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pmid:36769510

Construction and Verification of the Molecular Subtype and a Novel Prognostic Signature Based on Inflammatory Response-Related Genes in Uveal Melanoma.

Feng Zhang · Yan Deng · Dong Wang · Shuai Wang

Journal of clinical medicine · 2023 doi:10.3390/jcm12030861

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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title
Construction and Verification of the Molecular Subtype and a Novel Prognostic Signature Based on Inflammatory Response-Related Genes in Uveal Melanoma.
doi
10.3390/jcm12030861
pmid
36769510
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Feng Zhang
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Yan Deng
[2]
Dong Wang
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Shuai Wang
journal
Journal of clinical medicine
year
2023
abstract
The significance of inflammation in tumorigenesis and progression has become prominent. This study aimed to construct and validate the molecular subtype and a novel prognostic signature based on inflammatory response-related genes in uveal melanoma (UM). Patients from the TCGA, GSE84976, and GSE22138 UM cohorts were enrolled. According to the consensus cluster analysis, patients were divided into two molecular subtypes, namely IC1 and IC2. Survival curves showed that patients in IC1 had a better prognosis. The IC2 subgroup had higher levels of immune cell infiltration and more enriched immunological pathways. There were statistical differences in the immune-inflammation microenvironment, immune checkpoint genes expression, and drug sensitivity. The prognostic signature constructed based on inflammatory response-related genes exhibited a stable predictive power. Multivariate analysis confirmed that the signature was a prognostic factor independent of clinical characteristics. Functional analyses showed that the high-risk group was associated with immunological response, inflammatory cell activation, and tumor-related signal pathways. The riskscore had a negative relationship with tumor purity and was positively correlated with immune and stromal scores. Furthermore, the prognostic signature could sensitively predict the response to drug treatments. In conclusion, the prognostic signature might aid in stratifying patients at risk premised on the prognosis and immunotherapy sensitivity.
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Subsequently, to investigate if the predictive signature was independent of clinicopathological characteristics for predicting clinical outcome, univariate and multivariate analyses were conducted after merging the riskscore and clinical variables documented in all cohorts.
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sections[20] "3.7. Correlation Analysis and Independent Prognostic Analysis between the Riskscore and Clinicopathological Features" paragraph 4
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Subsequently, to investigate if the predictive signature was independent of clinicopathological characteristics for predicting clinical outcome, univariate and multivariate analyses were conducted after merging the riskscore and clinical variables documented in all cohorts.
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Publicly available datasets from the Cancer Genome Atlas “https://portal.gdc.cancer.gov/ (accessed on 1 October 2022)” and the Gene Expression Omnibus (GEO) “https://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 October 2022)” databases were analyzed in this study.
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sections[30] "Data Availability Statement" paragraph 2
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Publicly available datasets from the Cancer Genome Atlas “https://portal.gdc.cancer.gov/ (accessed on 1 October 2022)” and the Gene Expression Omnibus (GEO) “https://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 October 2022)” databases were analyzed in this study.
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After adjusting for other significant factors, multivariate analysis confirmed that the riskscore was an independent prognostic indicator (TCGA cohort: HR = 11.004; 95% CI: 3.830–31.610, p < 0.001; GSE84976 cohort: HR = 2.615; 95% CI: 1.006–6.794, p = 0.048, and GSE22138 cohort: HR = 2.443; 95% CI: 1.128–5.292, p = 0.023) (Figure 9B,D,F).
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After adjusting for other significant factors, multivariate analysis confirmed that the riskscore was an independent prognostic indicator (TCGA cohort: HR = 11.004; 95% CI: 3.830–31.610, p < 0.001; GSE84976 cohort: HR = 2.615; 95% CI: 1.006–6.794, p = 0.048, and GSE22138 cohort: HR = 2.443; 95% CI: 1.128–5.292, p = 0.023) (Figure 9B,D,F).
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sections[20] "3.7. Correlation Analysis and Independent Prognostic Analysis between the Riskscore and Clinicopathological Features" paragraph 4
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After adjusting for other significant factors, multivariate analysis confirmed that the riskscore was an independent prognostic indicator (TCGA cohort: HR = 11.004; 95% CI: 3.830–31.610, p < 0.001; GSE84976 cohort: HR = 2.615; 95% CI: 1.006–6.794, p = 0.048, and GSE22138 cohort: HR = 2.443; 95% CI: 1.128–5.292, p = 0.023) (Figure 9B,D,F).
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After adjusting for other significant factors, multivariate analysis confirmed that the riskscore was an independent prognostic indicator (TCGA cohort: HR = 11.004; 95% CI: 3.830–31.610, p < 0.001; GSE84976 cohort: HR = 2.615; 95% CI: 1.006–6.794, p = 0.048, and GSE22138 cohort: HR = 2.443; 95% CI: 1.128–5.292, p = 0.023) (Figure 9B,D,F).
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Expression Data from Uveal Melanoma primary tumors.
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Publicly available datasets from the Cancer Genome Atlas “https://portal.gdc.cancer.gov/ (accessed on 1 October 2022)” and the Gene Expression Omnibus (GEO) “https://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 October 2022)” databases were analyzed in this study.
