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Wang J, Dong L, Zheng Z, Zhu Z, Xie B, Xie Y, Li X, Chen B, Li P. Effects of different KRAS mutants and Ki67 expression on diagnosis and prognosis in lung adenocarcinoma. Sci Rep 2024; 14:4085. [PMID: 38374309 PMCID: PMC10876986 DOI: 10.1038/s41598-023-48307-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/24/2023] [Indexed: 02/21/2024] Open
Abstract
Lung adenocarcinoma (LUAD) is a prevalent form of non-small cell lung cancer with a rising incidence in recent years. Understanding the mutation characteristics of LUAD is crucial for effective treatment and prediction of this disease. Among the various mutations observed in LUAD, KRAS mutations are particularly common. Different subtypes of KRAS mutations can activate the Ras signaling pathway to varying degrees, potentially influencing the pathogenesis and prognosis of LUAD. This study aims to investigate the relationship between different KRAS mutation subtypes and the pathogenesis and prognosis of LUAD. A total of 63 clinical samples of LUAD were collected for this study. The samples were analyzed using targeted gene sequencing panels to obtain sequencing data. To complement the dataset, additional clinical and sequencing data were obtained from TCGA and MSK. The analysis revealed significantly higher Ki67 immunohistochemical scores in patients with missense mutations compared to controls. Moreover, the expression level of KRAS was found to be significantly correlated with Ki67 expression. Enrichment analysis indicated that KRAS missense mutations activated the SWEET_LUNG_CANCER_KRAS_DN and CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_2 pathways. Additionally, patients with KRAS missense mutations and high Ki67 IHC scores exhibited significantly higher tumor mutational burden levels compared to other groups, which suggests they are more likely to be responsive to ICIs. Based on the data from MSK and TCGA, it was observed that patients with KRAS missense mutations had shorter survival compared to controls, and Ki67 expression level could more accurately predict patient prognosis. In conclusion, when utilizing KRAS mutations as biomarkers for the treatment and prediction of LUAD, it is important to consider the specific KRAS mutant subtypes and Ki67 expression levels. These findings contribute to a better understanding of LUAD and have implications for personalized therapeutic approaches in the management of this disease.
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Affiliation(s)
- Jun Wang
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Liwen Dong
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Zhaowei Zheng
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Zhen Zhu
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Baisheng Xie
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Yue Xie
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Xiongwei Li
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Bing Chen
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
| | - Pan Li
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
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Pan RH, Zhang X, Chen ZP, Liu YJ. Arachidonate lipoxygenases 5 is a novel prognostic biomarker and correlates with high tumor immune infiltration in low-grade glioma. Front Genet 2023; 14:1027690. [PMID: 36777735 PMCID: PMC9911666 DOI: 10.3389/fgene.2023.1027690] [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: 08/25/2022] [Accepted: 01/16/2023] [Indexed: 01/28/2023] Open
Abstract
Objective: To investigate the prognostic value of arachidonate lipoxygenases 5 (ALOX5) expression and methylation, and explore the immune functions of arachidonate lipoxygenases 5 expression in low-grade glioma (LGG). Materials and Methods: Using efficient bioinformatics approaches, the differential expression of arachidonate lipoxygenases 5 and the association of its expression with clinicopathological characteristics were evaluated. Then, we analyzed the prognostic significance of arachidonate lipoxygenases 5 expression and its methylation level followed by immune cell infiltration analysis. The functional enrichment analysis was conducted to determine the possible regulatory pathways of arachidonate lipoxygenases 5 in low-grade glioma. Finally, the drug sensitivity analysis was performed to explore the correlation between arachidonate lipoxygenases 5 expression and chemotherapeutic drugs. Results: arachidonate lipoxygenases 5 mRNA expression was increased in low-grade glioma and its expression had a notable relation with age and subtype (p < 0.05). The elevated mRNA level of arachidonate lipoxygenases 5 could independently predict the disease-specific survival (DSS), overall survival (OS), and progression-free interval (PFI) (p < 0.05). Besides, arachidonate lipoxygenases 5 expression was negatively correlated with its methylation level and the arachidonate lipoxygenases 5 hypomethylation led to a worse prognosis (p < 0.05). The arachidonate lipoxygenases 5 expression also showed a positive connection with immune cells, while low-grade glioma patients with higher immune cell infiltration had poor survival probability (p < 0.05). Further, arachidonate lipoxygenases 5 might be involved in immune- and inflammation-related pathways. Importantly, arachidonate lipoxygenases 5 expression was negatively related to drug sensitivity. Conclusion: arachidonate lipoxygenases 5 might be a promising biomarker, and it probably occupies a vital role in immune cell infiltration in low-grade glioma.
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Wang Y, Xiang J, Liu C, Tang M, Hou R, Bao M, Tian G, He J, He B. Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization. Front Microbiol 2022; 13:1062281. [PMID: 36545200 PMCID: PMC9762482 DOI: 10.3389/fmicb.2022.1062281] [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: 10/05/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.
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Affiliation(s)
- Yibai Wang
- School of Information Engineering, Changsha Medical University, Changsha, China
| | - Ju Xiang
- School of Information Engineering, Changsha Medical University, Changsha, China,Academician Workstation, Changsha Medical University, Changsha, China,*Correspondence: Ju Xiang,
| | - Cuicui Liu
- School of Information Engineering, Changsha Medical University, Changsha, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Rui Hou
- Geneis (Beijing) Co., Ltd., Beijing, China,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Meihua Bao
- School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jianjun He
- Academician Workstation, Changsha Medical University, Changsha, China,School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China,Jianjun He,
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China,School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China,Binsheng He,
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He B, Wang K, Xiang J, Bing P, Tang M, Tian G, Guo C, Xu M, Yang J. DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network. Brief Bioinform 2022; 23:6712302. [PMID: 36151744 DOI: 10.1093/bib/bbac405] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/01/2022] [Accepted: 08/21/2022] [Indexed: 12/14/2022] Open
Abstract
The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease-disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease-gene associations to connect the disease-disease network and gene-gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease-gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease-gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson's disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene-disease associations were highly evidenced by independent experimental studies.
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Affiliation(s)
- Binsheng He
- Academician Workstation, Changsha Medical University, Changsha 410219, China.,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, P. R. China.,School of pharmacy, Changsha Medical University, Changsha 410219, P. R. China
| | - Kun Wang
- School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China
| | - Ju Xiang
- Academician Workstation, Changsha Medical University, Changsha 410219, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha 410219, China.,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, P. R. China.,School of pharmacy, Changsha Medical University, Changsha 410219, P. R. China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang 212001, Jiangsu, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing 100102, China
| | - Cheng Guo
- Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Miao Xu
- Broad institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Jialiang Yang
- Academician Workstation, Changsha Medical University, Changsha 410219, China.,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, P. R. China.,School of pharmacy, Changsha Medical University, Changsha 410219, P. R. China.,Geneis (Beijing) Co., Ltd., Beijing 100102, China
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MAGEA11 as a STAD Prognostic Biomarker Associated with Immune Infiltration. Diagnostics (Basel) 2022; 12:diagnostics12102506. [PMID: 36292195 PMCID: PMC9600629 DOI: 10.3390/diagnostics12102506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/23/2022] [Accepted: 10/11/2022] [Indexed: 11/17/2022] Open
Abstract
Expression of MAGE family member A11 (MAGEA11) is upregulated in different tumors. However, in gastric cancer, the prognostic significance of MAGEA11 and its relationship with immune infiltration remain largely unknown. The expression of MAGEA11 in pan-cancer and the receiver operating characteristic (ROC) and survival impact of gastric cancer were evaluated by The Cancer Genome Atlas (TCGA). Whether MAGEA11 was an independent risk factor was assessed by Cox analysis. Nomograms were constructed from MAGEA11 and clinical variables. Gene functional pathway enrichment was obtained based on MAGEA11 differential analysis. The relationship between MAGEA11 and immune infiltration was determined by the Tumor Immunity Estimation Resource (TIMER) and the Tumor Immune System Interaction Database (TISIDB). Finally, MAGEA11-sensitive drugs were predicted based on the CellMiner database. The results showed that the expression of MAGEA11 mRNA in gastric cancer tissues was significantly higher than that in normal tissues. The ROC curve indicated an AUC value of 0.667. Survival analysis showed that patients with high MAGEA11 had poor prognosis (HR = 1.43, p = 0.034). In correlation analysis, MAGEA11 mRNA expression was found to be associated with tumor purity and immune invasion. Finally, drug sensitivity analysis found that the expression of MAGEA11 was correlated with seven drugs. Our study found that upregulated MAGEA11 in gastric cancer was significantly associated with lower survival and invasion by immune infiltration. It is suggested that MAGEA11 may be a potential biomarker and immunotherapy target for gastric cancer.
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Leng X, Yang J, Liu T, Zhao C, Cao Z, Li C, Sun J, Zheng S. A bioinformatics framework to identify the biomarkers and potential drugs for the treatment of colorectal cancer. Front Genet 2022; 13:1017539. [PMID: 36238159 PMCID: PMC9551025 DOI: 10.3389/fgene.2022.1017539] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.
