Prognostic and immunological significance of calcium-related gene signatures in renal clear cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36588677", "kind": "science-record", "revision": 3, "content_hash": "sha256:f71ad4d942f654b7133798788ba9b01483de23ea7cc8351a70188a36888fff89", "created_at": "2026-11-01T19:01:08Z", "updated_at": "2026-11-01T22:08:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic and immunological significance of calcium-related gene signatures in renal clear cell carcinoma.", "doi": "10.3389/fphar.2022.1055841", "pmid": "36588677", "authors": ["An Liu", "Fei Li", "Bao Wang", "Le Yang", "Hai Xing", "Chang Su", "Li Gao", "Minggao Zhao", "Lanxin Luo"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Background: Calcium signaling is implicated in multiple processes including immune response that important in tumor progression. Kidney renal clear cell carcinoma (KIRC) is the most frequent histological type of renal cell carcinoma with up to a third of cases develop metastases. As a result of a lack of in-depth understanding of the mechanisms underlying KIRC, treatment options have been limited. Here, we aim to comprehensively investigate the landscape of Ca2+ channels, pumps and exchangers in KIRC patients. Methods: The mRNA expression profiles and gene variations of 58 calcium-related genes (CRGs) in KIRC patients and normal control cases were downloaded from TCGA database. CRGs-related risk score was constructed to quantify calcium patterns by using least absolute shrinkage and selection operator (LASSO) regression. The prognostic value, biological functions, immune landscape and therapeutic sensitivities based on CRGs-related risk score were then evaluated using multiple methods. Finally, key gene of CRGs was identified by weighted gene co-expression network analysis (WGCNA). TCGA-CPTAC, GSE53757 datasets, as well as human tissues were used for validation. Results: KIRC patients had significant differences in CRG expression, prognosis, and biological functions between two CRG clusters. CRGs-related risk score was then determined. The prognosis, tumor mutation burden, immune cell infiltration, immune checkpoints, and the response of targeted inhibitors were remarkably different between high and low CRGs-related risk subtypes. CRGs-related high-risk subtype was characterized by immunosuppressive microenvironment with poor prognosis. Meanwhile, several targeted drugs showed distinct sensitivity between CRGs-related risk subtypes. Finally, TRPM3 was identified as a key CRG based on risk score in KIRC patients. TRPM3 mRNA and protein expression were significantly lower in KIRC tumors than in normal controls. Low TRPM3 expression was associated with poor prognosis in KIRC patients. Conclusion: Our study highlighted the promising prognostic value of CRGs in KIRC tumors. The evaluation of CRGs-related risk score will contribute to predicting prognosis and clinical therapy in KIRC patients.", "claims": [{"id": "claim-method", "statement": "A Cox proportional hazards regression analysis was performed on each CRG in TCGA cohort to screen significant genes that associated with survival (Nagashima and Sato, 2017). Those genes were then regressed using LASSO Cox (Tibshirani, 1997).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36588677", "locator": "sections[8] \"2.6 CRGs-related risk score construction\" paragraph 2", "quote": "A Cox proportional hazards regression analysis was performed on each CRG in TCGA cohort to screen significant genes that associated with survival (Nagashima and Sato, 2017). Those genes were then regressed using LASSO Cox (Tibshirani, 1997)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36588677", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The CRGs have promising prognostic value in KIRC tumors.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36588677", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our study highlighted the promising prognostic value of CRGs in KIRC tumors."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The CRGs have promising prognostic value in KIRC tumors.", "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:36588677"], "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:GSE53757"], "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:GSE53757"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36588677", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2022.1055841", "sha256": "sha256:952ace62b66f8a2738afec59d242ee134381bcbe614ada8c8d1c09a6e323cca3", "bytes": 53157, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fphar.2022.1055841", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9795407", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE53757", "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=GSE53757", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE53757", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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