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pmid:37577321

Identification of lysosomal genes associated with prognosis in lung adenocarcinoma.

Houqiang Li · Xinyu Sha · Wenmiao Wang · Zhanghao Huang · Peng Zhang · Lei Liu · Silin Wang · Youlang Zhou · Shuai He · Jiahai Shi

Translational lung cancer research · 2023 doi:10.21037/tlcr-23-14

Paper

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Experiment

complete

Data

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Analysis

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title
Identification of lysosomal genes associated with prognosis in lung adenocarcinoma.
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10.21037/tlcr-23-14
pmid
37577321
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Houqiang Li
[1]
Xinyu Sha
[2]
Wenmiao Wang
[3]
Zhanghao Huang
[4]
Peng Zhang
[5]
Lei Liu
[6]
Silin Wang
[7]
Youlang Zhou
[8]
Shuai He
[9]
Jiahai Shi
journal
Translational lung cancer research
year
2023
abstract
Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, representing 40% of all cases of this tumor. Despite immense improvements in understanding the molecular basis, diagnosis, and treatment of LUAD, its recurrence rate is still high. Methods: RNA-seq data from The Cancer Genome Atlas (TCGA) LUAD cohort were download from Genomic Data Commons Portal. The GSE13213 dataset from Gene Expression Omnibus (GEO) was used for external validation. Differential prognostic lysosome-related genes (LRGs) were identified by overlapping survival-related genes obtained via univariate Cox regression analysis with differentially expressed genes (DEGs). The prognostic model was built using Kaplan-Meier curves and least absolute shrinkage and selection operator (LASSO) analyses. In addition, univariate and multivariate Cox analyses were employed to identify independent prognostic factors. The responses of patients to immune checkpoint inhibitors (ICIs) were further predicted. The pRRophetic package and rank-sum test were used to compute the half maximal inhibitory concentrations (IC50) of 56 chemotherapeutic drugs and their differential effects in the low- and high-risk groups. Moreover, quantitative real-time polymerase chain reaction, Western blot, and human protein atlas (HPA) database were used to verify the expression of the four prognostic biomarkers in LUAD. Results: Of the nine candidate differential prognostic LRGs, GATA2, TFAP2A, LMBRD1, and KRT8 were selected as prognostic biomarkers. The prediction of the risk model was validated to be reliable. Cox independent prognostic analysis revealed that risk score and stage were independent prognostic factors in LUAD. Furthermore, the nomogram and calibration curves of the independent prognostic factors performed well. Differential analysis of ICIs revealed CD276, ICOS, PDCD1LG2, CD27, TNFRSF18, TNFSF9, ENTPD1, and NT5E to be expressed differently in the low- and high-risk groups. The IC50 values of 12 chemotherapeutic drugs, including epothilone.B, JNK.inhibitor.VIII, and AKT.inhibitor.VIII, significantly differed between the two risk groups. KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in LUAD cell lines. In addition, KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in tumor tissues. Conclusions: Four key prognostic biomarkers-GATA2, TFAP2A, LMBRD1, and KRT8-were used to construct a significant prognostic model for LUAD patients.
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First, differential expression analysis was applied to the 59 normal and 526 LUAD samples to identify differentially expressed genes (DEGs) in LUAD using the limma R package (version 3.44.3) with the criteria of P value <0.05 and |log2(fold change)| >0.5 (15). Then, the 144 LRGs were analyzed to obtain prognostic LRGs using univariate Cox regression survival analysis with P values <0.05.
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sections[5] "Identification of differential prognostic LRGs in LUAD" paragraph 2
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First, differential expression analysis was applied to the 59 normal and 526 LUAD samples to identify differentially expressed genes (DEGs) in LUAD using the limma R package (version 3.44.3) with the criteria of P value <0.05 and |log2(fold change)| >0.5 (15). Then, the 144 LRGs were analyzed to obtain prognostic LRGs using univariate Cox regression survival analysis with P values <0.05.
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its recurrence rate is still high. Methods: RNA-seq data from The Cancer Genome Atlas (TCGA) LUA