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Drug Response Prediction Based on 1D Convolutional Neural Network and Attention Mechanism. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8671348. [PMID: 36164615 PMCID: PMC9509240 DOI: 10.1155/2022/8671348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/18/2022] [Indexed: 11/30/2022]
Abstract
There are multiple methods based on gene expression, copy number variation, and methylation biomarkers for screening drug response have been developed. On the other hand, many machine learning algorithms have been applied in recent years to predict drug response, such as neural networks and random forests for the discovery of genomic markers of drug sensitivity for individual drugs in cancer cell lines. In this paper, we propose a drug response prediction algorithm based on 1D convolutional neural networks with attention mechanism and combined with pathway networks, which combines the individual histological data affecting drug response and considers the topological nature of the pathways to find the subpathways highly correlated with drug response and use this as a feature to predict drug response by training using convolutional neural networks. Thus, the output values will represent the probability of occurrence of each of these two categories. In this experiment, using five-fold cross-validation, the identification accuracy reached an average of 84.6%, which is 4.5% higher than the direct random forest approach for drug prediction with an AUC value. This proves that the use of the one-dimensional1D convolutional neural network with attention mechanism to predict the response of low-grade glioma patients and drugs has better prediction results.
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8
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Wu M, Liang L, Dai X. Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report. Front Oncol 2022; 12:939022. [PMID: 35992799 PMCID: PMC9381827 DOI: 10.3389/fonc.2022.939022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 07/08/2022] [Indexed: 12/29/2022] Open
Abstract
There are many treatment options for advanced lung cancer, among which immunotherapy has developed rapidly and benefited a lot of patients. However, immunotherapy can only benefit a subgroup of patients, and how to select patients suitable for this therapy is critical. Tumor mutation burden (TMB) is one of the important reference indicators for immune checkpoint inhibitors (ICIs). However, there are many factors influencing the usage of this indicator, which will lead to considerable consequences if not treated well. In this study, we performed a case study on a male advanced lung squamous cell carcinoma patient of age 83. The patient suffered from “cough and sputum”, and did chest CT scans on 24 October 2018, which showed “a mass-like mass in the anterior segment of the right lung upper lobe, about 38mm×28mm”. He was treated with systemic chemotherapy; however, the tumor was still under progression. Although PD-L1 was not tested in gene testing, he had a TMB value of 10.26 mutations/Mb with a quantile value 88.63%. Thus, “toripalimab injection” was added as immunotherapy and the size of the lesion decreased. In summary, we adopted a clinical case as the basis to explore the value and significance of TMB in immunotherapy in this study. We hope that more predictive molecular markers will be discovered, which will bring more treatment methods for advanced lung cancer.
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Affiliation(s)
- Mingrui Wu
- Department of Respiratory and Critical Care Medicine, Affiliated People‘s Hospital of Chongqing Three Gorges Medical College, Chongqing, China
| | - Lan Liang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Army Medical University, Chongqing, China
- *Correspondence: Lan Liang,
| | - Xiaotian Dai
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Army Medical University, Chongqing, China
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Informative SNP Selection Based on a Fuzzy Clustering and Improved Binary Particle Swarm Optimization Algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3837579. [PMID: 35756402 PMCID: PMC9225903 DOI: 10.1155/2022/3837579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 12/04/2022]
Abstract
Single-nucleotide polymorphism (SNP) involves the replacement of a single nucleotide in a deoxyribonucleic acid (DNA) sequence and is often linked to the development of specific diseases. Although current genotyping methods can tag SNP loci within biological samples to provide accurate genetic information for a disease associated, they have limited prediction accuracy. Furthermore, they are complex to perform and may result in the prediction of an excessive number of tag SNP loci, which may not always be associated with the disease. Therefore in this manuscript, we aimed to evaluate the impact of a newly optimized fuzzy clustering and binary particle swarm optimization algorithm (FCBPSO) on the accuracy and running time of informative SNP selection. Fuzzy clustering and FCBPSO were first applied to identify the equivalence relation and the candidate tag SNP set to reduce the redundancy between loci. The FCBPSO algorithm was then optimized and used to obtain the final tag SNP set. The prediction performance and running time of the newly developed model were compared with other traditional methods, including NMC, SPSO, and MCMR. The prediction accuracy of the FCBPSO algorithm was always higher than that of the other algorithms especially as the number of tag SNPs increased. However, when the number of tag SNPs was low, the prediction accuracy of FCBPSO was slightly lower than that of MCMR (add prediction accuracy values for each algorithm). However, the running time of the FCBPSO algorithm was always lower than that of MCMR. FCBPSO not only reduced the size and dimension of the optimization problem but also simplified the training of the prediction model. This improved the prediction accuracy of the model and reduced the running time when compared with other traditional methods.
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A Framework to Predict the Molecular Classification and Prognosis of Breast Cancer Patients and Characterize the Landscape of Immune Cell Infiltration. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4635806. [PMID: 35720039 PMCID: PMC9201713 DOI: 10.1155/2022/4635806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 03/25/2022] [Accepted: 05/16/2022] [Indexed: 11/27/2022]
Abstract
It is known that all current cancer therapies can only benefit a limited proportion of patients; thus, molecular classification and prognosis evaluation are critical for correctly classifying breast cancer patients and selecting the best treatment strategy. These processes usually involve the disclosure of molecular information like mutation, expression, and immune microenvironment of a breast cancer patient, which are not been fully studied until now. Therefore, there is an urgent clinical need to identify potential markers to enhance molecular classification, precision prognosis, and therapy stratification for breast cancer patients. In this study, we explored the gene expression profiles of 1,721 breast cancer patients through CIBERSORT and ESTIMATE algorithms; then, we obtained a comprehensive intratumoral immune landscape. The immune cell infiltration (ICI) patterns of breast cancer were classified into 3 separate subtypes according to the infiltration levels of 22 immune cells. The differentially expressed genes between these subtypes were further identified, and ICI scores were calculated to assess the immune landscape of BRCA patients. Importantly, we demonstrated that ICI scores correlate with patients' survival, tumor mutation burden, neoantigens, and sensitivity to specific drugs. Based on these ICI scores, we were able to predict the prognosis of patients and their response to immunotherapy. Together, these findings provide a realistic scenario to stratify breast cancer patients for precision medicine.
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Xiao C, Dong T, Yang L, Jin L, Lin W, Zhang F, Han Y, Huang Z. Identification of Novel Immune Ferropotosis-Related Genes Associated With Clinical and Prognostic Features in Gastric Cancer. Front Oncol 2022; 12:904304. [PMID: 35664744 PMCID: PMC9157572 DOI: 10.3389/fonc.2022.904304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/19/2022] [Indexed: 12/08/2022] Open
Abstract
Background Gastric cancer (GC) is the fifth commonest cancer and the third commonest reason of death causing by cancer worldwide. Currently, tumor immunology and ferropotosis develop rapidly that has made gastric cancer be treated in new directions. So, finding the potential targets and prognostic biomarkers for immunotherapy combined with ferropotosis is urgent. Methods By mining TCGA, immune-related genes, ferropotosis-related genes and immune-ferropotosis-related differentially expressed genes (IFR-DEGs) were identified. The independent prognostic value of IFR-DEGs was determined by differential expression analysis, prognostic analysis, and univariate and lasso regression analysis. Then, based on the prognostic risk model, the correlation between IFR-DEGs and immune scores, immune checkpoints were evaluated. Besides, we predicted the response of high and low risk groups to drugs. Results A 15-gene prognostic feature was constructed. The high-risk group had a poorer prognosis than the low-risk group. High-risk group had higher level of Treg immune cell infiltration compared with that in the low-risk group, and the tumor purity, immune checkpoint PD-1 and CTLA4, and immunity in the high-risk group were higher than those in the low-risk group. These results indicate that immune ferropotosis-related genes migh be potential predictors of STAD's response to ICI immunotherapy biomarkers. In addition, the response of small molecule drugs such as Nilotini, Sunitinib, Imatinib, etc. for high and low risk groups was predicted. Conclusion IFRSig can be regarded as an independent prognostic feature and may estimate OS and clinical treatment response in patients with STAD. IFRSig also has important correlation with immune microenvironment. A new understanding of the immune-ferropotosis-related genes during the occurrence and development of STAD is provided in this study.
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Affiliation(s)
- Chen Xiao
- Department of Gastroenterology, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China
| | - Tao Dong
- Department of Digestion, Yidu Central Hospital of Weifang, Weifang, China
| | - Linhui Yang
- Graduate School of Fujian Medical University, Fuzhou, China
| | - Liangzi Jin
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China
| | - Weiguo Lin
- Department of Gastroenterology, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China
| | - Faqin Zhang
- Department of Gastroenterology, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China
| | - Yuanyuan Han
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China
| | - Zhijian Huang
- Department of Breast Surgical Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
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12
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Lang J, Li Y, Yang W, Dong R, Liang Y, Liu J, Chen L, Wang W, Ji B, Tian G, Che N, Meng B. Genomic and resistome analysis of Alcaligenes faecalis strain PGB1 by Nanopore MinION and Illumina Technologies. BMC Genomics 2022; 23:316. [PMID: 35443609 PMCID: PMC9022240 DOI: 10.1186/s12864-022-08507-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 03/28/2022] [Indexed: 12/24/2022] Open
Abstract
Background Drug-resistant bacteria are important carriers of antibiotic-resistant genes (ARGs). This fact is crucial for the development of precise clinical drug treatment strategies. Long-read sequencing platforms such as the Oxford Nanopore sequencer can improve genome assembly efficiency particularly when they are combined with short-read sequencing data. Results Alcaligenes faecalis PGB1 was isolated and identified with resistance to penicillin and three other antibiotics. After being sequenced by Nanopore MinION and Illumina sequencer, its entire genome was hybrid-assembled. One chromosome and one plasmid was assembled and annotated with 4,433 genes (including 91 RNA genes). Function annotation and comparison between strains were performed. A phylogenetic analysis revealed that it was closest to A. faecalis ZD02. Resistome related sequences was explored, including ARGs, Insert sequence, phage. Two plasmid aminoglycoside genes were determined to be acquired ARGs. The main ARG category was antibiotic efflux resistance and β-lactamase (EC 3.5.2.6) of PGB1 was assigned to Class A, Subclass A1b, and Cluster LSBL3. Conclusions The present study identified the newly isolated bacterium A. faecalis PGB1 and systematically annotated its genome sequence and ARGs. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08507-7.
