Combining autophagy and immune characterizations to predict prognosis and therapeutic response in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36177001", "kind": "science-record", "revision": 4, "content_hash": "sha256:aa1e92a9a4986e7346ce2fb71de6a1751c04a67abc6cd9d65ab8398efa676d6b", "created_at": "2026-11-01T18:15:27Z", "updated_at": "2026-11-01T22:07:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Combining autophagy and immune characterizations to predict prognosis and therapeutic response in lung adenocarcinoma.", "doi": "10.3389/fimmu.2022.944378", "pmid": "36177001", "authors": ["Qiaxuan Li", "Daipeng Xie", "Lintong Yao", "Hongrui Qiu", "Peimeng You", "Jialong Deng", "Congsen Li", "Weijie Zhan", "Maotao Weng", "Shaowei Wu", "Fasheng Li", "Yubo Zhou", "Fanjun Zeng", "Yong Zheng", "Haiyu Zhou"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Autophagy, a key regulator of programmed cell death, is critical for maintaining the stability of the intracellular environment. Increasing evidence has revealed the clinical importance of interactions between autophagy and immune status in lung adenocarcinoma. The present study evaluated the potential of autophagy-immune-derived biomarkers to predict prognosis and therapeutic response in patients with lung adenocarcinoma.\nMethods: Patients from the GSE72094 dataset were randomized 7:3 to a training set and an internal validation set. Three independent cohorts, TCGA, GSE31210, and GSE37745, were used for external verification. Unsupervised hierarchical clustering based on autophagy- and immune-associated genes was used to identify autophagy- and immune-associated molecular patterns, respectively. Significantly prognostic autophagy-immune genes were identified by LASSO analysis and by univariate and multivariate Cox regression analyses. Differences in tumor immune microenvironments, functional pathways, and potential therapeutic responses were investigated to differentiate high-risk and low-risk groups.\nResults: High autophagy status and high immune status were associated with improved overall survival. Autophagy and immune subtypes were merged into a two-dimensional index to characterize the combined prognostic classifier, with 535 genes defined as autophagy-immune-related differentially expressed genes (DEGs). Four genes (C4BPA, CD300LG, CD96, and S100P) were identified to construct an autophagy-immune-related prognostic risk model. Survival and receiver operating characteristic (ROC) curve analyses showed that this model was significantly prognostic of survival. Patterns of autophagy and immune genes differed in low- and high-risk patients. Enrichment of most immune infiltrating cells was greater, and the expression of crucial immune checkpoint molecules was higher, in the low-risk group. TIDE and immunotherapy clinical cohort analysis predicted that the low-risk group had more potential responders to immunotherapy. GO, KEGG, and GSEA function analysis identified immune- and autophagy-related pathways. Autophagy inducers were observed in patients in the low-risk group, whereas the high-risk group was sensitive to autophagy inhibitors. The expression of the four genes was assessed in clinical specimens and cell lines.\nConclusions: The autophagy-immune-based gene signature represents a promising tool for risk stratification in patients with lung adenocarcinoma, guiding individualized targeted therapy or immunotherapy.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis showed that the levels of 24 autophagy-immune-related genes in the training set correlated significantly with OS.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36177001", "locator": "sections[15] \"Construction and verification of a combined prognostic classifier and autophagy-immune-related prognostic risk model in lung adenocarcinoma\" paragraph 4", "quote": "Univariate Cox regression analysis showed that the levels of 24 autophagy-immune-related genes in the training set correlated significantly with OS."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36177001", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene prognostic model based on C4BPA, CD300LG, CD96, and S100P stratified lung adenocarcinoma patients by survival risk and was validated across four cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36177001", "locator": "sections[15] \"Construction and verification of a combined prognostic classifier and autophagy-immune-related prognostic risk model in lung adenocarcinoma\" paragraph 6", "quote": "Survival analysis showed that OS was significantly longer in the low-risk than in the high-risk group (p = 0.00014), and the areas under the curves (AUCs) for 1-, 3-, and 5-year survival were 0.686, 0.731, and 0.805, respectively ( Figure 3D )."}, {"asset": "source:pmid:36177001", "locator": "sections[15] \"Construction and verification of a combined prognostic classifier and autophagy-immune-related prognostic risk model in lung adenocarcinoma\" paragraph 6", "quote": "To confirm the reliability of the autophagy-immune-related prognostic risk model, it was used to assess the entire GSE72094 dataset, as well as the TCGA, GSE37745, and GSE31210 cohorts, with similar results observed in all of these cohorts (p< 0.0001, Figure 3F ; Supplementary Figures 4A–C )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A four-gene prognostic model based on C4BPA, CD300LG, CD96, and S100P stratified lung adenocarcinoma patients by survival risk and was validated across four cohorts.", "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:36177001"], "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:GSE100797"], "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:GSE100797"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36177001", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.944378", "sha256": "sha256:89cf45284adc75fd8f17740050941146f55e9eaa4ba4815725c334de1128b1d3", "bytes": 53828, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.944378", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9513242", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE100797", "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=GSE100797", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE100797", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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