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First, differential expression analysis was applied to the 59 normal and 526 LUAD samples to identify differentially expressed genes (DEGs) in LUAD using the limma R package (version 3.44.3) with the criteria of P value <0.05 and |log2(fold change)| >0.5 (15). Then, the 144 LRGs were analyzed to obtain prognostic LRGs using univariate Cox regression survival analysis with P values <0.05.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37577321", "kind": "science-record", "revision": 4, "content_hash": "sha256:e2f6835e812b7c9444b628ca9d4334754fcc2c57fcc44b9efc3b65a5df4b4461", "created_at": "2026-11-01T19:02:22Z", "updated_at": "2026-11-01T22:52:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of lysosomal genes associated with prognosis in lung adenocarcinoma.", "doi": "10.21037/tlcr-23-14", "pmid": "37577321", "authors": ["Houqiang Li", "Xinyu Sha", "Wenmiao Wang", "Zhanghao Huang", "Peng Zhang", "Lei Liu", "Silin Wang", "Youlang Zhou", "Shuai He", "Jiahai Shi"], "journal": "Translational lung cancer research", "year": 2023, "abstract": "Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, representing 40% of all cases of this tumor. Despite immense improvements in understanding the molecular basis, diagnosis, and treatment of LUAD, its recurrence rate is still high.\nMethods: RNA-seq data from The Cancer Genome Atlas (TCGA) LUAD cohort were download from Genomic Data Commons Portal. The GSE13213 dataset from Gene Expression Omnibus (GEO) was used for external validation. Differential prognostic lysosome-related genes (LRGs) were identified by overlapping survival-related genes obtained via univariate Cox regression analysis with differentially expressed genes (DEGs). The prognostic model was built using Kaplan-Meier curves and least absolute shrinkage and selection operator (LASSO) analyses. In addition, univariate and multivariate Cox analyses were employed to identify independent prognostic factors. The responses of patients to immune checkpoint inhibitors (ICIs) were further predicted. The pRRophetic package and rank-sum test were used to compute the half maximal inhibitory concentrations (IC50) of 56 chemotherapeutic drugs and their differential effects in the low- and high-risk groups. Moreover, quantitative real-time polymerase chain reaction, Western blot, and human protein atlas (HPA) database were used to verify the expression of the four prognostic biomarkers in LUAD.\nResults: Of the nine candidate differential prognostic LRGs, GATA2, TFAP2A, LMBRD1, and KRT8 were selected as prognostic biomarkers. The prediction of the risk model was validated to be reliable. Cox independent prognostic analysis revealed that risk score and stage were independent prognostic factors in LUAD. Furthermore, the nomogram and calibration curves of the independent prognostic factors performed well. Differential analysis of ICIs revealed CD276, ICOS, PDCD1LG2, CD27, TNFRSF18, TNFSF9, ENTPD1, and NT5E to be expressed differently in the low- and high-risk groups. The IC50 values of 12 chemotherapeutic drugs, including epothilone.B, JNK.inhibitor.VIII, and AKT.inhibitor.VIII, significantly differed between the two risk groups. KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in LUAD cell lines. In addition, KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in tumor tissues.\nConclusions: Four key prognostic biomarkers-GATA2, TFAP2A, LMBRD1, and KRT8-were used to construct a significant prognostic model for LUAD patients.", "claims": [{"id": "claim-method", "statement": "First, differential expression analysis was applied to the 59 normal and 526 LUAD samples to identify differentially expressed genes (DEGs) in LUAD using the limma R package (version 3.44.3) with the criteria of P value <0.05 and |log2(fold change)| >0.5 (15). Then, the 144 LRGs were analyzed to obtain prognostic LRGs using univariate Cox regression survival analysis with P values <0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37577321", "locator": "sections[5] \"Identification of differential prognostic LRGs in LUAD\" paragraph 2", "quote": "First, differential expression analysis was applied to the 59 normal and 526 LUAD samples to identify differentially expressed genes (DEGs) in LUAD using the limma R package (version 3.44.3) with the criteria of P value <0.05 and |log2(fold change)| >0.5 (15). Then, the 144 LRGs were analyzed to obtain prognostic LRGs using univariate Cox regression survival analysis with P values <0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-14/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37577321", "locator": "sections[36] \"Footnotes\" paragraph 2", "quote": "Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-14/dss"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:37577321"], "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:GSE13213"], "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:GSE13213"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37577321", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tlcr-23-14", "sha256": "sha256:8e10e92672383a20e0fb8aad83baaa2ec109c65fb6e2e7b7470e13c25b0e77f0", "bytes": 