Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data
The integration of transcriptome and proteome analysis can lead to the discovery of a myriad of biological insights into ovarian cancer. Proteome, clinical, and transcriptome data about ovarian cancer were downloaded from TCGA’s database. A LASSO–Cox regression was used to uncover prognostic-related...
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| Format: | Article |
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MDPI AG
2023-04-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/13/4/685 |
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| author | Jinghang Jiang Zhongyuan Chen Honghong Wang Yifu Wang Jie Zheng Yi Guo Yonghua Jiang Zengnan Mo |
| author_facet | Jinghang Jiang Zhongyuan Chen Honghong Wang Yifu Wang Jie Zheng Yi Guo Yonghua Jiang Zengnan Mo |
| author_sort | Jinghang Jiang |
| collection | DOAJ |
| description | The integration of transcriptome and proteome analysis can lead to the discovery of a myriad of biological insights into ovarian cancer. Proteome, clinical, and transcriptome data about ovarian cancer were downloaded from TCGA’s database. A LASSO–Cox regression was used to uncover prognostic-related proteins and develop a new protein prognostic signature for patients with ovarian cancer to predict their prognosis. Patients were brought together in subgroups using a consensus clustering analysis of prognostic-related proteins. To further investigate the role of proteins and protein-coding genes in ovarian cancer, additional analyses were performed using multiple online databases (HPA, Sangerbox, TIMER, cBioPortal, TISCH, and CancerSEA). The final resulting prognosis factors consisted of seven protective factors (P38MAPK, RAB11, FOXO3A, AR, BETACATENIN, Sox2, and IGFRb) and two risk factors (AKT_pS473 and ERCC5), which can be used to construct a prognosis-related protein model. A significant difference in overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI) curves were found in the training, testing, and whole sets when analyzing the protein-based risk score (<i>p</i> < 0.05). We also illustrated a wide range of functions, immune checkpoints, and tumor-infiltrating immune cells in prognosis-related protein signatures. Additionally, the protein-coding genes were significantly correlated with each other. EMTAB8107 and GSE154600 single-cell data revealed that the genes were highly expressed. Furthermore, the genes were related to tumor functional states (angiogenesis, invasion, and quiescence). We reported and validated a survivability prediction model for ovarian cancer based on prognostic-related protein signatures. A strong correlation was found between the signatures, tumor-infiltrating immune cells, and immune checkpoints. The protein-coding genes were highly expressed in single-cell RNA and bulk RNA sequencing, correlating with both each other and tumor functional states. |
| first_indexed | 2024-03-11T05:11:48Z |
| format | Article |
| id | doaj.art-2b00fd3d44f84ccfa16269ffa0bc8874 |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-11T05:11:48Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-2b00fd3d44f84ccfa16269ffa0bc88742023-11-17T18:29:54ZengMDPI AGBiomolecules2218-273X2023-04-0113468510.3390/biom13040685Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic DataJinghang Jiang0Zhongyuan Chen1Honghong Wang2Yifu Wang3Jie Zheng4Yi Guo5Yonghua Jiang6Zengnan Mo7Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, ChinaCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, ChinaCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, ChinaCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, ChinaCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, ChinaCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, ChinaCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, ChinaCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, ChinaThe integration of transcriptome and proteome analysis can lead to the discovery of a myriad of biological insights into ovarian cancer. Proteome, clinical, and transcriptome data about ovarian cancer were downloaded from TCGA’s database. A LASSO–Cox regression was used to uncover prognostic-related proteins and develop a new protein prognostic signature for patients with ovarian cancer to predict their prognosis. Patients were brought together in subgroups using a consensus clustering analysis of prognostic-related proteins. To further investigate the role of proteins and protein-coding genes in ovarian cancer, additional analyses were performed using multiple online databases (HPA, Sangerbox, TIMER, cBioPortal, TISCH, and CancerSEA). The final resulting prognosis factors consisted of seven protective factors (P38MAPK, RAB11, FOXO3A, AR, BETACATENIN, Sox2, and IGFRb) and two risk factors (AKT_pS473 and ERCC5), which can be used to construct a prognosis-related protein model. A significant difference in overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI) curves were found in the training, testing, and whole sets when analyzing the protein-based risk score (<i>p</i> < 0.05). We also illustrated a wide range of functions, immune checkpoints, and tumor-infiltrating immune cells in prognosis-related protein signatures. Additionally, the protein-coding genes were significantly correlated with each other. EMTAB8107 and GSE154600 single-cell data revealed that the genes were highly expressed. Furthermore, the genes were related to tumor functional states (angiogenesis, invasion, and quiescence). We reported and validated a survivability prediction model for ovarian cancer based on prognostic-related protein signatures. A strong correlation was found between the signatures, tumor-infiltrating immune cells, and immune checkpoints. The protein-coding genes were highly expressed in single-cell RNA and bulk RNA sequencing, correlating with both each other and tumor functional states.https://www.mdpi.com/2218-273X/13/4/685ovarian cancerproteometranscriptometumor-infiltrating immune cellssingle cell |
| spellingShingle | Jinghang Jiang Zhongyuan Chen Honghong Wang Yifu Wang Jie Zheng Yi Guo Yonghua Jiang Zengnan Mo Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data Biomolecules ovarian cancer proteome transcriptome tumor-infiltrating immune cells single cell |
| title | Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data |
| title_full | Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data |
| title_fullStr | Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data |
| title_full_unstemmed | Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data |
| title_short | Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data |
| title_sort | screening and identification of a prognostic model of ovarian cancer by combination of transcriptomic and proteomic data |
| topic | ovarian cancer proteome transcriptome tumor-infiltrating immune cells single cell |
| url | https://www.mdpi.com/2218-273X/13/4/685 |
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