Enhanced prognostic signature for lung adenocarcinoma through integration of adjacent normal and tumor gene expressions.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39391517", "kind": "science-record", "revision": 4, "content_hash": "sha256:6a2207d7b30986857840f1e5d4cc47077030b48158a6b14fd82c3cb36523f4ed", "created_at": "2026-11-01T20:29:37Z", "updated_at": "2026-11-01T22:55:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Enhanced prognostic signature for lung adenocarcinoma through integration of adjacent normal and tumor gene expressions.", "doi": "10.1016/j.heliyon.2024.e38527", "pmid": "39391517", "authors": ["Mingyue Hao", "Dandan Li", "Weihao Chen", "Ming Xiong", "Xinkun Wang", "Yuanyuan Qiao", "Wei Ma"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Cancer prognosis-related signatures have traditionally been constructed based on gene expression profiles derived from tumor or normal tissues. However, the potential benefits of incorporating gene expression profiles from both tumor and normal tissues to improve signature performance have not been explored.\nMethods: In this study, we developed three prognostic models for lung adenocarcinoma (LUAD) using gene expression profiles from tumor tissues, normal tissues, and a combination (COM) of both, sourced from The Cancer Genome Atlas (TCGA). To ensure comparability, the same workflow was followed for all three models.\nResults: When applied to the TCGA LUAD dataset, the tumor-derived model exhibited the best overall performance, except in calibration analysis, where the normal-derived model performed better. The COM-derived model demonstrated intermediate performance. Validation on three independent test datasets revealed that the COM-derived model showed the best performance, while the normal-derived model showed the worst. In overall survival (OS) analysis, the low-risk group defined by the COM-derived model consistently exhibited longer mean survival times. The tumor-derived model did not consistently show this trend, and the normal-derived model produced opposite results. In discrimination analysis, no significant differences were observed. The COM-derived model demonstrated good discrimination ability for short periods, while the tumor-derived model performed better for longer periods. In calibration analysis, both the COM and tumor-derived models had similar absolute prediction errors, which were better than those of the normal-derived model. However, the tumor-derived model tended to underestimate survival rates. The clinical feature analysis and validation in GSE229705 indicate that the risk score (RS) from the COM model is the most clinically significant. These results demonstrate that the COM model's RS aligns more closely with clinical data, maintaining stable performance and the strongest generalizability.\nConclusions: Overall, the COM-derived model demonstrated the best generalization ability. The superior performance of the tumor-derived model in the TCGA LUAD dataset might be due to overfitting. Our results suggest that appropriate combinations of gene expression data from tumor and normal tissues can enhance the predictive power of prognostic signatures.", "claims": [{"id": "claim-method", "statement": "Univariable Cox regression analysis was performed for each gene to explore OS-related genes in both tumor and normal samples. To reduce dimensionality and select genes from the candidate gene set, Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression was employed. Stepwise regression was then used to build the OS prognosis models. Survival curve analysis was calculated using the Kaplan–Meier (K-M) method, and differences between survival curves were assessed using the log-rank test via the survival R package. For discrimination analysis, the time-dependent receiver operating characteristic curve (timeROC) was employed to evaluate the discrimination of models using the timeROC R package. Calibration analysis was performed using the survival R package, evaluating calibration performance based on the absolute difference values between predicted and calculated survival rates.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39391517", "locator": "sections[4] \"Gene selection model construction\" paragraph 2", "quote": "Univariable Cox regression analysis was performed for each gene to explore OS-related genes in both tumor and normal samples."}, {"asset": "source:pmid:39391517", "locator": "sections[4] \"Gene selection model construction\" paragraph 2", "quote": "To reduce dimensionality and select genes from the candidate gene set, Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression was employed."}, {"asset": "source:pmid:39391517", "locator": "sections[4] \"Gene selection model construction\" paragraph 2", "quote": "Stepwise regression was then used to build the OS prognosis models."}, {"asset": "source:pmid:39391517", "locator": "sections[5] \"Model validation\" paragraph 2", "quote": "Survival curve analysis was calculated using the Kaplan–Meier (K-M) method, and differences between survival curves were assessed using the log-rank test via the survival R package."}, {"asset": "source:pmid:39391517", "locator": "sections[5] \"Model validation\" paragraph 2", "quote": "For discrimination analysis, the time-dependent receiver operating characteristic curve (timeROC) was employed to evaluate the discrimination of models using the timeROC R package."}, {"asset": "source:pmid:39391517", "locator": "sections[5] \"Model validation\" paragraph 2", "quote": "Calibration analysis was performed using the survival R package, evaluating calibration performance based on the absolute difference values between predicted and calculated survival rates."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used in this study were open access.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39391517", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "All data used in this study were open access."}], "state": "complete"}, {"id": "claim-target-result", "statement": "For the COM model, a 7-gene-based model was built, including 6 genes from SUM (APOD, FUT2, TUBE1, C1orf105, HIST1H3I, and OPALIN) 1 gene from LogTN (COX6B2) (Fig. 2A).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39391517", "locator": "sections[10] \"Construction of three prognosis models based on candidate genes of tumor, normal, and COM in TCGA LUAD\" paragraph 2", "quote": "For the COM model, a 7-gene-based model was built, including 6 genes from SUM (APOD, FUT2, TUBE1, C1orf105, HIST1H3I, and OPALIN) 1 gene from LogTN (COX6B2) (Fig. 2A)."}, {"asset": "source:pmid:39391517", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Overall, the COM-derived model demonstrated the best generalization ability."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "For the COM model, a 7-gene-based model was built, including 6 genes from SUM (APOD, FUT2, TUBE1, C1orf105, HIST1H3I, and OPALIN) 1 gene from LogTN (COX6B2) (Fig. 2A).", "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:39391517"], "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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