Cell Trajectory-Related Genes of Lung Adenocarcinoma Predict Tumor Immune Microenvironment and Prognosis of Patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35924143", "kind": "science-record", "revision": 4, "content_hash": "sha256:eeb6b8061f4bfe53b011ae4ab0ee82050c457434a966cb29f9c61792f2476355", "created_at": "2026-11-01T18:15:01Z", "updated_at": "2026-11-01T22:07:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Cell Trajectory-Related Genes of Lung Adenocarcinoma Predict Tumor Immune Microenvironment and Prognosis of Patients.", "doi": "10.3389/fonc.2022.911401", "pmid": "35924143", "authors": ["Yu Luo", "Xiaheng Deng", "Jun Que", "Zhihua Li", "Weiping Xie", "Guanqun Dai", "Liang Chen", "Hong Wang"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer which typically exhibits a diverse progression trajectory. Our study sought to explore the cell differentiation trajectory of LUAD and its clinical relevance.\nMethods: Utilizing a single-cell RNA-sequencing dataset (GSE117570), we identified LUAD cells of distinct differential status along with differentiation-related genes (DRGs). DRGs were applied to the analysis of bulk-tissue RNA-sequencing dataset (GSE72094) to classify tumors into different subtypes, whose clinical relevance was further analyzed. DRGs were also applied to gene co-expression network analysis (WGCNA) using another bulk-tissue RNA-sequencing dataset (TCGA-LUAD). Genes from modules that demonstrated a significant correlation with clinical traits and were differentially expressed between normal tissue and tumors were identified. Among these, genes with significant prognostic relevance were used for the development of a prognostic nomogram, which was tested on TCGA-LUAD dataset and validated in GSE72094. Finally, CCK-8, EdU, cell apoptosis, cell colony formation, and Transwell assays were used to verify the functions of the identified genes.\nResults: Four clusters of cells with distinct differentiation status were characterized, whose DRGs were predominantly correlated with pathways of immune regulation. Based on DRGs, tumors could be clustered into four subtypes associated with distinct immune microenvironment and clinical outcomes. DRGs were categorized into four modules. A total of nine DRGs (SFTPB, WFDC2, HLA-DPA1, TIMP1, MS4A7, HLA-DQA1, VCAN, KRT8, and FABP5) with most significant survival-predicting power were integrated to develop a prognostic model, which outperformed the traditional parameters in predicting clinical outcomes. Finally, we verified that knockdown of WFDC2 inhibited proliferation, migration, and invasion but promoted the apoptosis of A549 cells in vitro.\nConclusion: The cellular composition and cellular differentiation status of tumor mass can predict the clinical outcomes of LUAD patients. It also plays an important role in shaping the tumor immune microenvironment.", "claims": [{"id": "claim-method", "statement": "The candidate hub genes identified in WGCNA analysis were further applied to univariate survival analysis. Genes with a Univariate Cox value of p < 0.05 were further screened with the least absolute shrinkage and selection operator (Lasso) Cox model, which was implemented by package glmnet in R environment (36).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35924143", "locator": "sections[7] \"Identification of Hub Genes and Construction of the Risk Prediction Model\" paragraph 2", "quote": "The candidate hub genes identified in WGCNA analysis were further applied to univariate survival analysis. Genes with a Univariate Cox value of p < 0.05 were further screened with the least absolute shrinkage and selection operator (Lasso) Cox model, which was implemented by package glmnet in R environment (36)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35924143", "locator": "sections[29] \"Data Availability Statement\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of nine DRGs (SFTPB, WFDC2, HLA-DPA1, TIMP1, MS4A7, HLA-DQA1, VCAN, KRT8, and FABP5) with most significant survival-predicting power were integrated to develop a prognostic model, which outperformed the traditional parameters in predicting clinical outcomes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35924143", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of nine DRGs (SFTPB, WFDC2, HLA-DPA1, TIMP1, MS4A7, HLA-DQA1, VCAN, KRT8, and FABP5) with most significant survival-predicting power were integrated to develop a prognostic model, which outperformed the traditional parameters in predicting clinical outcomes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of nine DRGs (SFTPB, WFDC2, HLA-DPA1, TIMP1, MS4A7, HLA-DQA1, VCAN, KRT8, and FABP5) with most significant survival-predicting power were integrated to develop a prognostic model, which outperformed the traditional parameters in predicting clinical outcomes.", "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:35924143"], "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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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:35924143", "data:geo:GSE117570"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE117570"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:58:25Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 19706, "completion_tokens": 1404, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:35924143", "type": "paper", "label": "pmid:35924143"}, {"id": "source:pmid:35924143", "type": "manuscript", "label": "PMID 35924143 full text"}, {"id": "data:geo:GSE117570", "type": "dataset", "label": "GEO GSE117570"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35924143", "type": "paper", "label": "pmid:35924143"}, {"id": "source:pmid:35924143", "type": "manuscript", "label": "PMID 35924143 full text"}, {"id": "data:geo:GSE117570", "type": "dataset", "label": "GEO GSE117570"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35924143", "type": "extraction", "used": ["source:pmid:35924143"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE117570", "type": "accession_resolution", "used": ["data:geo:GSE117570"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:35924143", "parent_hash": "sha256:50451f67c84b6034e550fccaeb47a28842dd62efc27733ddb8fdc186276a149d", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE117570 licence not read", "asset:data:geo:GSE117570 not downloaded", "asset:data:geo:GSE72094 licence not read", "asset:data:geo:GSE72094 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["relevance. Methods: Utilizing a single-cell RNA-sequencing dataset (GSE117570), we identified LUAD cell", "were applied to the analysis of bulk-tissue RNA-sequencing dataset (GSE72094) to classify tumors into d", "k analysis (WGCNA) using another bulk-tissue RNA-sequencing dataset (TCGA-LUAD). Genes from modules that", "us clustering was carried out using the bulk transcriptome data of 442 LUAD tumors from the GSE72094 da"], "order_basis": "paper_text_order:347", "id": "assay:1"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["dated in GSE72094. Finally, CCK-8, EdU, cell apoptosis, cell colony formation, and Transwell assays", "on, migration, and invasion but promoted the apoptosis of A549 cells in vitro. Conclusion: The cell", "dataset and validated in GSE72094. Finally, CCK-8, EdU, cell apoptosis, cell colony formation,"], "order_basis": "paper_text_order:1150", "id": "assay:2"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["clinical outcomes. Finally, we verified that knockdown of WFDC2 inhibited proliferation, migration,"], "order_basis": "paper_text_order:1912", "id": "assay:3"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": [", and invasion but promoted the apoptosis of A549 cells in vitro. Conclusion: The cellular com", "e Culture Collection (ATCC, USA). Cells were cultured with Dulbecco’s Modified Eagle’s Medium/Nutrient", ". Cell Culture Human lung cancer cell lines (A549) were purchased from the American Type Cultu"], "order_basis": "paper_text_order:2010", "id": "assay:4"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The candidate hub genes identified in WGCNA analysis were further applied to univariate survival analysis. Genes with a Univariate Cox value of p < 0.05 were further screened with the least absolute shrinkage and selection operator (Lasso) Cox model, which was implemented by package glmnet in R environment (36)."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}