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Affiliation(s)
- Jidong Lang
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, People's Republic of China
| | - Yanju Li
- School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Wenjuan Yang
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China
| | - Ruyi Dong
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, People's Republic of China
| | - Yuebin Liang
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, People's Republic of China
| | - Jia Liu
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China
| | - Lanyou Chen
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China
| | - Weiwei Wang
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China
| | - Binbin Ji
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, People's Republic of China
| | - Nanying Che
- Department of Pathology, Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China.
| | - Bo Meng
- Geneis (Beijing) Co., Ltd, Beijing, 100102, China.
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13
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Tian X, Shen L, Gao P, Huang L, Liu G, Zhou L, Peng L. Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion. Front Microbiol 2022; 13:740382. [PMID: 35295301 PMCID: PMC8919055 DOI: 10.3389/fmicb.2022.740382] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 02/01/2022] [Indexed: 02/06/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.
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Affiliation(s)
- Xiongfei Tian
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Pengfei Gao
- College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, China
- The Future Laboratory, Tsinghua University, Beijing, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
- *Correspondence: Liqian Zhou,
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
- College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
- Lihong Peng,
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14
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Wang Y, Zheng K, Xiong H, Huang Y, Chen X, Zhou Y, Qin W, Su J, Chen R, Qiu H, Yuan X, Wang Y, Zou Y. PARP Inhibitor Upregulates PD-L1 Expression and Provides a New Combination Therapy in Pancreatic Cancer. Front Immunol 2022; 12:762989. [PMID: 34975854 PMCID: PMC8718453 DOI: 10.3389/fimmu.2021.762989] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/19/2021] [Indexed: 12/15/2022] Open
Abstract
Despite recent improvements in treatment modalities, pancreatic cancer remains a highly lethal tumor with mortality rate increasing every year. Poly (ADP-ribose) polymerase (PARP) inhibitors are now used in pancreatic cancer as a breakthrough in targeted therapy. This study focused on whether PARP inhibitors (PARPis) can affect programmed death ligand-1 (PD-L1) expression in pancreatic cancer and whether immune checkpoint inhibitors of PD-L1/programmed death 1 (PD-1) can enhance the anti-tumor effects of PARPis. Here we found that PARPi, pamiparib, up-regulated PD-L1 expression on the surface of pancreatic cancer cells in vitro and in vivo. Mechanistically, pamiparib induced PD-L1 expression via JAK2/STAT3 pathway, at least partially, in pancreatic cancer. Importantly, pamiparib attenuated tumor growth; while co-administration of pamiparib with PD-L1 blockers significantly improved the therapeutic efficacy in vivo compared with monotherapy. Combination therapy resulted in an altered tumor immune microenvironment with a significant increase in windiness of CD8+ T cells, suggesting a potential role of CD8+ T cells in the combination therapy. Together, this study provides evidence for the clinical application of PARPis with anti-PD-L1/PD-1 drugs in the treatment of pancreatic cancer.
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Affiliation(s)
- Yali Wang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kun Zheng
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hua Xiong
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yongbiao Huang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiuqiong Chen
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yilu Zhou
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom.,Institute for Life Sciences, University of Southampton, Southampton, United Kingdom
| | - Wan Qin
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinfang Su
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Rui Chen
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Qiu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xianglin Yuan
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yihua Wang
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom.,Institute for Life Sciences, University of Southampton, Southampton, United Kingdom
| | - Yanmei Zou
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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15
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Maiti S, MaitiDutta S, Chen G. Regulations of expressions of rat/human sulfotransferases by anticancer drug, nolatrexed, and micronutrients. Anticancer Drugs 2022; 33:e525-e533. [PMID: 34387600 DOI: 10.1097/cad.0000000000001155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Cancer is related to the cellular proliferative state. Increase in cell-cycle regulatory function augments cellular folate pool. This pathway is therapeutically targeted. A number of drugs influences this metabolism, that is, folic acid, folinic acid, nolatrexed, and methotrexate. Our previous study showed methotrexate influences on rat/human sulfotransferases. Present study explains the effect of nolatrexed (widely used in different cancers) and some micronutrients on the expressions of rat/human sulfotransferases. Female Sprague-Dawley rats were treated with nolatrexed (01-100 mg/kg) and rats of both sexes were treated to folic acid (100, 200, or 400 mg/kg) for 2-weeks and their aryl sulfotransferase-IV (AST-IV; β-napthol sulfation) and sulfotransferase (STa; DHEA sulfation) activities, protein expression (western blot) and mRNA expression (RT-PCR) were tested. In human-cultured hepatocarcinoma (HepG2) cells nolatrexed (1 nM-1.2 mM) or folinic acid (10 nM-10 μM) were applied for 10 days. Folic acid (0-10 μM) was treated to HepG2 cells. PPST (phenol catalyzing), MPST (dopamine and monoamine), DHEAST (dehydroepiandrosterone and DHEA), and EST (estradiol sulfating) protein expressions (western-blot) were tested in HepG2 cells. Present results suggest that nolatrexed significantly increased sulfotransferases expressions in rat (protein, STa, F = 4.87, P < 0.05/mRNA, AST-IV, F = 6.702, P < 0.014; Student's t test, P < 0.01-0.05) and HepG2 cells. Folic acid increased sulfotransferases activity/protein in gender-dependant manner. Both folic and folinic acid increased several human sulfotransferases isoforms with varied level of significance (least or no increase at highest dose) in HepG2 cells pointing its dose-dependent multiphasic responses. The clinical importance of this study may be furthered in the verification of sulfation metabolism of several exogenous/endogenous molecules, drug-drug interaction and their influences on cancer pathophysiological processes. Further studies are necessary.
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Affiliation(s)
- Smarajit Maiti
- Cell and Molecular Therapeutics Laboratory, Department of Biochemistry and Biotechnology, Oriental Institute of Science and Technology
- Epidemiology and Human Health Division, Founder and Secretary, Agricure Biotech Research Society
| | - Sangita MaitiDutta
- Department of Biological Sciences, Midnapore City College, Midnapore, West Bengal, India
| | - Guangping Chen
- Venture I OSU Laboratory, Oklahoma Technology & Research Park, Innovation Way, Stillwater, Oklahoma, USA
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16
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Chen Y, Juan L, Lv X, Shi L. Bioinformatics Research on Drug Sensitivity Prediction. Front Pharmacol 2021; 12:799712. [PMID: 34955863 PMCID: PMC8696280 DOI: 10.3389/fphar.2021.799712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 11/18/2021] [Indexed: 11/28/2022] Open
Abstract
Modeling-based anti-cancer drug sensitivity prediction has been extensively studied in recent years. While most drug sensitivity prediction models only use gene expression data, the remarkable impacts of gene mutation, methylation, and copy number variation on drug sensitivity are neglected. Drug sensitivity prediction can both help protect patients from some adverse drug reactions and improve the efficacy of treatment. Genomics data are extremely useful for drug sensitivity prediction task. This article reviews the role of drug sensitivity prediction, describes a variety of methods for predicting drug sensitivity. Moreover, the research significance of drug sensitivity prediction, as well as existing problems are well discussed.
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Affiliation(s)
- Yaojia Chen
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiao Lv
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Lei Shi
- Department of Spine Surgery Changzheng Hospital, Naval Medical University, Shanghai, China
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17
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Guo Y, Ju Y, Chen D, Wang L. Research on the Computational Prediction of Essential Genes. Front Cell Dev Biol 2021; 9:803608. [PMID: 34938741 PMCID: PMC8685449 DOI: 10.3389/fcell.2021.803608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 11/22/2021] [Indexed: 11/19/2022] Open
Abstract
Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body’s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.
