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Wei W, Su Y. Function of CD8 +, conventional CD4 +, and regulatory CD4 + T cell identification in lung cancer. Comput Biol Med 2023; 160:106933. [PMID: 37156220 DOI: 10.1016/j.compbiomed.2023.106933] [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: 02/18/2023] [Revised: 04/06/2023] [Accepted: 04/13/2023] [Indexed: 05/10/2023]
Abstract
Lung cancer is the malignant tumor with the highest mortality rate in the world. There is obvious heterogeneity within the tumor. Single cell sequencing technology enables scholars to obtain information about the cell type, status, subpopulation distribution and communication behavior between cells in the tumor microenvironment from the cellular level. However, due to the problem of sequencing depth, some genes with low expression cannot be detected, which results in that most of the specific genes of immune cells cannot be recognized, and lead to defects in the functional identification of immune cells. In this paper, we used single cell sequencing data of 12346 T cells in 14 treatment-naïve non-small-cell lung cancer patients to identify immune cell-specific genes and infer the function of three types of T cells. The method, named GRAPH-LC, implemented this function by gene interaction network and graph learning methods. Graph learning methods are used to extract genes feature and dense neural network is used to identify immune cell-specific genes. The experiments on 10-cross validation shows that the AUROC and AUPR reached at least 0.802, 0.815 on identifying cell-specific genes of three types of T cells. And we did functional enrichment analysis on the top 15 expressed genes. By functional enrichment analysis, we got 95 GO terms and 39 KEGG pathways that related to three types of T cells. The use of this technology will help to deeply understand the mechanism of the occurrence and development of lung cancer, find new diagnostic markers and therapeutic targets, and provide a theoretical reference for the precise treatment of lung cancer patients in the future.
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Affiliation(s)
- Wei Wei
- Department of Lung Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Lung Cancer Center, tianjin, China
| | - Yanjun Su
- Department of Lung Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Lung Cancer Center, tianjin, China.
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Cheng N, Liu J, Chen C, Zheng T, Li C, Huang J. Prediction of lung cancer metastasis by gene expression. Comput Biol Med 2023; 153:106490. [PMID: 36638618 DOI: 10.1016/j.compbiomed.2022.106490] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/14/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022]
Abstract
Tumor metastasis is the main cause of death in cancer patients. Early prediction of tumor metastasis can allow for timely intervention. At present, research on tumor metastasis mainly focuses on manual diagnosis by imaging or diagnosis by computational methods. With the deterioration of the tumor, gene expression levels in blood change greatly. It is feasible to measure the transcripts of key genes to predict whether cancer will metastasize. Therefore, in this paper, we obtained gene expression data from 226 patients from TCGA. These data included 239,322 transcripts. Background screening and LASSO analysis were used to select 31 transcripts as features. Finally, a deep neural network (DNN) was used to determine whether or not lung cancer would metastasize. We compared our methods with several other methods and found that our method achieved the best precision. In addition, in a previous study, we identified 7 genes that play a vital role in lung cancer. We added those gene transcripts into the DNN and found that the AUC and AUPR of the model were increased.
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Affiliation(s)
- Nitao Cheng
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Junliang Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Chen Chen
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, China
| | - Tang Zheng
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Changsheng Li
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jingyu Huang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
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Xu Y, Zhao J, Ma Y, Liu J, Cui Y, Yuan Y, Xiang C, Ma D, Liu H. The microbiome types of colorectal tissue are potentially associated with the prognosis of patients with colorectal cancer. Front Microbiol 2023; 14:1100873. [PMID: 37025624 PMCID: PMC10072283 DOI: 10.3389/fmicb.2023.1100873] [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: 11/17/2022] [Accepted: 02/28/2023] [Indexed: 04/08/2023] Open
Abstract
As the second leading cause of cancer worldwide, colorectal cancer (CRC) is associated with a poor prognosis. Although recent studies have explored prognostic markers in patients with CRC, whether tissue microbes carry prognostic information remains unknown. Here, by assessing the colorectal tissue microbes of 533 CRC patients, we found that Proteobacteria (43.5%), Firmicutes (25.3%), and Actinobacteria (23.0%) dominated the colorectal tissue microbiota, which was different from the gut microbiota. Moreover, two clear clusters were obtained by clustering based on the tissue microbes across all samples. By comparison, the relative abundances of Proteobacteria and Bacteroidetes in cluster 1 were significantly higher than those in cluster 2; while compared with cluster 1, Firmicutes and Actinobacteria were more abundant in cluster 2. In addition, the Firmicutes/Bacteroidetes ratios in cluster 1 were significantly lower than those in cluster 2. Further, compared with cluster 2, patients in cluster 1 had relatively poor survival (Log-rank test, p = 0.0067). By correlating tissue microbes with patient survival, we found that the relative abundance of dominant phyla, including Proteobacteria, Firmicutes, and Bacteroidetes, was significantly associated with survival in CRC patients. Besides, the co-occurrence network of tissue microbes at the phylum level of cluster 2 was more complicated than that of cluster 1. Lastly, we detected some pathogenic bacteria enriched in cluster 1 that promote the development of CRC, thus leading to poor survival. In contrast, cluster 2 showed significant increases in the abundance of some probiotics and genera that resist cancer development. Altogether, this study provides the first evidence that the tissue microbiome of CRC patients carries prognostic information and can help design approaches for clinically evaluating the survival of CRC patients.
