of temozolomide resistance, enhancing temozolomide sensitivity, and extending patient survival are urgent tasks for researchers.\nMethods: Temozolomide resistance hub differential genes were identified using differential analysis and protein interaction analysis from the GEO datasets (GSE100736 and GSE113510). These genes were further studied in glioma patients treated with temozolomide in the TCGA and CGGA databases. Patients from the mRNAseq_325 dataset (CGGA) were considered as the training set to construct a risk model for predicting glioma sensitivity to temozolomide, while patients from the mRNAseq_693 dataset (CGGA) and TCGA-GBM dataset were considered as the validation set to evaluate the performance of models. PCR and western blot were performed to determine the difference in expression of the feature gene DACH1 between glioma cells and temozolomide-resistant glioma cells. The alterations in the sensitivity of tumor cells to temozolomide were also observed after DACH1 was silenced. The patients were then divided into two groups based on the expression of DACH1, and the differences in patient survival rates, molecular pathway activation, and level of immune infiltration were compared.\nResults: Based on four signature genes (AHR, DACH1, MGMT, and YAP1), a risk model for predicting glioma sensitivity to temozolomide was constructed, and the results of timeROC in both the training and validation sets showed that the model had good predictive performance. The expression of the signature gene DACH1 was significantly downregulated in temozolomide-resistant cells, according to the results of the PCR and western blot experiments. The sensitivity of tumor cells to temozolomide was significantly reduced after DACH1 was silenced. DACH1 probably regulates temozolomide resistance in glioblastoma through the transcriptional dysregulation in cancer and ECM.\nDiscussion: This study constructs a risk model that can predict glioma susceptibility to temozolomide and validates the function of the feature gene DACH1, which provides a promising target for the research of temozolomide resistance.", "claims": [{"id": "claim-method", "statement": "The t-test was used to compare two groups, and double-factor variance analysis was used to compare several groups. The Kaplan-Meier method was used to compare the survival rates of high-risk and low-risk groups. Risk models were constructed by multivariate Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36959804", "locator": "sections[17] \"Statistical analysis\" paragraph 2", "quote": "The t-test was used to compare two groups, and double-factor variance analysis was used to compare several groups. The Kaplan-Meier method was used to compare the survival rates of high-risk and low-risk groups. Risk models were constructed by multivariate Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary material . Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36959804", "locator": "sections[32] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary material . Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The four-gene risk model demonstrated good predictive performance, with 1-, 3-, and 5-year AUC values of 0.728, 0.738, and 0.781 in the training set and 0.699, 0.712, and 0.708 in validation set I.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36959804", "locator": "sections[20] \"Construction and evaluation of a risk model for predicting glioma sensitivity to temozolomide\" paragraph 2", "quote": "The AUC values for 1, 3, and 5 years were 0.728, 0.738, and 0.781 in CGGA-mRNAseq_325 ( Figure 3G ) and 0.699, 0.712, and 0.708 in CGGA-mRNAseq_693 ( Figure 3H )."}, {"asset": "source:pmid:36959804", "locator": "sections[20] \"Construction and evaluation of a risk model for predicting glioma sensitivity to temozolomide\" paragraph 2", "quote": "The results showed that the model performed well in terms of prediction."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The four-gene risk model demonstrated good predictive performance, with 1-, 3-, and 5-year AUC values of 0.728, 0.738, and 0.781 in the training set and 0.699, 0.712, and 0.708 in validation set I.", "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:36959804"], "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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