Establishing a Macrophage Phenotypic Switch-Associated Signature-Based Risk Model for Predicting the Prognoses of Lung Adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35284334", "kind": "science-record", "revision": 5, "content_hash": "sha256:1ebff05f5742305c466bac6ff854089712ea28305c5a687db3db33603200f6d3", "created_at": "2026-11-01T18:14:04Z", "updated_at": "2026-11-01T22:48:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishing a Macrophage Phenotypic Switch-Associated Signature-Based Risk Model for Predicting the Prognoses of Lung Adenocarcinoma.", "doi": "10.3389/fonc.2021.771988", "pmid": "35284334", "authors": ["Jun Chen", "Chao Zhou", "Ying Liu"], "journal": "Frontiers in oncology", "year": 2021, "abstract": "Background: Tumor-associated macrophages are important components of the tumor microenvironment, and the macrophage phenotypic switch has been shown to correlate with tumor development. However, the use of a macrophage phenotypic switch-related gene (MRG)-based prognosis signature for lung adenocarcinoma (LADC) has not yet been investigated.\nMethods: In total, 1,114 LADC cases from two different databases were collected. The samples from TCGA were used as the training set (N = 490), whereas two independent datasets (GSE31210 and GSE72094) from the GEO database were used as the validation sets (N = 624). A robust MRG signature that predicted clinical outcomes of LADC patients was identified through multivariate COX and Lasso regression analysis. Gene set enrichment analysis was applied to analyze molecular pathways associated with the MRG signature. Moreover, the fractions of 22 immune cells were estimated using CIBERSORT algorithm.\nResults: An eight MRG-based signature comprising CTSL, ECT2, HCFC2, HNRNPK, LRIG1, OSBPL5, P4HA1, and TUBA4A was used to estimate the LADC patients' overall survival. The MRG model was capable of distinguishing high-risk patients from low-risk patients and accurately predict survival in both the training and validation cohorts. Subsequently, the eight MRG-based signature and other features were used to construct a nomogram to better predict the survival of LADC patients. Calibration plots and decision curve analysis exhibited good consistency between the nomogram predictions and actual observation. ROC curves displayed that the signature had good robustness to predict LADC patients' prognostic outcome.\nConclusions: We identified a phenotypic switch-related signature for predicting the survival of patients with LADC.", "claims": [{"id": "claim-method", "statement": "The univariate Cox regression analysis was used to screen out the genes significantly correlated with OS based on the 382 MRGs (for P-values < 0.05). Next, the overlapped prognosis-related MRGs from TCGA and GEO databases were selected for the least absolute shrinkage and selection operator (Lasso) with ten-fold cross-validation which was subsequently applied using “glmnet” and “survival” packages. Afterwards, a multivariate Cox regression was applied out to select candidate OS-related MRGs and determine a prognostic signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35284334", "locator": "sections[5] \"Development and Validation of a Prognostic Model\" paragraph 2", "quote": "The univariate Cox regression analysis was used to screen out the genes significantly correlated with OS based on the 382 MRGs (for P-values < 0.05). Next, the overlapped prognosis-related MRGs from TCGA and GEO databases were selected for the least absolute shrinkage and selection operator (Lasso) with ten-fold cross-validation which was subsequently applied using “glmnet” and “survival” packages. Afterwards, a multivariate Cox regression was applied out to select candidate OS-related MRGs and determine a prognostic signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: The transcriptome profiles and corresponding clinical data of LADC patients were downloaded from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35284334", "locator": "sections[19] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: The transcriptome profiles and corresponding clinical data of LADC patients were downloaded from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An eight-MRG prognostic signature distinguished high- from low-risk LADC patients and predicted overall survival in training and validation cohorts, with a training-set C-index of 0.72.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The results showed that the C-index for the prediction of OS of the identified MRG signature was 0.72 (95% CI = 0.65–0.76)."}, "evidence": [{"asset": "source:pmid:35284334", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "An eight MRG-based signature comprising CTSL, ECT2, HCFC2, HNRNPK, LRIG1, OSBPL5, P4HA1, and TUBA4A was used to estimate the LADC patients’ overall survival."}, {"asset": "source:pmid:35284334", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The MRG model was capable of distinguishing high-risk patients from low-risk patients and accurately predict survival in both the training and validation cohorts."}, {"asset": "source:pmid:35284334", "locator": "sections[13] \"Prognostic Value of the 10-MRG Signature\" paragraph 2", "quote": "The results showed that the C-index for the prediction of OS of the identified MRG signature was 0.72 (95% CI = 0.65–0.76)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An eight-MRG prognostic signature distinguished high- from low-risk LADC patients and predicted overall survival in training and validation cohorts, with a training-set C-index of 0.72.", "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:35284334"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE31210"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE31210"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35284334", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2021.771988", "sha256": "sha256:a5c9fadb7f3a26f893eeb76f15d2a49558848ba954e17beb1ede644ba8462fef", "bytes": 29510, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2021.771988", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8905507", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE31210", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31210", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31210", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: The transcriptome profiles and corresponding clinical data of LADC patients were downloaded from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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