DataSheet1_Bibliometric analysis of research on immunogenic cell death in cancer.docx

Background: Immunotherapy is changing the way we treat cancer. Immunogenic cell death (ICD) has received considerable attention in the treatments of various cancer types, due to the long-lasting antitumor responses elicited in human body. However, to date, no relevant bibliometric research has been reported.Methods: Publications related to ICD in cancer research were collected from the Web of Science Core Collection. Using CiteSpace, VOSviewer and an online platform, the analyses of co-author, co-citation, and co-occurrence of terms retrieved from literatures were carried out.Results: A total of 1,577 publications were included in this study. The global research literatures on ICD in cancer research have been increasing from 2005 to 2021. China, the United States and France dominated in this area and had close collaborations with many countries. Six of the top 10 most contributive institutions were from France. When it comes to author analysis, Kroemer G, Zitvogel L, Kepp O, Garg AD and Galluzzi L were in both the top 10 most productive authors and top 10 most co-cited authors lists. The co-occurring author keywords could be grouped into three clusters: “biomarkers of ICD”, “nanoparticles” and “combination therapy”. In terms of promising hotspots, keywords (author keywords and KeyWords Plus) with recent citation bursts could be summarized into two aspects: “tumor microenvironment” and “nanoparticles”.Conclusion: Increased attention has been paid to ICD in cancer treatment. However, there are still many unresolved domains in the field of ICD, such as clinical application and molecular mechanisms of this cell death process. ICD-inducing modalities combined with nanotechnology could potentiate the current immunotherapies, and will be hotspots for future research.</p

Table_1_Global research trends and hotspots on glioma stem cells.docx

BackgroundGlioma stem cells (GSCs) are a sub-population of cancer stem cells with capacity of self-renewal and differentiation. Accumulated evidence has revealed that GSCs were shown to contribute to gliomagenesis, distant metastasis as well as the resistance to radiotherapy and chemotherapy. As a result, GSCs were regarded as a promising therapeutic target in human glioma. The purpose of our study is to identify current state and hotspots of GSCs research by analyzing scientific publications through bibliometric methods.MethodsAll relevant publications on GSCs during 2003-2021 were extracted from the Science Citation Index Expanded of Web of Science Core Collection (WoSCC), and related information was collected and analyzed using Microsoft Excel 2016, GraphPad Prism 8 and VOSviewer software.ResultsA total of 4990 papers were included. The United States accounted for the largest number of publications (1852), the second average citations per item (ACI) value (67.54) as well as the highest H-index (157). Cancer Research was the most influential journal in this field. The most contributive institution was League of European Research Universities. RICH JN was the author with the most publications (109) and the highest H-index (59). All studies were clustered into 3 groups: “glioma stem cell properties”, “cell biological properties” and “oncology therapy”. The keywords “identification”, “CD133” and “side population” appeared earlier with the smaller average appearing years (AAY), and the keywords”radiotherapy” and “chemotherapy” had the latest AAY. The analysis of top cited articles showed that “temozolomide”, “epithelial-mesenchymal transition”, and “immunotherapy” emerged as new focused issues.ConclusionThere has been a growing number of researches on GSCs. The United States has always been a leading player in this domain. In general, the research focus has gradually shifted from basic cellular biology to the solutions of clinical concerns. “Temozolomide resistance”, “epithelial-mesenchymal transition”, and “immunotherapy” should be given more attention in the future.</p

DataSheet_3_A cuproptosis-related genes signature associated with prognosis and immune cell infiltration in osteosarcoma.csv

Osteosarcoma (OS) is one of the most prevalent primary bone tumors at all ages of human development. The objective of our study was to develop a model of Cuproptosis-Related Genes (CRGs) for predicting prognosis in OS patients. All datasets of OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database. We obtained the gene set (81 CRGs) related to cuproptosis by accessing the database and previous literature. All the CRGs were analyzed by univariate COX regression, least absolute shrinkage and selection operator (LASSO) COX regression analysis to screen for CRGs associated with prognosis in OS patients. Then these CRGs were used to construct a prognostic signature, which was further verified by independent cohort (GSE21257) and clinical correlation analysis. Afterward, to identify underlying mechanisms, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for the high-risk group by using the GSEA method. The association between the prognostic signature and 28 types of immune infiltrating cells in the tumor microenvironment was assessed. Ultimately, Lipoic Acid Synthetase (LIAS) (HR=0.632, P=0.004), Lipoyltransferase 1 (LIPT1) (HR=0.524, P=0.011), BCL2 Like 1 (BCL2L1/BCL-XL) (HR=0.593, P=0.022), and Pyruvate Dehydrogenase Kinase 1 (PDK1) (HR=0.662, P=0.025) were identified. Subsequently, they were used to calculate the risk score and build a prognostic model. In the training cohort, risk score (HR=1.878, P=0.003) could be considered as an independent prognostic factor, and OS patients with high-risk scores showed lower survival rates. Biological pathways related to substance metabolism and transport were enriched. There were significant differences in immune infiltrating cells in the tumor microenvironment. All in all, The CRGs signature is related to the tumor immune microenvironment and could be used as a credible predictor of the prognostic status in OS patients.</p

DataSheet_1_A cuproptosis-related genes signature associated with prognosis and immune cell infiltration in osteosarcoma.csv

