Landscape of transcriptional deregulation in lung cancer
Abstract Background Lung cancer is a very heterogeneous disease that can be pathologically classified into different subtypes including small-cell lung carcinoma (SCLC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large-cell carcinoma (LCC). Although much progress has been ma...
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| Format: | Article |
| Language: | English |
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BMC
2018-06-01
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| Series: | BMC Genomics |
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| Online Access: | http://link.springer.com/article/10.1186/s12864-018-4828-1 |
| _version_ | 1831572762295009280 |
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| author | Shu Zhang Mingfa Li Hongbin Ji Zhaoyuan Fang |
| author_facet | Shu Zhang Mingfa Li Hongbin Ji Zhaoyuan Fang |
| author_sort | Shu Zhang |
| collection | DOAJ |
| description | Abstract Background Lung cancer is a very heterogeneous disease that can be pathologically classified into different subtypes including small-cell lung carcinoma (SCLC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large-cell carcinoma (LCC). Although much progress has been made towards the oncogenic mechanism of each subtype, transcriptional circuits mediating the upstream signaling pathways and downstream functional consequences remain to be systematically studied. Results Here we trained a one-class support vector machine (OC-SVM) model to establish a general transcription factor (TF) regulatory network containing 325 TFs and 18724 target genes. We then applied this network to lung cancer subtypes and identified those deregulated TFs and downstream targets. We found that the TP63/SOX2/DMRT3 module was specific to LUSC, corresponding to squamous epithelial differentiation and/or survival. Moreover, the LEF1/MSC module was specifically activated in LUAD and likely to confer epithelial-to-mesenchymal transition, known important for cancer malignant progression and metastasis. The proneural factor, ASCL1, was specifically up-regulated in SCLC which is known to have a neuroendocrine phenotype. Also, ID2 was differentially regulated between SCLC and LUSC, with its up-regulation in SCLC linking to energy supply for fast mitosis and its down-regulation in LUSC linking to the attenuation of immune response. We further described the landscape of TF regulation among the three major subtypes of lung cancer, highlighting their functional commonalities and specificities. Conclusions Our approach uncovered the landscape of transcriptional deregulation in lung cancer, and provided a useful resource of TF regulatory network for future studies. |
| first_indexed | 2024-12-17T13:32:13Z |
| format | Article |
| id | doaj.art-d51871e803ca42a7b242aa32e8a3d1c3 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-17T13:32:13Z |
| publishDate | 2018-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-d51871e803ca42a7b242aa32e8a3d1c32022-12-21T21:46:32ZengBMCBMC Genomics1471-21642018-06-0119111310.1186/s12864-018-4828-1Landscape of transcriptional deregulation in lung cancerShu Zhang0Mingfa Li1Hongbin Ji2Zhaoyuan Fang3School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversitySchool of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityState Key Laboratory of Cell BiologyState Key Laboratory of Cell BiologyAbstract Background Lung cancer is a very heterogeneous disease that can be pathologically classified into different subtypes including small-cell lung carcinoma (SCLC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large-cell carcinoma (LCC). Although much progress has been made towards the oncogenic mechanism of each subtype, transcriptional circuits mediating the upstream signaling pathways and downstream functional consequences remain to be systematically studied. Results Here we trained a one-class support vector machine (OC-SVM) model to establish a general transcription factor (TF) regulatory network containing 325 TFs and 18724 target genes. We then applied this network to lung cancer subtypes and identified those deregulated TFs and downstream targets. We found that the TP63/SOX2/DMRT3 module was specific to LUSC, corresponding to squamous epithelial differentiation and/or survival. Moreover, the LEF1/MSC module was specifically activated in LUAD and likely to confer epithelial-to-mesenchymal transition, known important for cancer malignant progression and metastasis. The proneural factor, ASCL1, was specifically up-regulated in SCLC which is known to have a neuroendocrine phenotype. Also, ID2 was differentially regulated between SCLC and LUSC, with its up-regulation in SCLC linking to energy supply for fast mitosis and its down-regulation in LUSC linking to the attenuation of immune response. We further described the landscape of TF regulation among the three major subtypes of lung cancer, highlighting their functional commonalities and specificities. Conclusions Our approach uncovered the landscape of transcriptional deregulation in lung cancer, and provided a useful resource of TF regulatory network for future studies.http://link.springer.com/article/10.1186/s12864-018-4828-1Lung cancerTranscription factorsSupport-vector machinesTranscription regulatory network |
| spellingShingle | Shu Zhang Mingfa Li Hongbin Ji Zhaoyuan Fang Landscape of transcriptional deregulation in lung cancer BMC Genomics Lung cancer Transcription factors Support-vector machines Transcription regulatory network |
| title | Landscape of transcriptional deregulation in lung cancer |
| title_full | Landscape of transcriptional deregulation in lung cancer |
| title_fullStr | Landscape of transcriptional deregulation in lung cancer |
| title_full_unstemmed | Landscape of transcriptional deregulation in lung cancer |
| title_short | Landscape of transcriptional deregulation in lung cancer |
| title_sort | landscape of transcriptional deregulation in lung cancer |
| topic | Lung cancer Transcription factors Support-vector machines Transcription regulatory network |
| url | http://link.springer.com/article/10.1186/s12864-018-4828-1 |
| work_keys_str_mv | AT shuzhang landscapeoftranscriptionalderegulationinlungcancer AT mingfali landscapeoftranscriptionalderegulationinlungcancer AT hongbinji landscapeoftranscriptionalderegulationinlungcancer AT zhaoyuanfang landscapeoftranscriptionalderegulationinlungcancer |