The novel transcriptomic signature of angiogenesis predicts clinical outcome, tumor microenvironment and treatment response for prostate adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35836112", "kind": "science-record", "revision": 4, "content_hash": "sha256:c3e31c26a6fcaaeff3fc798dd72f1d8703f834cca82a7e26f6e82eec4a39e50e", "created_at": "2026-11-01T18:14:54Z", "updated_at": "2026-11-01T22:07:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The novel transcriptomic signature of angiogenesis predicts clinical outcome, tumor microenvironment and treatment response for prostate adenocarcinoma.", "doi": "10.1186/s10020-022-00504-6", "pmid": "35836112", "authors": ["Cheng-Yuan Gu", "Bo Dai", "Yao Zhu", "Guo-Wen Lin", "Hong-Kai Wang", "Ding-Wei Ye", "Xiao-Jian Qin"], "journal": "Molecular medicine (Cambridge, Mass.)", "year": 2022, "abstract": "Angiogenesis plays the critical roles in promoting tumor progression, aggressiveness, and metastasis. Although few studies have revealed some angiogenesis-related genes (ARGs) could serve as prognosis-related biomarkers for the prostate cancer (PCa), the integrated role of ARGs has not been systematically studied. The RNA-sequencing data and clinical information of prostate adenocarcinoma (PRAD) were downloaded from The Cancer Genome Atlas (TCGA) as discovery dataset. Twenty-three ARGs in total were identified to be correlated with prognosis of PRAD by the univariate Cox regression analysis, and a 19-ARG signature was further developed with significant correlation with the disease-free survival (DFS) of PRAD by the least absolute shrinkage and selection operator (LASSO) Cox regression with tenfold cross-validation. The signature stratified PRAD patients into high- and low-ARGs signature score groups, and those with high ARGs signature score were associated with significantly poorer outcomes (median DFS: 62.71 months vs unreached, p < 0.0001). The predicting ability of ARGs signature was subsequently validated in two independent cohorts of GSE40272 & PRAD_MSKCC. Notably, the 19-ARG signature outperformed the typical clinical features or each involved ARG in predicting the DFS of PRAD. Furthermore, a prognostic nomogram was constructed with three independent prognostic factors, including the ARGs signature, T stage and Gleason score. The predicted results from the nomogram (C-index = 0.799, 95%CI = 0.744-0.854) matched well with the observed outcomes, which was verified by the calibration curves. The values of area under receiver operating characteristic curve (AUC) for DFS at 1-, 3-, 5-year for the nomogram were 0.82, 0.83, and 0.83, respectively, indicating the performance of nomogram model is of reasonably high accuracy and robustness. Moreover, functional enrichment analysis demonstrated the potential targets of E2F targets, G2M checkpoint pathways, and cell cycle pathways to suppress the PRAD progression. Of note, the high-risk PRAD patients were more sensitive to immune therapies, but Treg might hinder benefits from immunotherapies. Additionally, this established tool also could predict response to neoadjuvant androgen deprivation therapy (ADT) and some chemotherapy drugs, such as cisplatin, paclitaxel, and docetaxel, etc. The novel ARGs signature, with prognostic significance, can further promote the application of targeted therapies in different stratifications of PCa patients.", "claims": [{"id": "claim-method", "statement": "The ARGs related to DFS (P < 0.05) were identified via a univariate Cox regression analysis using optimized algorithm. Following least absolute shrinkage and selection operator (LASSO) Cox regression with tenfold cross-validation was performed using the “glmnet” package in R studio (Sauerbrei et al. 2007; Wang et al. 2019). Multivariate Cox regression analysis was then conducted for the construction of this ARG signature, and the ARGs signature score formula was established.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35836112", "locator": "sections[5] \"Construction of the angiogenesis-related prognostic signature and nomogram\" paragraph 2", "quote": "The ARGs related to DFS (P < 0.05) were identified via a univariate Cox regression analysis using optimized algorithm. Following least absolute shrinkage and selection operator (LASSO) Cox regression with tenfold cross-validation was performed using the “glmnet” package in R studio (Sauerbrei et al. 2007; Wang et al. 2019). Multivariate Cox regression analysis was then conducted for the construction of this ARG signature, and the ARGs signature score formula was established."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in the present study was from the public database. The data can be downloaded from: https://www.cbioportal.org/study/summary?id=prad_tcga, https://gdc-hub.s3.us-east-1.amazonaws.com/download/TCGA-PRAD.htseq_counts.tsv.gz, https://www.ncbi.nlm.nih.gov/geo/, http://www.gsea-msigdb.org/gsea/msigdb/, https://www.cancerrxgene.org/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35836112", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The datasets analyzed in the present study was from the public database. The data can be downloaded from: https://www.cbioportal.org/study/summary?id=prad_tcga, https://gdc-hub.s3.us-east-1.amazonaws.com/download/TCGA-PRAD.htseq_counts.tsv.gz, https://www.ncbi.nlm.nih.gov/geo/, http://www.gsea-msigdb.org/gsea/msigdb/, https://www.cancerrxgene.org/."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The 19-ARG signature outperformed the typical clinical features or each involved ARG in predicting the DFS of PRAD.", "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:35836112"], "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:GSE102124"], "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:GSE102124"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35836112", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s10020-022-00504-6", "sha256": "sha256:61ec780d58e9077aa83d0944c9d274512923e6519fbced70d0a69d0bad867e5e", "bytes": 54478, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s10020-022-00504-6", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9284787", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE102124", "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=GSE102124", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE102124", "version": null, "note": "The datasets analyzed in the present study was from the public database. 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The RNA-sequencing data and clinical information of prostate ad"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["tein expression was not estimated.Fig. 11The immunohistochemistry (IHC) analysis. A The collection of IHC stai", "0.76, 0.68 and 0.63, respectively (Fig. 3H). IHC analysis of ARGs protein expression in human", "pression in human PRAD samples Additionally, IHC data were retrieved from the HPA database to", "ated in the cytoplasm and/or membrane by the IHC analysis (Fig. 11B). 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