Identification of protein signatures for lung cancer subtypes based on BPSO method.
The objective of this study was to identify protein biomarkers that can distinguish between LUAD and LUSC, critical for personalized treatment plans. The proteomic profiling data of LUAD and LUSC samples from TCPA database, along with phenotype and survival information from TCGA database were downlo...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294243&type=printable |
| _version_ | 1797377802875437056 |
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| author | Jihan Wang Hanping Wang Jing Xu Qiying Song Baozhen Zhou Jingbo Shangguan Mengju Xue Yangyang Wang |
| author_facet | Jihan Wang Hanping Wang Jing Xu Qiying Song Baozhen Zhou Jingbo Shangguan Mengju Xue Yangyang Wang |
| author_sort | Jihan Wang |
| collection | DOAJ |
| description | The objective of this study was to identify protein biomarkers that can distinguish between LUAD and LUSC, critical for personalized treatment plans. The proteomic profiling data of LUAD and LUSC samples from TCPA database, along with phenotype and survival information from TCGA database were downloaded and preprocessed for analysis. We used BPSO feature selection method and identified 10 candidate protein biomarkers that have better classifying performance, as analyzed by t-SNE and PCA algorithms. To explore the causalities among these proteins and their associations with tumor subtypes, we conducted the PCStable algorithm to construct a regulatory network. Results indicated that 4 proteins, MIG6, CD26, NF2, and INPP4B, were directly linked to the lung cancer subtypes and may be useful in guiding therapeutic decision-making. Besides, spearman correlation, Cox proportional hazard model and Kaplan-Meier curve was employed to validate the biological significance of the candidate proteins. In summary, our study highlights the importance of protein biomarkers in the classification of lung cancer subtypes and the potential of computational methods for identifying key biomarkers and understanding their underlying biological mechanisms. |
| first_indexed | 2024-03-08T19:57:40Z |
| format | Article |
| id | doaj.art-f0f00f86005145c39a70fc988dc1eae1 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-08T19:57:40Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f0f00f86005145c39a70fc988dc1eae12023-12-24T05:33:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-011812e029424310.1371/journal.pone.0294243Identification of protein signatures for lung cancer subtypes based on BPSO method.Jihan WangHanping WangJing XuQiying SongBaozhen ZhouJingbo ShangguanMengju XueYangyang WangThe objective of this study was to identify protein biomarkers that can distinguish between LUAD and LUSC, critical for personalized treatment plans. The proteomic profiling data of LUAD and LUSC samples from TCPA database, along with phenotype and survival information from TCGA database were downloaded and preprocessed for analysis. We used BPSO feature selection method and identified 10 candidate protein biomarkers that have better classifying performance, as analyzed by t-SNE and PCA algorithms. To explore the causalities among these proteins and their associations with tumor subtypes, we conducted the PCStable algorithm to construct a regulatory network. Results indicated that 4 proteins, MIG6, CD26, NF2, and INPP4B, were directly linked to the lung cancer subtypes and may be useful in guiding therapeutic decision-making. Besides, spearman correlation, Cox proportional hazard model and Kaplan-Meier curve was employed to validate the biological significance of the candidate proteins. In summary, our study highlights the importance of protein biomarkers in the classification of lung cancer subtypes and the potential of computational methods for identifying key biomarkers and understanding their underlying biological mechanisms.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294243&type=printable |
| spellingShingle | Jihan Wang Hanping Wang Jing Xu Qiying Song Baozhen Zhou Jingbo Shangguan Mengju Xue Yangyang Wang Identification of protein signatures for lung cancer subtypes based on BPSO method. PLoS ONE |
| title | Identification of protein signatures for lung cancer subtypes based on BPSO method. |
| title_full | Identification of protein signatures for lung cancer subtypes based on BPSO method. |
| title_fullStr | Identification of protein signatures for lung cancer subtypes based on BPSO method. |
| title_full_unstemmed | Identification of protein signatures for lung cancer subtypes based on BPSO method. |
| title_short | Identification of protein signatures for lung cancer subtypes based on BPSO method. |
| title_sort | identification of protein signatures for lung cancer subtypes based on bpso method |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294243&type=printable |
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