Prognostic significance of GPR132 in papillary thyroid carcinoma: insights from integrated machine learning and its role in regulating TPC-1 cell growth.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41123734", "kind": "science-record", "revision": 4, "content_hash": "sha256:420da21b4cc88fe13bfc25fa6f5ab331f95789a3f2796414b7b047427708c81a", "created_at": "2026-11-01T20:31:10Z", "updated_at": "2026-11-01T22:57:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic significance of GPR132 in papillary thyroid carcinoma: insights from integrated machine learning and its role in regulating TPC-1 cell growth.", "doi": "10.1007/s12672-025-03833-0", "pmid": "41123734", "authors": ["Jinghua Gao", "Zihan Cai", "Shoupeng Ding", "Lanxin Ma", "Jian Han", "Yi-Yi Luo", "Xueli Yang", "Liqin Zhou", "Wen Mei", "Xiangfang Li", "Lin Meng", "Heng Luo"], "journal": "Discover oncology", "year": 2025, "abstract": "OBJECT: This study utilizes machine learning and bioinformatics methods to analyze data identifying GPR132 as a reliable potential prognostic gene for papillary thyroid carcinoma (PTC).The experiments elucidated potential role of GPR132 in inhibiting tumor growth in PTC by regulating the cell cycle and apoptotic mechanisms. This research provides significant insights for future personalized therapeutic strategies aimed at targeting PTC.\nMETHODS: The study analyzed the GSE191288 RNA-seq dataset, which included six thyroid cancer tumor samples and one adjacent normal tissue sample, to identify genes associated with tumor-associated macrophages (TAMs). After conducting a thorough enrichment analysis, we used the CellChat tool to investigate the signaling pathways.Pseudotemporal analysis elucidated the differentiation status of TAMs, and weighted gene co-expression network analysis(WGCNA) identified M1-like TAM-related genes within the M1 macrophage module. Integration with the GEO database revealed that GPR132 is a key prognostic gene. The effects of GPR132 overexpression on the proliferation, migration, apoptosis, and cell cycle progression of thyroid papillary carcinoma (TPC-1) cells were evaluated through cell-based experiments.\nRESULTS: Single-cell sequencing revealed 20 distinct cell clusters, categorized as epithelial, stromal, or immune cells, with a focus on TAMs.Enrichment analysis associated TAM-expressed genes with immune response regulation. Pseudotime analysis identified TAMs differentiation states, while WGCNA linked a low abundance of M1 macrophages to favorable PTC prognosis. Integration with the GEO database confirmed GPR132 as a key prognostic gene. Cellular experiments showed that GPR132 overexpression markedly inhibited TPC-1 cell proliferation and migration, likely through G1 phase cell cycle arrest and enhanced apoptosis. Flow cytometry confirmed elevated early and total apoptosis rates in GPR132-overexpressing cells.\nCONCLUSION: GPR132 was identified as a critical prognostic gene for PTC, with evidence suggesting its role in tumor suppression via cell cycle modulation and apoptosis induction.", "claims": [{"id": "claim-method", "statement": "First, we used WGCNA to select genes highly correlated with M1 macrophages, which were then cross-referenced with validated TAMs marker genes from single-cell datasets to identify potential biomarkers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41123734", "locator": "sections[24] \"Machine Learning-Based selection of prognostic genes\" paragraph 2", "quote": "First, we used WGCNA to select genes highly correlated with M1 macrophages, which were then cross-referenced with validated TAMs marker genes from single-cell datasets to identify potential biomarkers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data analyzed in this study are available from the [NCBI]database.The direct links required to find each data set in the database are as follows: theGEO gene expression and clinical pathology data set: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE191288.Thedata set downloaded by this direct link is the original data set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41123734", "locator": "sections[35] \"Data availability\" paragraph 2", "quote": "The data analyzed in this study are available from the [NCBI]database.The direct links required to find each data set in the database are as follows: theGEO gene expression and clinical pathology data set: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE191288.Thedata set downloaded by this direct link is the original data set."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified GPR132 as a key prognostic gene for PTC and found that its overexpression inhibited TPC-1 cell proliferation and migration while promoting apoptosis through G1 phase cell-cycle arrest.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41123734", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Integration with the GEO database confirmed GPR132 as a key prognostic gene."}, {"asset": "source:pmid:41123734", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Cellular experiments showed that GPR132 overexpression markedly inhibited TPC-1 cell proliferation and migration, likely through G1 phase cell cycle arrest and enhanced apoptosis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study identified GPR132 as a key prognostic gene for PTC and found that its overexpression inhibited TPC-1 cell proliferation and migration while promoting apoptosis through G1 phase cell-cycle arrest.", "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:41123734"], "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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METHODS: The study analyzed the GSE191288 RNA-seq dataset, which included six thyroid cancer t", "trajectories This study utilized single-cell RNA sequencing data and employed the Monocle 3 package to p", "ls, and stromal cells (Fig. 1D). Single-cell RNA sequencing (scRNA-seq) analysis of thyroid cancer (THCA", "y differences between cell types Single-cell RNA sequencing analysis reveals immune cell populations in"], "order_basis": "paper_text_order:592", "id": "assay:1"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["rexpression on the proliferation, migration, apoptosis, and cell cycle progression of thyroid papil", "ough G1 phase cell cycle arrest and enhanced apoptosis. Flow cytometry confirmed elevated early and", "cytometry confirmed elevated early and total apoptosis rates in GPR132-overexpressing cells. CONCLU", "or suppression via cell cycle modulation and apoptosis induction."], "order_basis": "paper_text_order:1228", "id": "assay:2"}, {"role": "data_generation", "definition_id": "flow_cytometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["se cell cycle arrest and enhanced apoptosis. Flow cytometry confirmed elevated early and total apoptosis"], "order_basis": "paper_text_order:1982", "id": "assay:3"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["sis of cell trajectories This study utilized single-cell RNA sequencing data and employed the Monocle 3 package to p", ", immune cells, and stromal cells (Fig. 1D). Single-cell RNA sequencing (scRNA-seq) analysis of thyroid cancer (THCA", "hlighting key differences between cell types Single-cell RNA sequencing analysis reveals immune cell populations in", "cells (Fig. 1D). Single-cell RNA sequencing (scRNA-seq) analysis of thyroid cancer (THCA) and norma"], "order_basis": "paper_text_order:2342", "id": "assay:4"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["First, we used WGCNA to select genes highly correlated with M1 macrophages, which were then cross-referenced with validated TAMs marker genes from single-cell datasets to identify potential biomarkers."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}