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pmid:37928388

Hierarchical clustering identifies oxidative stress-related subgroups for the prediction of prognosis and immune microenvironment in gastric cancer

Heliyon · 2023 doi:10.1016/j.heliyon.2023.e20804

Paper

partial

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

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Hierarchical clustering identifies oxidative stress-related subgroups for the prediction of prognosis and immune microenvironment in gastric cancer
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10.1016/j.heliyon.2023.e20804
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37928388
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Heliyon
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Furthermore, when compared to GC patients from the C1 subgroup, GC patients in the C2 subgroup exhibited a better overall survival rate (Fig. 2E, p = 0.01).
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Univariate and multivariate Cox analyses assessed the independent prognostic value of ORGs risk score in GC patients.
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Initially, we conducted an assessment of 41 ORGs linked to the predictive significance of patients with GC from the TCGA database. To accomplish this, we employed the univariate Cox regression analysis technique. Subsequently, utilizing the R software's ConcensusClusterPlus tool, we executed consensus clustering by considering the gene expression profiles of the aforementioned 41 ORGs [31]. Afterwards, the optimal number of clusters was assessed, and this procedure was iterated 1000 times to guarantee the credibility of findings. Following that, a cluster map was created utilizing the pheatmap tool in R. To compare the overall survival rates between the two subgroups, we conducted Kaplan-Meier analysis employing the survminer and survival packages in R software.
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The C1 oxidative stress-related gastric cancer subgroup had a worse overall survival rate than the C2 subgroup (p = 0.01).
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Complete file
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