Single cell sequencing analysis and transcriptome analysis constructed the liquid-liquid phase separation(LLPS)-related prognostic model for endometrial cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36185238", "kind": "science-record", "revision": 3, "content_hash": "sha256:985a4cb44f3d667d4f6609600b18647eba7633631335909a201f1c7f2107d041", "created_at": "2026-11-01T18:15:28Z", "updated_at": "2026-11-01T21:29:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single cell sequencing analysis and transcriptome analysis constructed the liquid-liquid phase separation(LLPS)-related prognostic model for endometrial cancer.", "doi": "10.3389/fonc.2022.1005472", "pmid": "36185238", "authors": ["Jiayang Wang", "Fei Meng", "Fei Mao"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Background: Endometrial cancer is one of the most common gynecological tumors in developed countries. Our understanding of the pathogenesis of endometrial cancer and the changes in the immune microenvironment are still unclear. It is necessary to explore new biomarkers to guide the diagnosis and treatment of endometrial cancer.\nMethods: The GEO database was used to download the endometrial cancer single cell sequencing dataset GSE173682. The UCSC database was used to download transcriptome sequencing data. The validation set was the transcriptome dataset GSE119041, which was retrieved from the GEO database. On the DrLLPS website, liquid-liquid phase separation-related genes can be downloaded. Relevant hub genes were found using weighted co-expression network analysis and dimension reduction clustering analysis. Prognostic models were built using Lasso regression and univariate COX regression. Analyses of immune infiltration were employed to investigate the endometrial cancer immunological microenvironment. The expression of model genes in endometrial cancer was confirmed using a PCR test.\nResults: We created an LLPS-related predictive model for endometrial cancer by extensive study, and it consists of four genes: EIF2S2, SNRPC, PRELID1, and NDUFB9. Patients with endometrial cancer may be classified into high-risk and low-risk groups based on their risk scores, and those in the high-risk group had significantly worse prognoses (P<0.05). Additionally, there were notable variations in the immunological milieu between the groups at high and low risk. EIF2S2, SNRPC, PRELID1, and NDUFB9 were all up-regulated in endometrial cancer tissues, according to PCR results.\nConclusions: Our study can provide a certain reference for the diagnosis and treatment of endometrial cancer.", "claims": [{"id": "claim-method", "statement": "First, univariate COX regression was used to identify the genes associated with prognosis. After that, the prognosis-related genes were further examined using LASSO regression, with the family set to “Cox” and Maxit set to 1000.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36185238", "locator": "sections[8] \"Construction and validation of the prognostic model\" paragraph 2", "quote": "First, univariate COX regression was used to identify the genes associated with prognosis. After that, the prognosis-related genes were further examined using LASSO regression, with the family set to “Cox” and Maxit set to 1000."}], "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:36185238", "locator": "sections[24] \"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 four-gene LLPS-related prognostic model classified endometrial cancer patients into high- and low-risk groups, with the high-risk group showing poorer prognosis in the TCGA training cohort and the GSE119041 validation cohort.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "As shown in Figures 3D, E , in both the training cohort TCGA and the external validation cohort GSE119041, it was observed that the prognosis of the risk high group was poorer than that of the risk low group (P <0.05)."}, "evidence": [{"asset": "source:pmid:36185238", "locator": "sections[16] \"Construction and validation of the prognostic model\" paragraph 2", "quote": "As shown in Figures 3D, E , in both the training cohort TCGA and the external validation cohort GSE119041, it was observed that the prognosis of the risk high group was poorer than that of the risk low group (P <0.05)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A four-gene LLPS-related prognostic model classified endometrial cancer patients into high- and low-risk groups, with the high-risk group showing poorer prognosis in the TCGA training cohort and the GSE119041 validation cohort.", "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:36185238"], "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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