MicroRNAs (miRNAs) are single-stranded, non-coding RNA molecules that regulate gene expression via imperfect binding of the miRNA to specific sites in the 3' untranslated region of the mRNAs. Because prediction of miRNA targets is an essential step for understanding the functional roles of miRNAs, many computational approaches have been developed to identify miRNA targets. However, identifying targets remains challenging due to the inherent limitation of current prediction approaches based on imperfect complementarity between miRNA and its target mRNAs. To overcome these current limitations, we developed a novel correlation-based approach that is sequence independence to predict functional targets of miRNAs by step-wise integration of the expression data of miRNAs, mRNAs, and proteins from NCI-60 cell lines. A correlation matrix between expression of miRNAs and mRNAs was first generated and later integrated with the correlation matrix between expression of mRNAs and signaling proteins. Because these integrated matrices reflect the association of miRNAs and signaling pathways, they were used to predict potential signaling pathways regulated by certain miRNAs. We implemented a web-based tool, miRPP, based on our approach. As validation of our approach, we also demonstrated that miR-500 regulates the MAPK pathway in melanoma and breast cancer cells as predicted by our algorithms. In additional experiments, we further identified PPFIA1 as a direct target of miR-500 that regulates MAP2K1 in the MAPK pathway. In conclusion, we developed a systematic analysis approach that can predict signaling pathways regulated by particular miRNAs. Our approach can be used to investigate the unknown regulatory role of miRNAs in signaling pathways and gene regulatory networks.
","abstract_html":"<p>MicroRNAs (miRNAs) are single-stranded, non-coding RNA molecules that regulate gene expression via imperfect binding of the miRNA to specific sites in the 3' untranslated region of the mRNAs. Because prediction of miRNA targets is an essential step for understanding the functional roles of miRNAs, many computational approaches have been developed to identify miRNA targets. However, identifying targets remains challenging due to the inherent limitation of current prediction approaches based on imperfect complementarity between miRNA and its target mRNAs. To overcome these current limitations, we developed a novel correlation-based approach that is sequence independence to predict functional targets of miRNAs by step-wise integration of the expression data of miRNAs, mRNAs, and proteins from NCI-60 cell lines. A correlation matrix between expression of miRNAs and mRNAs was first generated and later integrated with the correlation matrix between expression of mRNAs and signaling proteins. Because these integrated matrices reflect the association of miRNAs and signaling pathways, they were used to predict potential signaling pathways regulated by certain miRNAs. We implemented a web-based tool, miRPP, based on our approach. As validation of our approach, we also demonstrated that miR-500 regulates the MAPK pathway in melanoma and breast cancer cells as predicted by our algorithms. In additional experiments, we further identified <em>PPFIA1</em> as a direct target of miR-500 that regulates <em>MAP2K1</em> in the MAPK pathway. In conclusion, we developed a systematic analysis approach that can predict signaling pathways regulated by particular miRNAs. Our approach can be used to investigate the unknown regulatory role of miRNAs in signaling pathways and gene regulatory networks.</p>","abstract_has_math":false,"creators":["Kim, sangbae"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation (PhD)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Ju-Seog Lee, Ph.D.","Shiaw-Yih Lin, Ph.D.","Prahlad Ram, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-01T08:00:00Z","date_published":"2014-12-01T08:00:00Z","updated_at":"2026-07-24T05:50:31Z","subjects":["miRNA","signaling pathway","systmems biology","microarray","cancer","Genomics","Medicine and Health Sciences","Systems Biology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/543","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ju-Seog Lee, Ph.D.","Shiaw-Yih Lin, Ph.D.","Prahlad Ram, Ph.D."]