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  • Frontiers Media SA  (9)
  • Cao, Jianping  (9)
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  • Frontiers Media SA  (9)
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  • 1
    Online Resource
    Online Resource
    DataSheet_1.zip
    Frontiers Media SA ; 2021
    In:  Frontiers in Oncology Vol. 11 ( 2021-3-9)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 11 ( 2021-3-9)
    Abstract: Lower-grade glioma (LGG) is a type of central nervous system tumor that includes WHO grade II and grade III gliomas. Despite developments in medical science and technology and the availability of several treatment options, the management of LGG warrants further research. Surgical treatment for LGG treatment poses a challenge owing to its often inaccessible locations in the brain. Although radiation therapy (RT) is the most important approach in this condition and offers more advantages compared to surgery and chemotherapy, it is associated with certain limitations. Responses can vary from individual to individual based on genetic differences. The relationship between non-coding RNA and the response to radiation therapy, especially at the molecular level, is still undefined. Methods In this study, using The Cancer Genome Atlas dataset and bioinformatics, the gene co-expression network that is involved in the response to radiation therapy in lower-grade gliomas was determined, and the ceRNA network of radiotherapy response was constructed based on three databases of RNA interaction. Next, survival analysis was performed for hub genes in the co-expression network, and the high-efficiency biomarkers that could predict the prognosis of patients with LGG undergoing radiotherapy was identified. Results We found that some modules in the co-expression network were related to the radiotherapy responses in patients with LGG. Based on the genes in those modules and the three databases, we constructed a ceRNA network for the regulation of radiotherapy responses in LGG. We identified the hub genes and found that the long non-coding RNA, DRAIC, is a potential molecular biomarker to predict the prognosis of radiotherapy in LGG.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2021.622880
    DOI: 10.3389/fonc.2021.622880.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2649216-7
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  • 2
    Online Resource
    Online Resource
    Table_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 11 ( 2022-1-6)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 11 ( 2022-1-6)
    Abstract: We developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients. Methods and Materials Analyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed. Results In the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group. Conclusions The 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2021.816053
    DOI: 10.3389/fonc.2021.816053.s001
    DOI: 10.3389/fonc.2021.816053.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
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  • 3
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    DataSheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-6-16)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-6-16)
    Abstract: Existing studies suggest that m 6 A methylation is closely related to the prognosis of cancer. We developed three prognostic models based on m 6 A-related transcriptomics in lung adenocarcinoma patients and performed external validations. The TCGA-LUAD cohort served as the derivation cohort and six GEO data sets as external validation cohorts. The first model (mRNA model) was developed based on m 6 A-related mRNA. LASSO and stepwise regression were used to screen genes and the prognostic model was developed from multivariate Cox regression model. The second model (lncRNA model) was constructed based on m 6 A related lncRNAs. The four steps of random survival forest, LASSO, best subset selection and stepwise regression were used to screen genes and develop a Cox regression prognostic model. The third model combined the risk scores of the first two models with clinical variable. Variables were screened by stepwise regression. The mRNA model included 11 predictors. The internal validation C index was 0.736. The lncRNA model has 15 predictors. The internal validation C index was 0.707. The third model combined the risk scores of the first two models with tumor stage. The internal validation C index was 0.794. In validation sets, all C-indexes of models were about 0.6, and three models had good calibration accuracy. Freely online calculator on the web at https://lhj0520.shinyapps.io/LUAD_prediction_model/ .
