Establishment of a lysosome-related prognostic signature in breast cancer to predict immune infiltration and therapy response.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38162504", "kind": "science-record", "revision": 3, "content_hash": "sha256:55359dc83d9b014abaf5146f6f57828cf70e9e583ca605d8b2c7a340e2f55076", "created_at": "2026-11-01T19:20:05Z", "updated_at": "2026-11-01T22:10:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment of a lysosome-related prognostic signature in breast cancer to predict immune infiltration and therapy response.", "doi": "10.3389/fonc.2023.1325452", "pmid": "38162504", "authors": ["Hairong Su", "Ying Chen", "Fengye Lin", "Wanhua Li", "Xiangyu Gu", "Weijie Zeng", "Dan Liu", "Man Li", "Shaowen Zhong", "Qianjun Chen", "Qubo Chen"], "journal": "Frontiers in oncology", "year": 2023, "abstract": "Background: Lysosomes are instrumental in intracellular degradation and recycling, with their functional alterations holding significance in tumor growth. Nevertheless, the precise role of lysosome-related genes (LRGs) in breast cancer (BC) remains elucidated. This study aimed to establish a prognostic model for BC based on LRGs.\nMethods: Employing The Cancer Genome Atlas (TCGA) BC cohort as a training dataset, this study identified differentially expressed lysosome-related genes (DLRGs) through intersecting LRGs with differential expression genes (DEGs) between tumor and normal samples. A prognostic model of BC was subsequently developed using Cox regression analysis and validated within two Gene Expression Omnibus (GEO) external validation sets. Further analyses explored functional pathways, the immune microenvironment, immunotherapeutic responses, and sensitivity to chemotherapeutic drugs in different risk groups. Additionally, the mRNA and protein expression levels of genes within the risk model were examined by utilizing the Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases. Clinical tissue specimens obtained from patients were gathered to validate the expression of the model genes via Real-Time Polymerase Chain Reaction (RT-PCR).\nResults: We developed a risk model of BC based on five specific genes (ATP6AP1, SLC7A5, EPDR1, SDC1, and PIGR). The model was validated for overall survival (OS) in two GEO validation sets (p=0.00034 for GSE20685 and p=0.0095 for GSE58812). In addition, the nomogram incorporating clinical factors showed better predictive performance. Compared to the low-risk group, the high-risk group had a higher level of certain immune cell infiltration, including regulatory T cells (Tregs) and type 2 T helper cells (Th2). The high-risk patients appeared to respond less well to general immunotherapy and chemotherapeutic drugs, according to the Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenotype Score (IPS), and drug sensitivity scores. The RT-PCR results validated the expression trends of some prognostic-related genes in agreement with the previous differential expression analysis.\nConclusion: Our innovative lysosome-associated signature can predict the prognosis for BC patients, offering insights for guiding subsequent immunotherapeutic and chemotherapeutic interventions. Furthermore, it has the potential to provide a scientific foundation for identifying prospective therapeutic targets.", "claims": [{"id": "claim-method", "statement": "The results of stepwise multivariate Cox regression finalized five genes (SLC7A5, SDC1, PIGR, ATP6AP1, and EPDR1) for prognostic model development", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38162504", "locator": "sections[17] \"Construction and validation of the LRGs signature\" paragraph 2", "quote": "The results of stepwise multivariate Cox regression finalized five genes (SLC7A5, SDC1, PIGR, ATP6AP1, and EPDR1) for prognostic model development"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38162504", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A five-gene lysosome-related risk model predicted overall survival in breast cancer and was validated in two GEO cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38162504", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We developed a risk model of BC based on five specific genes (ATP6AP1, SLC7A5, EPDR1, SDC1, and PIGR). The model was validated for overall survival (OS) in two GEO validation sets (p=0.00034 for GSE20685 and p=0.0095 for GSE58812)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A five-gene lysosome-related risk model predicted overall survival in breast cancer and was validated in two GEO cohorts.", "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:38162504"], "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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