},{"key":"dc:creator","label":"Author","values":["Kim, sangbae"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-12-19T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation (PhD)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["miRNA","signaling pathway","systmems biology","microarray","cancer","Genomics","Medicine and Health Sciences","Systems Biology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/543"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["MicroRNAs (miRNAs) are single-stranded, non-coding RNA molecules that regulate gene expression via imperfect binding of the miRNA to specific sites in the 3' untranslated region of the mRNAs. Because prediction of miRNA targets is an essential step for understanding the functional roles of miRNAs, many computational approaches have been developed to identify miRNA targets. However, identifying targets remains challenging due to the inherent limitation of current prediction approaches based on imperfect complementarity between miRNA and its target mRNAs. To overcome these current limitations, we developed a novel correlation-based approach that is sequence independence to predict functional targets of miRNAs by step-wise integration of the expression data of miRNAs, mRNAs, and proteins from NCI-60 cell lines. A correlation matrix between expression of miRNAs and mRNAs was first generated and later integrated with the correlation matrix between expression of mRNAs and signaling proteins. Because these integrated matrices reflect the association of miRNAs and signaling pathways, they were used to predict potential signaling pathways regulated by certain miRNAs. We implemented a web-based tool, miRPP, based on our approach. As validation of our approach, we also demonstrated that miR-500 regulates the MAPK pathway in melanoma and breast cancer cells as predicted by our algorithms. In additional experiments, we further identified PPFIA1 as a direct target of miR-500 that regulates MAP2K1 in the MAPK pathway. In conclusion, we developed a systematic analysis approach that can predict signaling pathways regulated by particular miRNAs. Our approach can be used to investigate the unknown regulatory role of miRNAs in signaling pathways and gene regulatory networks.
"]},{"key":"dc:title","label":"Title","values":["Identification of Cell Signaling Pathway Regulated By Micrornas In Cancer Cells Using A Systems Biological Approach"]}]}],"canonical_facts":{"dc:contributor":["Ju-Seog Lee, Ph.D.","Shiaw-Yih Lin, Ph.D.","Prahlad Ram, Ph.D."],"dc:creator":["Kim, sangbae"],"dc:date.available":["2015-12-19T08:00:00Z"],"dc:description.abstract":["MicroRNAs (miRNAs) are single-stranded, non-coding RNA molecules that regulate gene expression via imperfect binding of the miRNA to specific sites in the 3' untranslated region of the mRNAs. Because prediction of miRNA targets is an essential step for understanding the functional roles of miRNAs, many computational approaches have been developed to identify miRNA targets. However, identifying targets remains challenging due to the inherent limitation of current prediction approaches based on imperfect complementarity between miRNA and its target mRNAs. To overcome these current limitations, we developed a novel correlation-based approach that is sequence independence to predict functional targets of miRNAs by step-wise integration of the expression data of miRNAs, mRNAs, and proteins from NCI-60 cell lines. A correlation matrix between expression of miRNAs and mRNAs was first generated and later integrated with the correlation matrix between expression of mRNAs and signaling proteins. Because these integrated matrices reflect the association of miRNAs and signaling pathways, they were used to predict potential signaling pathways regulated by certain miRNAs. We implemented a web-based tool, miRPP, based on our approach. As validation of our approach, we also demonstrated that miR-500 regulates the MAPK pathway in melanoma and breast cancer cells as predicted by our algorithms. In additional experiments, we further identified PPFIA1 as a direct target of miR-500 that regulates MAP2K1 in the MAPK pathway. In conclusion, we developed a systematic analysis approach that can predict signaling pathways regulated by particular miRNAs. Our approach can be used to investigate the unknown regulatory role of miRNAs in signaling pathways and gene regulatory networks.
"],"dc:identifier":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/543"],"dc:subject":["miRNA","signaling pathway","systmems biology","microarray","cancer","Genomics","Medicine and Health Sciences","Systems Biology"],"dc:title":["Identification of Cell Signaling Pathway Regulated By Micrornas In Cancer Cells Using A Systems Biological Approach"],"thesis:degree_level":["Dissertation (PhD)"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T05:50:31Z"}