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2022.895148
    DOI: 10.3389/fonc.2022.895148.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
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  • 4
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    Table_1.docx
    Frontiers Media SA ; 2021
    In:  Frontiers in Oncology Vol. 11 ( 2021-7-30)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 11 ( 2021-7-30)
    Abstract: Lower grade glioma (LGG) is one of the leading causes of death world worldwide. We attempted to develop and validate a radiosensitivity model for predicting the survival of lower grade glioma by using spike-and-slab lasso Cox model. Methods In this research, differentially expressed genes based on tumor microenvironment was obtained to further analysis. Log-rank test was used to identify genes in patients who received radiotherapy and patients who did not receive radiotherapy, respectively. Then, spike-and-slab lasso was performed to select genes in patients who received radiotherapy. Finally, three genes (INA, LEPREL1 and PTCRA) were included in the model. A radiosensitivity-related risk score model was established based on overall rate of TCGA dataset in patients who received radiotherapy. The model was validated in TCGA dataset that PFS as endpoint and two CGGA datasets that OS as endpoint. A novel nomogram integrated risk score with age and tumor grade was developed to predict the OS of LGG patients. Results We developed and verified a radiosensitivity-related risk score model. The radiosensitivity-related risk score is served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, the nomogram integrated risk score with age and tumor grade was established to perform better for predicting 1, 3, 5-year survival rate. Conclusions This model can be used by clinicians and researchers to predict patient’s survival rates and achieve personalized treatment of LGG.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2021.701500
    DOI: 10.3389/fonc.2021.701500.s001
    DOI: 10.3389/fonc.2021.701500.s002
    DOI: 10.3389/fonc.2021.701500.s003
    DOI: 10.3389/fonc.2021.701500.s004
    DOI: 10.3389/fonc.2021.701500.s005
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2649216-7
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  • 5
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    Identification of Uncommon Cryptosporidiumviatorum (a Novel Subtype XVcA2G1c) and Cryptosporidium andersoni as Well as Common Giardia duodenalis Assemblages A and B in Humans in Myanmar
    Frontiers Media SA ; 2020
    In:  Frontiers in Cellular and Infection Microbiology Vol. 10 ( 2020-11-25)
    Details
    In: Frontiers in Cellular and Infection Microbiology, Frontiers Media SA, Vol. 10 ( 2020-11-25)
    Abstract: Cryptosporidium and Giardia are two important zoonotic intestinal protozoa responsible for diarrheal diseases in humans and animals worldwide. Feces from infected hosts, water and food contaminated by Cryptosporidium oocysts and Giardia cysts as well as predictors such as poverty have been involved in their transmission. Myanmar is one of the world’s most impoverished countries. To date, there are few epidemiological studies of Cryptosporidium and Giardia in humans. To understand the prevalence and genetic characterization of Cryptosporidium spp. and Giardia duodenalis in humans in Myanmar, a molecular epidemiological investigation of the two protozoa was conducted in four villages of Shan State. 172 fecal specimens were collected from Wa people (one each) and identified for the presence of Cryptosporidium spp. and G. duodenalis by sequence analysis of their respective small subunit ribosomal RNA genes. 1.74% of investigated people were infected with Cryptosporidium spp.— C. andersoni (n = 2) and C. viatorum (n = 1) while 11.05% infected with G. duodenalis —assemblages A (n = 6) and B (n = 13). By sequence analysis of 60-kDa glycoprotein gene, the C. viatorum isolate belonged to a novel subtype XVcA2G1c. DNA preparations positive for G. duodenalis were further subtyped. Five of them were amplified and sequenced successfully: different assemblage B sequences (n = 2) at the triosephosphate isomerase (tpi) locus; sub-assemblage AII sequence (n = 1) and identical assemblage B sequences (n = 2) at the β-giardin (bg) locus. This is the first molecular epidemiological study of Cryptosporidium spp. and G. duodenalis in humans in Myanmar at both genotype and subtype levels. Due to unclear transmission patterns and dynamics of Cryptosporidium spp. and G. duodenalis , future research effort should focus on molecular epidemiological investigations of the two parasites in humans and animals living in close contact in the investigated areas, even in whole Myanmar. These data will aid in making efficient control strategies to intervene with and prevent occurrence of both diseases.
