Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer.
CRC cancer is one of the deadliest diseases in Western countries. In order to develop prognostic biomarkers for CRC (colorectal cancer) aggressiveness, we analyzed retrospectively 267 CRC patients via a novel, multidimensional biomarker platform. Using nanofluidic technology for qPCR analysis and qu...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4079703?pdf=render |
| _version_ | 1818196842989486080 |
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| author | Marisa Mariani Shiquan He Mark McHugh Mirko Andreoli Deep Pandya Steven Sieber Zheyang Wu Paul Fiedler Shohreh Shahabi Cristiano Ferlini |
| author_facet | Marisa Mariani Shiquan He Mark McHugh Mirko Andreoli Deep Pandya Steven Sieber Zheyang Wu Paul Fiedler Shohreh Shahabi Cristiano Ferlini |
| author_sort | Marisa Mariani |
| collection | DOAJ |
| description | CRC cancer is one of the deadliest diseases in Western countries. In order to develop prognostic biomarkers for CRC (colorectal cancer) aggressiveness, we analyzed retrospectively 267 CRC patients via a novel, multidimensional biomarker platform. Using nanofluidic technology for qPCR analysis and quantitative fluorescent immunohistochemistry for protein analysis, we assessed 33 microRNAs, 124 mRNAs and 9 protein antigens. Analysis was conducted in each single dimension (microRNA, gene or protein) using both the multivariate Cox model and Kaplan-Meier method. Thereafter, we simplified the censored survival data into binary response data (aggressive vs. non aggressive cancer). Subsequently, we integrated the data into a diagnostic score using sliced inverse regression for sufficient dimension reduction. Accuracy was assessed using area under the receiver operating characteristic curve (AUC). Single dimension analysis led to the discovery of individual factors that were significant predictors of outcome. These included seven specific microRNAs, four genes, and one protein. When these factors were quantified individually as predictors of aggressive disease, the highest demonstrable area under the curve (AUC) was 0.68. By contrast, when all results from single dimensions were combined into integrated biomarkers, AUCs were dramatically increased with values approaching and even exceeding 0.9. Single dimension analysis generates statistically significant predictors, but their predictive strengths are suboptimal for clinical utility. A novel, multidimensional integrated approach overcomes these deficiencies. Newly derived integrated biomarkers have the potential to meaningfully guide the selection of therapeutic strategies for individual patients while elucidating molecular mechanisms driving disease progression. |
| first_indexed | 2024-12-12T01:40:31Z |
| format | Article |
| id | doaj.art-f865e35ebaa54640a1fe11e41559304a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T01:40:31Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj.art-f865e35ebaa54640a1fe11e41559304a2022-12-22T00:42:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0197e10106510.1371/journal.pone.0101065Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer.Marisa MarianiShiquan HeMark McHughMirko AndreoliDeep PandyaSteven SieberZheyang WuPaul FiedlerShohreh ShahabiCristiano FerliniCRC cancer is one of the deadliest diseases in Western countries. In order to develop prognostic biomarkers for CRC (colorectal cancer) aggressiveness, we analyzed retrospectively 267 CRC patients via a novel, multidimensional biomarker platform. Using nanofluidic technology for qPCR analysis and quantitative fluorescent immunohistochemistry for protein analysis, we assessed 33 microRNAs, 124 mRNAs and 9 protein antigens. Analysis was conducted in each single dimension (microRNA, gene or protein) using both the multivariate Cox model and Kaplan-Meier method. Thereafter, we simplified the censored survival data into binary response data (aggressive vs. non aggressive cancer). Subsequently, we integrated the data into a diagnostic score using sliced inverse regression for sufficient dimension reduction. Accuracy was assessed using area under the receiver operating characteristic curve (AUC). Single dimension analysis led to the discovery of individual factors that were significant predictors of outcome. These included seven specific microRNAs, four genes, and one protein. When these factors were quantified individually as predictors of aggressive disease, the highest demonstrable area under the curve (AUC) was 0.68. By contrast, when all results from single dimensions were combined into integrated biomarkers, AUCs were dramatically increased with values approaching and even exceeding 0.9. Single dimension analysis generates statistically significant predictors, but their predictive strengths are suboptimal for clinical utility. A novel, multidimensional integrated approach overcomes these deficiencies. Newly derived integrated biomarkers have the potential to meaningfully guide the selection of therapeutic strategies for individual patients while elucidating molecular mechanisms driving disease progression.http://europepmc.org/articles/PMC4079703?pdf=render |
| spellingShingle | Marisa Mariani Shiquan He Mark McHugh Mirko Andreoli Deep Pandya Steven Sieber Zheyang Wu Paul Fiedler Shohreh Shahabi Cristiano Ferlini Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer. PLoS ONE |
| title | Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer. |
| title_full | Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer. |
| title_fullStr | Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer. |
| title_full_unstemmed | Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer. |
| title_short | Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer. |
| title_sort | integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer |
| url | http://europepmc.org/articles/PMC4079703?pdf=render |
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