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Liu L, Huang Y, Zheng Y, Liao Y, Ma S, Wang Q. ScnML models single-cell transcriptome to predict spinal cord neuronal cell status. Front Genet 2024; 15:1413484. [PMID: 38894722 PMCID: PMC11183327 DOI: 10.3389/fgene.2024.1413484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Injuries to the spinal cord nervous system often result in permanent loss of sensory, motor, and autonomic functions. Accurately identifying the cellular state of spinal cord nerves is extremely important and could facilitate the development of new therapeutic and rehabilitative strategies. Existing experimental techniques for identifying the development of spinal cord nerves are both labor-intensive and costly. In this study, we developed a machine learning predictor, ScnML, for predicting subpopulations of spinal cord nerve cells as well as identifying marker genes. The prediction performance of ScnML was evaluated on the training dataset with an accuracy of 94.33%. Based on XGBoost, ScnML on the test dataset achieved 94.08% 94.24%, 94.26%, and 94.24% accuracies with precision, recall, and F1-measure scores, respectively. Importantly, ScnML identified new significant genes through model interpretation and biological landscape analysis. ScnML can be a powerful tool for predicting the status of spinal cord neuronal cells, revealing potential specific biomarkers quickly and efficiently, and providing crucial insights for precision medicine and rehabilitation recovery.
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Affiliation(s)
- Lijia Liu
- School of Recreation and Community Sport, Capital University of Physical Education and Sports, Beijing, China
| | - Yuxuan Huang
- Department of Neuroscience in the Behavioral Sciences, Duke University and Duke Kunshan University, Suzhou, Jiangsu, China
| | - Yuan Zheng
- Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Luqiao, China
| | - Yihan Liao
- Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Luqiao, China
| | - Siyuan Ma
- School of Recreation and Community Sport, Capital University of Physical Education and Sports, Beijing, China
| | - Qian Wang
- Department of Neurology, The First Hospital of Tsinghua University, Beijing, China
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Jin W, Jia J, Si Y, Liu J, Li H, Zhu H, Wu Z, Zuo Y, Yu L. Identification of Key lncRNAs Associated with Immune Infiltration and Prognosis in Gastric Cancer. Biochem Genet 2024:10.1007/s10528-024-10801-w. [PMID: 38658494 DOI: 10.1007/s10528-024-10801-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
Long non-coding RNAs (lncRNAs), as promising novel biomarkers for cancer treatment and prognosis, can function as tumor suppressors and oncogenes in the occurrence and development of many types of cancer, including gastric cancer (GC). However, little is known about the complex regulatory system of lncRNAs in GC. In this study, we systematically analyzed lncRNA and miRNA transcriptomic profiles of GC based on bioinformatics methods and experimental validation. An lncRNA-miRNA interaction network related to GC was constructed, and the nine crucial lncRNAs were identified. These 9 lncRNAs were found to be associated with the prognosis of GC patients by Cox proportional hazards regression analysis. Among them, the expression of lncRNA SNHG14 can affect the survival of GC patients as a potential prognostic marker. Moreover, it was shown that SNHG14 was involved in immune-related pathways and significantly correlated with immune cell infiltration in GC. Meanwhile, we found that SNHG14 affected immune function in many cancers, such as breast cancer and esophageal carcinoma. Such information revealed that SNHG14 may serve as a potential target for cancer immunotherapy. As well, our study could provide practical and theoretical guiding significance for clinical application of non-coding RNAs.
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Affiliation(s)
- Wen Jin
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China
| | - Jianchao Jia
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China
| | - Yangming Si
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Jianli Liu
- School of Water Resource and Environment Engineering, China University of Geosciences, Beijing, 100083, China
| | - Hanshuang Li
- College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Hao Zhu
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China
| | - Zhouying Wu
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China
| | - Yongchun Zuo
- College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
- Digital College, Inner Mongolia Intelligent Union Big Data Academy, Hohhot, 010010, China.
- Inner Mongolia International Mongolian Hospital, Hohhot, 010065, China.
| | - Lan Yu
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China.
- Department of Endocrine and Metabolic Diseases, Inner Mongolia People's Hospital, Hohhot, 010010, China.