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Affiliation(s)
- Yixin Xu
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jing Zhao
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yu Ma
- Department of Pathology, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jia Liu
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yingying Cui
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yuqing Yuan
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Chenxi Xiang
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Dongshen Ma
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- *Correspondence: Hui Liu, ; Dongshen Ma,
| | - Hui Liu
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Department of Pathology, Xuzhou Medical University, Xuzhou, Jiangsu, China
- *Correspondence: Hui Liu, ; Dongshen Ma,
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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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Hu H, Xia Q, Hu J, Wang S. Oncolytic Viruses for the Treatment of Bladder Cancer: Advances, Challenges, and Prospects. J Clin Med 2022; 11:jcm11236997. [PMID: 36498574 PMCID: PMC9738443 DOI: 10.3390/jcm11236997] [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: 09/08/2022] [Revised: 11/16/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
Bladder cancer is one of the most prevalent cancers. Despite recent advancements in bladder cancer therapy, new strategies are still required for improving patient outcomes, particularly for those who experienced Bacille Calmette-Guerin failure and those with locally advanced or metastatic bladder cancer. Oncolytic viruses are either naturally occurring or purposefully engineered viruses that have the ability to selectively infect and lyse tumor cells while avoiding harming healthy cells. In light of this, oncolytic viruses serve as a novel and promising immunotherapeutic strategy for bladder cancer. A wide diversity of viruses, including adenoviruses, herpes simplex virus, coxsackievirus, Newcastle disease virus, vesicular stomatitis virus, alphavirus, and vaccinia virus, have been studied in many preclinical and clinical studies for their potential as oncolytic agents for bladder cancer. This review aims to provide an overview of the advances in oncolytic viruses for the treatment of bladder cancer and highlights the challenges and research directions for the future.
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Affiliation(s)
| | | | - Jia Hu
- Correspondence: (J.H.); (S.W.)
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Li L, Chen F, Liu J, Zhu W, Lin L, Chen L, Shi Y, Lin A, Chen G. Molecular classification grade 3 endometrial endometrioid carcinoma using a next-generation sequencing–based gene panel. Front Oncol 2022; 12:935694. [PMID: 36003784 PMCID: PMC9394115 DOI: 10.3389/fonc.2022.935694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
Over the past two decades, the incidence of endometrial cancer (EC) is increasing, and there is a need for molecular biomarkers to predict prognosis and guide treatment. A recent study from The Cancer Genome Atlas suggested to implement the EC analysis by molecular profile for improving diagnosis, prognosis, and therapeutic treatment. In this study, next-generation sequencing was performed on 70 cases of G3 endometrioid ECs (EECs) using an 11-gene panel (TP53, MLH1, MSH2, MSH6, PMS2, EPCAM, PIK3CA, CTNNB1, KRAS, PTEN, and POL) for molecular classification. The molecular classification based on the 11-gene NGS panel identified four molecular subgroups: POLE-ultramutated (n = 20, 28.6%), MSI-H (n = 27, 38.6%), NSMP (n = 13, 18.6%) and TP53mut (n = 10, 14.3%). The NGS method showed 98.6% (69 of 70 cases, kappa value 98%) in concordance with the cases assessed by immunohistochemistry (IHC). Among the seven dead cases, four were MSI-H tumors, two were TP53mut/p53abn tumors, and one was NSMP tumors with an average overall survival (OS) of 14.7 months. TP53mut subgroup showed that poor OS rates and POLE group have favorable prognosis. Our work suggested that the 11-gene panel is suitable for molecular classification in G3 EECs and for guiding prognosis and treatment decisions.
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Affiliation(s)
- Ling Li
- Department of Gynecological Oncology Surgery, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Fangfang Chen
- Department of Molecular pathology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Jingcheng Liu
- Department of Pathology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Weifeng Zhu
- Department of Pathology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Liang Lin
- Department of Gynecological Oncology Surgery, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Li Chen
- Department of Gynecological Oncology Surgery, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Yi Shi
- Department of Molecular pathology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - An Lin
- Department of Gynecological Oncology Surgery, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Gang Chen
- Department of Pathology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
- *Correspondence: Gang Chen,
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