Osteosarcoma (OS) is one of the most prevalent primary bone tumors at all ages of human development. The objective of our study was to develop a model of Cuproptosis-Related Genes (CRGs) for predicting prognosis in OS patients. All datasets of OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database. We obtained the gene set (81 CRGs) related to cuproptosis by accessing the database and previous literature. All the CRGs were analyzed by univariate COX regression, least absolute shrinkage and selection operator (LASSO) COX regression analysis to screen for CRGs associated with prognosis in OS patients. Then these CRGs were used to construct a prognostic signature, which was further verified by independent cohort (GSE21257) and clinical correlation analysis. Afterward, to identify underlying mechanisms, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for the high-risk group by using the GSEA method. The association between the prognostic signature and 28 types of immune infiltrating cells in the tumor microenvironment was assessed. Ultimately, Lipoic Acid Synthetase (LIAS) (HR=0.632, P=0.004), Lipoyltransferase 1 (LIPT1) (HR=0.524, P=0.011), BCL2 Like 1 (BCL2L1/BCL-XL) (HR=0.593, P=0.022), and Pyruvate Dehydrogenase Kinase 1 (PDK1) (HR=0.662, P=0.025) were identified. Subsequently, they were used to calculate the risk score and build a prognostic model. In the training cohort, risk score (HR=1.878, P=0.003) could be considered as an independent prognostic factor, and OS patients with high-risk scores showed lower survival rates. Biological pathways related to substance metabolism and transport were enriched. There were significant differences in immune infiltrating cells in the tumor microenvironment. All in all, The CRGs signature is related to the tumor immune microenvironment and could be used as a credible predictor of the prognostic status in OS patients.</p

DataSheet_2_A cuproptosis-related genes signature associated with prognosis and immune cell infiltration in osteosarcoma.csv

Osteosarcoma (OS) is one of the most prevalent primary bone tumors at all ages of human development. The objective of our study was to develop a model of Cuproptosis-Related Genes (CRGs) for predicting prognosis in OS patients. All datasets of OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database. We obtained the gene set (81 CRGs) related to cuproptosis by accessing the database and previous literature. All the CRGs were analyzed by univariate COX regression, least absolute shrinkage and selection operator (LASSO) COX regression analysis to screen for CRGs associated with prognosis in OS patients. Then these CRGs were used to construct a prognostic signature, which was further verified by independent cohort (GSE21257) and clinical correlation analysis. Afterward, to identify underlying mechanisms, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for the high-risk group by using the GSEA method. The association between the prognostic signature and 28 types of immune infiltrating cells in the tumor microenvironment was assessed. Ultimately, Lipoic Acid Synthetase (LIAS) (HR=0.632, P=0.004), Lipoyltransferase 1 (LIPT1) (HR=0.524, P=0.011), BCL2 Like 1 (BCL2L1/BCL-XL) (HR=0.593, P=0.022), and Pyruvate Dehydrogenase Kinase 1 (PDK1) (HR=0.662, P=0.025) were identified. Subsequently, they were used to calculate the risk score and build a prognostic model. In the training cohort, risk score (HR=1.878, P=0.003) could be considered as an independent prognostic factor, and OS patients with high-risk scores showed lower survival rates. Biological pathways related to substance metabolism and transport were enriched. There were significant differences in immune infiltrating cells in the tumor microenvironment. All in all, The CRGs signature is related to the tumor immune microenvironment and could be used as a credible predictor of the prognostic status in OS patients.</p

进出液方式对全钒液流电池性能影响模拟研究

The phylogenetic tree of R-genes with typical domains showing the different divergence rates among legumes. The numbers above the lines indicate the divergence rates of different R-genes with (A) NBS-LRR, (B) TIR-NBS, (C) CC-NBS-LRR, and (D) CC-NBS domains. (PDF 480 kb

Additional file 2: Figure S1. of Molecular phylogeny and dynamic evolution of disease resistance genes in the legume family

The orthologous gene families within the legumes with grape as the out-group. (A) Category of gene orthologs in legumes; (B) A Venn diagram showing the number of genes common among wild soybean (G. soja), cultivated soybean (G. max), barrel clover (M. truncatula) and chickpea (C. arietinum). (PDF 428 kb

Additional file 4: Dataset S1. of Molecular phylogeny and dynamic evolution of disease resistance genes in the legume family

Details and sequences of R-gene identified in each species. Grape (Vv): Vitis vinifera; Cultivated soybean (Gm): Glycine max; Wild soybean (Gs): Glycine soja; Barrel clover (Mt): Medicago truncatula; Bird’s-foot trefoil (Lj): Lotus japonicas; Pigeonpea (Cc): Cajanus cajan; Chickpea (Ca): Cicer arietinum; Common bean (Pv): Phaseolus vulgaris. (XLSX 1716 kb

Additional file 7: Figure S5. of Molecular phylogeny and dynamic evolution of disease resistance genes in the legume family

Chromosomal distributions of R-genes in the legume family. The different colors represent different species, and the Y-axis denotes the number of R-genes on each chromosome. Note that the legumes have different chromosomes and some genome assembly was not anchored to chromosomes. (PDF 591 kb

Additional file 1: Table S1. of Molecular phylogeny and dynamic evolution of disease resistance genes in the legume family

Overview information of the seven published genomes in the legume family. Table S2. The genome assembly and number of identified R-genes in the seven legume species and grape. Table S3. The number of R-genes in each R-gene family in seven species of legumes and grape. Each row represents one R-gene family. Table S4. Global statistics of R-genes in families or clusters in seven species of legumes and grape. Table S5. Statistics on the expansions and contractions of R-genes during the evolution of legumes. Table S6. The gene pairs with outlier Ka/Ks values between wild and cultivated soybeans. The threshold for an outlier Ka/Ks value was set at 0.8. (XLSX 29 kb