    Type of Medium: Online Resource
    ISSN: 2235-2988
    URL: Article
    DOI: 10.3389/fcimb.2020.614053
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
    detail.hit.zdb_id: 2619676-1
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  • 6
    Online Resource
    Online Resource
    DataSheet_1.docx
    Frontiers Media SA ; 2023
    In:  Frontiers in Oncology Vol. 12 ( 2023-1-10)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2023-1-10)
    Abstract: Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is how to integrate highly sparse genetic genomics data with a mass of minor effects into a prediction model for improving prediction power. We find that the deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower-dimensional continuous data in a non-linear way. This may provide benefits in risk prediction-associated genotype data. We developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for cancer prognosis. Specifically, we first reduced the size of binary biomarkers via a univariable regression model to a moderate size. Then, a trainable auto-encoder was used to learn compact features from the reduced data. Next, we performed a LASSO problem process to select the optimal combination of extracted features. Lastly, we applied such feature combination to real cancer prognostic models and evaluated the raw predictive effect of the models. The results indicated that these compressed transformation features could better improve the model’s original predictive performance and might avoid an overfitting problem. This idea may be enlightening for everyone involved in cancer research, risk reduction, treatment, and patient care via integrating genomics data.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2022.1091767
    DOI: 10.3389/fonc.2022.1091767.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
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  • 7
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    Image_2.tif
    Frontiers Media SA ; 2022
    In:  Frontiers in Immunology Vol. 13 ( 2022-12-8)
    Details
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 13 ( 2022-12-8)
    Abstract: Hydatid cysts and angiogenesis are the key characteristics of cystic echinococcosis, with immune cells and endothelial cells mediating essential roles in disease progression. Recent single-cell analysis studies demonstrated immune cell infiltration after Echinococcus granulosus infection, highlighting the diagnostic and therapeutic potential of targeting certain cell types in the lesion microenvironment. However, more detailed immune mechanisms during different periods of E. granulosus infection were not elucidated. Methods Herein, we characterized immune and endothelial cells from the liver samples of mice in different stages by single-cell RNA sequencing. Results We profiled the transcriptomes of 45,199 cells from the liver samples of mice at 1, 3, and 6 months after infection (two replicates) and uninfected wild-type mice. The cells were categorized into 26 clusters with four distinct cell types: natural killer (NK)/T cells, B cells, myeloid cells, and endothelial cells. An SPP1 + macrophage subset with immunosuppressive and pro-angiogenic functions was identified in the late infection stage. Single-cell regulatory network inference and clustering (SCENIC) analysis suggested that Cebpe, Runx3, and Rora were the key regulators of the SPP1 + macrophages. Cell communication analysis revealed that the SPP1 + macrophages interacted with endothelial cells and had pro-angiogenic functions. There was an obvious communicative relationship between SPP1 + macrophages and endothelial cells via Vegfa–Vegfr1/Vegfr2, and SPP1 + macrophages interacted with other immune cells via specific ligand–receptor pairs, which might have contributed to their immunosuppressive function. Discussion Our comprehensive exploration of the cystic echinococcosis ecosystem and the first discovery of SPP1 + macrophages with infection period specificity provide deeper insights into angiogenesis and the immune evasion mechanisms associated with later stages of infection.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fimmu.2022.1067338
    DOI: 10.3389/fimmu.2022.1067338.s001
    DOI: 10.3389/fimmu.2022.1067338.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606827-8
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  • 8
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    Analysis of gene expression profile of peripheral blood in alveolar and cystic echinococcosis
    Frontiers Media SA ; 2022
    In:  Frontiers in Cellular and Infection Microbiology Vol. 12 ( 2022-8-11)
    Details
    In: Frontiers in Cellular and Infection Microbiology, Frontiers Media SA, Vol. 12 ( 2022-8-11)
    Abstract: RNA-sequencing (RNA-seq) is a versatile, high-throughput technology that is being widely employed for screening differentially expressed genes (DEGs) in various diseases. Echinococcosis, a globally distributed zoonosis, has been reported to impose a heavy disease burden in pastoral areas of China. Herein we aimed to explore the molecular mechanisms underlying echinococcosis. In this study, peripheral blood samples were collected from six patients with alveolar echinococcosis (AE), six patients with cystic echinococcosis (CE), and six healthy controls. RNA-Seq (mRNA) was performed to detect gene transcript and expression levels, and DEGs were subjected to bioinformatic analyses. In comparison with healthy controls, 492 DEGs (270 upregulated, 222 downregulated) were found in the AE group and 424 DEGs (170 upregulated, 254 downregulated) were found in the CE group (|log 2 (fold change)| & gt; 1 and P & lt; 0.05). Further, 60 genes were upregulated and 39 were downregulated in both the AE and CE groups. Gene ontology enrichment analysis indicated that DEGs were mainly involved in molecular functions, including extracellular space, extracellular region, organ and system development, and anatomical structure development. Protein–protein interaction (PPI) networks were constructed to depict the complex relationship between DEGs and interacting proteins.
