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pmid:39170367

Construction of a prognostic model for ovarian cancer based on a comprehensive bioinformatics analysis of cuproptosis-associated long non-coding RNA signatures.

Rujun Chen · Yating Huang · Ke Sun · Fuyun Dong · Xiaoqin Wang · Junhua Guan · Lina Yang · He Fei

Heliyon · 2024 doi:10.1016/j.heliyon.2024.e35004

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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title
Construction of a prognostic model for ovarian cancer based on a comprehensive bioinformatics analysis of cuproptosis-associated long non-coding RNA signatures.
doi
10.1016/j.heliyon.2024.e35004
pmid
39170367
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[0]
Rujun Chen
[1]
Yating Huang
[2]
Ke Sun
[3]
Fuyun Dong
[4]
Xiaoqin Wang
[5]
Junhua Guan
[6]
Lina Yang
[7]
He Fei
journal
Heliyon
year
2024
abstract
Ovarian cancer (OCa) is a common malignancy in women, and the role of cuproptosis and its related genes in OCa is unclear. Using the GSE14407 dataset, we analyzed the expression and correlation of cuproptosis-related genes (CRGs) between tumor and normal groups. From the TCGA-OV dataset, we identified 20 cuproptosis-related long non-coding RNAs (CuLncs) associated with patient survival through univariate Cox analysis. OCa patients were divided into early-stage and late-stage groups to analyze CuLncs expression. Cluster analysis classified patients into two clusters, with Cluster1 having a poorer prognosis. Significant differences in "Lymphatic Invasion" and "Cancer status" were observed between clusters. Seven CRGs showed significant expression differences, validated using the human protein atlas (HPA) databases. Immune analysis revealed a higher ImmuneScore in Cluster1. GSEA identified associated signaling pathways. LASSO regression included 11 CuLncs to construct and validate a survival prediction model, classifying patients into high-risk and low-risk groups. Correlations between riskScore, Cluster phenotype, ImmuneScore, and immune cell infiltration were explored. Cell experiments showed that knocking down AC023644.1 decreases OCa cell viability. In conclusion, we constructed an accurate prognostic model for OCa based on 11 CuLncs, providing a basis for prognosis assessment and potential immunotherapy targets.
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By performing a Spearman's correlation analysis on the expression profiles of CRGs and lncRNAs, we obtained an expression matrix specifically for cuproptosis-related lncRNAs, referred to as CuLncs.
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LASSO regression included 11 CuLncs to construct and validate a survival prediction model, classifying patients into high-risk and low-risk groups.
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Ovarian Cancer gene expression profiling identifies the surface of the ovary as a stem cell niche
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Complete file
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