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Qiu W, Wu X, Shi H, Liu B, Li L, Wu W, Lin J. ASF1B: A Possible Prognostic Marker, Therapeutic Target, and Predictor of Immunotherapy in Male Thyroid Carcinoma. Front Oncol 2022; 12:678025. [PMID: 35174076 PMCID: PMC8841667 DOI: 10.3389/fonc.2022.678025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 01/06/2022] [Indexed: 12/19/2022] Open
Abstract
Background Thyroid carcinoma (TC) is the most common malignant endocrine tumor worldwide. Several studies have documented that male patients with TC have a higher rate of metastasis and disease recurrence than female patients. However, the mechanism underlying this observation is not completely clear. The goal of our research was to investigate the potential key candidate genes and pathways related to TC progression in male patients at the molecular level. Methods A total of 320 samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Hub genes were screened out using weighted gene coexpression network analysis (WGCNA) and a protein–protein interaction (PPI) network analysis. Survival analysis was used to identify hub genes associated with disease-free survival (DFS) rates. Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression (ESTIMATE) data were used to assess the relationship between hub genes and immune cell infiltration. The molecular mechanism and biological functions of hub genes were explored using RT-qPCR, Western blot, Cell Counting Kit-8 Assay, flow cytometry, Transwell assays, and scratch assays. Results Forty-seven hub genes were identified, and the survival analysis demonstrated that anti-silencing function 1B (ASF1B) was the sole independent risk factor for poor DFS in male TC patients. Possible associations between the results from the ESTIMATE analysis showed that the ASF1B expression level was related to the ESTIMATE score, immune score, and T-cell regulatory (Treg) infiltration level. Through in vitro cell function experiments, we verified that knockdown of ASF1B inhibited KTC-1 cell proliferation, promoted cell apoptosis, and blocked cell cycle. The silencing of ASF1B reduced protein kinase B (AKT), phospho-AKT (p-AKT), and forkhead box p3 (FOXP3) in KTC-1 cells. Moreover, FOXP3 overexpression markedly restored the cell migration, invasion, and proliferation abilities repressed by ASF1B knockdown. Conclusions Our results indicate that ASF1B can be considered a prognostic marker, therapeutic target, and predictor of immunotherapy response in male thyroid cancer patients. However, further in-depth studies are required to validate this finding.
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Affiliation(s)
| | - Xinquan Wu
- *Correspondence: Xinquan Wu, ; orcid.org/0000-0003-0779-8708
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Zhong L, Meng Q, Chen Y, Du L, Wu P. A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data. BMC Bioinformatics 2021; 22:475. [PMID: 34600466 PMCID: PMC8487515 DOI: 10.1186/s12859-021-04391-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 09/22/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. RESULTS In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. CONCLUSION The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.
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Affiliation(s)
- Lianxin Zhong
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Qingfang Meng
- School of Information Science and Engineering, University of Jinan, Jinan, China.
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China.
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Lei Du
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Peng Wu
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
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Liu S, Ye T, Li Z, Li J, Jamil AM, Zhou Y, Hua G, Liang A, Deng T, Yang L. Identifying Hub Genes for Heat Tolerance in Water Buffalo ( Bubalus bubalis) Using Transcriptome Data. Front Genet 2019; 10:209. [PMID: 30918514 PMCID: PMC6424900 DOI: 10.3389/fgene.2019.00209] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Accepted: 02/26/2019] [Indexed: 12/25/2022] Open
Abstract
Heat stress has a detrimental effect on the physiological and production performance of buffaloes. Elucidating the underlying mechanisms of heat stress is challenging, therefore identifying candidate genes is urgent and necessary. We evaluated the response of buffaloes (n = 30) to heat stress using the physiological parameters, ELISA indexes, and hematological parameters. We then performed mRNA and microRNA (miRNA) expression profiles analysis between heat tolerant (HT, n = 4) and non-heat tolerant (NHT, n = 4) buffaloes, as well as the specific modules, significant genes, and miRNAs related to the heat tolerance identified using the weighted gene co-expression network analysis (WGCNA). The results indicated that the buffaloes in HT had a significantly lower rectal temperature (RT) and respiratory rate (RR) and displayed a higher plasma heat shock protein (HSP70 and HSP90) and cortisol (COR) levels than those of NHT buffaloes. Differentially expressed analysis revealed a total of 753 differentially expressed genes (DEGs) and 16 differentially expressed miRNAs (DEmiRNAs) were identified between HT and NHT. Using the WGCNA analysis, these DEGs assigned into 5 modules, 4 of which were significantly correlation with the heat stress indexes. Interestingly, 158 DEGs associated with heat tolerance in the turquoise module were identified, 35 of which were found within the protein-protein interaction network. Several hub genes (IL18RAP, IL6R, CCR1, PPBP, IL1B, and IL1R1) were identified that significantly enriched in the Cytokine-cytokine receptor interaction. The findings may help further elucidate the underlying mechanisms of heat tolerance in buffaloes.
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Affiliation(s)
- Shenhe Liu
- Ministry of Education, Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Tingzhu Ye
- Ministry of Education, Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Zipeng Li
- Ministry of Education, Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Jun Li
- Department of Immunology, Zunyi Medical College, Zunyi, China
| | - Ahmad Muhammad Jamil
- Ministry of Education, Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yang Zhou
- Ministry of Education, Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Guohua Hua
- Ministry of Education, Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Aixin Liang
- Ministry of Education, Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Tingxian Deng
- Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Liguo Yang
- Ministry of Education, Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
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