Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning
Abstract Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented pro...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-11-01
|
| Series: | BioData Mining |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13040-022-00312-y |
| _version_ | 1811329128640544768 |
|---|---|
| author | Davide Chicco Abbas Alameer Sara Rahmati Giuseppe Jurman |
| author_facet | Davide Chicco Abbas Alameer Sara Rahmati Giuseppe Jurman |
| author_sort | Davide Chicco |
| collection | DOAJ |
| description | Abstract Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them. |
| first_indexed | 2024-04-13T15:38:21Z |
| format | Article |
| id | doaj.art-1311db58c66748e99f8d48ec5518a469 |
| institution | Directory Open Access Journal |
| issn | 1756-0381 |
| language | English |
| last_indexed | 2024-04-13T15:38:21Z |
| publishDate | 2022-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj.art-1311db58c66748e99f8d48ec5518a4692022-12-22T02:41:13ZengBMCBioData Mining1756-03812022-11-0115112310.1186/s13040-022-00312-yTowards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learningDavide Chicco0Abbas Alameer1Sara Rahmati2Giuseppe Jurman3Institute of Health Policy Management and Evaluation, University of TorontoDepartment of Biological Sciences, Kuwait UniversityKrembil Research InstituteFondazione Bruno KesslerAbstract Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.https://doi.org/10.1186/s13040-022-00312-yGenetic signaturePrognostic signatureMicroarrayGene expressionCancerPan-cancer |
| spellingShingle | Davide Chicco Abbas Alameer Sara Rahmati Giuseppe Jurman Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning BioData Mining Genetic signature Prognostic signature Microarray Gene expression Cancer Pan-cancer |
| title | Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning |
| title_full | Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning |
| title_fullStr | Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning |
| title_full_unstemmed | Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning |
| title_short | Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning |
| title_sort | towards a potential pan cancer prognostic signature for gene expression based on probesets and ensemble machine learning |
| topic | Genetic signature Prognostic signature Microarray Gene expression Cancer Pan-cancer |
| url | https://doi.org/10.1186/s13040-022-00312-y |
| work_keys_str_mv | AT davidechicco towardsapotentialpancancerprognosticsignatureforgeneexpressionbasedonprobesetsandensemblemachinelearning AT abbasalameer towardsapotentialpancancerprognosticsignatureforgeneexpressionbasedonprobesetsandensemblemachinelearning AT sararahmati towardsapotentialpancancerprognosticsignatureforgeneexpressionbasedonprobesetsandensemblemachinelearning AT giuseppejurman towardsapotentialpancancerprognosticsignatureforgeneexpressionbasedonprobesetsandensemblemachinelearning |