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Affiliation(s)
- Yuxin Guo
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China
| | - Dong Chen
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Lihong Wang
- Beidahuang Industry Group General Hospital, Harbin, China
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18
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Pang H, Zhang G, Yan N, Lang J, Liang Y, Xu X, Cui Y, Wu X, Li X, Shan M, Wang X, Meng X, Liu J, Tian G, Cai L, Yuan D, Wang X. Evaluating the Risk of Breast Cancer Recurrence and Metastasis After Adjuvant Tamoxifen Therapy by Integrating Polymorphisms in Cytochrome P450 Genes and Clinicopathological Characteristics. Front Oncol 2021; 11:738222. [PMID: 34868931 PMCID: PMC8639703 DOI: 10.3389/fonc.2021.738222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Tamoxifen (TAM) is the most commonly used adjuvant endocrine drug for hormone receptor-positive (HR+) breast cancer patients. However, how to accurately evaluate the risk of breast cancer recurrence and metastasis after adjuvant TAM therapy is still a major concern. In recent years, many studies have shown that the clinical outcomes of TAM-treated breast cancer patients are influenced by the activity of some cytochrome P450 (CYP) enzymes that catalyze the formation of active TAM metabolites like endoxifen and 4-hydroxytamoxifen. In this study, we aimed to first develop and validate an algorithm combining polymorphisms in CYP genes and clinicopathological signatures to identify a subpopulation of breast cancer patients who might benefit most from TAM adjuvant therapy and meanwhile evaluate major risk factors related to TAM resistance. Specifically, a total of 256 patients with invasive breast cancer who received adjuvant endocrine therapy were selected. The genotypes at 10 loci from three TAM metabolism-related CYP genes were detected by time-of-flight mass spectrometry and multiplex long PCR. Combining the 10 loci with nine clinicopathological characteristics, we obtained 19 important features whose association with cancer recurrence was assessed by importance score via random forests. After that, a logistic regression model was trained to calculate TAM risk-of-recurrence score (TAM RORs), which is adopted to assess a patient's risk of recurrence after TAM treatment. The sensitivity and specificity of the model in an independent test cohort were 86.67% and 64.56%, respectively. This study showed that breast cancer patients with high TAM RORs were less sensitive to TAM treatment and manifested more invasive characteristics, whereas those with low TAM RORs were highly sensitive to TAM treatment, and their conditions were stable during the follow-up period. There were some risk factors that had a significant effect on the efficacy of TAM. They were tissue classification (tumor Grade < 2 vs. Grade ≥ 2, p = 2.2e-16), the number of lymph node metastases (Node-Negative vs. Node < 4, p = 5.3e-07; Node < 4 vs. Node ≥ 4, p = 0.003; Node-Negative vs. Node ≥ 4, p = 7.2e-15), and the expression levels of estrogen receptor (ER) and progesterone receptor (PR) (ER < 50% vs. ER ≥ 50%, p = 1.3e-12; PR < 50% vs. PR ≥ 50%, p = 2.6e-08). The really remarkable thing is that different genotypes of CYP2D6*10(C188T) show significant differences in prediction function (CYP2D6*10 CC vs. TT, p < 0.019; CYP2D6*10 CT vs. TT, p < 0.037). There are more than 50% Chinese who have CYP2D6*10 mutation. So the genotype of CYP2D6*10(C188T) should be tested before TAM therapy.
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Affiliation(s)
- Hui Pang
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Guoqiang Zhang
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Na Yan
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
- Department of Science, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jidong Lang
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
- Department of Science, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Yuebin Liang
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
- Department of Science, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xinyuan Xu
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yaowen Cui
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xueya Wu
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xianjun Li
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ming Shan
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xiaoqin Wang
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
| | - Xiangzhi Meng
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiaxiang Liu
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Geng Tian
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
- Department of Science, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Li Cai
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Dawei Yuan
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
| | - Xin Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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19
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Lu JY, Zhang FR, Zou WZ, Huang WT, Guo Z. Peptide-based system for sensing Pb 2+ and molecular logic computing. Anal Biochem 2021; 630:114333. [PMID: 34400145 DOI: 10.1016/j.ab.2021.114333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/14/2021] [Accepted: 08/08/2021] [Indexed: 12/25/2022]
Abstract
Peptides with recognition, assembly, various activities exhibit strong power and application prospects in sensing, material science, biomedicine. However, peptide-based sensing and expanding application is still at an early stage. Herein, a peptide-based sensing and logic system was developed for highly sensitive and selective detection of Pb2+ and implementation of logic operations. Our Pb2+ assay method was ultra-rapid (less than 1 min), direct, simple with detection limit of 0.75 nM. Flexibility and scalability of peptide-based solution system facilitated the execution of sensing and logic operations from simple to complex. This research will not only inspire discovery and comprehensive applications (such as sensing and assembly) of more functional peptides, but also provide more opportunities for development and design of peptide-based systems and molecular information technologies.
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Affiliation(s)
- Jiao Yang Lu
- Academician Workstation, Changsha Medical University, Changsha, 410219, PR China
| | - Fu Rui Zhang
- State Key Laboratory of Developmental Biology of Freshwater Fish, Hunan Provincial Key Laboratory of Microbial Molecular Biology, College of Life Science, Hunan Normal University, Changsha, 410081, PR China
| | - Wen Zi Zou
- State Key Laboratory of Developmental Biology of Freshwater Fish, Hunan Provincial Key Laboratory of Microbial Molecular Biology, College of Life Science, Hunan Normal University, Changsha, 410081, PR China
| | - Wei Tao Huang
- State Key Laboratory of Developmental Biology of Freshwater Fish, Hunan Provincial Key Laboratory of Microbial Molecular Biology, College of Life Science, Hunan Normal University, Changsha, 410081, PR China
| | - Zhen Guo
- Academician Workstation, Changsha Medical University, Changsha, 410219, PR China.
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20
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Hu A, Wei Z, Zheng Z, Luo B, Yi J, Zhou X, Zeng C. A Computational Framework to Identify Transcriptional and Network Differences between Hepatocellular Carcinoma and Normal Liver Tissue and Their Applications in Repositioning Drugs. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9921195. [PMID: 34604388 PMCID: PMC8483911 DOI: 10.1155/2021/9921195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022]
Abstract
Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies worldwide. Although there have been extensive studies on the molecular mechanisms of its carcinogenesis, FDA-approved drugs for HCC are rare. Side effects, development time, and cost of these drugs are the major bottlenecks, which can be partially overcome by drug repositioning. In this study, we developed a computational framework to study the mechanisms of HCC carcinogenesis, in which drug perturbation-induced gene expression signatures were utilized for repositioning of potential drugs. Specifically, we first performed differential expression analysis and coexpression network module analysis on the HCC dataset from The Cancer Genome Atlas database. Differential gene expression analysis identified 1,337 differentially expressed genes between HCC and adjacent normal tissues, which were significantly enriched in functions related to various pathways, including α-adrenergic receptor activity pathway and epinephrine binding pathway. Weighted gene correlation network analysis (WGCNA) suggested that the number of coexpression modules was higher in HCC tissues than in normal tissues. Finally, by correlating differentially expressed genes with drug perturbation-related signatures, we prioritized a few potential drugs, including nutlin and eribulin, for the treatment of hepatocellular carcinoma. The drugs have been reported by a few experimental studies to be effective in killing cancer cells.
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Affiliation(s)
- Aimin Hu
- Xiantao First People's Hospital Affiliated to Yangtze University, Xiantao 433000, China
| | - Zheng Wei
- Xiantao First People's Hospital Affiliated to Yangtze University, Xiantao 433000, China
| | - Zuxiang Zheng
- Xiantao First People's Hospital Affiliated to Yangtze University, Xiantao 433000, China
| | - Bichao Luo
- Xiantao First People's Hospital Affiliated to Yangtze University, Xiantao 433000, China
| | - Jieming Yi
- Xiantao First People's Hospital Affiliated to Yangtze University, Xiantao 433000, China
| | - Xinhong Zhou
- Xiantao First People's Hospital Affiliated to Yangtze University, Xiantao 433000, China
| | - Changjiang Zeng
- Xiantao First People's Hospital Affiliated to Yangtze University, Xiantao 433000, China
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21
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Yao Y, Ji B, Lv Y, Li L, Xiang J, Liao B, Gao W. Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks. Front Genet 2021; 12:712170. [PMID: 34490041 PMCID: PMC8417042 DOI: 10.3389/fgene.2021.712170] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 07/23/2021] [Indexed: 02/05/2023] Open
Abstract
Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA–disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA–disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA–disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA–disease associations.
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Affiliation(s)
- Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou, China
| | - Binbin Ji
- Geneis Beijing Co., Ltd., Beijing, China
| | - Yaping Lv
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Ling Li
- Basic Courses Department, Zhejiang Shuren University, Hangzhou, China
| | - Ju Xiang
- School of Computer Science and Engineering, Central South University, Changsha, China.,Department of Basic Medical Sciences, Changsha Medical University, Changsha, China.,Department of Computer Science, Changsha Medical University, Changsha, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Wei Gao
- Departments of Internal Medicine-Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, China
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22
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Yang J, Hui Y, Zhang Y, Zhang M, Ji B, Tian G, Guo Y, Tang M, Li L, Guo B, Ma T. Application of Circulating Tumor DNA as a Biomarker for Non-Small Cell Lung Cancer. Front Oncol 2021; 11:725938. [PMID: 34422670 PMCID: PMC8375502 DOI: 10.3389/fonc.2021.725938] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 07/19/2021] [Indexed: 12/21/2022] Open
Abstract
Background Non-small cell lung cancer (NSCLC) is one of the most prevalent causes of cancer-related death worldwide. Recently, there are many important medical advancements on NSCLC, such as therapies based on tyrosine kinase inhibitors and immune checkpoint inhibitors. Most of these therapies require tumor molecular testing for selecting patients who would benefit most from them. As invasive biopsy is highly risky, NSCLC molecular testing based on liquid biopsy has received more and more attention recently. Objective We aimed to introduce liquid biopsy and its potential clinical applications in NSCLC patients, including cancer diagnosis, treatment plan prioritization, minimal residual disease detection, and dynamic monitoring on the response to cancer treatment. Method We reviewed recent studies on circulating tumor DNA (ctDNA) testing, which is a minimally invasive approach to identify the presence of tumor-related mutations. In addition, we evaluated potential clinical applications of ctDNA as blood biomarkers for advanced NSCLC patients. Results Most studies have indicated that ctDNA testing is critical in diagnosing NSCLC, predicting clinical outcomes, monitoring response to targeted therapies and immunotherapies, and detecting cancer recurrence. Moreover, the changes of ctDNA levels are associated with tumor mutation burden and cancer progression. Conclusion The ctDNA testing is promising in guiding the therapies on NSCLC patients.
