Comprehensive Analysis of circRNA-Related mRNAs as Prognostic Factors in Non-Smoking Women with Lung Adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39650787", "kind": "science-record", "revision": 3, "content_hash": "sha256:0d2e702d6d9846be541ce89ba5d08eed677ea559f5c510af9dac5f9ed11551af", "created_at": "2026-11-01T20:29:56Z", "updated_at": "2026-11-01T22:13:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive Analysis of circRNA-Related mRNAs as Prognostic Factors in Non-Smoking Women with Lung Adenocarcinoma.", "doi": "10.2147/IJGM.S490478", "pmid": "39650787", "authors": ["Hao Wang", "Song Wei", "Lijun Wang", "Zhihong Zhang"], "journal": "International journal of general medicine", "year": 2024, "abstract": "Background: Non-smoking women with lung adenocarcinoma(NSWLA) is a significant health problem globally; the carcinogenesis and prognostic signature remain poorly understood. Circular RNAs (circRNAs) are gradually implicated in cancer formation through sponging miRNAs to regulate mRNAs.\nMethods: Tumor and paracancerous specimens from non-smoking women after lung adenocarcinoma surgery were collected for high-throughput sequencing of circRNA. miRNA and mRNA datasets were downloaded from TCGA. A circRNA-miRNA-mRNA network was built using differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs). Following that, GSEA was applied to analyze the biological function of mRNAs in the ceRNA network. Utilizing the mRNAs associated with prognosis, we created an original prognostic risk score model. The expression of DEmRNA in the ceRNA network was verified by mRNA-seq and scRNA-seq data (GSE131907). The expression of BTBD3 and EIF4EBP2 was then verified by immunohistochemistry.\nResults: 16 pairs of adenocarcinoma tissues and their corresponding para-tumor samples were collected from thoracic surgery. We created a circRNA related ceRNA in NSWLA. The hsa_circ_0002346 regulation of the cancer cell proliferation may through the hsa_circ_0002346/miR-96-5p/EIF4EBP2 axis. Hsa_circ_0072309 may affect proliferation of lung adenocarcinoma and activate Nature Killer cells by targeting miR-32-5p to regulate PCMTD1 expression. Based upon mRNA (BTBD3, CFL2, EIF4EBP2, EVI5, PCMTD1, SYDE2) related to overall survival, we also created a predictive signature. According to mRNA-seq, scRNA-seq, and immunohistochemical data, the expression of BTBD3 and EIF4EBP2 was lower in tumor samples than in normal tissues nearby.\nConclusion: The circRNA related mRNAs played an important role in predicting the overall survival of NSWLA. The circRNA in the ceRNA network might unravel the pathogenesis and be potential novel targets for NSWLA.", "claims": [{"id": "claim-method", "statement": "According to the mRNAs contained within the ceRNA network, we applied the univariate Cox regression analysis to test the overall survival-associated genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39650787", "locator": "sections[7] \"Survival Analysis\" paragraph 2", "quote": "According to the mRNAs contained within the ceRNA network, we applied the univariate Cox regression analysis to test the overall survival-associated genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The miRNA-seq data, mRNA-seq data and clinicopathological data used in this study can be acquired free of charge from the corresponding authors by email, and the rest of the data can be retrieved from TCGA (https://portal.gdc.cancer.gov/), and GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131907).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39650787", "locator": "sections[25] \"Data Sharing Statement\" paragraph 2", "quote": "The miRNA-seq data, mRNA-seq data and clinicopathological data used in this study can be acquired free of charge from the corresponding authors by email, and the rest of the data can be retrieved from TCGA (https://portal.gdc.cancer.gov/), and GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131907)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 1-, 3-, and 5-year ROC curves’ area under the curve (AUC) values were 0.705, 0.647, and 0.680, respectively, demonstrating the model’s moderate accuracy and specificity.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39650787", "locator": "sections[17] \"Risk Scoring Model and Survival\" paragraph 2", "quote": "The 1-, 3-, and 5-year ROC curves’ area under the curve (AUC) values were 0.705, 0.647, and 0.680, respectively, demonstrating the model’s moderate accuracy and specificity."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The 1-, 3-, and 5-year ROC curves’ area under the curve (AUC) values were 0.705, 0.647, and 0.680, respectively, demonstrating the model’s moderate accuracy and specificity.", "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:39650787"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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