Identification of prognostic biomarkers for breast cancer brain metastases based on the bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35059509", "kind": "science-record", "revision": 4, "content_hash": "sha256:f5b89bd6b3f0c6c62022952d35facd36a4dcd7f1aeaa2559e76af6197541884c", "created_at": "2026-11-01T17:36:47Z", "updated_at": "2026-11-01T22:05:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of prognostic biomarkers for breast cancer brain metastases based on the bioinformatics analysis.", "doi": "10.1016/j.bbrep.2022.101203", "pmid": "35059509", "authors": ["Zhuoyi Wu", "Jinghai Wan", "Jiawei Wang", "Xiaoli Meng", "Haipeng Qian"], "journal": "Biochemistry and biophysics reports", "year": 2022, "abstract": "PURPOSE: The prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.\nPATIENTS AND METHODS: The gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.\nRESULTS: A total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.\nCONCLUSION: Our study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.", "claims": [{"id": "claim-method", "statement": "DEMGs were first input to the univariate Cox regression analysis to screen out the DEMGs significantly associated with the survival of BC patients (p-value <0.05).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35059509", "locator": "sections[5] \"The overall survival and the metastatic recurrence survival analyses of DEMGs\" paragraph 2", "quote": "DEMGs were first input to the univariate Cox regression analysis to screen out the DEMGs significantly associated with the survival of BC patients (p-value <0.05)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression profiles of 907 M0 and 22 M1 for patients with BC were downloaded from TCGA database. The gene expression data and the clinical information were downloaded from GSE12276, which comprised 16 BC patients with BMs and 188 BC patients with other metastases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35059509", "locator": "sections[3] \"Data source\" paragraph 2", "quote": "The gene expression profiles of 907 M0 and 22 M1 for patients with BC were downloaded from TCGA database. The gene expression data and the clinical information were downloaded from GSE12276, which comprised 16 BC patients with BMs and 188 BC patients with other metastases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35059509", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC.", "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:35059509"], "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:GSE12276"], "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:GSE12276"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35059509", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.bbrep.2022.101203", "sha256": "sha256:636f0d00c5c2226f391368a5c67b7a5a412782ac96c6a41562f25517dc4c9028", "bytes": 27988, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.bbrep.2022.101203", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8760394", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE12276", "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=GSE12276", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE12276", "version": null, "note": "The gene expression profiles of 907 M0 and 22 M1 for patients with BC were downloaded from TCGA database. 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