51531, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tlcr-23-14", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10413022", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13213", "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=GSE13213", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13213", "version": null, "note": "Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-14/dss", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-14/dss", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM333673", "GSM333674", "GSM333675", "GSM333676", "GSM333677", "GSM333678", "GSM333679", "GSM333680", "GSM333681", "GSM333682", "GSM333683", "GSM333684", "GSM333685", "GSM333686", "GSM333687", "GSM333688", "GSM333689", "GSM333690", "GSM333691", "GSM333692", "GSM333693", "GSM333694", "GSM333695", "GSM333696", "GSM333697", "GSM333698", "GSM333699", "GSM333700", "GSM333701", "GSM333702", "GSM333703", "GSM333704", "GSM333705", "GSM333706", "GSM333707", "GSM333708", "GSM333709", "GSM333710", "GSM333711", "GSM333712", "GSM333713", "GSM333714", "GSM333715", "GSM333716", "GSM333717", "GSM333718", "GSM333719", "GSM333720", "GSM333721", "GSM333722", "GSM333723", "GSM333724", "GSM333725", "GSM333726", "GSM333727", "GSM333728", "GSM333729", "GSM333730", "GSM333731", "GSM333732", "GSM333733", "GSM333734", "GSM333735", "GSM333736", "GSM333737", "GSM333738", "GSM333739", "GSM333740", "GSM333741", "GSM333742", "GSM333743", "GSM333744", "GSM333745", "GSM333746", "GSM333747", "GSM333748", "GSM333749", "GSM333750", "GSM333751", "GSM333752", "GSM333753", "GSM333754", "GSM333755", "GSM333756", "GSM333757", "GSM333758", "GSM333759", "GSM333760", "GSM333761", "GSM333762", "GSM333763", "GSM333764", "GSM333765", "GSM333766", "GSM333767", "GSM333768", "GSM333769", "GSM333770", "GSM333771", "GSM333772", "GSM333773", "GSM333774", "GSM333775", "GSM333776", "GSM333777", "GSM333778", "GSM333779", "GSM333780", "GSM333781", "GSM333782", "GSM333783", "GSM333784", "GSM333785", "GSM333786", "GSM333787", "GSM333788", "GSM333789"], "sample_count": 117, "group_sizes": null, "group_sizes_basis": null, "platform": "6480", "taxon": "Homo sapiens", "title": "Relapse-related molecular signature in lung adenocarcinomas identifies patients with dismal prognosis", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE13213 not downloaded", "asset:data:geo:GSE13213 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "First, differential expression analysis was applied to the 59 normal and 526 LUAD samples to identify differentially expressed genes (DEGs) in LUAD using the limma R package (version 3.44.3) with the criteria of P value <0.05 and |log2(fold change)| >0.5 (15). Then, the 144 LRGs were analyzed to obtain prognostic LRGs using univariate Cox regression survival analysis with P values <0.05."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-3", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-4", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "protein-protein interaction"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE13213"], "resolved_accessions": ["GSE13213"], "statements": ["Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-14/dss"], "basis": "The paper names GSE13213 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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Methods: RNA-seq data from The Cancer Genome Atlas (TCGA) LUA"], "order_basis": "paper_text_order:312", "id": "assay:1"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["itative real-time polymerase chain reaction, Western blot, and human protein atlas (HPA) database were", "epithelial cells (Figure 10A). In addition, Western blot analysis also confirmed that the expression"], "order_basis": "paper_text_order:1304", "id": "assay:2"}, {"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": ["om the American Type Culture Collection, and cultured in RPMI 1640 (Gibco, Thermo Fisher Scientific,", "ls (16HBE) and lung cancer cells (H1299, and A549) were purchased from the American Type Cultu", "se of GATA2 and LMBRD1 were downregulated in A549 and H1299 cell lines compared with human bro"], "order_basis": "paper_text_order:6187", "id": "assay:3"}, {"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": ["First, differential expression analysis was applied to the 59 normal and 526 LUAD samples to identify differentially expressed genes (DEGs) in LUAD using the limma R package (version 3.44.3) with the criteria of P value <0.05 and |log2(fold change)| >0.5 (15). Then, the 144 LRGs were analyzed to obtain prognostic LRGs using univariate Cox regression survival analysis with P values <0.05."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}