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Xin R, Cheng Q, Chi X, Feng X, Zhang H, Wang Y, Duan M, Xie T, Song X, Yu Q, Fan Y, Huang L, Zhou F. Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer. Genes (Basel) 2023; 14:2169. [PMID: 38136991 PMCID: PMC10742656 DOI: 10.3390/genes14122169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/19/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.
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Affiliation(s)
- Ruihao Xin
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Qian Cheng
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Xiaohang Chi
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin 132000, China;
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130012, China;
| | - Hang Zhang
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Yueying Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
| | - Meiyu Duan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
| | - Tunyang Xie
- Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK;
| | - Xiaonan Song
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130012, China;
| | - Qiong Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130012, China;
| | - Yusi Fan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130012, China;
| | - Lan Huang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, China
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Lv X, Li X, Chen S, Zhang G, Li K, Wang Y, Duan M, Zhou F, Liu H. Transcriptional Dysregulations of Seven Non-Differentially Expressed Genes as Biomarkers of Metastatic Colon Cancer. Genes (Basel) 2023; 14:1138. [PMID: 37372321 DOI: 10.3390/genes14061138] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 05/18/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
Background: Colon cancer (CC) is common, and the mortality rate greatly increases as the disease progresses to the metastatic stage. Early detection of metastatic colon cancer (mCC) is crucial for reducing the mortality rate. Most previous studies have focused on the top-ranked differentially expressed transcriptomic biomarkers between mCC and primary CC while ignoring non-differentially expressed genes. Results: This study proposed that the complicated inter-feature correlations could be quantitatively formulated as a complementary transcriptomic view. We used a regression model to formulate the correlation between the expression levels of a messenger RNA (mRNA) and its regulatory transcription factors (TFs). The change between the predicted and real expression levels of a query mRNA was defined as the mqTrans value in the given sample, reflecting transcription regulatory changes compared with the model-training samples. A dark biomarker in mCC is defined as an mRNA gene that is non-differentially expressed in mCC but demonstrates mqTrans values significantly associated with mCC. This study detected seven dark biomarkers using 805 samples from three independent datasets. Evidence from the literature supports the role of some of these dark biomarkers. Conclusions: This study presented a complementary high-dimensional analysis procedure for transcriptome-based biomarker investigations with a case study on mCC.
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Affiliation(s)
- Xiaoying Lv
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang 550025, China
| | - Xue Li
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang 550025, China
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China
| | - Shihong Chen
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang 550025, China
| | - Gongyou Zhang
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang 550025, China
| | - Kewei Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Yueying Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Meiyu Duan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang 550025, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
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Xin R, Feng X, Zhang H, Wang Y, Duan M, Xie T, Dong L, Yu Q, Huang L, Zhou F. Seven non-differentially expressed ' dark biomarkers' show transcriptional dysregulation in chronic lymphocytic leukemia. Per Med 2023. [PMID: 36705049 DOI: 10.2217/pme-2022-0123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Aim: Transcriptional regulation is actively involved in the onset and progression of various diseases. This study used the feature-engineering approach model-based quantitative transcription regulation to quantitatively measure the correlation between mRNA and transcription factors in a reference dataset of chronic lymphocytic leukemia (CLL) transcriptomes. Methods: A comprehensive investigation of transcriptional regulation changes in CLL was conducted using 973 samples in six independent datasets. Results & conclusion: Seven mRNAs were detected to have significantly differential model-based quantitative transcription regulation values but no differential expression between CLL patients and controls. We called these genes 'dark biomarkers' because their original expression levels did not show differential changes in the CLL patients. The overlapping lncRNAs might have contributed their transcripts to the expression miscalculations of these dark biomarkers.
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Affiliation(s)
- Ruihao Xin
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China.,College of Information & Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132000, China
| | - Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin,132000, China.,Department of Epidemiology & Biostatistics, School of Public Health, Jilin University, Changchun, 130012, China
| | - Hang Zhang
- College of Information & Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132000, China
| | - Yueying Wang
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Meiyu Duan
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Tunyang Xie
- Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, UK
| | - Lin Dong
- Department of Epidemiology & Biostatistics, School of Public Health, Jilin University, Changchun, 130012, China
| | - Qiong Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Jilin University, Changchun, 130012, China
| | - Lan Huang
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Fengfeng Zhou
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
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