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Affiliation(s)
- Jialiang Yang
- Chifeng Municipal Hospital, Chifeng, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Geneis Beijing Co., Ltd., Beijing, China
| | - Yan Hui
- Chifeng Municipal Hospital, Chifeng, China
| | | | | | - Binbin Ji
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Geneis Beijing Co., Ltd., Beijing, China
| | - Geng Tian
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Geneis Beijing Co., Ltd., Beijing, China
| | - Yangqiang Guo
- China National Intellectual Property Administration, Beijing, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, China
| | | | - Bella Guo
- Genetron Health (Beijing) Co. Ltd., Beijing, China
| | - Tonghui Ma
- Genetron Health (Beijing) Co. Ltd., Beijing, China
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He B, Hou F, Ren C, Bing P, Xiao X. A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds. Front Oncol 2021; 11:711225. [PMID: 34367996 PMCID: PMC8340770 DOI: 10.3389/fonc.2021.711225] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 07/07/2021] [Indexed: 12/23/2022] Open
Abstract
Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.
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Affiliation(s)
- Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Fangxing Hou
- Queen Mary School, Nanchang University, Jiangxi, China
| | - Changjing Ren
- School of Science, Dalian Maritime University, Dalian, China.,Genies Beijing Co., Ltd., Beijing, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Xiangzuo Xiao
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Jiangxi, China
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24
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Liu Z, Hong ZP, Xi SX. RUNX3 Expression Level Is Correlated with the Clinical and Pathological Characteristics in Endometrial Cancer: A Systematic Review and Meta-analysis. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9995384. [PMID: 34337071 PMCID: PMC8298141 DOI: 10.1155/2021/9995384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/07/2021] [Accepted: 06/30/2021] [Indexed: 11/20/2022]
Abstract
Human Runt-associated transcription factor 3 (RUNX3) plays an important role in the development and progression of endometrial cancer (EC). However, the clinical and pathological significance of RUNX3 in EC needs to be further studied. In order to clarify the clinical and pathological significance of RUNX3, a systematic review and meta-analysis was conducted in EC patients. Keywords RUNX3, endometrial cancer, and uterine cancer were searched in Cochrane Library, Web of Knowledge, PubMed, CBM, MEDLINE, and Chinese CNKI database for data up to Dec 31, 2018. References, abstracts, and meeting proceedings were manually searched in supplementary. Outcomes were various clinical and pathological features. The two reviewers performed the literature searching, data extracting, and method assessing independently. Meta-analysis was performed by RevMan5.3.0. A total of 563 EC patients were enrolled from eight studies. Meta-analysis results showed that the expression of RUNX3 has significant differences in these comparisons: lymph node (LN) metastasis vs. non-LN metastasis (P = 0.26), EC tissues vs. normal tissues (P < 0.00001), clinical stages I/II vs. II/IV (P < 0.00001), muscular infiltration < 1/2 vs. muscular infiltration ≥ 1/2 (P < 0.00001), and G1 vs. G2/G3 (P < 0.00001). The decreasing expression of RUNX3 is associated with poor TNM stage and muscular infiltration. It is indicated that RUNX3 was involved in the suppression effect of EC. However, further multicenter randomized controlled trials are needed considering the small sample size of the included trials.
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Affiliation(s)
- Zhen Liu
- Department of Gynecology, Chifeng Municipal Hospital, Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng, China
| | - Zhi-pan Hong
- Department of Tumor Surgery, Chifeng Municipal Hospital, Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng, China
| | - Shu-xue Xi
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
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25
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Hellesøy M, Engen C, Grob T, Löwenberg B, Valk PJM, Gjertsen BT. Sex disparity in acute myeloid leukaemia with FLT3 internal tandem duplication mutations: implications for prognosis. Mol Oncol 2021; 15:2285-2299. [PMID: 34101344 PMCID: PMC8410575 DOI: 10.1002/1878-0261.13035] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/22/2021] [Accepted: 06/07/2021] [Indexed: 12/13/2022] Open
Abstract
Incidence, molecular presentation and outcome of acute myeloid leukaemia (AML) are influenced by sex, but little attention has been directed at untangling sex‐related molecular and phenotypic differences between female and male patients. While increased incidence and poor risk are generally associated with a male phenotype, the poor prognostic FLT3 internal tandem duplication (FLT3‐ITD) mutation and co‐mutations with NPM1 and DNMT3A are overrepresented in female AML. Here, we have investigated the relationship between sex and FLT3‐ITD mutation status by comparing clinical data, mutational profiles, gene expression and ex vivo drug sensitivity in four cohorts: Beat AML, LAML‐TCGA and two independent HOVON/SAKK cohorts, comprising 1755 AML patients in total. We found prevalent sex‐associated molecular differences. Co‐occurrence of FLT3‐ITD, NPM1 and DNMT3A mutations was overrepresented in females, while males with FLT3‐ITDs were characterized by additional mutations in RNA splicing and epigenetic modifier genes. We observed diverging expression of multiple leukaemia‐associated genes as well as discrepant ex vivo drug responses, suggestive of discrete functional properties. Importantly, significant prognostication was observed only in female FLT3‐ITD‐mutated AML. Thus, we suggest optimization of FLT3‐ITD mutation status as a clinical tool in a sex‐adjusted manner and hypothesize that prognostication, prediction and development of therapeutic strategies in AML could be improved by including sex‐specific considerations.
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Affiliation(s)
- Monica Hellesøy
- Haematology Section, Department of Medicine, Haukeland University Hospital, Helse Bergen HF, Norway
| | - Caroline Engen
- Department of Clinical Science, Center for Cancer Biomarkers CCBIO, University of Bergen, Norway
| | - Tim Grob
- Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Bob Löwenberg
- Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Peter J M Valk
- Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Bjørn T Gjertsen
- Haematology Section, Department of Medicine, Haukeland University Hospital, Helse Bergen HF, Norway.,Department of Clinical Science, Center for Cancer Biomarkers CCBIO, University of Bergen, Norway
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Ma S, Guo Z, Wang B, Yang M, Yuan X, Ji B, Wu Y, Chen S. A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them. Front Genet 2021; 12:832627. [PMID: 35116059 PMCID: PMC8804649 DOI: 10.3389/fgene.2021.832627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 12/29/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Recurrence is still a major obstacle to the successful treatment of gliomas. Understanding the underlying mechanisms of recurrence may help for developing new drugs to combat gliomas recurrence. This study provides a strategy to discover new drugs for recurrent gliomas based on drug perturbation induced gene expression changes. Methods: The RNA-seq data of 511 low grade gliomas primary tumor samples (LGG-P), 18 low grade gliomas recurrent tumor samples (LGG-R), 155 glioblastoma multiforme primary tumor samples (GBM-P), and 13 glioblastoma multiforme recurrent tumor samples (GBM-R) were downloaded from TCGA database. DESeq2, key driver analysis and weighted gene correlation network analysis (WGCNA) were conducted to identify differentially expressed genes (DEGs), key driver genes and coexpression networks between LGG-P vs LGG-R, GBM-P vs GBM-R pairs. Then, the CREEDS database was used to find potential drugs that could reverse the DEGs and key drivers. Results: We identified 75 upregulated and 130 downregulated genes between LGG-P and LGG-R samples, which were mainly enriched in human papillomavirus (HPV) infection, PI3K-Akt signaling pathway, Wnt signaling pathway, and ECM-receptor interaction. A total of 262 key driver genes were obtained with frizzled class receptor 8 (FZD8), guanine nucleotide-binding protein subunit gamma-12 (GNG12), and G protein subunit β2 (GNB2) as the top hub genes. By screening the CREEDS database, we got 4 drugs (Paclitaxel, 6-benzyladenine, Erlotinib, Cidofovir) that could downregulate the expression of up-regulated genes and 5 drugs (Fenofibrate, Oxaliplatin, Bilirubin, Nutlins, Valproic acid) that could upregulate the expression of down-regulated genes. These drugs may have a potential in combating recurrence of gliomas. Conclusion: We proposed a time-saving strategy based on drug perturbation induced gene expression changes to find new drugs that may have a potential to treat recurrent gliomas.
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Affiliation(s)
- Shuzhi Ma
- Department of Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhen Guo
- Academician Workstation, Changsha Medical University, Changsha, China
- Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Bo Wang
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Min Yang
- Geneis (Beijing) Co., Ltd., Beijing, China
| | | | - Binbin Ji
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Yan Wu
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Size Chen
- Department of Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Guangdong Provincial Engineering Research Center for Esophageal Cancer Precise Therapy, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Central Laboratory, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- *Correspondence: Size Chen,
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Ammer LM, Vollmann-Zwerenz A, Ruf V, Wetzel CH, Riemenschneider MJ, Albert NL, Beckhove P, Hau P. The Role of Translocator Protein TSPO in Hallmarks of Glioblastoma. Cancers (Basel) 2020; 12:cancers12102973. [PMID: 33066460 PMCID: PMC7602186 DOI: 10.3390/cancers12102973] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/09/2020] [Accepted: 10/09/2020] [Indexed: 12/18/2022] Open
Abstract
Simple Summary The translocator protein (TSPO) has been under extensive investigation as a specific marker in positron emission tomography (PET) to visualize brain lesions following injury or disease. In recent years, TSPO is increasingly appreciated as a potential novel therapeutic target in cancer. In Glioblastoma (GBM), the most malignant primary brain tumor, TSPO expression levels are strongly elevated and scientific evidence accumulates, hinting at a pivotal role of TSPO in tumorigenesis and glioma progression. The aim of this review is to summarize the current literature on TSPO with respect to its role both in diagnostics and especially with regard to the critical hallmarks of cancer postulated by Hanahan and Weinberg. Overall, our review contributes to a better understanding of the functional significance of TSPO in Glioblastoma and draws attention to TSPO as a potential modulator of treatment response and thus an important factor that may influence the clinical outcome of GBM. Abstract Glioblastoma (GBM) is the most fatal primary brain cancer in adults. Despite extensive treatment, tumors inevitably recur, leading to an average survival time shorter than 1.5 years. The 18 kDa translocator protein (TSPO) is abundantly expressed throughout the body including the central nervous system. The expression of TSPO increases in states of inflammation and brain injury due to microglia activation. Not least due to its location in the outer mitochondrial membrane, TSPO has been implicated with a broad spectrum of functions. These include the regulation of proliferation, apoptosis, migration, as well as mitochondrial functions such as mitochondrial respiration and oxidative stress regulation. TSPO is frequently overexpressed in GBM. Its expression level has been positively correlated to WHO grade, glioma cell proliferation, and poor prognosis of patients. Several lines of evidence indicate that TSPO plays a functional part in glioma hallmark features such as resistance to apoptosis, invasiveness, and proliferation. This review provides a critical overview of how TSPO could regulate several aspects of tumorigenesis in GBM, particularly in the context of the hallmarks of cancer proposed by Hanahan and Weinberg in 2011.