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Subsequently, to investigate if the predictive signature was independent of clinicopathological characteristics for predicting clinical outcome, univariate and multivariate analyses were conducted after merging the riskscore and clinical variables documented in all cohorts.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36769510", "kind": "science-record", "revision": 3, "content_hash": "sha256:c1cbea679a9d4b8e84119509431639121b27e2304c1bb8bdcb35e27b59355145", "created_at": "2026-11-01T19:01:21Z", "updated_at": "2026-11-01T22:08:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and Verification of the Molecular Subtype and a Novel Prognostic Signature Based on Inflammatory Response-Related Genes in Uveal Melanoma.", "doi": "10.3390/jcm12030861", "pmid": "36769510", "authors": ["Feng Zhang", "Yan Deng", "Dong Wang", "Shuai Wang"], "journal": "Journal of clinical medicine", "year": 2023, "abstract": "The significance of inflammation in tumorigenesis and progression has become prominent. This study aimed to construct and validate the molecular subtype and a novel prognostic signature based on inflammatory response-related genes in uveal melanoma (UM). Patients from the TCGA, GSE84976, and GSE22138 UM cohorts were enrolled. According to the consensus cluster analysis, patients were divided into two molecular subtypes, namely IC1 and IC2. Survival curves showed that patients in IC1 had a better prognosis. The IC2 subgroup had higher levels of immune cell infiltration and more enriched immunological pathways. There were statistical differences in the immune-inflammation microenvironment, immune checkpoint genes expression, and drug sensitivity. The prognostic signature constructed based on inflammatory response-related genes exhibited a stable predictive power. Multivariate analysis confirmed that the signature was a prognostic factor independent of clinical characteristics. Functional analyses showed that the high-risk group was associated with immunological response, inflammatory cell activation, and tumor-related signal pathways. The riskscore had a negative relationship with tumor purity and was positively correlated with immune and stromal scores. Furthermore, the prognostic signature could sensitively predict the response to drug treatments. In conclusion, the prognostic signature might aid in stratifying patients at risk premised on the prognosis and immunotherapy sensitivity.", "claims": [{"id": "claim-method", "statement": "Subsequently, to investigate if the predictive signature was independent of clinicopathological characteristics for predicting clinical outcome, univariate and multivariate analyses were conducted after merging the riskscore and clinical variables documented in all cohorts.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36769510", "locator": "sections[20] \"3.7. Correlation Analysis and Independent Prognostic Analysis between the Riskscore and Clinicopathological Features\" paragraph 4", "quote": "Subsequently, to investigate if the predictive signature was independent of clinicopathological characteristics for predicting clinical outcome, univariate and multivariate analyses were conducted after merging the riskscore and clinical variables documented in all cohorts."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets from the Cancer Genome Atlas “https://portal.gdc.cancer.gov/ (accessed on 1 October 2022)” and the Gene Expression Omnibus (GEO) “https://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 October 2022)” databases were analyzed in this study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36769510", "locator": "sections[30] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets from the Cancer Genome Atlas “https://portal.gdc.cancer.gov/ (accessed on 1 October 2022)” and the Gene Expression Omnibus (GEO) “https://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 October 2022)” databases were analyzed in this study."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After adjusting for other significant factors, multivariate analysis confirmed that the riskscore was an independent prognostic indicator (TCGA cohort: HR = 11.004; 95% CI: 3.830–31.610, p < 0.001; GSE84976 cohort: HR = 2.615; 95% CI: 1.006–6.794, p = 0.048, and GSE22138 cohort: HR = 2.443; 95% CI: 1.128–5.292, p = 0.023) (Figure 9B,D,F).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "After adjusting for other significant factors, multivariate analysis confirmed that the riskscore was an independent prognostic indicator (TCGA cohort: HR = 11.004; 95% CI: 3.830–31.610, p < 0.001; GSE84976 cohort: HR = 2.615; 95% CI: 1.006–6.794, p = 0.048, and GSE22138 cohort: HR = 2.443; 95% CI: 1.128–5.292, p = 0.023) (Figure 9B,D,F)."}, "evidence": [{"asset": "source:pmid:36769510", "locator": "sections[20] \"3.7. Correlation Analysis and Independent Prognostic Analysis between the Riskscore and Clinicopathological Features\" paragraph 4", "quote": "After adjusting for other significant factors, multivariate analysis confirmed that the riskscore was an independent prognostic indicator (TCGA cohort: HR = 11.004; 95% CI: 3.830–31.610, p < 0.001; GSE84976 cohort: HR = 2.615; 95% CI: 1.006–6.794, p = 0.048, and GSE22138 cohort: HR = 2.443; 95% CI: 1.128–5.292, p = 0.023) (Figure 9B,D,F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "After adjusting for other significant factors, multivariate analysis confirmed that the riskscore was an independent prognostic indicator (TCGA cohort: HR = 11.004; 95% CI: 3.830–31.610, p < 0.001; GSE84976 cohort: HR = 2.615; 95% CI: 1.006–6.794, p = 0.048, and GSE22138 cohort: HR = 2.443; 95% CI: 1.128–5.292, p = 0.023) (Figure 9B,D,F).", "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:36769510"], "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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