    Type of Medium: Online Resource
    ISSN: 2235-2988
    URL: Article
    DOI: 10.3389/fcimb.2022.913393
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2619676-1
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  • 9
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    Table_7.xlsx
    Frontiers Media SA ; 2021
    In:  Frontiers in Cellular and Infection Microbiology Vol. 11 ( 2021-5-13)
    Details
    In: Frontiers in Cellular and Infection Microbiology, Frontiers Media SA, Vol. 11 ( 2021-5-13)
    Abstract: Toxoplasma gondii , a representative model organism belonging to the phylum Apicomplexa, can infect almost all warm-blooded organisms, including humans. The invasion of host cells via host–parasite interaction is the key step for T. gondii to complete its life cycle. Herein we performed tandem mass tag analysis to investigate global proteomic changes in host cells (human foreskin fibroblasts, HFFs) [HFFs infected with T. gondii (HT) vs . HFFs (H)] and T. gondii [HT vs . T. gondii (T)] during intracellular infection. Overall, 3477 and 1434 proteins were quantified, of which 375 and 1099 proteins were differentially expressed (adjusted p-value & lt; 0.05 and & gt;1.5 or & lt;0.67-fold change) in host cells and T. gondii , respectively. T. gondii invasion relies on the secretion of numerous secretory proteins, which originate from three secretory organelles: micronemes, rhoptries, and dense granules. In the HT vs . T group, few secretory proteins were upregulated, such as microneme proteins (MICs: MIC6, MIC10), rhoptry bulb proteins (ROPs: ROP5, ROP17), and dense granule proteins (GRAs: GRA4, GRA5, GRA12). In contrast, dozens of known secretory proteins were significantly downregulated in T. gondii -infected HFFs. In HFFs, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed a large number of differentially expressed proteins (DEPs) enriched in metabolic processes and immune-associated signaling pathways, such as NF-κB, cAMP, and Rap1 signaling pathways. Further, in case of T. gondii , DEPs were involved in ribosome biogenesis, citrate cycle, and galactose metabolism, indicating that cell biosynthesis and metabolism of T. gondii were altered after host cell invasion. These findings reveal novel modifications in the proteome of host cells as well as T. gondii , helping us better understand the mechanisms underlying host–parasite interaction.
    Type of Medium: Online Resource
    ISSN: 2235-2988
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fcimb.2021.643001
    DOI: 10.3389/fcimb.2021.643001.s001
    DOI: 10.3389/fcimb.2021.643001.s002
    DOI: 10.3389/fcimb.2021.643001.s003
    DOI: 10.3389/fcimb.2021.643001.s004
    DOI: 10.3389/fcimb.2021.643001.s005
    DOI: 10.3389/fcimb.2021.643001.s006
    DOI: 10.3389/fcimb.2021.643001.s007
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2619676-1
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