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Wang H, Lin YN, Yan S, Hong JP, Tan JR, Chen YQ, Cao YS, Fang W. NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning. PLANT METHODS 2023; 19:119. [PMID: 37925413 PMCID: PMC10625708 DOI: 10.1186/s13007-023-01092-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/15/2023] [Indexed: 11/06/2023]
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity. RESULTS To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses. CONCLUSION Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp .
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Affiliation(s)
- Hao Wang
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yu-Nan Lin
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Shen Yan
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jing-Peng Hong
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jia-Rui Tan
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yan-Qing Chen
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Yong-Sheng Cao
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Wei Fang
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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Wang H, Zhang Z, Li H, Li J, Li H, Liu M, Liang P, Xi Q, Xing Y, Yang L, Zuo Y. A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery. Cell Biosci 2023; 13:41. [PMID: 36849879 PMCID: PMC9972636 DOI: 10.1186/s13578-023-00991-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 02/15/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. RESULTS Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28-32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server ( http://bioinfor.imu.edu.cn/placenta ). CONCLUSION Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.
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Affiliation(s)
- Hao Wang
- grid.411643.50000 0004 1761 0411The 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
| | - Zhaoyue Zhang
- grid.54549.390000 0004 0369 4060School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054 China
| | - Haicheng Li
- grid.411643.50000 0004 1761 0411The 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
| | - Jinzhao Li
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China
| | - Hanshuang Li
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China
| | - Mingzhu Liu
- grid.411643.50000 0004 1761 0411The 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
| | - Pengfei Liang
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China
| | - Qilemuge Xi
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China
| | - Yongqiang Xing
- School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
| | - Lei Yang
- 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.
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Wang S, Sun ST, Zhang XY, Ding HR, Yuan Y, He JJ, Wang MS, Yang B, Li YB. The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives. Int J Mol Sci 2023; 24:ijms24032943. [PMID: 36769267 PMCID: PMC9918030 DOI: 10.3390/ijms24032943] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/01/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
As an emerging sequencing technology, single-cell RNA sequencing (scRNA-Seq) has become a powerful tool for describing cell subpopulation classification and cell heterogeneity by achieving high-throughput and multidimensional analysis of individual cells and circumventing the shortcomings of traditional sequencing for detecting the average transcript level of cell populations. It has been applied to life science and medicine research fields such as tracking dynamic cell differentiation, revealing sensitive effector cells, and key molecular events of diseases. This review focuses on the recent technological innovations in scRNA-Seq, highlighting the latest research results with scRNA-Seq as the core technology in frontier research areas such as embryology, histology, oncology, and immunology. In addition, this review outlines the prospects for its innovative application in traditional Chinese medicine (TCM) research and discusses the key issues currently being addressed by scRNA-Seq and its great potential for exploring disease diagnostic targets and uncovering drug therapeutic targets in combination with multiomics technologies.
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Affiliation(s)
| | | | | | | | | | | | | | - Bin Yang
- Correspondence: (B.Y.); (Y.-B.L.)
| | - Yu-Bo Li
- Correspondence: (B.Y.); (Y.-B.L.)
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The Cumulative Formation of R-loop Interacts with Histone Modifications to Shape Cell Reprogramming. Int J Mol Sci 2022; 23:ijms23031567. [PMID: 35163490 PMCID: PMC8835745 DOI: 10.3390/ijms23031567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/26/2022] [Accepted: 01/26/2022] [Indexed: 12/27/2022] Open
Abstract
R-loop, a three-stranded RNA/DNA structure, plays important roles in modulating genome stability and gene expression, but the molecular mechanism of R-loops in cell reprogramming remains elusive. Here, we comprehensively profiled the genome-wide landscape of R-loops during cell reprogramming. The results showed that the R-loop formation on most different types of repetitive elements is stage-specific in cell reprogramming. We unveiled that the cumulative deposition of an R-loop subset is positively correlated with gene expression during reprogramming. More importantly, the dynamic turnover of this R-loop subset is accompanied by the activation of the pluripotent transcriptional regulatory network (TRN). Moreover, the large accumulation of the active histone marker H3K4me3 and the reduction in H3K27me3 were also observed in these R-loop regions. Finally, we characterized the dynamic network of R-loops that facilitates cell fate transitions in reprogramming. Together, our study provides a new clue for deciphering the interplay mechanism between R-loops and HMs to control cell reprogramming.
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