An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35300428", "kind": "science-record", "revision": 3, "content_hash": "sha256:4ab47623f69d15ea7a4eddd3df78e38da60b29a721377bca6fcf84fe38472fd8", "created_at": "2026-11-01T18:14:05Z", "updated_at": "2026-11-01T21:27:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma.", "doi": "10.3389/fcell.2022.770550", "pmid": "35300428", "authors": ["Wen-Yu Zhai", "Fang-Fang Duan", "Si Chen", "Jun-Ye Wang", "Ze-Rui Zhao", "Yi-Zhi Wang", "Bing-Yu Rao", "Yao-Bin Lin", "Hao Long"], "journal": "Frontiers in cell and developmental biology", "year": 2022, "abstract": "Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586-0.671) in the TCGA cohort and 0.648 (95% CI: 0.535-0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC.", "claims": [{"id": "claim-method", "statement": "The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35300428", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA databases (https://tcga-data.nci.nih.gov/tcga/), GEO databases (https://www.ncbi.nlm.nih.gov/geo/) and Human Protein Atlas (https://www.proteinatlas.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35300428", "locator": "sections[19] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA databases (https://tcga-data.nci.nih.gov/tcga/), GEO databases (https://www.ncbi.nlm.nih.gov/geo/) and Human Protein Atlas (https://www.proteinatlas.org/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After integrating age, gender, smoking history, and T, N, and M classification, the risk score was still significantly associated with OS (HR = 2.68, 95%CI = 1.86–3.86, p < 0.001) (Figure 3F).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "After integrating age, gender, smoking history, and T, N, and M classification, the risk score was still significantly associated with OS (HR = 2.68, 95%CI = 1.86–3.86, p < 0.001) (Figure 3F)."}, "evidence": [{"asset": "source:pmid:35300428", "locator": "sections[12] \"Prognostic Value of the ARG Signature in the Training Cohort\" paragraph 2", "quote": "After integrating age, gender, smoking history, and T, N, and M classification, the risk score was still significantly associated with OS (HR = 2.68, 95%CI = 1.86–3.86, p < 0.001) (Figure 3F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "After integrating age, gender, smoking history, and T, N, and M classification, the risk score was still significantly associated with OS (HR = 2.68, 95%CI = 1.86–3.86, p < 0.001) (Figure 3F).", "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:35300428"], "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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