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pmid:33889524
A signature of estimate-stromal-immune score-based genes associated with the prognosis of lung adenocarcinoma.
Qianli Ma · Yang Chen · Fei Xiao · Yang Hao · Zhiyi Song · Jin Zhang · Katsuhiro Okuda · Sang-Won Um · Mario Silva · Yoshihisa Shimada · Chaozeng Si · Chaoyang Liang
Translational lung cancer research · 2021 doi:10.21037/tlcr-21-223
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Experiment
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Complete file
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We aimed to develop a gene signature based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE)-stromal-immune score in LUAD.\nMETHODS: The transcriptomic profiles of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA), and the immune and stromal scores were derived using the ESTIMATE algorithm. The prognostic signature genes were selected from the differentially expressed genes (DEGs) using the robust partial likelihood-based cox proportional hazards regression method. The negative log-likelihood and the Akaike Information Criterion (AIC) were used to identify the optimal gene signature. The validation was carried out in 2 independent datasets from the Gene Expression Omnibus (GSE68571 and GSE72094).\nRESULTS: Patients with high ESTIMATE, stromal, and immune scores had better overall survivals (P=0.0035, P=0.066, and P=0.0077). The expression of thirty-seven genes was related to ESTIMATE-stromal-immune score. A risk stratification model was developed based on a gene signature containing CD74, JCHAIN, and PTGDS. The ESTIMATE-stromal-immune risk score was revealed to be a prognostic factor (P=0.009) after multivariate analysis. Four groups were classified based on this risk stratification model, yielding increasing survival outcomes (log-rank test, P=0.0051). This risk stratification model and other clinicopathological factors were combined to generate a nomogram. The calibration curves showed perfect agreement between the nomogram-predicted outcomes and those actually observed. Similar observations were made in 2 independent cohorts.\nCONCLUSIONS: The gene signature based on the ESTIMATE-stromal-immune score could predict the prognosis of patients with LUAD.", "claims": [{"id": "claim-method", "statement": "The Kaplan–Meier estimator was used to estimate the patients’ overall survival. The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33889524", "locator": "sections[6] \"Relationship of estimate-stromal-immune scores and prognosis\" paragraph 2", "quote": "The Kaplan–Meier estimator was used to estimate the patients’ overall survival. The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33889524", "locator": "sections[3] \"Population\" paragraphs 1-4", "quote": "❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome."}], "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:33889524"], "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:GSE68571"], "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:GSE68571"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33889524", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tlcr-21-223", "sha256": "sha256:216ea8519ce015fcd86333ba8c716a43643e283cc06c253dfed69ff69c7d8364", "bytes": 46806, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tlcr-21-223", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8044489", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE68571", "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=GSE68571", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE68571", "version": null, "note": "❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. 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Log-rank tests were used to compare the survival outcomes of the 2 groups."}}, {"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": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-3", "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": 0.7, "band": "medium", "quote": "cox proportional hazards"}}], "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": ["logrank_test"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: log_rank_test."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups."], "order_basis": "analysis_only", "id": "assay:1"}]}