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Affiliation(s)
- Laura-Marie Ammer
- Wilhelm Sander-NeuroOncology Unit and Department of Neurology, University Hospital Regensburg, 93053 Regensburg, Germany; (L.-M.A.); (A.V.-Z.)
| | - Arabel Vollmann-Zwerenz
- Wilhelm Sander-NeuroOncology Unit and Department of Neurology, University Hospital Regensburg, 93053 Regensburg, Germany; (L.-M.A.); (A.V.-Z.)
| | - Viktoria Ruf
- Center for Neuropathology and Prion Research, Ludwig Maximilians University of Munich, 81377 Munich, Germany;
| | - Christian H. Wetzel
- Molecular Neurosciences, Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany;
| | | | - Nathalie L. Albert
- Department of Nuclear Medicine, Ludwig-Maximilians-University Munich, 81377 Munich, Germany;
| | - Philipp Beckhove
- Regensburg Center for Interventional Immunology (RCI) and Department Internal Medicine III, University Hospital Regensburg, 93053 Regensburg, Germany;
| | - Peter Hau
- Wilhelm Sander-NeuroOncology Unit and Department of Neurology, University Hospital Regensburg, 93053 Regensburg, Germany; (L.-M.A.); (A.V.-Z.)
- Correspondence:
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Liu C, Wei D, Xiang J, Ren F, Huang L, Lang J, Tian G, Li Y, Yang J. An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 21:676-686. [PMID: 32759058 PMCID: PMC7403773 DOI: 10.1016/j.omtn.2020.07.003] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 06/10/2020] [Accepted: 07/06/2020] [Indexed: 12/16/2022]
Abstract
In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.
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Affiliation(s)
- Chuanying Liu
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Dong Wei
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Ju Xiang
- College of Information Engineering, Changsha Medical University, Changsha, Hunan 410219, China; School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Fuquan Ren
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Li Huang
- Tianhang Experiment School, Hangzhou, Zhejiang 310004, China
| | - Jidong Lang
- Geneis Beijing Co., Ltd., Beijing 100102, China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing 100102, China
| | - Yushuang Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China.
| | - Jialiang Yang
- College of Information Engineering, Changsha Medical University, Changsha, Hunan 410219, China; Geneis Beijing Co., Ltd., Beijing 100102, China.
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29
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Dong Q, Li F, Xu Y, Xiao J, Xu Y, Shang D, Zhang C, Yang H, Tian Z, Mi K, Li X, Zhang Y. RNAactDrug: a comprehensive database of RNAs associated with drug sensitivity from multi-omics data. Brief Bioinform 2019; 21:2167-2174. [PMID: 31799597 DOI: 10.1093/bib/bbz142] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 09/30/2019] [Accepted: 10/17/2019] [Indexed: 12/16/2022] Open
Abstract
Drug sensitivity has always been at the core of individualized cancer chemotherapy. However, we have been overwhelmed by large-scale pharmacogenomic data in the era of next-generation sequencing technology, which makes it increasingly challenging for researchers, especially those without bioinformatic experience, to perform data integration, exploration and analysis. To bridge this gap, we developed RNAactDrug, a comprehensive database of RNAs associated with drug sensitivity from multi-omics data, which allows users to explore drug sensitivity and RNA molecule associations directly. It provides association data between drug sensitivity and RNA molecules including mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) at four molecular levels (expression, copy number variation, mutation and methylation) from integrated analysis of three large-scale pharmacogenomic databases (GDSC, CellMiner and CCLE). RNAactDrug currently stores more than 4 924 200 associations of RNA molecules and drug sensitivity at four molecular levels covering more than 19 770 mRNAs, 11 119 lncRNAs, 438 miRNAs and 4155 drugs. A user-friendly interface enriched with various browsing sections augmented with advance search facility for querying the database is offered for users retrieving. RNAactDrug provides a comprehensive resource for RNA molecules acting in drug sensitivity, and it could be used to prioritize drug sensitivity-related RNA molecules, further promoting the identification of clinically actionable biomarkers in drug sensitivity and drug development more cost-efficiently by making this knowledge accessible to both basic researchers and clinical practitioners. Database URL: http://bio-bigdata.hrbmu.edu.cn/RNAactDrug.
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Affiliation(s)
- Qun Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jing Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingqi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Desi Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zihan Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kai Mi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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30
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Lv Z, Jin S, Ding H, Zou Q. A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features. Front Bioeng Biotechnol 2019; 7:215. [PMID: 31552241 PMCID: PMC6737778 DOI: 10.3389/fbioe.2019.00215] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 08/22/2019] [Indexed: 02/01/2023] Open
Abstract
To gain insight into the malfunction of the Golgi apparatus and its relationship to various genetic and neurodegenerative diseases, the identification of sub-Golgi proteins, both cis-Golgi and trans-Golgi proteins, is of great significance. In this study, a state-of-art random forests sub-Golgi protein classifier, rfGPT, was developed. The rfGPT used 2-gap dipeptide and split amino acid composition for the feature vectors and was combined with the synthetic minority over-sampling technique (SMOTE) and an analysis of variance (ANOVA) feature selection method. The rfGPT was trained on a sub-Golgi protein sequence data set (137 sequences), with sequence identity less than 25%. For the optimal rfGPT classifier with 93 features, the accuracy (ACC) was 90.5%; the Matthews correlation coefficient (MCC) was 0.811; the sensitivity (Sn) was 92.6%; and the specificity (Sp) was 88.4%. The independent testing scores for the rfGPT were ACC = 90.6%; MCC = 0.696; Sn = 96.1%; and Sp = 69.2%. Although the independent testing accuracy was 4.4% lower than that for the best reported sub-Golgi classifier trained on a data set with 40% sequence identity (304 sequences), the rfGPT is currently the top sub-Golgi protein predictor utilizing feature vectors without any position-specific scoring matrix and its derivative features. Therefore, the rfGPT is a more practical tool, because no sequence alignment is required with tens of millions of protein sequences. To date, the rfGPT is the Golgi classifier with the best independent testing scores, optimized by training on smaller benchmark data sets. Feature importance analysis proves that the non-polar and aliphatic residues composition, the (aromatic residues) + (non-polar, aliphatic residues) dipeptide and aromatic residues composition between NH2-termial and COOH-terminal of protein sequences are the three top biological features for distinguishing the sub-Golgi proteins.
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Affiliation(s)
- Zhibin Lv
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Shunshan Jin
- Department of Neurology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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31
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More P, Goedtel-Armbrust U, Shah V, Mathaes M, Kindler T, Andrade-Navarro MA, Wojnowski L. Drivers of topoisomerase II poisoning mimic and complement cytotoxicity in AML cells. Oncotarget 2019; 10:5298-5312. [PMID: 31523390 PMCID: PMC6731103 DOI: 10.18632/oncotarget.27112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 06/19/2019] [Indexed: 11/25/2022] Open
Abstract
Recently approved cancer drugs remain out-of-reach to most patients due to prohibitive costs and only few produce clinically meaningful benefits. An untapped alternative is to enhance the efficacy and safety of existing cancer drugs. We hypothesized that the response to topoisomerase II poisons, a very successful group of cancer drugs, can be improved by considering treatment-associated transcript levels. To this end, we analyzed transcriptomes from Acute Myeloid Leukemia (AML) cell lines treated with the topoisomerase II poison etoposide. Using complementary criteria of co-regulation within networks and of essentiality for cell survival, we identified and functionally confirmed 11 druggable drivers of etoposide cytotoxicity. Drivers with pre-treatment expression predicting etoposide response (e.g., PARP9) generally synergized with etoposide. Drivers repressed by etoposide (e.g., PLK1) displayed standalone cytotoxicity. Drivers, whose modulation evoked etoposide-like gene expression changes (e.g., mTOR), were cytotoxic both alone and in combination with etoposide. In summary, both pre-treatment gene expression and treatment-driven changes contribute to the cell killing effect of etoposide. Such targets can be tweaked to enhance the efficacy of etoposide. This strategy can be used to identify combination partners or even replacements for other classical anticancer drugs, especially those interfering with DNA integrity and transcription.
