MOESM2 of MicroRNA-144 inhibits cell proliferation, migration and invasion in human hepatocellular carcinoma by targeting CCNB1

Additional file 2: Figure S2. Overexpression of miR-144 using lentivirus vector inhibited CCNB1 expression in SMMC-7721 cells. Lentivirus vector Lenti-NC or Lenti-miR-144 was constructed and infected SMMC-7721 cells. (A) the relative miR-144 expression level was examined by RT-PCR and (B) the CCNB1 protein level was examined by western blot. Actin was used as internal control. Experiments were repeated twice and representative data was shown

MOESM1 of MicroRNA-144 inhibits cell proliferation, migration and invasion in human hepatocellular carcinoma by targeting CCNB1

Additional file 1: Figure S1. miRNA targeting CCNB1 was screened by TargetScan, miRwalk and miRand

Image_1_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.tif

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

Image_3_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.tif

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

Image_2_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.tif

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

Table_1_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.docx

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

Image_4_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.tif

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

DataSheet2_Current Research Progress of the Role of LncRNA LEF1-AS1 in a Variety of Tumors.docx

Long non-coding RNAs (lncRNA), as key regulators of cell proliferation and death, are involved in the regulation of various processes in the nucleus and cytoplasm, involving biological developmental processes in the fields of immunology, neurobiology, cancer, and stress. There is great scientific interest in exploring the relationship between lncRNA and tumors. Many researches revealed that lymph enhancer-binding factor 1-antisense RNA 1 (LEF1-AS1), a recently discovered lncRNA, is downregulated in myeloid malignancy, acting mainly as a tumor suppressor, while it is highly expressed and carcinogenic in glioblastoma (GBM), lung cancer, hepatocellular carcinoma (HCC), osteosarcoma, colorectal cancer (CRC), oral squamous cell carcinoma (OSCC), prostatic carcinoma, retinoblastoma, and other malignant tumors. Furthermore, abnormal LEF1-AS1 expression was associated with tumorigenesis, development, survival, and prognosis via the regulation of target genes and signaling pathways. This review summarizes the existing data on the expression, functions, underlying mechanism, relevant signaling pathways, and clinical significance of LEF1-AS1 in cancer. It is concluded that LEF1-AS1 can serve as a novel biomarker for the diagnosis and prognosis of various tumors, thus deserves further attention in the future.</p

Additional file 1 of Multidimensional optimization for accelerating light-powered biocatalysis in Rhodopseudomonas palustris

Additional file 1: Table S1. Strains used in this study. Table S2. Oligonucleotides used in this study. Table S3. Plasmids used in this study. Figure S1. R. palustris contained two plasmids for synthesis of vanillyl alcohol (VA) or p-hydroxybenzyl alcohol (pHBA). Figure S2. Effects of aldh deletions on R. palustris. Figure S3. Effects of the decreases in ispA and crtE expressions on R. palustris. Figure S4. Synthesis of pHBA from pCA using whole-cell biocatalysis. Figure S5. The HPLC result for the production of pHBA from pCA. Figure S6. R. palustris contained two plasmids for pinene synthesis from isoprenol. Figure S7. The GC result for the synthesis of pinene from isoprenol

Understanding the Ingenious Dual Role-Playing of CO₂ in One-Pot Pressure-Swing Synthesis of Linear Carbonate

CO2, as an engaging C1 building block, shines brightly in green conversion; besides, its unique identity as an acidic molecule is worthy of further development. Here, a novel mode of CO2 utilization was explored to play a dual role in the one-pot pressure-swing synthesis of dimethyl carbonate (DMC) over 2-hydroxypyridine anion functionalized poly­(ionic liquid)­s (OPy-PILs), that is, CO2 could act as both a raw material of coupling reaction and an acidic inhibitor of side-reaction. Specifically, OPy-PILs, constructed by free radical copolymerization followed by ion exchange, were applied to the one-pot coupling reaction of CO2, epoxide, and CH3OH. As an efficient heterogeneous catalyst, OPy-PILs could cover basic sites with acidic CO2 during the insertion of activated CO2 into epoxide by intermediate [OPy-CO2–] so as to reduce the yield of the epoxide alcoholysis side-reaction from 27 to 12%. Moreover, various comparative experiments fully exhibited the high activity and stability of OPy-PILs, and theoretical calculations convincingly explained the dual role-playing of CO2 in a one-pot pressure-swing reaction. This work paves a novel avenue for the deeper exploration of the CO2 utilization potential in the catalytic process