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...
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| Veröffentlicht in: | PloS one 2023-12, Vol.18 (12), p.e0294243-e0294243 |
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| creator | Wang, Jihan Wang, Hanping Xu, Jing Song, Qiying Zhou, Baozhen Shangguan, Jingbo Xue, Mengju Wang, Yangyang |
| 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. |
| doi_str_mv | 10.1371/journal.pone.0294243 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0294243</identifier><identifier>PMID: 38060494</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Biomarkers ; Care and treatment ; Classification ; Cytokeratin ; Datasets ; Decision making ; Diagnosis ; Dipeptidyl-peptidase IV ; Evaluation ; Feature selection ; Head & neck cancer ; Lung cancer ; Lung diseases ; Mathematical optimization ; Medical prognosis ; Medical research ; Methods ; Optimization techniques ; Phenotypes ; Protein expression ; Proteins ; Proteomics ; Squamous cell carcinoma ; Statistical models ; Swarm intelligence ; Tumors ; Velocity</subject><ispartof>PloS one, 2023-12, Vol.18 (12), p.e0294243-e0294243</ispartof><rights>Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c586t-35efbc468a63c2f5ea19a688d35ef651aa450032df68c52d14fc8994571d87d3</cites><orcidid>0000-0002-4753-7193</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294243&type=printable$$EPDF$$P50$$Gplos$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0294243$$EHTML$$P50$$Gplos$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,2915,23845,27901,27902,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38060494$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Wu, Bingli</contributor><creatorcontrib>Wang, Jihan</creatorcontrib><creatorcontrib>Wang, Hanping</creatorcontrib><creatorcontrib>Xu, Jing</creatorcontrib><creatorcontrib>Song, Qiying</creatorcontrib><creatorcontrib>Zhou, Baozhen</creatorcontrib><creatorcontrib>Shangguan, Jingbo</creatorcontrib><creatorcontrib>Xue, Mengju</creatorcontrib><creatorcontrib>Wang, Yangyang</creatorcontrib><title>Identification of protein signatures for lung cancer subtypes based on BPSO method</title><title>PloS one</title><addtitle>PLoS One</addtitle><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.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Biomarkers</subject><subject>Care and treatment</subject><subject>Classification</subject><subject>Cytokeratin</subject><subject>Datasets</subject><subject>Decision making</subject><subject>Diagnosis</subject><subject>Dipeptidyl-peptidase IV</subject><subject>Evaluation</subject><subject>Feature selection</subject><subject>Head & neck cancer</subject><subject>Lung cancer</subject><subject>Lung diseases</subject><subject>Mathematical optimization</subject><subject>Medical prognosis</subject><subject>Medical research</subject><subject>Methods</subject><subject>Optimization techniques</subject><subject>Phenotypes</subject><subject>Protein 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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>38060494</pmid><doi>10.1371/journal.pone.0294243</doi><tpages>e0294243</tpages><orcidid>https://orcid.org/0000-0002-4753-7193</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analysis Biomarkers Care and treatment Classification Cytokeratin Datasets Decision making Diagnosis Dipeptidyl-peptidase IV Evaluation Feature selection Head & neck cancer Lung cancer Lung diseases Mathematical optimization Medical prognosis Medical research Methods Optimization techniques Phenotypes Protein expression Proteins Proteomics Squamous cell carcinoma Statistical models Swarm intelligence Tumors Velocity |
| title | Identification of protein signatures for lung cancer subtypes based on BPSO method |
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