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Affiliation(s)
- Piyush More
- Department of Pharmacology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Ute Goedtel-Armbrust
- Department of Pharmacology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Viral Shah
- Department of Hematology, Medical Oncology and Pneumology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany.,University Cancer Center of Mainz, Mainz, Germany
| | - Marianne Mathaes
- Department of Pharmacology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Thomas Kindler
- Department of Hematology, Medical Oncology and Pneumology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany.,University Cancer Center of Mainz, Mainz, Germany
| | - Miguel A Andrade-Navarro
- Computational Biology and Data Mining, Faculty of Biology, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Leszek Wojnowski
- Department of Pharmacology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
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32
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Robert BM, Brindha GR, Santhi B, Kanimozhi G, Prasad NR. Computational models for predicting anticancer drug efficacy: A multi linear regression analysis based on molecular, cellular and clinical data of oral squamous cell carcinoma cohort. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 178:105-112. [PMID: 31416538 DOI: 10.1016/j.cmpb.2019.06.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 04/15/2019] [Accepted: 06/11/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVES The computational prediction of drug responses based on the analysis of multiple clinical features of the tumor will be a novel strategy for accomplishing the long-term goal of precision medicine in oncology. The cancer patients will be benefitted if we computationally account all the tumor characteristics (data) for the selection of most effective and precise therapeutic drug. In this study, we developed and validated few computational models to predict anticancer drug efficacy based on molecular, cellular and clinical features of 31 oral squamous cell carcinoma (OSCC) cohort using computational methods. METHODS We developed drug efficacy prediction models using multiple tumor features by employing the statistical methods like multi linear regression (MLR), modified MLR-weighted least square (MLR-WLS) and enhanced MLR-WLS. All the three developed drug efficacy prediction models were then validated using the data of actual OSCC samples (train-test ratio 31: 31) and actual Vs hypothetical samples (train-test ratio 31: 30). The selected best statistical model i.e. enhanced MLR-WLS has then been cross-validated (CV) using 341 theoretical tumor data. Finally, the performances of the models were assessed by the level of learning confidence, significance, accuracy and error terms. RESULTS The train-test process for the real tumor samples of MLR-WLS method revealed the drug efficacy prediction enhancement and we observed that there was very less priming difference between actual and predicted. Furthermore, we found there was a less difference between actual apoptotic priming and predicted apoptotic priming for the tumors 6, 8, 21 and 30 whereas, for the remaining tumors there were no differences between predicted and actual priming data. The error terms (Actual Vs Predicted) also revealed the reliability of enhanced MLR-WLS model for drug efficacy prediction. CONCLUSION We developed effective computational prediction models using MLR analysis for anticancer drug efficacy which will be useful in the field of precision medicine to choose the choice of drug in a personalized manner. We observed that the enhanced MLR-WLS model was the best fit to predict anticancer drug efficacy which may have translational applications.
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Affiliation(s)
- Beaulah Mary Robert
- Department of Biochemistry and Biotechnology, Annamalai University, Annamalainagar 608 002, Tamilnadu, India
| | - G R Brindha
- School of Computing, SASTRA Deemed to be University, Tirumalaisamudram, Thanjavur 613401, Tamilnadu, India.
| | - B Santhi
- School of Computing, SASTRA Deemed to be University, Tirumalaisamudram, Thanjavur 613401, Tamilnadu, India
| | - G Kanimozhi
- Department of Biochemistry, Dharmapuramn Gnanambigai Government Arts and Science College for Women, Mayiladuthurai, Tamilnadu, India
| | - Nagarajan Rajendra Prasad
- Department of Biochemistry and Biotechnology, Annamalai University, Annamalainagar 608 002, Tamilnadu, India.
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Xu Y, Dong Q, Li F, Xu Y, Hu C, Wang J, Shang D, Zheng X, Yang H, Zhang C, Shao M, Meng M, Xiong Z, Li X, Zhang Y. Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data. J Transl Med 2019; 17:255. [PMID: 31387579 PMCID: PMC6685260 DOI: 10.1186/s12967-019-2010-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 07/31/2019] [Indexed: 12/19/2022] Open
Abstract
Background Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy. Methods In this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations. Results Totally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP (http://bio-bigdata.hrbmu.edu.cn/CancerDAP/) available to explore 2751 subpathways relevant with 191 anticancer drugs response. Conclusions Taken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions. Electronic supplementary material The online version of this article (10.1186/s12967-019-2010-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Qun Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yingqi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Congxue Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jingwen Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Desi Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xuan Zheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Mengting Shao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Mohan Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Zhiying Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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Qi R, Ma A, Ma Q, Zou Q. Clustering and classification methods for single-cell RNA-sequencing data. Brief Bioinform 2019; 21:1196-1208. [PMID: 31271412 DOI: 10.1093/bib/bbz062] [Citation(s) in RCA: 104] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 04/24/2019] [Accepted: 04/25/2019] [Indexed: 12/12/2022] Open
Abstract
Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs.
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Affiliation(s)
- Ren Qi
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, USA
| | - Qin Ma
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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35
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Xu X, Gu H, Wang Y, Wang J, Qin P. Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response. Front Genet 2019; 10:233. [PMID: 30972101 PMCID: PMC6445890 DOI: 10.3389/fgene.2019.00233] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 03/04/2019] [Indexed: 12/14/2022] Open
Abstract
Anticancer drug responses can be varied for individual patients. This difference is mainly caused by genetic reasons, like mutations and RNA expression. Thus, these genetic features are often used to construct classification models to predict the drug response. This research focuses on the feature selection issue for the classification models. Because of the vast dimensions of the feature space for predicting drug response, the autoencoder network was first built, and a subset of inputs with the important contribution was selected. Then by using the Boruta algorithm, a further small set of features was determined for the random forest, which was used to predict drug response. Two datasets, GDSC and CCLE, were used to illustrate the efficiency of the proposed method.
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Affiliation(s)
- Xiaolu Xu
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Hong Gu
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Yang Wang
- Institute of Cancer Stem Cell, Dalian Medical University, Dalian, China
| | - Jia Wang
- Department of Breast Surgery, Institute of Breast Disease, Second Hospital of Dalian Medical University, Dalian, China
| | - Pan Qin
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
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36
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Fernández-Torras A, Duran-Frigola M, Aloy P. Encircling the regions of the pharmacogenomic landscape that determine drug response. Genome Med 2019; 11:17. [PMID: 30914058 PMCID: PMC6436215 DOI: 10.1186/s13073-019-0626-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 03/05/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics. METHODS To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome. RESULTS We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses. CONCLUSIONS Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.
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Affiliation(s)
- Adrià Fernández-Torras
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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37
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Xu L, Liang G, Liao C, Chen GD, Chang CC. An Efficient Classifier for Alzheimer's Disease Genes Identification. Molecules 2018; 23:molecules23123140. [PMID: 30501121 PMCID: PMC6321377 DOI: 10.3390/molecules23123140] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 11/17/2018] [Accepted: 11/19/2018] [Indexed: 11/16/2022] Open
Abstract
Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518055, China.
| | - Guangmin Liang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518055, China.
| | - Changrui Liao
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
| | - Gin-Den Chen
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
| | - Chi-Chang Chang
- School of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.
- IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
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38
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Structure Analysis of Effective Chemical Compounds against Dengue Viruses Isolated from Isatis tinctoria. CANADIAN JOURNAL OF INFECTIOUS DISEASES & MEDICAL MICROBIOLOGY 2018; 2018:3217473. [PMID: 29808104 PMCID: PMC5902107 DOI: 10.1155/2018/3217473] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 01/28/2018] [Indexed: 11/17/2022]
Abstract
The history of Chinese herb research can be traced back to thousands of years ago, and the abundant knowledge accumulated for these herbs makes them good candidates for developing new natural drugs. Isatis tinctoria is probably the most well-studied Chinese herb, which has been identified to be effective against dengue fever. However, the underlying biological mechanisms are still unclear. In this study, we adopt combined methods of bioactive trace technology and phytochemical extraction and separation, to guide the isolation and purification of the effective chemical constituents on the water-soluble components of aerial parts of Isatis tinctoria. In addition, we apply polarimetry and 1D or 2D nuclear magnetic resonance (NMR) spectroscopy to identify their structures, which lay a foundation for further study on the biological mechanisms underlying medicinal effects of Isatis tinctoria using in vitro and in vivo experiments. Specifically, we identify and infer the structures of 27 types of chemical compounds named GB-1, GB-2, …, GB-27, respectively, among which GB-7 is a novel compound. Further study of these compounds is critical to reveal the secrets behind the medicinal effects of Isatis tinctoria.
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39
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A novel heterogeneous network-based method for drug response prediction in cancer cell lines. Sci Rep 2018; 8:3355. [PMID: 29463808 PMCID: PMC5820329 DOI: 10.1038/s41598-018-21622-4] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 02/06/2018] [Indexed: 02/01/2023] Open
Abstract
An enduring challenge in personalized medicine lies in selecting a suitable drug for each individual patient. Here we concentrate on predicting drug responses based on a cohort of genomic, chemical structure, and target information. Therefore, a recently study such as GDSC has provided an unprecedented opportunity to infer the potential relationships between cell line and drug. While existing approach rely primarily on regression, classification or multiple kernel learning to predict drug responses. Synthetic approach indicates drug target and protein-protein interaction could have the potential to improve the prediction performance of drug response. In this study, we propose a novel heterogeneous network-based method, named as HNMDRP, to accurately predict cell line-drug associations through incorporating heterogeneity relationship among cell line, drug and target. Compared to previous study, HNMDRP can make good use of above heterogeneous information to predict drug responses. The validity of our method is verified not only by plotting the ROC curve, but also by predicting novel cell line-drug sensitive associations which have dependable literature evidences. This allows us possibly to suggest potential sensitive associations among cell lines and drugs. Matlab and R codes of HNMDRP can be found at following https://github.com/USTC-HIlab/HNMDRP.
