Po-Wu Liu, Zhao-Yi Liu, Shi-Jia Deng, Xiu Zhang, Zhi-Bin Wang, Na-Yiyuan Wu, Chao-Shui Liu, Ming-Hua Hu, Jing Wang, He Li
{"title":"一个预测卵巢癌预后、免疫特征和药物敏感性的焦热相关LncRNA标记。","authors":"Po-Wu Liu, Zhao-Yi Liu, Shi-Jia Deng, Xiu Zhang, Zhi-Bin Wang, Na-Yiyuan Wu, Chao-Shui Liu, Ming-Hua Hu, Jing Wang, He Li","doi":"10.2147/OTT.S491130","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Multiple studies have suggested that lncRNAs and pyroptosis play important roles in ovarian cancer (OC). However, the function of pyroptosis-related lncRNAs (PRLs) in OC is not fully understood.</p><p><strong>Methods: </strong>Clinical information and RNA-seq data of OC patients (n = 379) were collected from TCGA database. Pearson correlation analysis and univariate Cox analysis were performed to identify prognostic PRLs, respectively. LASSO-COX regression was utilized to construct a prognostic PRLs signature. Kaplan-Meier (K-M) curve analyses and receiver operating characteristics (ROC) were used to evaluate the prognostic prediction of the signature. The association between risk score and tumor microenvironment infiltration, immunotherapy response and chemotherapy sensitivity were also analyzed. In addition, the function of TYMSOS on OC and pyroptosis was experimentally confirmed in cell lines.</p><p><strong>Results: </strong>Firstly, 32 prognostic PRLs were identified, and a novel prognostic PRLs signature was constructed and validated. Surprisingly, the prognostic PRLs signature could solidly predict the clinical outcome of patients with OC and patients with high-risk score shown a short overall survival. GSEA results suggested that the RPLs were mainly enriched in the inflammatory response pathway, p53 pathway, TGF-β signaling and TNFα signaling. Besides, our results demonstrated that the risk score was significantly associated with patients with immune infiltration, immunotherapy response and the sensitivity of veliparib and metformin. Furthermore, the oncogene effect of TYMSOS on OC by inhibiting pyroptosis was verified by experiments.</p><p><strong>Conclusion: </strong>This study found that the prognostic PRLs signature may serve as an efficient biomarker in predicting the prognosis, tumor microenvironment infiltration, and sensitivity of chemotherapeutic agents. TYMSOS is a potential biomarker in OC, and it might promote tumor progression by inhibiting pyroptosis.</p>","PeriodicalId":19534,"journal":{"name":"OncoTargets and therapy","volume":"18 ","pages":"585-601"},"PeriodicalIF":2.7000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034292/pdf/","citationCount":"0","resultStr":"{\"title\":\"A Pyroptosis-Related LncRNA Signature for Predicting Prognosis, Immune Features and Drug Sensitivity in Ovarian Cancer.\",\"authors\":\"Po-Wu Liu, Zhao-Yi Liu, Shi-Jia Deng, Xiu Zhang, Zhi-Bin Wang, Na-Yiyuan Wu, Chao-Shui Liu, Ming-Hua Hu, Jing Wang, He Li\",\"doi\":\"10.2147/OTT.S491130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Multiple studies have suggested that lncRNAs and pyroptosis play important roles in ovarian cancer (OC). However, the function of pyroptosis-related lncRNAs (PRLs) in OC is not fully understood.</p><p><strong>Methods: </strong>Clinical information and RNA-seq data of OC patients (n = 379) were collected from TCGA database. Pearson correlation analysis and univariate Cox analysis were performed to identify prognostic PRLs, respectively. LASSO-COX regression was utilized to construct a prognostic PRLs signature. Kaplan-Meier (K-M) curve analyses and receiver operating characteristics (ROC) were used to evaluate the prognostic prediction of the signature. The association between risk score and tumor microenvironment infiltration, immunotherapy response and chemotherapy sensitivity were also analyzed. In addition, the function of TYMSOS on OC and pyroptosis was experimentally confirmed in cell lines.</p><p><strong>Results: </strong>Firstly, 32 prognostic PRLs were identified, and a novel prognostic PRLs signature was constructed and validated. Surprisingly, the prognostic PRLs signature could solidly predict the clinical outcome of patients with OC and patients with high-risk score shown a short overall survival. GSEA results suggested that the RPLs were mainly enriched in the inflammatory response pathway, p53 pathway, TGF-β signaling and TNFα signaling. Besides, our results demonstrated that the risk score was significantly associated with patients with immune infiltration, immunotherapy response and the sensitivity of veliparib and metformin. Furthermore, the oncogene effect of TYMSOS on OC by inhibiting pyroptosis was verified by experiments.</p><p><strong>Conclusion: </strong>This study found that the prognostic PRLs signature may serve as an efficient biomarker in predicting the prognosis, tumor microenvironment infiltration, and sensitivity of chemotherapeutic agents. 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A Pyroptosis-Related LncRNA Signature for Predicting Prognosis, Immune Features and Drug Sensitivity in Ovarian Cancer.
