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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] [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] [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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Su D, Xiong Y, Wei H, Wang S, Ke J, Liang P, Zhang H, Yu Y, Zuo Y, Yang L. Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance. Heliyon 2023; 9:e16147. [PMID: 37215759 PMCID: PMC10199194 DOI: 10.1016/j.heliyon.2023.e16147] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/04/2023] [Accepted: 05/07/2023] [Indexed: 05/24/2023] Open
Abstract
Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.
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Affiliation(s)
- Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jiawei Ke
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Pengfei Liang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Haoxin Zhang
- Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Yao Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
- Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
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Xiong Y, Wang S, Wei H, Li H, Lv Y, Chi M, Su D, Lu Q, Yu Y, Zuo Y, Yang L. Deep learning-based transcription factor activity for stratification of breast cancer patients. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2022; 1865:194838. [PMID: 35690313 DOI: 10.1016/j.bbagrm.2022.194838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/19/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients.
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Affiliation(s)
- Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hanshuang Li
- The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yingli Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Meng Chi
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Qianzi Lu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yao Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yongchun Zuo
- The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mingolia Wesure Date Technology Co., Ltd., Hohhot 010010, China.
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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Xiao Y, Ma J, Guo C, Liu D, Pan J, Huang X. Cyclin B2 overexpression promotes tumour growth by regulating jagged 1 in hepatocellular carcinoma. Aging (Albany NY) 2022; 14:2855-2867. [PMID: 35349480 PMCID: PMC9004552 DOI: 10.18632/aging.203979] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/27/2022] [Indexed: 12/24/2022]
Abstract
Background: Our previous study showed that Cyclin B2 (CCNB2) is closely related to the occurrence and progression of hepatocellular carcinoma (HCC). Aim of the study: This study aimed to clarify the effect of CCNB2 gene silencing on tumorigenesis in nude mice and to detect the potential mechanism. Methods: The effect of CCNB2 on HCC was tested in vivo. The downstream target genes of CCNB2 were predicted by proteomics and confirmed by western blot assay. The regulatory functions of CCNB2 in the proliferation and migration of HCC cells were determined through functional recovery experiments. The expression of the downstream target genes of CCNB2 was detected by immunohistochemistry. Results: Knockdown of CCNB2 decreased tumour formation rate and tumour volume and weight and inhibited tumour proliferation. A total of 130 differentially expressed proteins were detected by proteomics, and Jagged 1 (JAG1) was predicted as the potential downstream target of CCNB2. Western blot assay revealed that CCNB2 and JAG1 expression was significantly correlated in HCC cells. The results of functional recovery experiments suggested that CCNB2 knockdown weakened the proliferation and migration ability of HCC cells, while JAG1 overexpression restored this ability of HCC cells that was weakened by CCNB2 knockdown. Immunohistochemistry showed that JAG1 expression was higher in HCC tissues than in paracancerous tissues and was related to tumour size and number and tumour thrombus formation. Conclusions: The proliferation of HCC cells in vivo was inhibited by CCNB2 knockdown. CCNB2 may accelerate the proliferation and metastasis of HCC cells by increasing JAG1 expression.
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Affiliation(s)
- Yening Xiao
- Department of Gastroenterology, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570028, China
| | - Jiamei Ma
- Department of Gastroenterology, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570028, China
| | - Chunliu Guo
- Department of Gastroenterology, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570028, China
| | - Danni Liu
- Department of Gastroenterology, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570028, China
| | - Jing Pan
- Department of Gastroenterology, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570028, China
| | - Xiaoxi Huang
- Department of Gastroenterology, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570028, China
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