Development of a relapse-related RiskScore model to predict the drug sensitivity and prognosis for patients with ovarian cancer
Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC. Gene Expression Omnibus (GEO) and...
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| Published in | PeerJ (San Francisco, CA) Vol. 13; p. e19764 |
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| Abstract | Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC.
Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the "Seurat" package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying "timeROC" package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using "pRRophetic" package. The effects of relapse-related prognostic genes on OC cells were detected with
assays.
The single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (
,
,
,
, and
) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC
values to these drugs, the low-risk group was more sensitive to the six drugs. In addition,
silencing markedly inhibited the invasion and migration of OC cells.
This study established a relapse-related RiskScore model based on five prognostic genes (
,
,
,
, and
), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies. |
|---|---|
| AbstractList | Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC.BackgroundOvarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC.Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the "Seurat" package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying "timeROC" package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using "pRRophetic" package. The effects of relapse-related prognostic genes on OC cells were detected with in vitro assays.MethodsGene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the "Seurat" package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying "timeROC" package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using "pRRophetic" package. The effects of relapse-related prognostic genes on OC cells were detected with in vitro assays.The single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC50 values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, KRT19 silencing markedly inhibited the invasion and migration of OC cells.ResultsThe single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC50 values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, KRT19 silencing markedly inhibited the invasion and migration of OC cells.This study established a relapse-related RiskScore model based on five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies.ConclusionThis study established a relapse-related RiskScore model based on five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies. Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the "Seurat" package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying "timeROC" package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using "pRRophetic" package. The effects of relapse-related prognostic genes on OC cells were detected with assays. The single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes ( , , , , and ) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, silencing markedly inhibited the invasion and migration of OC cells. This study established a relapse-related RiskScore model based on five prognostic genes ( , , , , and ), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies. Background Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC. Methods Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the “Seurat” package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying “timeROC” package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using “pRRophetic” package. The effects of relapse-related prognostic genes on OC cells were detected with in vitro assays. Results The single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC50 values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, KRT19 silencing markedly inhibited the invasion and migration of OC cells. Conclusion This study established a relapse-related RiskScore model based on five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies. BackgroundOvarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC. MethodsGene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the “Seurat” package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying “timeROC” package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using “pRRophetic” package. The effects of relapse-related prognostic genes on OC cells were detected with in vitro assays. ResultsThe single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC50 values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, KRT19 silencing markedly inhibited the invasion and migration of OC cells. ConclusionThis study established a relapse-related RiskScore model based on five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies. Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the "Seurat" package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying "timeROC" package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using "pRRophetic" package. The effects of relapse-related prognostic genes on OC cells were detected with in vitro assays. The single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC.sub.50 values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, KRT19 silencing markedly inhibited the invasion and migration of OC cells. This study established a relapse-related RiskScore model based on five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies. Background Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC. Methods Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the "Seurat" package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying "timeROC" package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using "pRRophetic" package. The effects of relapse-related prognostic genes on OC cells were detected with in vitro assays. Results The single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC.sub.50 values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, KRT19 silencing markedly inhibited the invasion and migration of OC cells. Conclusion This study established a relapse-related RiskScore model based on five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies. |
| ArticleNumber | e19764 |
| Audience | Academic |
| Author | Yang, Shasha Fei, Jiaojiao Wang, Xuegu Dou, Chengli Jin, Zhixin Ding, Biao Li, Xiang Wang, Xiaojing |
| Author_xml | – sequence: 1 givenname: Zhixin surname: Jin fullname: Jin, Zhixin organization: Anhui Key Laboratory of Respiratory Tumors and Infectious Diseases, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China – sequence: 2 givenname: Xuegu surname: Wang fullname: Wang, Xuegu organization: Department of Obstetrics and Gynecology (Center for Reproductive Medicine), The First Affiliated Hospital of Bengbu Medical University, Bengbu, China – sequence: 3 givenname: Xiang surname: Li fullname: Li, Xiang organization: Molecular Diagnostic Center, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China – sequence: 4 givenname: Shasha surname: Yang fullname: Yang, Shasha organization: Anhui Key Laboratory of Respiratory Tumors and Infectious Diseases, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China – sequence: 5 givenname: Biao surname: Ding fullname: Ding, Biao organization: Department of Obstetrics and Gynecology (Center for Reproductive Medicine), The First Affiliated Hospital of Bengbu Medical University, Bengbu, China – sequence: 6 givenname: Jiaojiao surname: Fei fullname: Fei, Jiaojiao organization: Anhui Key Laboratory of Respiratory Tumors and Infectious Diseases, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China – sequence: 7 givenname: Xiaojing surname: Wang fullname: Wang, Xiaojing organization: Anhui Key Laboratory of Respiratory Tumors and Infectious Diseases, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China – sequence: 8 givenname: Chengli surname: Dou fullname: Dou, Chengli organization: Molecular Diagnostic Center, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40821986$$D View this record in MEDLINE/PubMed |
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| Keywords | Prognostic model RiskScore Single cell atlas Relapse Drug sensitivity Ovarian cancer |
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| Snippet | Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to... Background Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was... BackgroundOvarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was... |
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| SubjectTerms | Analysis Antineoplastic Agents - pharmacology Antineoplastic Agents - therapeutic use Bioinformatics Biomarkers, Tumor - genetics Cancer Cancer patients Cancer therapies Cell Biology Cell migration Cells Chemotherapy Datasets Drug Resistance, Neoplasm - genetics Drug sensitivity Drugs Epithelial cells Epithelium Female Gene expression Gene Expression Profiling Gene Expression Regulation, Neoplastic Genes Genetic aspects Genomes Genomics Gynecology and Obstetrics Humans Malignancy Medical prognosis Neoplasm Recurrence, Local - drug therapy Neoplasm Recurrence, Local - genetics Oncology Ovarian cancer Ovarian Neoplasms - drug therapy Ovarian Neoplasms - genetics Ovarian Neoplasms - pathology Patients Prognosis Prognostic model Regression analysis Relapse Reproductive system Risk Risk Assessment Risk groups RiskScore Sensitivity analysis Single cell atlas Vinorelbine Women’s Health |
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| Title | Development of a relapse-related RiskScore model to predict the drug sensitivity and prognosis for patients with ovarian cancer |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40821986 https://www.proquest.com/docview/3239092916 https://www.proquest.com/docview/3239209893 https://www.proquest.com/docview/3240482433 https://pubmed.ncbi.nlm.nih.gov/PMC12352420 https://doaj.org/article/48d1a1e794ac475696fa21796ba3d940 |
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