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40
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Thangavel C, Boopathi E, Liu Y, McNair C, Haber A, Perepelyuk M, Bhardwaj A, Addya S, Ertel A, Shoyele S, Birbe R, Salvino JM, Dicker AP, Knudsen KE, Den RB. Therapeutic Challenge with a CDK 4/6 Inhibitor Induces an RB-Dependent SMAC-Mediated Apoptotic Response in Non-Small Cell Lung Cancer. Clin Cancer Res 2018; 24:1402-1414. [PMID: 29311118 DOI: 10.1158/1078-0432.ccr-17-2074] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 11/13/2017] [Accepted: 01/02/2018] [Indexed: 12/12/2022]
Abstract
Purpose: The retinoblastoma tumor suppressor (RB), a key regulator of cell-cycle progression and proliferation, is functionally suppressed in up to 50% of non-small cell lung cancer (NSCLC). RB function is exquisitely controlled by a series of proteins, including the CyclinD-CDK4/6 complex. In this study, we interrogated the capacity of a CDK4/6 inhibitor, palbociclib, to activate RB function.Experimental Design and Results: We employed multiple isogenic RB-proficient and -deficient NSCLC lines to interrogate the cytostatic and cytotoxic capacity of CDK 4/6 inhibition in vitro and in vivo We demonstrate that while short-term exposure to palbociclib induces cellular senescence, prolonged exposure results in inhibition of tumor growth. Mechanistically, CDK 4/6 inhibition induces a proapoptotic transcriptional program through suppression of IAPs FOXM1 and Survivin, while simultaneously augmenting expression of SMAC and caspase-3 in an RB-dependent manner.Conclusions: This study uncovers a novel function of RB activation to induce cellular apoptosis through therapeutic administration of a palbociclib and provides a rationale for the clinical evaluation of CDK 4/6 inhibitors in the treatment of patients with NSCLC. Clin Cancer Res; 24(6); 1402-14. ©2018 AACR.
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Affiliation(s)
- Chellappagounder Thangavel
- Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.
| | - Ettickan Boopathi
- Department of Medicine, Center for Translational Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Yi Liu
- Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Christopher McNair
- Department of Cancer Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Alex Haber
- Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Maryna Perepelyuk
- Department of Pharmaceutical Science, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Anshul Bhardwaj
- Department of Biochemistry and Molecular Biology, X-ray Crystallography and Molecular Interactions, Sidney Kimmel Cancer Center, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Sankar Addya
- Cancer Genomics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Adam Ertel
- Cancer Genomics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Sunday Shoyele
- Department of Pharmaceutical Science, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Ruth Birbe
- Department of Anatomy & Cell Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Joseph M Salvino
- The Wistar Cancer Center Molecular Screening, The Wistar Institute, Philadelphia, Pennsylvania
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.,Cancer Genomics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Karen E Knudsen
- Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.,Department of Cancer Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.,Cancer Genomics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.,Department of Urology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Robert B Den
- Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania. .,Department of Cancer Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.,Department of Urology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
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41
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Yang J, Qiu J, Wang K, Zhu L, Fan J, Zheng D, Meng X, Yang J, Peng L, Fu Y, Zhang D, Peng S, Huang H, Zhang Y. Using molecular functional networks to manifest connections between obesity and obesity-related diseases. Oncotarget 2017; 8:85136-85149. [PMID: 29156709 PMCID: PMC5689599 DOI: 10.18632/oncotarget.19490] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 06/05/2017] [Indexed: 01/04/2023] Open
Abstract
Obesity is a primary risk factor for many diseases such as certain cancers. In this study, we have developed three algorithms including a random-walk based method OBNet, a shortest-path based method OBsp and a direct-overlap method OBoverlap, to reveal obesity-disease connections at protein-interaction subnetworks corresponding to thousands of biological functions and pathways. Through literature mining, we also curated an obesity-associated disease list, by which we compared the methods. As a result, OBNet outperforms other two methods. OBNet can predict whether a disease is obesity-related based on its associated genes. Meanwhile, OBNet identifies extensive connections between obesity genes and genes associated with a few diseases at various functional modules and pathways. Using breast cancer and Type 2 diabetes as two examples, OBNet identifies meaningful genes that may play key roles in connecting obesity and the two diseases. For example, TGFB1 and VEGFA are inferred to be the top two key genes mediating obesity-breast cancer connection in modules associated with brain development. Finally, the top modules identified by OBNet in breast cancer significantly overlap with modules identified from TCGA breast cancer gene expression study, revealing the power of OBNet in identifying biological processes involved in the disease.
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Affiliation(s)
- Jialiang Yang
- College of Information Engineering, Changsha Medical University, Changsha 410219, P. R. China
| | - Jing Qiu
- Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
| | - Kejing Wang
- Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
| | - Lijuan Zhu
- Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
| | - Jingjing Fan
- Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
| | - Deyin Zheng
- Department of Mathematics, Hangzhou Normal University, Hangzhou 311121, P. R. China
| | - Xiaodi Meng
- Department of Food Science, Fujian Agriculture and Forestry University, Fuzhou 35002, P. R. China
| | - Jiasheng Yang
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Lihong Peng
- College of Information Engineering, Changsha Medical University, Changsha 410219, P. R. China
| | - Yu Fu
- Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
| | - Dahan Zhang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, P. R. China
| | - Shouneng Peng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Haiyun Huang
- Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
| | - Yi Zhang
- Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
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42
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Sá ACC, Sadee W, Johnson JA. Whole Transcriptome Profiling: An RNA-Seq Primer and Implications for Pharmacogenomics Research. Clin Transl Sci 2017; 11:153-161. [PMID: 28945944 PMCID: PMC5866981 DOI: 10.1111/cts.12511] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 09/03/2017] [Indexed: 12/16/2022] Open
Affiliation(s)
- Ana Caroline C Sá
- Center for Pharmacogenomics & Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida, USA.,Genetics & Genomic Graduate Program, Genetics Institute, University of Florida, Gainesville, Florida, USA
| | - Wolfgang Sadee
- Center for Pharmacogenomics, Department of Cancer Biology and Genetic, College of Medicine, Ohio State University, Columbus, Ohio, USA
| | - Julie A Johnson
- Center for Pharmacogenomics & Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida, USA.,Genetics & Genomic Graduate Program, Genetics Institute, University of Florida, Gainesville, Florida, USA.,Division of Cardiovascular Medicine, Colleges of Pharmacy and Medicine, University of Florida, Gainesville, Florida, USA
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43
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Emad A, Cairns J, Kalari KR, Wang L, Sinha S. Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance. Genome Biol 2017; 18:153. [PMID: 28800781 PMCID: PMC5554409 DOI: 10.1186/s13059-017-1282-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Accepted: 07/18/2017] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance. RESULTS We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group. CONCLUSIONS Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.
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Affiliation(s)
- Amin Emad
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
| | - Junmei Cairns
- Department of Molecular Pharmacology and Experimental Therapeutics, Gonda 19, Mayo Clinic Rochester, 200, 1st St. SW, Rochester, MN 55905 USA
| | - Krishna R. Kalari
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905 USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Gonda 19, Mayo Clinic Rochester, 200, 1st St. SW, Rochester, MN 55905 USA
| | - Saurabh Sinha
- Department of Computer Science and Institute of Genomic Biology, University of Illinois at Urbana-Champaign, 2122 Siebel Center, 201N. Goodwin Ave, Urbana, IL 61801 USA
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CoQ10 Deficiency May Indicate Mitochondrial Dysfunction in Cr(VI) Toxicity. Int J Mol Sci 2017; 18:ijms18040816. [PMID: 28441753 PMCID: PMC5412400 DOI: 10.3390/ijms18040816] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Revised: 04/03/2017] [Accepted: 04/07/2017] [Indexed: 01/18/2023] Open
Abstract
To investigate the toxic mechanism of hexavalent chromium Cr(VI) and search for an antidote for Cr(VI)-induced cytotoxicity, a study of mitochondrial dysfunction induced by Cr(VI) and cell survival by recovering mitochondrial function was performed. In the present study, we found that the gene expression of electron transfer flavoprotein dehydrogenase (ETFDH) was strongly downregulated by Cr(VI) exposure. The levels of coenzyme 10 (CoQ10) and mitochondrial biogenesis presented by mitochondrial mass and mitochondrial DNA copy number were also significantly reduced after Cr(VI) exposure. The subsequent, Cr(VI)-induced mitochondrial damage and apoptosis were characterized by reactive oxygen species (ROS) accumulation, caspase-3 and caspase-9 activation, decreased superoxide dismutase (SOD) and ATP production, increased methane dicarboxylic aldehyde (MDA) content, mitochondrial membrane depolarization and mitochondrial permeability transition pore (MPTP) opening, increased Ca2+ levels, Cyt c release, decreased Bcl-2 expression, and significantly elevated Bax expression. The Cr(VI)-induced deleterious changes were attenuated by pretreatment with CoQ10 in L-02 hepatocytes. These data suggest that Cr(VI) induces CoQ10 deficiency in L-02 hepatocytes, indicating that this deficiency may be a biomarker of mitochondrial dysfunction in Cr(VI) poisoning and that exogenous administration of CoQ10 may restore mitochondrial function and protect the liver from Cr(VI) exposure.
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BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research. Sci Rep 2016; 6:37140. [PMID: 27876826 PMCID: PMC5120305 DOI: 10.1038/srep37140] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 10/24/2016] [Indexed: 02/06/2023] Open
Abstract
Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.
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