LRRC3B and its promoter hypomethylation status predicts response to anti-PD-1 based immunotherapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36798137", "kind": "science-record", "revision": 3, "content_hash": "sha256:802a7c179fd211f99111e28cac8f56a3d636449eeef1903487cd5ee574200664", "created_at": "2026-11-01T19:01:23Z", "updated_at": "2026-11-01T22:51:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "LRRC3B and its promoter hypomethylation status predicts response to anti-PD-1 based immunotherapy.", "doi": "10.3389/fimmu.2023.959868", "pmid": "36798137", "authors": ["Linfeng Luo", "Sha Fu", "Wei Du", "Li-Na He", "Xuanye Zhang", "Yixing Wang", "Yixin Zhou", "Shaodong Hong"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background: The leucine rich repeat containing 3B (LRRC3B) gene is a tumor suppressor gene involved in the anti-tumor immune microenvironment. Expression of LRRC3B and DNA methylation at the LRRC3B promoter region may serve as a useful marker to predict response to anti-PD-1 therapy. However, no studies have yet systematically explored the protective role of LRRC3B methylation in tumor progression and immunity.\nMethods: Expression of LRRC3B of 33 cancer types in The Cancer Genome Atlas (TCGA) was downloaded from UCSC Xena (http://xena.ucsc.edu/). And, we evaluated the differential expression of LRRC3B according to tumor stage, overall survival, and characteristics of the tumor microenvironment. The immunotherapeutic cohorts included IMvigor21, GSE119144, and GSE72308 which were obtained from the Gene Expression Omnibus database. We conducted pearson correlation analysis of LRRC3B and tumor microenvironment (TME) in pan-cancer. Also, six immune cell types (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells) and tumor purity were analyzed using the Tumor IMmune Estimation Resource (TIMER1.0) (Tumor IMmune Estimation Resource (TIMER2.0). And, a \"silencing score\" model base on LRRC3B promoter methylation to predict overall survival (OS) by multivariate Cox regression analysis was constructed. Finally, the model was applied to predict anti-PD-1 therapy in non-small cell lung cancer (NSCLC) and breast cancer (BRCA).\nResults: LRRC3B expression associated with less tumor invasion, less severe tumor stage, and decreased metastasis. The inactivation of LRRC3B promoted the enrichment of immuneosuppressive cells, including myeloid-derived suppressor cells (MDSCs), cancer-associated fibroblasts (CAFs), M2 subtype of tumor-associated macrophages (M2-TAMs), M1 subtype of tumor-associated macrophages (M1-TAMs), and regulatory T (Treg) cells. A high silencing score was significantly associated with immune inhibition, low expression of LRRC3B, poor patient survival, and activation of cancer-related pathways.\nConclusion: Our comprehensive analysis demonstrated the potential role of LRRC3B in the anti-tumor microenvironment, clinicopathological features of cancer, and disease prognosis. It suggested that LRRC3B methylation could be used as a powerful biomarker to predict immunotherapy responses in NSCLC and BRCA.", "claims": [{"id": "claim-method", "statement": "First, all CpG sites were incorporated into the multivariate Cox regression, and then probes which exhibited a differential methylation at p< 0.05 for each type of cancer were selected to construct the silencing score.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36798137", "locator": "sections[11] \"DNA methylation analysis and the LRRC3B silencing score\" paragraph 2", "quote": "First, all CpG sites were incorporated into the multivariate Cox regression, and then probes which exhibited a differential methylation at p< 0.05 for each type of cancer were selected to construct the silencing score."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://xenabrowser.net/datapages/, and GEO(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119144/GSE72308). Gene expression data for immunotherapy response and overall survival data were obtained from the IMvigor210 public cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36798137", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://xenabrowser.net/datapages/, and GEO(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119144/GSE72308). Gene expression data for immunotherapy response and overall survival data were obtained from the IMvigor210 public cohort."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Overexpression of LRRC3B was associated with better overall survival in BLCA (p = 0.014), BRCA (p = 0.011), LUAD (p< 0.001), KIRC (p< 0.0001), and LICH (p = 0.011).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36798137", "locator": "sections[18] \"Higher LRRC3B is associated with lower TMN stage and better cancer prognoses\" paragraph 2", "quote": "Consistently, overexpression of LRRC3B was associated with better OS of patients with BLCA (p = 0.014), BRCA (p = 0.011), LUAD (p< 0.001), KIRC (p< 0.0001), and LICH (p = 0.011) ( Figures 3G, H, J, K, L )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Overexpression of LRRC3B was associated with better overall survival in BLCA (p = 0.014), BRCA (p = 0.011), LUAD (p< 0.001), KIRC (p< 0.0001), and LICH (p = 0.011).", "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:36798137"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36798137", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.959868", "sha256": "sha256:6c3c904d6007e3be986ca899dd2e34f682544eb2d9d6cc102b0bfcb1bb779d7c", "bytes": 43039, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.959868", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9928207", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "First, all CpG sites were incorporated into the multivariate Cox regression, and then probes which exhibited a differential methylation at p< 0.05 for each type of cancer were selected to construct the silencing score."}}, {"id": "method-2", "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 regression"}}, {"id": "method-3", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.7, "band": "medium", "quote": "pearson correlation"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-5", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}], "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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "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": "R4", "reachable": false, "accessions": ["GSE119144/GSE72308"], "resolved_accessions": [], "statements": ["Publicly available datasets were analyzed in this study. This data can be found here: https://xenabrowser.net/datapages/, and GEO(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119144/GSE72308). Gene expression data for immunotherapy response and overall survival data were obtained from the IMvigor210 public cohort."], "basis": "The paper names GSE119144/GSE72308, but the accession does not resolve in the repository (no reason recorded), so the data cannot be reached from here. 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