Background: Multiple studies have suggested that lncRNAs and pyroptosis play important roles in ovarian cancer (OC). However, the function of pyroptosis-related lncRNAs (PRLs) in OC is not fully understood.
Methods: Clinical information and RNA-seq data of OC patients (n = 379) were collected from TCGA database. Pearson correlation analysis and univariate Cox analysis were performed to identify prognostic PRLs, respectively. LASSO-COX regression was utilized to construct a prognostic PRLs signature. Kaplan-Meier (K-M) curve analyses and receiver operating characteristics (ROC) were used to evaluate the prognostic prediction of the signature. The association between risk score and tumor microenvironment infiltration, immunotherapy response and chemotherapy sensitivity were also analyzed. In addition, the function of TYMSOS on OC and pyroptosis was experimentally confirmed in cell lines.
Results: Firstly, 32 prognostic PRLs were identified, and a novel prognostic PRLs signature was constructed and validated. Surprisingly, the prognostic PRLs signature could solidly predict the clinical outcome of patients with OC and patients with high-risk score shown a short overall survival. GSEA results suggested that the RPLs were mainly enriched in the inflammatory response pathway, p53 pathway, TGF-β signaling and TNFα signaling. Besides, our results demonstrated that the risk score was significantly associated with patients with immune infiltration, immunotherapy response and the sensitivity of veliparib and metformin. Furthermore, the oncogene effect of TYMSOS on OC by inhibiting pyroptosis was verified by experiments.
Conclusion: This study found that the prognostic PRLs signature may serve as an efficient biomarker in predicting the prognosis, tumor microenvironment infiltration, and sensitivity of chemotherapeutic agents. TYMSOS is a potential biomarker in OC, and it might promote tumor progression by inhibiting pyroptosis.
期刊介绍：
OncoTargets and Therapy is an international, peer-reviewed journal focusing on molecular aspects of cancer research, that is, the molecular diagnosis of and targeted molecular or precision therapy for all types of cancer.
The journal is characterized by the rapid reporting of high-quality original research, basic science, reviews and evaluations, expert opinion and commentary that shed novel insight on a cancer or cancer subtype.
Specific topics covered by the journal include:
-Novel therapeutic targets and innovative agents
-Novel therapeutic regimens for improved benefit and/or decreased side effects
-Early stage clinical trials
Further considerations when submitting to OncoTargets and Therapy:
-Studies containing in vivo animal model data will be considered favorably.
-Tissue microarray analyses will not be considered except in cases where they are supported by comprehensive biological studies involving multiple cell lines.
-Biomarker association studies will be considered only when validated by comprehensive in vitro data and analysis of human tissue samples.
-Studies utilizing publicly available data (e.g. GWAS/TCGA/GEO etc.) should add to the body of knowledge about a specific disease or relevant phenotype and must be validated using the authors’ own data through replication in an independent sample set and functional follow-up.
-Bioinformatics studies must be validated using the authors’ own data through replication in an independent sample set and functional follow-up.
-Single nucleotide polymorphism (SNP) studies will not be considered.
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